Crypto Forecasting - Team CryptoCast.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "31846381",
   "metadata": {},
   "source": [
    "Notebook Prepared by:\n",
    "    Akanksha Singh\n",
    "    Harshraj Jadeja"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7b68710",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b692220f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "57617b20",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1514764860</td>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2376.5800</td>\n",
       "      <td>2399.5000</td>\n",
       "      <td>2357.1400</td>\n",
       "      <td>2374.5900</td>\n",
       "      <td>19.233005</td>\n",
       "      <td>2373.116392</td>\n",
       "      <td>-0.004218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1514764860</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>78.380000</td>\n",
       "      <td>8.530000</td>\n",
       "      <td>-0.014399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1514764860</td>\n",
       "      <td>1</td>\n",
       "      <td>229.0</td>\n",
       "      <td>13835.1940</td>\n",
       "      <td>14013.8000</td>\n",
       "      <td>13666.1100</td>\n",
       "      <td>13850.1760</td>\n",
       "      <td>31.550062</td>\n",
       "      <td>13827.062093</td>\n",
       "      <td>-0.014643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1514764860</td>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.6596</td>\n",
       "      <td>7.6596</td>\n",
       "      <td>7.6567</td>\n",
       "      <td>7.6576</td>\n",
       "      <td>6626.713370</td>\n",
       "      <td>7.657713</td>\n",
       "      <td>-0.013922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1514764860</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>25.8740</td>\n",
       "      <td>25.8770</td>\n",
       "      <td>121.087310</td>\n",
       "      <td>25.891363</td>\n",
       "      <td>-0.008264</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    timestamp  Asset_ID  Count        Open        High         Low  \\\n",
       "0  1514764860         2   40.0   2376.5800   2399.5000   2357.1400   \n",
       "1  1514764860         0    5.0      8.5300      8.5300      8.5300   \n",
       "2  1514764860         1  229.0  13835.1940  14013.8000  13666.1100   \n",
       "3  1514764860         5   32.0      7.6596      7.6596      7.6567   \n",
       "4  1514764860         7    5.0     25.9200     25.9200     25.8740   \n",
       "\n",
       "        Close       Volume          VWAP    Target  \n",
       "0   2374.5900    19.233005   2373.116392 -0.004218  \n",
       "1      8.5300    78.380000      8.530000 -0.014399  \n",
       "2  13850.1760    31.550062  13827.062093 -0.014643  \n",
       "3      7.6576  6626.713370      7.657713 -0.013922  \n",
       "4     25.8770   121.087310     25.891363 -0.008264  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trade_df = pd.read_csv('train.csv')\n",
    "# trade_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "10b1702e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24236806, 10)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trade_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f0185b9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6.779922</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>EOS.IO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>Ethereum Classic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Asset_ID    Weight        Asset_Name\n",
       "0         2  2.397895      Bitcoin Cash\n",
       "1         0  4.304065      Binance Coin\n",
       "2         1  6.779922           Bitcoin\n",
       "3         5  1.386294            EOS.IO\n",
       "4         7  2.079442  Ethereum Classic"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# asset = pd.read_csv('asset_details.csv')\n",
    "# asset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "589c2bbd",
   "metadata": {},
   "source": [
    "### Merging Trade df with Asset Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5dc335bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# trade_df = trade_df.merge(asset, on = 'Asset_ID', how = 'left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4dc88531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "timestamp          0\n",
       "Asset_ID           0\n",
       "Count              0\n",
       "Open               0\n",
       "High               0\n",
       "Low                0\n",
       "Close              0\n",
       "Volume             0\n",
       "VWAP               9\n",
       "Target        750338\n",
       "Weight             0\n",
       "Asset_Name         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trade_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aadee8fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Count</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01 00:01:00</td>\n",
       "      <td>2</td>\n",
       "      <td>40.0</td>\n",
       "      <td>2376.5800</td>\n",
       "      <td>2399.5000</td>\n",
       "      <td>2357.1400</td>\n",
       "      <td>2374.5900</td>\n",
       "      <td>19.233005</td>\n",
       "      <td>2373.116392</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-01 00:01:00</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>78.380000</td>\n",
       "      <td>8.530000</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-01 00:01:00</td>\n",
       "      <td>1</td>\n",
       "      <td>229.0</td>\n",
       "      <td>13835.1940</td>\n",
       "      <td>14013.8000</td>\n",
       "      <td>13666.1100</td>\n",
       "      <td>13850.1760</td>\n",
       "      <td>31.550062</td>\n",
       "      <td>13827.062093</td>\n",
       "      <td>6.779922</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-01 00:01:00</td>\n",
       "      <td>5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>7.6596</td>\n",
       "      <td>7.6596</td>\n",
       "      <td>7.6567</td>\n",
       "      <td>7.6576</td>\n",
       "      <td>6626.713370</td>\n",
       "      <td>7.657713</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>EOS.IO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-01 00:01:00</td>\n",
       "      <td>7</td>\n",
       "      <td>5.0</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>25.9200</td>\n",
       "      <td>25.8740</td>\n",
       "      <td>25.8770</td>\n",
       "      <td>121.087310</td>\n",
       "      <td>25.891363</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>Ethereum Classic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            timestamp  Asset_ID  Count        Open        High         Low  \\\n",
       "0 2018-01-01 00:01:00         2   40.0   2376.5800   2399.5000   2357.1400   \n",
       "1 2018-01-01 00:01:00         0    5.0      8.5300      8.5300      8.5300   \n",
       "2 2018-01-01 00:01:00         1  229.0  13835.1940  14013.8000  13666.1100   \n",
       "3 2018-01-01 00:01:00         5   32.0      7.6596      7.6596      7.6567   \n",
       "4 2018-01-01 00:01:00         7    5.0     25.9200     25.9200     25.8740   \n",
       "\n",
       "        Close       Volume          VWAP    Weight        Asset_Name  \n",
       "0   2374.5900    19.233005   2373.116392  2.397895      Bitcoin Cash  \n",
       "1      8.5300    78.380000      8.530000  4.304065      Binance Coin  \n",
       "2  13850.1760    31.550062  13827.062093  6.779922           Bitcoin  \n",
       "3      7.6576  6626.713370      7.657713  1.386294            EOS.IO  \n",
       "4     25.8770   121.087310     25.891363  2.079442  Ethereum Classic  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import datetime\n",
    "\n",
    "# # Convert 'timestamp' to a human-readable date-time format\n",
    "# trade_df['timestamp'] = pd.to_datetime(trade_df['timestamp'], unit='s')\n",
    "\n",
    "# # Remove the 'Target' column from the DataFrame\n",
    "# trade_df = trade_df.drop(columns=['Target'])\n",
    "\n",
    "# # Display the first few rows of the updated DataFrame to verify changes\n",
    "# trade_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "378762bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2018-01-01 00:01:00')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trade_df['timestamp'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d3ea467",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-09-21 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# trade_df['timestamp'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "535810b0",
   "metadata": {},
   "source": [
    "### Converting the minute level trade_df to Daily Level DF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1fe83fb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import pandas as pd\n",
    "\n",
    "# # Convert timestamps to pandas datetime\n",
    "# trade_df['timestamp'] = pd.to_datetime(trade_df['timestamp'])\n",
    "\n",
    "# # Group by Asset_ID and resample to daily, aggregating the data appropriately\n",
    "# daily_df = trade_df.groupby('Asset_ID').resample('D', on='timestamp').agg({\n",
    "#     'Open': 'first',\n",
    "#     'High': 'max',\n",
    "#     'Low': 'min',\n",
    "#     'Close': 'last',\n",
    "#     'Volume': 'sum',\n",
    "#     'Count': 'sum',\n",
    "#     'VWAP': 'last',\n",
    "#     'Weight': 'last',\n",
    "#     'Asset_Name': 'max'\n",
    "# }).reset_index(level='Asset_ID')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9565a70",
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_df = pd.read_csv('daily_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "45a774dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-01</th>\n",
       "      <td>0</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.7000</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>8.4500</td>\n",
       "      <td>654145.36</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>8.416289</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>0</td>\n",
       "      <td>8.4500</td>\n",
       "      <td>9.1133</td>\n",
       "      <td>8.0922</td>\n",
       "      <td>8.7150</td>\n",
       "      <td>1262489.43</td>\n",
       "      <td>29923.0</td>\n",
       "      <td>8.715540</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>0</td>\n",
       "      <td>8.7151</td>\n",
       "      <td>9.4697</td>\n",
       "      <td>8.2653</td>\n",
       "      <td>9.3040</td>\n",
       "      <td>947392.65</td>\n",
       "      <td>24572.0</td>\n",
       "      <td>9.327360</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>0</td>\n",
       "      <td>9.3040</td>\n",
       "      <td>9.6511</td>\n",
       "      <td>8.5595</td>\n",
       "      <td>9.1430</td>\n",
       "      <td>964494.94</td>\n",
       "      <td>26176.0</td>\n",
       "      <td>9.131448</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>0</td>\n",
       "      <td>9.1350</td>\n",
       "      <td>16.8172</td>\n",
       "      <td>8.9651</td>\n",
       "      <td>14.8908</td>\n",
       "      <td>2764977.04</td>\n",
       "      <td>44906.0</td>\n",
       "      <td>14.872271</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Asset_ID    Open     High     Low    Close      Volume    Count  \\\n",
       "timestamp                                                                     \n",
       "2018-01-01         0  8.5300   8.7000  7.8000   8.4500   654145.36  18200.0   \n",
       "2018-01-02         0  8.4500   9.1133  8.0922   8.7150  1262489.43  29923.0   \n",
       "2018-01-03         0  8.7151   9.4697  8.2653   9.3040   947392.65  24572.0   \n",
       "2018-01-04         0  9.3040   9.6511  8.5595   9.1430   964494.94  26176.0   \n",
       "2018-01-05         0  9.1350  16.8172  8.9651  14.8908  2764977.04  44906.0   \n",
       "\n",
       "                 VWAP    Weight    Asset_Name  \n",
       "timestamp                                      \n",
       "2018-01-01   8.416289  4.304065  Binance Coin  \n",
       "2018-01-02   8.715540  4.304065  Binance Coin  \n",
       "2018-01-03   9.327360  4.304065  Binance Coin  \n",
       "2018-01-04   9.131448  4.304065  Binance Coin  \n",
       "2018-01-05  14.872271  4.304065  Binance Coin  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b6602e",
   "metadata": {},
   "source": [
    "### Feature Engineering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7d0935d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>VWAP</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "      <th>sma_10</th>\n",
       "      <th>sma_50</th>\n",
       "      <th>RSI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>8.5300</td>\n",
       "      <td>8.7000</td>\n",
       "      <td>7.8000</td>\n",
       "      <td>8.4500</td>\n",
       "      <td>654145.36</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>8.416289</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>8.4500</td>\n",
       "      <td>9.1133</td>\n",
       "      <td>8.0922</td>\n",
       "      <td>8.7150</td>\n",
       "      <td>1262489.43</td>\n",
       "      <td>29923.0</td>\n",
       "      <td>8.715540</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>8.7151</td>\n",
       "      <td>9.4697</td>\n",
       "      <td>8.2653</td>\n",
       "      <td>9.3040</td>\n",
       "      <td>947392.65</td>\n",
       "      <td>24572.0</td>\n",
       "      <td>9.327360</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>9.3040</td>\n",
       "      <td>9.6511</td>\n",
       "      <td>8.5595</td>\n",
       "      <td>9.1430</td>\n",
       "      <td>964494.94</td>\n",
       "      <td>26176.0</td>\n",
       "      <td>9.131448</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>9.1350</td>\n",
       "      <td>16.8172</td>\n",
       "      <td>8.9651</td>\n",
       "      <td>14.8908</td>\n",
       "      <td>2764977.04</td>\n",
       "      <td>44906.0</td>\n",
       "      <td>14.872271</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Asset_ID    Open     High     Low    Close      Volume    Count  \\\n",
       "0 2018-01-01         0  8.5300   8.7000  7.8000   8.4500   654145.36  18200.0   \n",
       "1 2018-01-02         0  8.4500   9.1133  8.0922   8.7150  1262489.43  29923.0   \n",
       "2 2018-01-03         0  8.7151   9.4697  8.2653   9.3040   947392.65  24572.0   \n",
       "3 2018-01-04         0  9.3040   9.6511  8.5595   9.1430   964494.94  26176.0   \n",
       "4 2018-01-05         0  9.1350  16.8172  8.9651  14.8908  2764977.04  44906.0   \n",
       "\n",
       "        VWAP    Weight    Asset_Name  sma_10  sma_50  RSI  \n",
       "0   8.416289  4.304065  Binance Coin     NaN     NaN  NaN  \n",
       "1   8.715540  4.304065  Binance Coin     NaN     NaN  NaN  \n",
       "2   9.327360  4.304065  Binance Coin     NaN     NaN  NaN  \n",
       "3   9.131448  4.304065  Binance Coin     NaN     NaN  NaN  \n",
       "4  14.872271  4.304065  Binance Coin     NaN     NaN  NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Reset the index\n",
    "daily_df.reset_index(inplace=True)\n",
    "# Rename the 'index' column to 'Date'\n",
    "daily_df.rename(columns={'timestamp': 'Date'}, inplace=True)\n",
    "\n",
    "# Calculate SMA for 10 and 50 days\n",
    "daily_df['sma_10'] = daily_df['Close'].rolling(window=10).mean()\n",
    "daily_df['sma_50'] = daily_df['Close'].rolling(window=50).mean()\n",
    "\n",
    "# Calculate RSI\n",
    "def calculate_rsi(series, period=14):\n",
    "    delta = series.diff(1)\n",
    "    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()\n",
    "    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()\n",
    "    rs = gain / loss\n",
    "    rsi = 100 - (100 / (1 + rs))\n",
    "    return rsi\n",
    "\n",
    "daily_df['RSI'] = calculate_rsi(daily_df['Close'], period=14)\n",
    "\n",
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5a41070",
   "metadata": {},
   "source": [
    "### Dropping Null Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ff6013cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop rows with any null values\n",
    "daily_df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3dc8a54",
   "metadata": {},
   "source": [
    "### Checking for Multicollinearity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a2616ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asset_ID</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Open</td>\n",
       "      <td>1563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>High</td>\n",
       "      <td>2732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Low</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Close</td>\n",
       "      <td>1654608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Volume</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Count</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>VWAP</td>\n",
       "      <td>1637253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Weight</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>sma_10</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>sma_50</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RSI</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     feature      VIF\n",
       "0      const       24\n",
       "1   Asset_ID        2\n",
       "2       Open     1563\n",
       "3       High     2732\n",
       "4        Low      899\n",
       "5      Close  1654608\n",
       "6     Volume        1\n",
       "7      Count        2\n",
       "8       VWAP  1637253\n",
       "9     Weight        2\n",
       "10    sma_10      116\n",
       "11    sma_50       18\n",
       "12       RSI        1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "# Selecting numerical columns (excluding 'Date' and 'Asset_Name' as they are not numeric)\n",
    "numerical_df = daily_df.select_dtypes(include=[np.number])\n",
    "\n",
    "# Adding a constant column for intercept\n",
    "X = add_constant(numerical_df)\n",
    "\n",
    "# Calculating VIF for each feature\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "# Here we round the VIF scores to 0 decimal places and convert to integers\n",
    "vif_data[\"VIF\"] = [int(round(variance_inflation_factor(X.values, i), 0)) for i in range(X.shape[1])]\n",
    "\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "de56fe21",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing fields with High VIF\n",
    "\n",
    "daily_df = daily_df.drop(columns=['VWAP', 'Open', 'High', 'Low'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "923382e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping sma_10, sma_50, RSI at this point (will be calculating them again post the test-train split to avoid data leakage)\n",
    "\n",
    "daily_df = daily_df.drop(columns=['sma_10', 'sma_50', 'RSI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c72f48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>feature</th>\n",
       "      <th>VIF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>const</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Asset_ID</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Close</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Volume</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Count</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Weight</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    feature  VIF\n",
       "0     const   16\n",
       "1  Asset_ID    2\n",
       "2     Close    2\n",
       "3    Volume    1\n",
       "4     Count    2\n",
       "5    Weight    2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.stats.outliers_influence import variance_inflation_factor\n",
    "from statsmodels.tools.tools import add_constant\n",
    "\n",
    "# Selecting numerical columns (excluding 'Date' and 'Asset_Name' as they are not numeric)\n",
    "numerical_df = daily_df.select_dtypes(include=[np.number])\n",
    "\n",
    "# Adding a constant column for intercept\n",
    "X = add_constant(numerical_df)\n",
    "\n",
    "# Calculating VIF for each feature\n",
    "vif_data = pd.DataFrame()\n",
    "vif_data[\"feature\"] = X.columns\n",
    "vif_data[\"VIF\"] = [variance_inflation_factor(X.values, i) for i in range(X.shape[1])]\n",
    "# Here we round the VIF scores to 0 decimal places and convert to integers\n",
    "vif_data[\"VIF\"] = [int(round(variance_inflation_factor(X.values, i), 0)) for i in range(X.shape[1])]\n",
    "\n",
    "vif_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7f0bdb35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>2018-02-19</td>\n",
       "      <td>0</td>\n",
       "      <td>11.0601</td>\n",
       "      <td>1316213.81</td>\n",
       "      <td>51420.0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>2018-02-20</td>\n",
       "      <td>0</td>\n",
       "      <td>9.8888</td>\n",
       "      <td>2357501.48</td>\n",
       "      <td>75120.0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>2018-02-21</td>\n",
       "      <td>0</td>\n",
       "      <td>9.3799</td>\n",
       "      <td>2277445.93</td>\n",
       "      <td>82114.0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>2018-02-22</td>\n",
       "      <td>0</td>\n",
       "      <td>8.8600</td>\n",
       "      <td>1792778.94</td>\n",
       "      <td>67214.0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>2018-02-23</td>\n",
       "      <td>0</td>\n",
       "      <td>9.6930</td>\n",
       "      <td>1621549.49</td>\n",
       "      <td>60333.0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Asset_ID    Close      Volume    Count    Weight    Asset_Name\n",
       "49 2018-02-19         0  11.0601  1316213.81  51420.0  4.304065  Binance Coin\n",
       "50 2018-02-20         0   9.8888  2357501.48  75120.0  4.304065  Binance Coin\n",
       "51 2018-02-21         0   9.3799  2277445.93  82114.0  4.304065  Binance Coin\n",
       "52 2018-02-22         0   8.8600  1792778.94  67214.0  4.304065  Binance Coin\n",
       "53 2018-02-23         0   9.6930  1621549.49  60333.0  4.304065  Binance Coin"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.to_csv(\"daily_df.csv\", index=False)\n",
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01c0abbc",
   "metadata": {},
   "source": [
    "# Model Building"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1ddfcd2",
   "metadata": {},
   "source": [
    "## Sequential Neural Network Model"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a5e9e8dc",
   "metadata": {},
   "source": [
    "Optimizer: Adam optimizer\n",
    "Loss Function: Mean Squared Error (MSE)\n",
    "Epochs: The model is trained for 50 epochs\n",
    "Batch Size: Set to 32\n",
    "Scaling: StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b3a0f321",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Asset ID: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 339us/step\n",
      "Processing Asset ID: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 309us/step\n",
      "Processing Asset ID: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 311us/step\n",
      "Processing Asset ID: 3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 328us/step\n",
      "Processing Asset ID: 4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 315us/step\n",
      "Processing Asset ID: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 311us/step\n",
      "Processing Asset ID: 6\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 349us/step\n",
      "Processing Asset ID: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 316us/step\n",
      "Processing Asset ID: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 323us/step\n",
      "Processing Asset ID: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 348us/step\n",
      "Processing Asset ID: 10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 319us/step\n",
      "Processing Asset ID: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 362us/step\n",
      "Processing Asset ID: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 356us/step\n",
      "Processing Asset ID: 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1671871286.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 342us/step\n",
      "Evaluation Metrics:\n",
      "    Asset_ID         MAPE           MAE          RMSE\n",
      "0          0    16.118191     44.996548     64.075124\n",
      "1          1    52.110524  22503.672605  23758.298598\n",
      "2          2    12.079047     76.183960    118.419781\n",
      "3          3    19.358150      0.202817      0.273273\n",
      "4          4  3314.974123      4.847666      6.483432\n",
      "5          5     7.861252      0.413603      0.633224\n",
      "6          6    11.640882    237.613821    352.122192\n",
      "7          7     7.828231      3.181303      5.569851\n",
      "8          8    13.784349      0.137331      0.195759\n",
      "9          9    13.588930     25.630635     38.933479\n",
      "10        10    58.045178   1603.284049   1927.675075\n",
      "11        11    12.540928     31.849944     53.749174\n",
      "12        12    51.739633      0.198868      0.266192\n",
      "13        13   182.364253      0.157081      0.227549\n",
      "\n",
      "Detailed Predictions:\n",
      "           Date  Asset_ID  Actual_Close  Predicted_Close\n",
      "0    2021-01-01         0     37.751100        52.939125\n",
      "1    2021-01-02         0     38.216500        53.210873\n",
      "2    2021-01-03         0     41.236100        54.392971\n",
      "3    2021-01-04         0     41.117650        55.039581\n",
      "4    2021-01-05         0     41.775950        53.790928\n",
      "...         ...       ...           ...              ...\n",
      "3691 2021-09-17        13      0.105388        -0.283839\n",
      "3692 2021-09-18        13      0.105905         0.209872\n",
      "3693 2021-09-19        13      0.103692         0.197962\n",
      "3694 2021-09-20        13      0.091326        -0.330274\n",
      "3695 2021-09-21        13      0.091349         0.110647\n",
      "\n",
      "[3696 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from math import sqrt\n",
    "\n",
    "daily_df.sort_values('Date', inplace=True)\n",
    "\n",
    "# Function to calculate MAPE\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "# Manually calculate SMA\n",
    "def calculate_sma(data, window):\n",
    "    return data.rolling(window=window).mean()\n",
    "\n",
    "# Manually calculate RSI\n",
    "def calculate_rsi(data, window=14):\n",
    "    delta = data.diff()\n",
    "    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()\n",
    "    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()\n",
    "    rs = gain / loss\n",
    "    rsi = 100 - (100 / (1 + rs))\n",
    "    return rsi\n",
    "\n",
    "# Placeholder for results and detailed predictions\n",
    "results = []\n",
    "detailed_predictions = []\n",
    "\n",
    "# Temporal split settings\n",
    "split_date = '2021-01-01'  # Adjust this date to your dataset and needs\n",
    "\n",
    "# Split your dataset based on the split_date\n",
    "train_data = daily_df[daily_df['Date'] < split_date]\n",
    "test_data = daily_df[daily_df['Date'] >= split_date]\n",
    "\n",
    "# Iterate through each Asset_ID\n",
    "for asset_id, asset_train_data in train_data.groupby('Asset_ID'):\n",
    "    print(f\"Processing Asset ID: {asset_id}\")\n",
    "    asset_test_data = test_data[test_data['Asset_ID'] == asset_id]\n",
    "    \n",
    "    # Calculate Moving Averages and RSI for the training part\n",
    "    asset_train_data['SMA_10'] = calculate_sma(asset_train_data['Close'], window=10)\n",
    "    asset_train_data['SMA_50'] = calculate_sma(asset_train_data['Close'], window=50)\n",
    "    asset_train_data['RSI'] = calculate_rsi(asset_train_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values generated by SMA and RSI calculations in the training set\n",
    "    asset_train_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Repeat calculation for the test part (important to avoid data leakage)\n",
    "    asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
    "    asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
    "    asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values for the test set\n",
    "    asset_test_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Prepare data for model training and testing\n",
    "    X_train = asset_train_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_train = asset_train_data['Close'].values\n",
    "    X_test = asset_test_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_test = asset_test_data['Close'].values\n",
    "    \n",
    "    # Scale the features\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "    # Define the neural network model\n",
    "    model = Sequential([\n",
    "        Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
    "        Dense(32, activation='relu'),\n",
    "        Dense(1)\n",
    "    ])\n",
    "    \n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    \n",
    "    # Train the model\n",
    "    model.fit(X_train_scaled, y_train, epochs=50, batch_size=32, verbose=0)\n",
    "    \n",
    "    # Predict the Close values for the test set\n",
    "    y_pred = model.predict(X_test_scaled).flatten()\n",
    "    \n",
    "    # Calculate evaluation metrics for the test set\n",
    "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    # Store the evaluation metrics\n",
    "    results.append({\n",
    "        'Asset_ID': asset_id,\n",
    "        'MAPE': mape,\n",
    "        'MAE': mae,\n",
    "        'RMSE': rmse\n",
    "    })\n",
    "    \n",
    "    # Store detailed predictions\n",
    "    detailed_predictions.append(pd.DataFrame({\n",
    "        'Date': asset_test_data['Date'],\n",
    "        'Asset_ID': asset_id,\n",
    "        'Actual_Close': y_test,\n",
    "        'Predicted_Close': y_pred\n",
    "    }))\n",
    "\n",
    "# Convert results to DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "print(\"Evaluation Metrics:\")\n",
    "print(results_df)\n",
    "\n",
    "# Concatenate detailed predictions into a single DataFrame\n",
    "final_predictions_df = pd.concat(detailed_predictions, ignore_index=True)\n",
    "print(\"\\nDetailed Predictions:\")\n",
    "print(final_predictions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "1be9f3cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA9wAAAJOCAYAAABFiQ/hAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hU1dbH8e+ZyaQXSChJ6CIdBCxYUCmKFMGCXAuKWK4Fr/IqXlHUS7uigg0blquCih2xYEFREFBQQARFUCxBFEKHJKROOe8fZ2YykzrBJBPC7/M8eSZz6p5zJmXNXnttwzRNExERERERERGpVrZwN0BERERERESkPlLALSIiIiIiIlIDFHCLiIiIiIiI1AAF3CIiIiIiIiI1QAG3iIiIiIiISA1QwC0iIiIiIiJSAxRwi4iIiIiIiNQABdwiIiIiIiIiNUABt4iIiIiIiEgNUMAtIvXKY489hmEYdO3a9ZCPsX37diZPnsy6deuqr2EV6Nu3L3379q2Vc1WkdevWGIbh/4qPj+fEE0/kpZdeqpXzz5kzB8Mw2LJli3/ZoV6be++9l3fffbfa2uazZcsWDMNgzpw5IW3/+++/c+ONN9K+fXtiYmKIjY2lS5cu3H333Wzbts2/3RVXXEHr1q2rvb1/V8+ePWnWrBlut7vcbXr37k2jRo0oKioK6ZhVvYbhcPfdd9OyZUsiIiJo0KBBrZ133LhxGIbB0KFDa+2cFVmxYgWTJ0/mwIEDVd7X9/O8Zs0a/7LJkycH/Y6JjY2lefPmDBw4kMcff5ycnJxqbH1ofO/Hsr5ef/31Wm+PiNQ/CrhFpF554YUXAPjxxx/55ptvDukY27dvZ8qUKbUWcNclvXv3ZuXKlaxcudL/D/Po0aN56qmnwtKeWbNmMWvWrCrvV1MBd1V88MEHHHPMMXzwwQdce+21fPDBB/7vFyxYUGeCqopcffXVbN++nU8++aTM9Zs3b2bFihWMGjWKyMjIWm5dzXjvvfeYNm0al19+OUuXLuWzzz6rlfM6nU7mzp0LwMKFC4M+kAmXFStWMGXKlEMKuCuycOFCVq5cycKFC3nwwQdp2bIl48ePp0uXLqxfv75azxWqm266yf+7z/c1YMCAsLRFROqXiHA3QESkuqxZs4b169dz9tln8+GHH/L8889z4oknhrtZh5UGDRpw0kkn+Z+feeaZtGrViocffpgxY8aUuY/b7cblchEVFVXt7encuXO1H7M2ZGRkcPHFF9O+fXuWLFlCUlKSf13//v0ZO3Ys77zzThhbGJpLL72U2267jRdeeIEhQ4aUWu/7gOuqq66q7abVmA0bNgAwduxYmjRpUi3HzMvLIzY2tsJt3nvvPXbv3u3//fXiiy9y5513Vsv565rjjjuORo0a+Z9ffPHF3HjjjfTp04dzzjmHzZs318jvk4q0bNky6HefiEh1UQ+3iNQbzz//PAD3338/p5xyCq+//jp5eXmlttu2bRvXXnstLVq0IDIykvT0dEaMGMHOnTv54osvOOGEEwC48sor/amFkydPBspPcS4rJXjKlCmceOKJJCcnk5iYyLHHHsvzzz+PaZpVfm3nnXcerVq1wuPxlFp34okncuyxx/qfv/XWW5x44okkJSURGxvLUUcddcgBUYMGDejQoQN//PEHUJx+OWPGDO655x7atGlDVFQUS5YsAawPPc455xySk5OJjo6mZ8+evPnmm6WO+/XXX9O7d2+io6NJT09nwoQJOJ3OUtuVdb0LCwuZOnUqnTp1Ijo6mpSUFPr168eKFSsAMAyD3NxcXnzxRf/9CzzGjh07uO6662jevDmRkZG0adOGKVOm4HK5gs6zfft2LrzwQhISEkhKSuKiiy5ix44dIV23hx9+mNzcXGbNmhUUbPsYhsHw4cMrPEZBQQETJkygTZs2REZG0qxZM/71r3+V6m1cvHgxffv2JSUlhZiYGFq2bMkFF1wQ9N4vKirinnvuoWPHjkRFRdG4cWOuvPJKdu/eXWEbGjZsyPnnn8+CBQvYu3dv0Dq3283LL7/MCSecQLdu3fj111+58soradeuHbGxsTRr1oxhw4bxww8/VHK1yk+p96UgBzJNk1mzZtGjRw9iYmJo2LAhI0aM4Pfffw/a7rvvvmPo0KE0adKEqKgo0tPTOfvss/nrr7/KbUfr1q25++67AWjatGnQz77H42HGjBn+a9ikSRMuv/zyUsfr27cvXbt2ZdmyZZxyyinExsaG9PP3/PPPExkZyezZs2nRogWzZ88u9bvC4/Fwzz330KFDB2JiYmjQoAHHHHMMjz76qH+b3bt3+3+/+e517969S/XUf/bZZ5xxxhkkJiYSGxtL7969+fzzz/3rJ0+ezG233QZAmzZt/D9LX3zxRaWv5VB0796du+66i61bt/LGG2/UyDlERMJBAbeI1Av5+fm89tprnHDCCXTt2pWrrrqKnJwc3nrrraDttm3bxgknnMA777zDuHHj+Pjjj5k5cyZJSUns37+fY489ltmzZwPWOE5fauE///nPKrdpy5YtXHfddbz55pvMnz+f4cOHc9NNN/Hf//63yse66qqr2Lp1K4sXLw5a/tNPP7Fq1SquvPJKAFauXMlFF13EUUcdxeuvv86HH37IxIkTSwWToXI6nfzxxx80btw4aPljjz3G4sWLefDBB/n444/p2LEjS5YsoXfv3hw4cICnn36a9957jx49enDRRRcFjdfduHEjZ5xxBgcOHGDOnDk8/fTTfPfdd9xzzz2VtsflcjF48GD++9//MnToUN555x3mzJnDKaecwtatW/3XICYmhiFDhvjvny8tfceOHfTq1YtPPvmEiRMn8vHHH3P11Vdz3333cc011/jPk5+fz5lnnsmnn37Kfffdx1tvvUVqaioXXXRRSNft008/pWnTpofcY2aaJueddx4PPvggo0aN4sMPP2TcuHG8+OKL9O/fn8LCQsB6j5199tlERkbywgsvsHDhQu6//37i4uL8Y6o9Hg/nnnsu999/PyNHjuTDDz/k/vvvZ9GiRfTt25f8/PwK23L11VdTVFTkT3f2+eSTT9i+fTtXX301YH1AkZKSwv3338/ChQt58skniYiI4MQTT+Tnn38+pOtQluuuu46bb76ZM888k3fffZdZs2bx448/csopp7Bz504AcnNzGTBgADt37uTJJ59k0aJFzJw5k5YtW1Y4Tvidd97xvx5f2rPvZ3/MmDHcfvvtDBgwgPfff5///ve/LFy4kFNOOYU9e/YEHSczM5PLLruMkSNH8tFHH3HDDTdU+Jr++usvPv30U84991waN27M6NGj+fXXX1m2bFnQdjNmzGDy5MlccsklfPjhh7zxxhtcffXVQR/CjBo1infffZeJEyfy6aef8txzz3HmmWcGfWAyd+5czjrrLBITE3nxxRd58803SU5OZuDAgf6g+5///Cc33XQTAPPnz/f/LAV+uFfdzjnnHIBSr7sk0zRxuVwhfYXq/vvvJzIyktjYWE499VTef//9v/VaRET8TBGReuCll14yAfPpp582TdM0c3JyzPj4ePO0004L2u6qq64yHQ6HuXHjxnKPtXr1ahMwZ8+eXWpdnz59zD59+pRaPnr0aLNVq1blHtPtdptOp9OcOnWqmZKSYno8nkqPGcjpdJpNmzY1R44cGbR8/PjxZmRkpLlnzx7TNE3zwQcfNAHzwIEDFR6vLK1atTKHDBliOp1O0+l0mhkZGebo0aNNwLzttttM0zTNjIwMEzDbtm1rFhUVBe3fsWNHs2fPnqbT6QxaPnToUDMtLc10u92maZrmRRddZMbExJg7duzwb+NyucyOHTuagJmRkeFfXvLa+O7z//73vwpfS1xcnDl69OhSy6+77jozPj7e/OOPP4KW+67bjz/+aJqmaT711FMmYL733ntB211zzTXlvjcCRUdHmyeddFKF2wQq+f5ZuHChCZgzZswI2u6NN94wAfPZZ581TdM0582bZwLmunXryj32a6+9ZgLm22+/HbTc9z6fNWtWhW3zeDxmmzZtzGOOOSZo+QUXXGDGxsaaWVlZZe7ncrnMoqIis127duYtt9ziX+57DwVew/J+fiZNmmQG/quycuVKEzAfeuihoO3+/PNPMyYmxhw/frxpmqa5Zs0aEzDffffdCl9bWXzn3L17t3/Zpk2bTMC84YYbgrb95ptvTMC88847/cv69OljAubnn38e8jmnTp1qAubChQtN0zTN33//3TQMwxw1alTQdkOHDjV79OhR4bHi4+PNm2++udz1ubm5ZnJysjls2LCg5W632+zevbvZq1cv/7IHHnig1M9kqGbPnm0C5urVq/3Lyrq2gfLz803AHDx4cIXHXrJkiQmE9FVZ27dv325ec8015ptvvmkuX77cfOWVV8yTTjoppN8zIiKhUA+3iNQLzz//PDExMVx88cUAxMfH849//IPly5fzyy+/+Lf7+OOP6devH506darxNi1evJgzzzyTpKQk7HY7DoeDiRMnsnfvXnbt2lWlY0VERHDZZZcxf/58srKygOKU3nPPPZeUlBQAfzr8hRdeyJtvvlnlwksfffQRDocDh8NBmzZtePPNN7nppptK9T6fc845OBwO//Nff/2Vn376iUsvvRQgqIdpyJAhZGZm+ns5lyxZwhlnnEHTpk39+9vt9pB6jz/++GOio6MPOUX+gw8+oF+/fqSnpwe1cfDgwQAsXbrU38aEhAR/j5vPyJEjD+m8VeXLZLjiiiuClv/jH/8gLi7O3wvZo0cPIiMjufbaa3nxxRdLpVWD9ZobNGjAsGHDgl5zjx49SE1NrTRF2DAMrrzySr7//nu+/fZbAPbu3cuCBQu44IILSExMBKx7fu+999K5c2ciIyOJiIggMjKSX375hU2bNv3NK1L8WgzD4LLLLgt6LampqXTv3t3/Wo4++mgaNmzI7bffztNPP83GjRv/1nl9QyZK3o9evXrRqVOnoFRssFLx+/fvH9KxTdP0p5H7inS1adOGvn378vbbb5OdnR10vvXr13PDDTfwySefBK0L3GbOnDncc889fP3116WGaqxYsYJ9+/YxevTooGvo8XgYNGgQq1evJjc3N6S2VzczxOE2xx13HKtXrw7pKz09vcJjpaWl8eyzz/KPf/yDU089lZEjR7Js2TJ69uzJHXfcccjZQSIiPgq4ReSw50u9PPvsszFNkwMHDnDgwAFGjBgBFBd2Amt8Y/PmzWu8TatWreKss84C4H//+x9fffUVq1ev5q677gKoNI23LFdddRUFBQX+qWo++eQTMjMz/enkAKeffjrvvvsuLpeLyy+/nObNm9O1a1dee+21kM5x6qmnsnr1atasWcPGjRs5cOAAjz32WKkK1GlpaUHPfam8//73v/0Bu+/Ll07rS7vdu3cvqamppc5d1rKSdu/eTXp6Ojbbof352rlzJwsWLCjVxi5dupRqY+AHAlVpI1gFmDIyMg6pjb7zR0RElErlNwyD1NRUf3pw27Zt+eyzz2jSpAn/+te/aNu2LW3btg0a07tz504OHDhAZGRkqde9Y8eOUunQZbnyyiux2Wz+4RavvPIKRUVF/vRrsKa0+s9//sN5553HggUL+Oabb1i9ejXdu3c/pPd7WXbu3IlpmjRt2rTUa/n666/9ryUpKYmlS5fSo0cP7rzzTrp06UJ6ejqTJk0qs1ZAZXzXu+T7HiA9Pb3U+PaytivP4sWLycjI4B//+AfZ2dn+318XXngheXl5QT+7EyZM4MEHH+Trr79m8ODBpKSkcMYZZwRNvfXGG28wevRonnvuOU4++WSSk5O5/PLL/fUHfD+rI0aMKHUNp0+fjmma7Nu3L/SLU418tSIqC5Lj4+Pp0aNHSF+HUj3f4XBw0UUXsXfv3qAPbEVEDoWqlIvIYe+FF17ANE3mzZvHvHnzSq1/8cUXueeee7Db7TRu3LjCokmViY6O9vcwByoZtLz++us4HA4++OADoqOj/cv/zlRVnTt3plevXsyePZvrrruO2bNnk56e7g/sfc4991zOPfdcCgsL+frrr7nvvvsYOXIkrVu35uSTT67wHElJSRx//PGVtqVkIStfxeEJEyaUWwysQ4cOAKSkpJRZfCyUgmSNGzfmyy+/xOPxHFLQ3ahRI4455himTZtW5nrfP/opKSmsWrXqkNoI+OcV/vrrrw9pHHdKSgoul4vdu3cHBd2mabJjxw5/JgPAaaedxmmnnYbb7WbNmjU8/vjj3HzzzTRt2pSLL76YRo0akZKSwsKFC8s8V0JCQqXtad68OWeddRavvvoqDz30ELNnz+boo4/m9NNP928zd+5cLr/8cu69996gfffs2VPpXNbR0dH+cekl9w3UqFEjDMNg+fLlZVaxDlzWrVs3Xn/9dUzT5Pvvv2fOnDlMnTqVmJgY7rjjjkpfcyBfBklmZmapD+y2b98eVHEbSv98VMRX7PHhhx/m4YcfLnP9ddddB1iZLuPGjWPcuHEcOHCAzz77jDvvvJOBAwfy559/EhsbS6NGjZg5cyYzZ85k69atvP/++9xxxx3s2rWLhQsX+tv6+OOPl/veLOvDptrgGzddVmHKQEuXLqVfv34hHTMjI+OQ5rj39bYf6od7IiI+CrhF5LDmdrt58cUXadu2Lc8991yp9R988AEPPfQQH3/8MUOHDmXw4MG8/PLL/Pzzz/4AsCTfP+1l9cq1bt2at956i8LCQv92e/fuZcWKFf7UWrD+4Y6IiMBut/uX5efn8/LLL/+t13vllVcyZswYvvzySxYsWMC4ceOCzlHydfTp04cGDRrwySef8N1331UacB+qDh060K5dO9avX18q4CqpX79+vP/+++zcudP/j73b7Q6pMvHgwYN57bXXmDNnToVp5VFRUWXev6FDh/LRRx/Rtm1bGjZsWGEb33zzTd5///2gtPJXX3210jYC3HLLLbzwwgvccMMNpaYFA+uf+XfffZfzzz+/zP3POOMMZsyYwdy5c7nlllv8y99++21yc3M544wzSu1jt9s58cQT6dixI6+88gpr167l4osvZujQobz++uu43e6/NU3e1VdfzcKFC5k4cSLr1q1j2rRpQYGlYRilguAPP/yQbdu2cfTRR1d47NatW7Nr166g90RRUVGp+b+HDh3K/fffz7Zt27jwwgtDardhGHTv3p1HHnmEOXPmsHbt2pD2C+RLD587d27Qhx2rV69m06ZN/syVqtq/fz/vvPMOvXv3LrNo4HPPPccrr7zChg0b6Nq1a9C6Bg0aMGLECLZt28bNN9/Mli1bSk2j17JlS2688UY+//xzvvrqKwB69+5NgwYN2LhxIzfeeGOF7avod2F18/3uaN26daX31pdSHorKesvL4nQ6eeONN2jUqFGl710Rkcoo4BaRw9rHH3/M9u3bmT59epm9Il27duWJJ57g+eefZ+jQoUydOpWPP/6Y008/nTvvvJNu3bpx4MABFi5cyLhx4+jYsSNt27YlJiaGV155hU6dOhEfH096ejrp6emMGjWKZ555hssuu4xrrrmGvXv3MmPGjKBgG+Dss8/m4YcfZuTIkVx77bXs3buXBx988G/PLXvJJZcwbtw4LrnkEgoLC0uNKZ04cSJ//fUXZ5xxBs2bN+fAgQM8+uijOBwO+vTp87fOXZlnnnmGwYMHM3DgQK644gqaNWvGvn372LRpE2vXrvVXjL/77rt5//336d+/PxMnTiQ2NpYnn3wypHGjl1xyCbNnz+b666/n559/pl+/fng8Hr755hs6derkH8PfrVs3vvjiCxYsWEBaWhoJCQl06NCBqVOnsmjRIk455RTGjh1Lhw4dKCgoYMuWLXz00Uc8/fTTNG/enMsvv5xHHnmEyy+/nGnTptGuXTs++uijUgFgedq0acPrr7/ORRddRI8ePbjxxhvp2bMnYFVp92VllBdwDxgwgIEDB3L77beTnZ1N7969+f7775k0aRI9e/Zk1KhRADz99NMsXryYs88+m5YtW1JQUOAfQnHmmWcC1hzHr7zyCkOGDOH//u//6NWrFw6Hg7/++oslS5Zw7rnnltuOQOeccw6NGjXigQcewG63M3r06KD1Q4cOZc6cOXTs2JFjjjmGb7/9lgceeCCkIRwXXXQREydO5OKLL+a2226joKCAxx57DLfbHbRd7969ufbaa7nyyitZs2YNp59+OnFxcWRmZvLll1/SrVs3xowZwwcffMCsWbM477zzOOqoozBNk/nz53PgwAH/OOmq6NChA9deey2PP/44NpuNwYMHs2XLFv7zn//QokWLoA9FquKVV16hoKCAsWPHlvn7KyUlhVdeeYXnn3+eRx55hGHDhtG1a1eOP/54GjduzB9//MHMmTNp1aoV7dq1Iysri379+jFy5Eg6duxIQkICq1evZuHChf7Mk/j4eB5//HFGjx7Nvn37GDFiBE2aNGH37t2sX7+e3bt389RTTwHWzxHAo48+yujRo3E4HHTo0CGkrIiKfPvttyQlJeF0Otm+fTuff/45L7/8Mk2aNGHBggWVpoEnJCSElIkTinHjxuF0Ounduzepqan8+eefPP7446xbt47Zs2eX+4GmiEjIwlOrTUSkepx33nlmZGSkuWvXrnK3ufjii82IiAh/Vew///zTvOqqq8zU1FTT4XCY6enp5oUXXmju3LnTv89rr71mduzY0XQ4HCZgTpo0yb/uxRdfNDt16mRGR0ebnTt3Nt94440yqyy/8MILZocOHcyoqCjzqKOOMu+77z7z+eefr7QSd2VGjhxpAmbv3r1Lrfvggw/MwYMHm82aNTMjIyPNJk2amEOGDDGXL19e6XFbtWplnn322RVu46sw/cADD5S5fv369eaFF15oNmnSxHQ4HGZqaqrZv39/f/V4n6+++so86aSTzKioKDM1NdW87bbbzGeffTaka5Ofn29OnDjRbNeunRkZGWmmpKSY/fv3N1esWOHfZt26dWbv3r3N2NhYEwg6xu7du82xY8eabdq0MR0Oh5mcnGwed9xx5l133WUePHjQv91ff/1lXnDBBWZ8fLyZkJBgXnDBBeaKFStCqlLu89tvv5k33HCDefTRR5tRUVFmTEyM2blzZ3PcuHFBr7Os909+fr55++23m61atTIdDoeZlpZmjhkzxty/f79/m5UrV5rnn3++2apVKzMqKspMSUkx+/TpY77//vtBx3I6neaDDz5odu/e3YyOjjbj4+PNjh07mtddd535yy+/hPRaTNM0b7nlFhMwhwwZUmrd/v37zauvvtps0qSJGRsba5566qnm8uXLS93DsqqUm6ZpfvTRR2aPHj3MmJgY86ijjjKfeOKJUlXKfV544QXzxBNPNOPi4syYmBizbdu25uWXX26uWbPGNE3T/Omnn8xLLrnEbNu2rRkTE2MmJSWZvXr1MufMmVPpayyvkrbb7TanT59utm/f3nQ4HGajRo3Myy67zPzzzz+DtuvTp4/ZpUuXSs9jmqbZo0cPs0mTJmZhYWG525x00klmo0aNzMLCQvOhhx4yTznlFLNRo0ZmZGSk2bJlS/Pqq682t2zZYpqmaRYUFJjXX3+9ecwxx5iJiYlmTEyM2aFDB3PSpElmbm5u0HGXLl1qnn322WZycrLpcDjMZs2amWeffbb51ltvBW03YcIEMz093bTZbCZgLlmyJKTXVlGVct9XVFSUmZaWZp511lnmo48+amZnZ4d07Or0/PPPm7169TKTk5PNiIgIs2HDhubAgQPNTz75pNbbIiL1k2GaIZaEFBEREREREZGQqRKEiIiIiIiISA3QGG4RERERCYnH48Hj8VS4TUSE/r0UEfFRD7eIiIiIhGTq1Kml5u8u+bVly5ZwN1NEpM7QGG4RERERCcn27dvZvn17hdscc8wxlVYaFxE5UijgFhEREREREakBSikXERERERERqQH1vqqFx+Nh+/btJCQkYBhGuJsjIiIiIiIiYWaaJjk5OaSnp2Oz1Vw/dL0PuLdv306LFi3C3QwRERERERGpY/7880+aN29eY8ev9wF3QkICYF3IxMTEMLembE6nk08//ZSzzjoLh8MR7uYckXQPapaub/jpHoSHrnv46R6Eh657+OkehJ/uQXiEet2zs7Np0aKFP16sKfU+4PalkScmJtbpgDs2NpbExET9MIaJ7kHN0vUNP92D8NB1Dz/dg/DQdQ8/3YPw0z0Ij6pe95oedqyiaSIiIiIiIiI1QAG3iIiIiIiISA1QwC0iIiIiIiJSA+r9GG4REREREak73G43Tqcz3M2ocU6nk4iICAoKCnC73eFuzhHDd93dbnedGDuvgFtERERERGqcaZrs2LGDAwcOhLsptcI0TVJTU/nzzz9rvDCXFPNd999//52GDRuSmpoa1uuvgFtERERERGqcL9hu0qQJsbGx9T4I9Xg8HDx4kPj4eGw2jeStLR6Ph5ycHGw2G3v27AEgLS0tbO1RwC0iIiIiIjXK7Xb7g+2UlJRwN6dWeDweioqKiI6OVsBdi3zXPTExEZvNxq5du2jSpAl2uz0s7dGdFxERERGRGuUbsx0bGxvmlsiRxPd+C2fNAAXcIiIiIiJSK+p7GrnULXXh/aaAW0RERERERKQGKOAWERERERE5TBmGwbvvvltnjiPBFHCLiIiIiMhhw+0xWfnbXt5bt42Vv+3F7TFr5bwrVqzAbrczaNCgKu/bunVrZs6cWf2NCtGOHTu46aabOOqoo4iKiqJFixYMGzaMzz//PGxtOlKoSrmIiIiIiBwWFm7IZMqCjWRmFfiXpSVFM2lYZwZ1rdmpn1544QVuuukmnnvuObZu3UrLli1r9HzVZcuWLfTu3ZsGDRowY8YMjjnmGJxOJ5988gn/+te/+Omnn8LdxHpNPdwiIiIiIlLnLdyQyZi5a4OCbYAdWQWMmbuWhRsya+zcubm5vPnmm4wZM4ahQ4cyZ86cUtu8//77HH/88URHR9OoUSMuuOACAPr3788ff/zBLbfcgmEY/kJekydPpkePHkHHmDlzJq1bt/Y/X716NQMGDKBRo0YkJSXRp08f1q5dW6W233DDDRiGwapVqxgxYgTt27enS5cujBs3jq+//rrc/X744Qf69+9PTEwMKSkpXHvttRw8eNC//osvvqBXr17ExcXRoEEDevfuzR9//OFfv2DBAo477jiio6M56qijmDJlCi6Xq0ptrw8UcIuIiNQz4Uq3FBGpCtM0yStyhfSVU+Bk0vs/UtZvM9+yye9vJKfAGdLxTLNqvxffeOMNOnToQIcOHbjsssuYPXt20DE+/PBDhg8fztlnn813333H559/zvHHHw/AvHnzaN68OVOnTiUzM5PMzNA/GMjJyWH06NEsX76cr7/+mnbt2jFkyBBycnJC2n/fvn0sXLiQf/3rX8TFxZVa36BBgzL3y8vLY9CgQTRs2JDVq1fz1ltv8dlnn3HjjTcC4HK5OO+88+jTpw/ff/89K1eu5Nprr/V/mPDJJ59w2WWXMXbsWDZu3MgzzzzDnDlzmDZtWsivvb5QSrmIiEg9Es50SxGRqsh3uuk88ZNqOZYJ7MguoNvkT0PafuPUgcRGhh4KPf/881x22WUADBo0iIMHD/L5559z5plnAjBt2jQuvvhipkyZ4t+nW7duZGdnk5ycjN1uJyEhgdTU1NBfFFbveKBnnnmGhg0bsnTpUoYOHVrp/r/++iumadKxY8cqnfeVV14hPz+fl156yR+oP/HEEwwbNozp06fjcDjIyspi6NChtG3bFoBOnTr59582bRp33HEHo0ePBuCoo47iv//9L+PHj2fSpElVasvhTj3cIiIi9UQ40y1FROqrn3/+mVWrVnHxxRcDEBERwUUXXcQLL7zg32bdunWcccYZ1X7uXbt2cf3119O+fXuSkpJISkri4MGDbN26NaT9fb3wVZ2PetOmTXTv3j2oV7x37954PB5+/vlnkpOTueKKKxg4cCDDhg3j0UcfDeq5//bbb5k6dSrx8fH+r2uuuYbMzEzy8vKq1JbDnXq4RURE6gG3x2TKgo3lplsawJQFGxnQORW7rWr/eImI1IQYh52NUweGtO2qjH1cMXt1pdvNufIEerVJDuncoXr++edxuVw0a9bMv8w0TRwOB/v376dhw4bExMSEfDwfm81WKrXd6XQGPb/iiivYvXs3M2fOpFWrVkRFRXHyySdTVFQU0jnatWuHYRhs2rSJ8847L+S2maZZbpDuWz579mzGjh3LwoULeeONN7j77rtZtGgRJ510Eh6PhylTpjB8+PBS+0dHR4fcjvpAPdwiIiL1wKqMfaV6tgOZQGZWAasy9tVeo0REKmAYBrGRESF9ndauMWlJ0ZT3caGBNXzmtHaNQzpeqD2+LpeLl156iYceeoh169b5v9avX0+rVq145ZVXADjmmGMqnGIrMjISt9sdtKxx48bs2LEjKOhet25d0DbLly9n7NixDBkyhC5duhAVFcWePXtCajtAcnIyAwcO5MknnyQ3N7fU+gMHDpS5X+fOnVm3bl3QPl999RU2m4327dv7l/Xs2ZMJEyawYsUKunbtyquvvgrAsccey88//8zRRx9d6stmO7JC0CPr1YqIiNRTu3LKD7YPZTsRkbrEbjOYNKwzQKmg2/d80rDO1Z7B88EHH7B//36uvvpqunbtGvQ1YsQInn/+eevckybx2muvMWnSJDZt2sQPP/zAAw884D9O69atWbZsGdu2bfMHzH379mX37t3MmDGD3377jSeffJKPP/446PxHH300L7/8Mps2beKbb77h0ksvrXJv+qxZs3C73fTq1Yu3336bX375hU2bNvHYY49x8sknl7nPpZdeSnR0NKNHj2bDhg0sWbKEm266iVGjRtG0aVMyMjKYMGECK1eu5I8//uDTTz9l8+bN/nHcEydO5KWXXmLy5Mn8+OOPbNq0yd8LfqRRwC0iIlIPNEkILUUv1O1EROqaQV3TeOqyY0lNCv49lpoUzVOXHVsjhSGff/55zjzzTJKSkkqtu+CCC1i3bh1r166lb9++vPXWW7z//vv06NGD/v3788033/i3nTp1Klu2bKFt27Y0btwYsIqMzZo1iyeffJLu3buzatUq/v3vfwed44UXXmD//v307NmTUaNGMXbsWJo0aVKl19CmTRvWrl1Lv379uPXWW+natSsDBgzg888/56mnnipzn9jYWD755BP27dvHCSecwIgRIzjjjDN44okn/Ot/+uknLrjgAtq3b8+1117LjTfeyHXXXQfAwIED+eCDD1i0aBEnnHACJ510Eg8//DCtWrWqUtvrA43hFhERqQd6tUkmLSmaHVkFZY7jNrD+KQ1lbKOISF01qGsaAzqnsipjH7tyCmiSYP1eq6naFAsWLCh33bHHHhuUDj58+PCgMcsej4fs7GwATjrpJNavX1/qGNdffz3XX3990LI777zT/33Pnj1ZvTp47PqIESOCnocyxVlaWhpPPPGEP2AuS8njdOvWjcWLF5e5bdOmTXnnnXcqPOfAgQMZODC0Mfr1mQJuERGResCXbjlm7loMCAq6azLdUkSkttltBie3TQl3M0RCopRyERGReiIc6ZYiIiJSPgXcIiIi9cigrml8eXt/miZGAXDpiS348vb+CrZFRETCQAG3iIhIPWO3GTjs1p/49AaxSiMXEREJEwXcIiIi9ZDLbY3idro9YW6JiIjIkUsBt4iISD3k8lgBty/wFhERkdqngFtERKQecnusnm31cIuIiISPAm4REZF6qDilXD3cIiIi4aKAW0REpB7ypZSrh1tERCR8FHCLiIjUQ27fGG6PAm4RkcPF5MmT6dGjh//5FVdcwXnnnVfr7diyZQuGYbBu3bo6cZzDWVgD7mXLljFs2DDS09MxDIN333233G2vu+46DMNg5syZtdY+ERGRw5XTG2gXuZRSLiLyd1xxxRUYhoFhGDgcDo466ij+/e9/k5ubW+PnfvTRR5kzZ05I24YjuP3111+58sorad68OVFRUbRp04ZLLrmENWvW1Fob6rqwBty5ubl0796dJ554osLt3n33Xb755hvS09NrqWUiIiKHL4/HxPTG2erhFpF6Y8l9sHRG2euWzrDW15BBgwaRmZnJ77//zj333MOsWbP497//Xea2Tqez2s6blJREgwYNqu141WnNmjUcd9xxbN68mWeeeYaNGzfyzjvv0LFjR2699dZwN6/OCGvAPXjwYO655x6GDx9e7jbbtm3jxhtv5JVXXsHhcNRi60RERA5PvvHboGnBRKQesdlhybTSQffSGdZym73GTh0VFUVqaiotWrRg5MiRXHrppf7sXF8a+AsvvMBRRx1FVFQUpmmSlZXFzTffTGpqKomJifTv35/169cHHff++++nadOmJCQkcPXVV1NQUBC0vmRKucfjYfr06Rx99NFERUXRsmVLpk2bBkCbNm0A6NmzJ4Zh0LdvX/9+s2fPplOnTkRHR9OxY0dmzZoVdJ5Vq1bRs2dPoqOjOf744/nuu+8qvB6maXLFFVfQrl07li9fztlnn03btm3p0aMHkyZN4r333it336VLl9KrVy+ioqJIS0vjjjvuwOVy+dfPmzePbt26ERMTQ0pKCmeeeWZQNkFlr6WuiQh3Ayri8XgYNWoUt912G126dAl3c0RERA4Lgb3aRSqaJiJ1lWmCMy/07U/+F7iLrODaXQSn3gJfPgLLHoDTb7PWF4WY5u2IBcM4tHYDMTExQT3Zv/76K2+++SZvv/02drsV+A8bNoyEhAQ++OADGjZsyDPPPMMZZ5zB5s2bSU5O5s0332TSpEk8+eSTnHbaabz88ss89thjHHXUUeWed8KECfzvf//jkUce4dRTTyUzM5OffvoJsILmXr168dlnn9GlSxciIyMB+N///sekSZN44okn6NmzJ9999x3XXHMNcXFxjB49mtzcXIYOHUr//v2ZO3cuGRkZ/N///V+Fr3/dunX8+OOPvPrqq9hspftwy+uV37ZtG0OGDOGKK67gpZde4qeffuKaa64hOjqayZMnk5mZySWXXMKMGTM4//zzycnJYfny5ZjetK3KXktdVKcD7unTpxMREcHYsWND3qewsJDCwkL/8+zsbMBK7ajO9I7q5GtXXW3fkUD3oGbp+oaf7kF4hOu6FxQWn6/I5T6i77ve++Gh6x5+de0eOJ1OTNPE4/Hg8X0oWJSL7f7mh3bAZQ9YX+U9r4Tnjr8gMi6kbU3T9LcdrMD21VdfpX///ng8HkzTpKioiBdffJHGjRsD8Pnnn/PDDz+wefNmGjVqhGEYzJgxg3fffZc333yTa6+9lpkzZ3LllVdy1VVXATB16lQ+++wzCgoK/OcKPHdOTg6PPvoojz32GKNGjQKsXu1TTjkFj8dDSkoKAA0bNqRJkybW6/R4+O9//8sDDzzg7ylv1aoVP/74I8888wyjRo3i5Zdfxu1289xzzxEbG0unTp3YunUr//rXv4LvV4Cff/4ZgPbt25e53n+dvet8x3nyySdp0aIFjz32GIZh0L59e7Zt28Ydd9zB3XffzbZt23C5XJx33nm0bNkSwN/xGspr8V0z36Pv/jidTv8HIT619bNRZwPub7/9lkcffZS1a9diVOHTp/vuu48pU6aUWv7pp58SGxtbnU2sdosWLQp3E454ugc1S9c3/HQPwqO2r3uuE3x/4jN37uKjjz6q1fPXRXrvh4eue/jVlXsQERFBamoqBw8epKioyFrozKNBmNqTnZMDDndI2zqdTj788EMSExNxuVw4nU6GDBnCtGnTyM7OprCwkBYtWhAVFeXv7FuxYgUHDx6kbdu2QcfKz89n06ZNZGdns3HjRi6//HL/PgDHHnssy5cvD+o0dLlcZGdn8+2331JYWMiJJ54YtI/PwYMHAatOlm/9nj17+PPPP7nmmmu47rrr/Nu6XC4SExPJzs7m+++/p0uXLv7zAHTr1q3UsQLl5eX5H8taX16bfvjhB4477jhycnL82xxzzDEcPHiQTZs20aZNG/r06UP37t3p378//fr149xzz6VBgwYhvZZAOTk5FBUVkZ+fz7Jly4LS1gNfQ02rswH38uXL2bVrl/+TDQC3282tt97KzJkz2bJlS5n7TZgwgXHjxvmfZ2dn06JFC8466ywSExNrutmHxOl0smjRIgYMGKBx6mGie1CzdH3DT/cgPMJ13XfnFMKapQA0aJjCkCEn1Nq56xq998ND1z386to9KCgo4M8//yQ+Pp7o6GhroZlg9TRX1VczsS1/ENMeieEuwnPav6H3zVU6RGIVUsodDgd9+/Zl1qxZOBwO0tPTg65pVFQUCQkJQbFGZGQkaWlpvP/++8TFxQV1IDZo0IDExEQMwyA6OrrUfna73b/M4XAQERFBYmIijRo1AiA+Pr7MuCY+Ph6AuLg4//r8/HwAnnnmGU488cSg7X3nCTxHRccK1L17dwC2bt1K7969y712JY9jt9uJjIwMOmZcnJVpkJiYSMOGDfn8889ZsWIFixYt4vnnn2fatGmsXLnSv11FrwWsnu2cnBwSEhIoLCwkJiaG008/vfh951XRBwXVqc4G3KNGjeLMM88MWjZw4EBGjRrFlVdeWe5+UVFRREVFlVrucDjqxC+bihwObazvdA9qlq5v+OkehEdtX3fDXvwpvstj6p6j93646LqHX125B263G8MwsNlswWN+7QlVO9DSGbD8Qeh3F0af8bB0BrYl0yAiCvqMr95GexmGQXx8PO3bty93PRD0uo477jh27NhBREQE7dq1K3Occ6dOnVi1ahVXXHGFf9k333wTdCzfdGQ2m40OHToQExPDkiVLSvWcA/6A0jRN//5paWk0a9aMLVu2+FOuS+rSpQtz5871B6dgpc372lFW24899lg6d+7MI488wiWXXFJqmwMHDtCgQQP/ct9xunTpwttvv+1/XQBff/01CQkJtGjRwr/9aaedxmmnncakSZNo1aoV7733HuPGjav0tUBxGrvvuvmmcyv5c1BbPxdhDbgPHjzIr7/+6n+ekZHBunXrSE5OpmXLlv5xCD4Oh4PU1FQ6dOhQ200VERE5bARWJg+sWC4icljzVSPvd1dxcO17XDIt+HmYnXnmmZx88slceumlzJgxg06dOrF9+3Y++ugjzjvvPI4//nj+7//+j9GjR3P88cdz6qmn8sorr/Djjz+WWzQtOjqa22+/nfHjxxMZGUnv3r3ZvXs3P/74I1dffTVNmjQhJiaGhQsX0rx5c6Kjo0lKSmLy5MmMHTuWxMREBg8eTGFhIWvWrGH//v2MGzeOkSNHctddd3H11Vdz9913s2XLFh588MEKX59hGMyePZszzzyT008/nTvvvJOOHTty8OBBFixYwKeffsrSpUtL7XfDDTcwc+ZMbrrpJm688UZ+/vlnJk2axLhx47DZbHzzzTd8/vnnnHXWWTRp0oRvvvmG3bt306lTJ4BKX0tdFNaAe82aNfTr18//3HeRRo8eHfIE7yIiIhIsMMgucqlKuYjUEx53cLDt43vuCW1Mdm0wDIMPPviA8ePH889//pPdu3eTmprK6aefTtOmTQG46KKL+O2337j99tspKCjgggsuYMyYMXzyySflHvc///kPERERTJw4ke3bt5OWlsb1118PWOPkH3vsMaZOncrEiRM57bTT+OKLL/jnP/9JbGwsDzzwAOPHjycuLo5u3bpx8803A1ba94IFC7j++uvp2bMnnTt3Zvr06VxwwQUVvsZevXqxZs0apk2bxjXXXMOePXtIS0vjlFNOYebMmWXu06xZMz766CNuu+02unfvTnJysj/QByutfNmyZcycOZPs7GxatWrFQw89xODBgwEqfS11kWH6yrjVU9nZ2SQlJZGVlVWnx3B/9NFHDBkypE6k/ByJdA9qlq5v+OkehEe4rvuvu3I48+FlABzdJJ7PxvWptXPXNXrvh4eue/jVtXtQUFBARkYGbdq0KTWWtr7yeDxkZ2eTmJhYZlq21IzA615UVFTu+6624kTdeRERkXomsIfbqXm4RUREwkYBt4iISD0TNIbbXa8T2UREROo0BdwiIiL1TNAYbvVwi4iIhI0CbhERkXrG7SkOsl0KuEVERMJGAbeIiEg943SbZX4vIiIitUsBt4iISD3jVtE0EamjPB79TpLaUxfeb2Gdh1tERESqn6qUi0hdExkZic1mY/v27TRu3JjIyEgMwwh3s2qUx+OhqKiIgoICTQtWi3zXPTs7mz179mCz2YiMjAxbexRwi4iI1DOB47Y9Jng8JjZb/f7HVkTqNpvNRps2bcjMzGT79u3hbk6tME2T/Px8YmJi6v2HC3VJ4HWPi4ujZcuWYf3AQwG3iIhIPRPYww3g9HiIstnD1BoREUtkZCQtW7bE5XLhdrvD3Zwa53Q6WbZsGaeffjoOhyPczTli+K57//79iY6ODvuHHQq4RURE6pmSc2873SZR+osvInWAYRg4HI4jIgC12+24XC6io6OPiNdbV/iue0RERNiDbVDRNBERkXrHVaJIjKYGExERCQ8F3CIiIvWMu0RKeZECbhERkbBQwC0iIlLPlEwpL/lcREREaocCbhERkXqmVNE09XCLiIiEhQJuERGResZdYgy3Uz3cIiIiYaGAW0REpJ4pGWCrh1tERCQ8FHCLiIjUMyWLpmkMt4iISHgo4BYREalnSo7hVpVyERGR8FDALSIiUs+UnHdb83CLiIiEhwJuERGReqZ0lXKllIuIiISDAm4REZF6puQYbqdHPdwiIiLhoIBbRESknikZYDtdCrhFRETCQQG3iIhIPeMukUJeMsVcREREaocCbhERkXqm9Bhu9XCLiIiEgwJuERGResZVMqVcRdNERETCQgG3iIhIPVOyaJqmBRMREQkPBdwiIiL1jMutlHIREZG6QAG3iIhIPaN5uEVEROoGBdwiIiL1jIqmiYiI1A0KuEVEROoZd4miaZoWTEREJDwUcIuIiNQzvhRyu80AoMilHm4REZFwiAh3A0RERKSaLLkPbHbcnn4AxDjsHCx0WdOELZ0BHjf0mxDmRoqIiBw51MMtIiJSX9jssGQaZ+9/GYBohx2AXlufhyXTrPUiIiJSa9TDLSIiUl/0GQ/ABUumsT9iFzts7Yi1b6PPtreh313+9SIiIlI7FHCLiIjUJ33G88bqrfzz4MtQ9DE44LPUf3Kmgm0REZFap5RyERGReubNuJH4CpO7TBufNR4d3gaJiIgcoRRwi4iI1DPDc17BW6CcCMNDnx2zw9sgERGRI5QCbhERkfpk6QwuzZvLbk8SAF+4j2Hw7hesKuUiIiJSqxRwi4iI1BdLZ8CSabwUfSkHiAdgtacj7ydfaVUpV9AtIiJSqxRwi4iI1BceN/S7ixcdFxKBC4AYo5D3ky6zqpR73GFuoIiIyJFFVcpFRETqi34TAHCvWoLDsILrGIpwuj2aEkxERCQM1MMtIiJSz7g8Jg5fDzeFuDyeMLdIRETkyKSAW0REpJ5xuU0isHq4o40inG4zzC0SERE5MingFhERqWesHm5fSnmhlVIuIiIitU4Bt4iISD3j9nj8PdwxFOFSD7eIiEhYKOAWERGpZ6yU8uIq5erhFhERCQ8F3CIiIvWMy+Mh0lulPNpXpVxERERqnQJuERGResbwuPzfW2O4lVIuIiISDgq4RURE6hnT4/R/b43hVg+3iIhIOCjgFhERqUc8HpMI0+1/Hm0UUqQebhERkbBQwC0iIlKPuDzFBdPA28PtUQ+3iIhIOCjgFhERqUdcAVOCgXcMt8tdwR4iIiJSUxRwi4iI1CMuj4kjIOC2GyYEjOkWERGR2qOAW0REpB5xu00ijOAe7Qh3fphaIyIicmRTwC0iIlKPOD0eHAFjuAGiTCdujwqniYiI1DYF3CIiIvWIu0RKOUCMUYhTU4OJiIjUOgXcIiIi9YjLHVylHKxK5Qq4RUREap8CbhERkXqkZNE08FYq11zcIiIitU4Bt4iISD3iLjEtGEC0UYRLPdwiIiK1TgG3iIhIPeLylK5SHkMhRQq4RUREal1YA+5ly5YxbNgw0tPTMQyDd99917/O6XRy++23061bN+Li4khPT+fyyy9n+/bt4WuwiIhIHedym6WqlMdQhEsp5SIiIrUurAF3bm4u3bt354knnii1Li8vj7Vr1/Kf//yHtWvXMn/+fDZv3sw555wThpaKiIgcHlwes1RKeYxRiMujHm4REZHaFhHOkw8ePJjBgweXuS4pKYlFixYFLXv88cfp1asXW7dupWXLlrXRRBERkcOKu4x5uKMposilHm4REZHaFtaAu6qysrIwDIMGDRqUu01hYSGFhYX+59nZ2YCVou50Omu6iYfE16662r4jge5BzdL1DT/dg/AIx3XPL3SWWaW8oKjoiLz/eu+Hh657+OkehJ/uQXiEet1r674YpmnWiY+8DcPgnXfe4bzzzitzfUFBAaeeeiodO3Zk7ty55R5n8uTJTJkypdTyV199ldjY2OpqroiISJ30c5bB9p9WMjNyln/Zw84R2DqdQ5uEMDZMRESkDsnLy2PkyJFkZWWRmJhYY+c5LHq4nU4nF198MR6Ph1mzZlW47YQJExg3bpz/eXZ2Ni1atOCss86q0Qv5dzidThYtWsSAAQNwOBzhbs4RSfegZun6hp/uQXiE47on/LKHj39eHrQsxiikW6+TOLFNcq20oS7Rez88dN3DT/cg/HQPwiPU6+7LhK5pdT7gdjqdXHjhhWRkZLB48eJKg+aoqCiioqJKLXc4HHX+jX44tLG+0z2oWbq+4ad7EB61ed1Nw1Z6Hm6KMA3bEX3v9d4PD1338NM9CD/dg/Co7LrX1j2p0wG3L9j+5ZdfWLJkCSkpKeFukoiISJ1WZpVyCjUtmIiISBiENeA+ePAgv/76q/95RkYG69atIzk5mfT0dEaMGMHatWv54IMPcLvd7NixA4Dk5GQiIyPD1WwREZE6y+0xSxdNM4oocmtaMBERkdoW1oB7zZo19OvXz//cN/Z69OjRTJ48mffffx+AHj16BO23ZMkS+vbtW1vNFBEROWw43aWnBYuhEKd6uEVERGpdWAPuvn37UlGR9DpSQF1EROSw4Q5MKbdHgbuQaIrIUw+3iIhIrbOFuwEiIiJSfVweE4fh7eGOtgqNxhiFOBVwi4iI1DoF3CIiIvWIyx3Qwx3lDbgpUkq5iIhIGCjgFhERqUfcHk9xwB2dBHirlHvUwy0iIlLbFHCLiIjUI67AKuXelPJoo4gilwJuERGR2qaAW0REpB6xUsq9Y7j9KeWFuDxKKRcREaltCrhFRETqEauHO7hoWjROnOrhFhERqXUKuEVEROoRt8dTnFIeZY3hjlWVchERkbAI6zzcIiIiUja3x2RVxj525RTQJCGaXm2SsduMSvdzuk0ijOAx3AC48muopSIiIlIeBdwiIiJ1zMINmUxZsJHMrAL/srSkaCYN68ygrmkV7usOLJoWFRBwOxVwi4iI1DallIuIiNQhCzdkMmbu2qBgG2BHVgFj5q5l4YbMCvd3Bk4LFhGFy3B4VyjgFhERqW0KuEVEROoIt8dkyoKNlFVP3LdsyoKNuCuoOO4OrFJuj8RliwbAUMAtIiJS6xRwi4iI1BGrMvaV6tkOZAKZWQWsythX7jZB83DbHbjsVsBt0xhuERGRWqcx3CIiInXErpwCbo6Yh9u08bh7eKn1N9nnYzc87MrpUe4xXB5P8bRgtgjc3oDbUMAtIiJS69TDLSIiUkc0SYjGbdq41TGPm+zzg9bdZJ/PrQ4rGG+SEF3uMdyegCrldgduewwANnf5PeciIiJSM9TDLSIiUkf0apPMuPiRGAfhVsc8koxc7nVdyr/s73KrYx4PO0cwL34kN7dJLvcYLndASrnN4e/htquHW0REpNYp4BYREakj7DaDScM6M2bucFobO/hnxMdcaf8Eu+HhYecIHncP56lhnSucj9vlCSya5sATYfVw290KuEVERGqbUspFRETqkEFd03jqsmP5ydYWALvhodCM4K34kTx12bGVzsNtBdy+Hu4IPBHeomlKKRcREal16uEWERGpYwZ1TaPNwk1wEDymQZTh4quT12Drekal+7o9noAq5ZH+Hu4IBdwiIiK1TgG3iIhIXbN0Bh0OrgLge/Momh53Lmlf3AuGAX3GV7ir0x3Qw213YPoD7tIp5W6PyaqMfezKKaBJQjS92iRXmK4uIiIiVaOAW0REpC5ZOgOWTOPnmJ50yP+OGAr5qcMY0pJiYMk0a5sKgm63x8RhFE8L5g+4PYVB2y3ckMmUBRuD5v1OS4pm0rDOlaati4iISGg0hltERKQu8bih3138EtkZgFgK2XewyAqy+91lra+AyxNQpdzuwHRYAbfDUxxYL9yQyZi5a4OCbYAdWQWMmbuWhRsyq/EFiYiIHLnUwy0iIlKX9JtgPa65BoAYo5B9uUXWskrSyQFcbk9xlXKbAxyxQHHA7faYTFmwEbOMfU3AAKYs2MiAzqlKLxcREfmb1MMtIiJSBxkeJ2D1cO/1BdwhCKpSbneAt4c70htwr8rYV6pnO5AJZGYVsCpj36E1XERERPwUcIuIiNRFbm/AbRSy/2BhJRsH7BaYUm6LwIj09nCb1jF25YRWrTzU7URERKR8CrhFRETqIF8PN0DOwZyQ97NSyounBTO8KeVR3oC7SUJ0SMcJdTsREREpnwJuERGROigw4M7LzQ55P5fbg8MoTik3Ir0p5abVY92rTTJpSdGUNzrbwKpW3qtN8qE0W0RERAIo4BYREamDDI/L/31BFQJuAvbDFoEtMg4o7uG22wwmDetc9jm9j5OGdVbBNBERkWqggFtERKQOspkBAXf+wdB3dAcUWLM7sEf6UsqLlw/qmsakc0oH3alJ0Tx12bGah1tERKSaaFowERGROsjmcfq7nM3CXJxuDw575Z+TB6aiY3Ngi/IG3AQXXktPigl6fvugDlx7elv1bIuIiFQj9XCLiIjUQYE93LFGIftDnRosMKXc7sDuTSmPpgjTLJ59+9fdwb3mKfFRCrZFRESqmQJuERGROsbl9hAREHDHVGUubre1n2nYwTCwe3u4YyjE7QkIuHcFB9y7c0KfekxERERCo4BbRESkjil0eYrn0gZiKWRfyD3cVkq5abNGjUVEWz3cMRThdBcH3L95A+42jaz1CrhFRESqnwJuERGROqbQ5SHCCOjhNkLv4bb5Am57JIC/hzvKcOJ0edeZJr/tzgXg5LYpgAJuERGRmqCAW0REpI4pcLqDerhjOIQx3N4ebkdUnH+Vs8AKsndkF3Cw0IXdZnBC64aAAm4REZGaoIBbRESkjrFSygOKplVhDLevhxubw3qILK5G7i7KA4rHb7dKifVXK9+VU/C32y0iIiLBFHCLiIjUMYUuNxGBPdxGIftyQ+uB9lc3t1sBN4ZBnhkFgKsgOOA+unE8TRKjAfVwi4iI1AQF3CIiInVMgdMTFHAfStE0X0o5QCHWeG5PiR7uo5vE0zjBCsZzi9zkFgZMKSYiIiJ/mwJuERGROqbQ6SayREp5KAG3x2MSYXoDdV8PN1BgWEG1uzAXt8dk7R/7/euiI2zEOOyAerlFRESqW0Tlm4iIiEhtsqqUl0wprzzgdnnM4v0CAm5fD/f637dz8QeLycyyxmvP+uI33vluG/FRdvKdbnblFNK6UVyp44qIiMihUcAtIiJSxxS6PEQcQg+3y1M8f7fhnRYMoNCIBhPe/uZXMj3BAfWOrAJ8s3Orh1tERKR6KaVcRESkjikolVJewP48Jx6PGbSd22Oy8re9vLduGyt/2xsUqBu5u2DpDAAKDSv4jsYKqG+yz+fmiHkABB5xR3Z+Tb0kERGRI5J6uEVEROoYK3AOTil3e0yy8p00jLOC54UbMpmyYKM/PRygaUIUPX37GRGwZBoABVhjuGMo4ib7fG51zOMh54hS5/3+z6yaekkiIiJHJAXcIiIidUzJacHiDSudfF9eEQ3jIlm4IZMxc9dilthvZ04hDpu3hzu5NRx3OSyZRhOjGQBn279mgH0tDzlH8Lh7eKnz7sjWXNwiIiLVSSnlIiIidUxBkZsoozilPN7mDbhzi3B7TKYs2Fgq2PbxBeqmzQF9xkO/u2htbgOoMNgGK5VdREREqo8CbhERkTqmyBlcIC3GsMZe7z1YxKqMfUFp5CX5qpQfKPSG5H3G4/H+uXeZtnKDbYAil+fvNFtERERKUMAtIiJSxziLSgTc3mJn+3KL2JVTcdq3r0p5gcf7J37pDGxYgXSE4eEm+/yg7Y2A73cfrLwSuoiIiIROAbeIiEgd43IGT88VZVpB9le/7mZPJVN3+aqUOyKjrSrlS6bxteNEAH7zpHGrY15Q0J2aFM30C7oBsC/XKs4mIiIi1UNF00REROoYV4mU8gjTRQQuPvxhBx/+sAPDALOcuNjXw52cvwWWfMiz9otZmduCkyK/IZ8oHnGN4FbHPIZ0S+PACbfQq00yABPm/4DHhL0HC2mSGF2TL09EROSIoYBbRESkjikZcAPEUki29892ecE2gMPbw32w0M1zzhE8VnAOXYwMAJoYB3jUNRzThHMMDye3TfHvlxIfxe6cQnblKOAWERGpLkopFxERqWPc3pTyQhy4TOtPtW8cd0UaxUf6q5QvzmnBY94CabvMBgCkkIUND4+7hzPq9zOC0scbx1tzde+uJGVdREREQqeAW0REpI5xO50AFJkR5GEFwrFGxYFwjMPGE5f0xOGtUp4d0Em+lyTcpoHdMEkhCxPIzCpgVcY+/zaNEyIB+HTjDlb+tldjuUVERKqBAm4REZE6xu2ygmsXdvJ9AXclPdz5Tg8/Zmb7i6a5sPvXebCxlyTASiv38VU8X7ghk1UZ+wF4bdWfXPK/rzl1+mIWbsisnhckIiJyhFLALSIiUsd4XFb3tJMI8kwr4I6h4unAwJo2zJdS7gwIuAF2etPKAwPuJgnRLNyQyZi5a8l3uoO235FVwJi5axV0i4iI/A0KuEVEROoYlzfgdht28rEKmFWWUg6QEO3wVymPjooKmmN7l9kQsAJuA0hLiua4Vg2ZsmAjZSWP+5ZNWbBR6eUiIiKHSAG3iIhIHeNxWWO4Y6Ki/WO4YyhduTxQpN1G20Zx/h7u3u3TAPxBt69wWlOs1PFJwzrz7R/7ycwqv+e8rLHeIiIiEjoF3CIiInWM6e3hjoyKpnVqI6DyKuVNEqLwgH8M99FpDXnqsmNJTbJ6yHfRAIDWUTk8ddmxDOqa5h/DXZkdWfmH8CpERERE83CLiIjUMR631cONPYJGDRvCLrjptHSSXa15d9129uUW93Y3io9kz8EiTMDtMYn0VinH5mBQ1zQGdE5lVcY+Yr//Cda/w/nt7Bhdrd7vJgmhzbf93w83ERNpZ5B3PxEREQmNAm4REZE6xvSmlBt2B0TGAtA2yWDiKV246+zOrMrYx66cApokRNMkIYozHl7KgbwinG6Pv4cbu8N6sBmc3DYFijrAejAO7vSfp1ebZNKSotmRVVDmOG6f/blFjJm71t8zLiIiIqFRSrmIiEhd47Z6sA17JDisgBtnHlAcQJ/boxknt00hJd6aPzu3yE2B0+0fw43NEXzMhFTrMac44LbbDCYN61xpc1RATURE5NCENeBetmwZw4YNIz09HcMwePfdd4PWm6bJ5MmTSU9PJyYmhr59+/Ljjz+Gp7EiIiK1xPSnlDsgMs76vii3zG0Toh0Y3spoe3OL/FXKsZdIYvMF3Ad3gsfjXzyoaxpPXXYsyXElAvSSbUIF1ERERKoqrAF3bm4u3bt354knnihz/YwZM3j44Yd54oknWL16NampqQwYMICcnJxabqmIiEjtME3TH3Db7I5SPdwl2W0GidFWsLwnp6j8Hu64Jtajxwn5wUHzoK5p/Gdol5DaF2qhNREREQnzGO7BgwczePDgMteZpsnMmTO56667GD58OAAvvvgiTZs25dVXX+W6666rzaaKiIjUCpfHJMK0xmEbEVH+MdwUlR1wAzSIdZCV72RvbiEO/xjuyOCNIiIhNgXy9kLODohrFLQ6NTG0AmqhFloTERGROjyGOyMjgx07dnDWWWf5l0VFRdGnTx9WrFgRxpaJiIjUnEKXhwhvpXFbhAMc3pRyZ9kp5QANYq3geu/BgB7ukinlAPG+tPIdpVb5CqgZpdZYDCAtKZpebZJDeRkiIiJCHa5SvmOH9c9A06ZNg5Y3bdqUP/74o9z9CgsLKSwsnqs0OzsbAKfTidPprIGW/n2+dtXV9h0JdA9qlq5v+OkehMehXPeDecW91IbNjsseRQTgKTyIu5zjJEXbAdidU4DDsPZ1mTbMEtvb45tg2/UjrgPbS60DuGtwB256fT0GBFUtNwLWe9wuPO6QX07Y6b0fHrru4ad7EH66B+ER6nWvrftSZwNuH8MI/qzdNM1SywLdd999TJkypdTyTz/9lNjY2GpvX3VatGhRuJtwxNM9qFm6vuGnexAeVbnu+wrxB9zbd+5lR8HPHA/s3fEXKz76qMx9cvfbABvb9uXg8PaOr/luPTt/D05k63nASUtg87fL+GVbUpnHurK9wfwtNg4UFf+tTYo0Gd7ag/uPb/mo/M+86zS998ND1z38dA/CT/cgPCq77nl55Q/Vqk51NuBOTbXS3nbs2EFaWvGcn7t27SrV6x1owoQJjBs3zv88OzubFi1acNZZZ5GYmFhzDf4bnE4nixYtYsCAATgcFVeJlZqhe1CzdH3DT/cgPA7lumfsyWX7+oUApLdoSWrHU+CPp0hJjGHIkCFl7rPmg018u+dP8lwGEQ4r4D6+10mYbc8I2s625FtY8SUdmjWg3cCyjzUEGO8xGfv6ej7dtIth3VJ5YEQ37LbyP+yuy/TeDw9d9/DTPQg/3YPwCPW6+zKha1qdDbjbtGlDamoqixYtomfPngAUFRWxdOlSpk+fXu5+UVFRREVFlVrucDjq/Bv9cGhjfad7ULN0fcNP9yA8qnLd3dj8U3vZIiKxxVgfFtuc+djKOUbDeKuQmcfEP4Y7IjIaSm6fmA6APW8X9gra4wC6t2zIp5t2ERUZQXRUQAG2JfeBzQ59xpfecekM8Lih34RQXmqt0ns/PHTdw0/3IPx0D8KjsuteW/ckrAH3wYMH+fXXX/3PMzIyWLduHcnJybRs2ZKbb76Ze++9l3bt2tGuXTvuvfdeYmNjGTlyZBhbLSIiUnMKnO7gSuOhFE2LKf6nwb9vyWnBoHgu7pzSRdNKahxvfXi952Bh8AqbHZZMs74PDLqXzrCW97ur0mOLiIgcKcIacK9Zs4Z+/fr5n/tSwUePHs2cOXMYP348+fn53HDDDezfv58TTzyRTz/9lISEhHA1WUREpEYFVinH5gh5WjCf4irlkaU3rELA3SjB2n93TomA2xdkL5kG7iLoOBQ2fwJf3GsF22X1fIuIiByhwhpw9+3bF9M0y11vGAaTJ09m8uTJtdcoERGRMCp0efwp5dgd4PAG3M4QA26jomnBvDVQDu4E04QKipA29qapl+rhhuCge9kD1vcKtkVEREqps/Nwi4iIHImCUsptERDpSynPA4+nzH2SYop7syNDSSl3FUBBVoXt8PVw7z1YhMdTxofjgcF1eWO6RUREjnAKuEVEROqQQpeHiKAx3AFTWrryy9yn7JTyMgJuRwxEeacDO7jTelxynzX+uoSUuChuss/nRttbHMgvY67SRZOLv/e4yzyGiIjIkU4Bt4iISB1S6HSXnVIO5Y7jDiya5g+4bSVSyn2BdYI3rfyrmd7tvEXQ5gy1ni+dAUvuI/KrB7nVMQ+3aSudVr50Bnz1SPHzVr2tYyjoFhERCVJnpwUTERE5EhW4PEQHppTbbBARY/VuO3OBxqX2SSqrSnnJHm5fYN2glfV83avQsE3x+i3L4bkz4a/V0OZ0yFjGnKiRPF4wlJNyCmnf1Fuw1FeNvE0fyFhqLUvrDkf1Lbt6uYiIyBFMAbeIiEgdUuh0E2+UqDTu8Abc5fRwR9htJERFkFPoCujhLhFwBxY6A2jbv/j7Yy6Gnz+ygm2AjGXQ7y4++fl0yNob3MPtcVsF0nZ8X7ys6CAMuq94vYiIiAAKuEVEROqUUlXKwSqclr8PnGWP4QZIinWQU+gkwvAE7xuoz3j49TP48xv4bXHx8u9fD97OWwSt0fbvgBJTg/WbYD0+0q14WVFu8fFFRETET2O4RURE6hAr4A5IKYeAqcFyy92vQayjOFCHsgNugDMmlXOEgCnCPG74fCqN46MA2H2w0D+2G4C8fZC1tXj7ovLbJSIiciRTwC0iIlKHFDrdAZXGvSnlkd6Au5yUcoAGMZHFgTqUPS0YwB9fBR8bwLADZvH4boDlDzFw74sA9Pj9WSv93Ga31nl7vv0UcIuIiJRJAbeIiEgdEtTD7euldvjm4i4/sE2KdRRPJxa4byBfwbN+d8HptxUvN91WobQDf0CTztYyw8aJW57mFcc9DN79grWPL2U8c5316JtirDCnai9SRETkCHFIAfeBAwd47rnnmDBhAvv27QNg7dq1bNu2rVobJyIicqQpdAVMC+ZLKQ+ph7tESnnJacECg20o/r71adbz1qdZz3dthKhEMD2YGPS2b+Sl6EuDx2dvX2c9tjrF2y71cIuIiJSlygH3999/T/v27Zk+fToPPvggBw4cAOCdd95hwoQJ1d0+ERGRI0qB00OE4evh9lUp943hriDgjnX4U9E9RgQYRvAGvurifcYHf3/FB9b3Hrf1vN9d0Pk8AAxMiswIHncPDz6Wr4e7dW/rUQG3iIhImaoccI8bN44rrriCX375hejoaP/ywYMHs2zZsmptnIiIyJEmqIc7sEo5VBjYNoiJxOGdTsws2bsNVnVxXy914PfgDbQnFH+f1Ny/KtJw8Vjhf/B8Md1akLcPDngLpvl6uPP2hvz6REREjiRVnhZs9erVPPPMM6WWN2vWjB07dlRLo0RERI5Uhc7AKuW+MdyV93AHjuH2GBHYD7UBS2fAF/dCSjvY+wuL3T3ob18HX2zEA/xk70BnID+hFVEb3rE+uXcXgWmW7lUXERE5wlW5hzs6Oprs7OxSy3/++WcaN25cLY0SERE5UhW4AquU+wLuGOuxkjHcvv3M8qYEq0zgOO9u/wAgLyKRh5wjALB9cS97P30AgD+y3NhWPu7d0QRXYVlHFBEROaJVOeA+99xzmTp1Kk6nEwDDMNi6dSt33HEHF1xwQbU3UERE5EhS6PSUDrgjK69SnhjtINLbw+007bg9ZtVPHji2O70nAN2MDB53D+ch5whWuDtxmn0DAB1tf/GIM+DvftHBqp9PRESknqtywP3ggw+ye/dumjRpQn5+Pn369OHoo48mISGBadOm1UQbRUREjhiFLo8/cGbdq1avs6OMKuVLZ8CS+wBYuCGTG19b6w/U9xfCqdMXs3BDZtVOHji2O70HAC08fxFHPo+7hzPSeTemN453mnYedV9AvmkVdnMXaGowERGRkqoccCcmJvLll1/y9ttvc//993PjjTfy0UcfsXTpUuLi4mqijSIiIkeMQpebCCOgh3vJNPjjK+u5bwy3L/XbZmfhhkzGzF3LnoNF/oDbZdrZkVXAmLlrqx50+8Q3gcTm2DDpYmwB4J6IFzAMa7i2w3Bzk30+B7EKqG7I2H6oL1lERKTeqnLRNJ/+/fvTv3//6myLiIjIEa/A6fEXP+OEf0JCmhVcgxVwB4yzdp92G1OmL8aXPO6rbu7CjgkYwJQFGxnQORW77RAKmqX3gOy/6Gb7nRPZxGURnwOw0HMCGz2tuNUxjwOeODAgK/vA33jVIiIi9VOVA+6pU6dWuH7ixImH3BgREZEjXdC0YDaHleK9axP8OB9+WwK/LfaPs171214yswr8+/rm73Z6a5SbQGZWAasy9nFy25SqNya9J/z0ASPsy+hk+5OdngY0tR3gM/dxvO05HYBbHfMAaORwHvqLFhERqaeqHHC/8847Qc+dTicZGRlERETQtm1bBdwiIiJ/Q6ErYFowX9G0npdZATcm2CP946x35RQE7RvYwx2o5HYh8xZOSzX28azzbK51fIjHNFji6QHA4+7hXGxfQjPbXjqkVHmUmoiISL1X5YD7u+++K7UsOzubK664gvPPP79aGiUiInKkKnC6ibCVqFL+0wfFG7iLrLTyPuNpkhAdtK9vDLezxJ/3ktuF6vOsNM4AGhq57CcBgLVmO/aRCFgp67+Z6TRjL/YK5ggXERE5UlXLx9GJiYlMnTqV//znP9VxOBERkSOSaZqlU8qXzoA1LxQ/73unNYZ76Qx6tUkmLSka3+jskgG3AaQlRdOrTXKV27JwQyb/fCuDrZ7GAFwTYQX9n7mP9W+TmhRN+5ap1pNCVSkXEREpqdryvw4cOEBWVlZ1HU5EROSI43Sb2EwPNsNbBu2bp6zg+nTvVF0eJ5x0vTWGe8k07MsfYNKwzoAVXPumE3OZdn8QPmlY5yoXTHN7TP56ZyI32ufzvXkUAMmGNc/2Z55juck+n9ui5rP0tn6kNmpk7VRU/hzhIiIiR6oqp5Q/9thjQc9N0yQzM5OXX36ZQYMGVVvDREREjjQFLre/l9rPWyCNlU+CMxdy9xTPle1xM6hrGk9ddixTFmwkIscbcGMnNSmaScM6M6hrWpXbsSpjH1kFHm51zGOZu5t/+RZPUwbbVnGrYx4POUfw7R/7OTky3lqpgFtERKSUKgfcjzzySNBzm81G48aNGT16NBMmTKi2homIiBxpCp0BBdMA+twBDu/467gUOJALeXshpW1x0A0M6prGgM6pbPn0Z/gaurVsxJdX9z+0qcCwiqw97h4OFFchB9hrJvqD7cfdwzk6pwAi46yVCrhFRERKqXLAnZGRURPtEBEROeJZ47cDAm5f0TSA2EZwYKvVw10Gu82gbUoUAMkJsXCIwTYUF1l73D2cKJzc6HgPgOPsv/iDbf92Wb4e7oOHfD4REZH6SnN4iIiI1BGFLk9xSrlhA1vA9F6x3nm088oOuAHwlJhO7BAFFmN70H0RbtP6d6HQjPAH2wnREVYxNvVwi4iIlCukHu7hw4eHfMD58+cfcmNERESOZAXOEhXKA8V5i5Pl7S3/AG5n2ftWkd1mMGlYZ8bMXctY+3zshodCM4Iow8VN9vk87h5On/aNrZR1f8CtHm4REZGSQgq4k5KSarodIiIiR7xClweH4euljgxe6evhLielHLCqmJe17yEY1DWNT4/9mnYbi8ds32Sf7x/T7Um5zdpQPdwiIiLlCingnj17dk23Q0RE5IhX6AxIKbeX+BNdlR7ukvseiqUzaLfxMTx97+SUFv/k6JwCmiScxNLFDbl12/9YujUZeACiEqzt1cMtIiJSSjX8RRYREZHqUOCqIKU81htwV9TDXU0p5QB43NDvLmx9xnNywOIHf72GNVv2c5zb2xOvHm4REZFyHVLAPW/ePN588022bt1KUVFR0Lq1a9dWS8NERESONEHTgpVMC/f3cIeSUl4NAXe/sqf6dNhtPOIezsimLekLCrhFREQqUOUq5Y899hhXXnklTZo04bvvvqNXr16kpKTw+++/M3jw4Jpoo4iIyBEhaFqwkmnh/irloRRNq7kENkeENd2Y0+WxFkRqWjAREZHyVDngnjVrFs8++yxPPPEEkZGRjB8/nkWLFjF27FiysrJqoo0iIiJHhKAx3KVSyn1F0yoIuKtpWrCKRNqtfx2cbl/AHdDDbZo1dl4REZHDUZUD7q1bt3LKKacAEBMTQ05ODgCjRo3itddeq97WiYiIHCHcHpOfdmT7q5SbJYNmX0q5Mxec+eUcpBrHcJcjMsIXcHuDa1/A7XGBq7DGzisiInI4qnLAnZqayt691qfrrVq14uuvvwYgIyMDU59si4iIVNnCDZmcOn0xL3y1xV807efdBSzckFm8UVRicSBdXuG06hzDXQ6Ht4e7sGRKOWgct4iISAlVDrj79+/PggULALj66qu55ZZbGDBgABdddBHnn39+tTdQRESkPlu4IZMxc9eSmVUA4E8pz3fbGDN3bXHQbRiVF05z117A7U8pt9khIsb6XuO4RUREgoRcVeXdd99l2LBhPPvss3g81h/Z66+/nuTkZL788kuGDRvG9ddfX2MNFRERqW/cHpMpCzYSmB/mK5pW5P0TPWXBRgZ0TsVuM6ypwXIyyy+cVqsp5Z6AhXHgylcPt4iISAkh93CPGDGCZs2aMWHCBH777Tf/8gsvvJDHHnuMsWPHEhkZWcERREREJNCqjH3+nm0fX0q5y7RjAplZBazK2GetjE22HtfMhqUzSh/Ql1L+2+IaajFE2r1VyksG3KCAW0REpISQA+6tW7dy00038c4779C5c2dOPfVUZs+eTW6u/riKiIgcil05BaWWRXh7uF3YS2/nSyl3FcCSaaWD7p2brMeanBbMm1Je5AoMuDU1mIiISFlCDrjT09O566672Lx5M4sXL6Zt27aMHTuWtLQ0/vnPf7Jy5cqabKeIiEi90yQhutQyh2H1cBcFjPrybxfrDbhTj4F+dwUH3UtnwN7N1vedhtZYm30p5UXugER4fw+3Am4REZFAVS6aBtCnTx9efPFFMjMzefjhh9m0aROnnnoqXbp0qe72iYjIEcztMVn5217eW7eNlb/txe2pX7Nh9GqTTFpSNEbAMkdAD7cBpCVF06uNN5XcXzRtL/QZXxx0T0m2Hhu2sdbX4BjuUkXTAKJ8PdzKehMREQn0t3LO4uPj6devH1u2bOGnn35i8+bN1dUuERE5wi3ckMmUBRuDxjinJUUzaVhnBnVNC2PLqo/dZjBpWGfGzF2LAZgUVyn3pZRPGtbZKpgGEJtiPfqKph13hRVom24rjTypOezPqJUq5cEp5erhFhERKcsh9XDn5eXx4osv0qdPH9q3b88bb7zBuHHj2LJlSzU3T0REjkQlp8ry2ZFVEDxVVj0wqGsaT112LKlJVtq4r4c7whHFU5cdG/zhgi/g9s3D/cndxes8LtiXYX1fgwF3ZFk93JHq4RYRESlLlXq4v/rqK1544QXeeustXC4Xw4cP57PPPqNfv3411T4RETnClDVVlo8JGJSYKqseGNQ1jQGdUxn86DIce6we7oHHtMBWsic/cB7upTPghzcCVhqQ/Zf1bTimBQMF3CIiIiWEHHC3b9+e3377jZ49ezJ9+nRGjhxJUlJSTbZNRESOQGVNlRUocKqsk9um1F7DapjdZuCw2/wp5bayeql9RdOy/rJSyVPaW4XSDLuVVu7z4zvQYVCNtNPhnRas7JRyBdwiIiKBQg64Bw0axNVXX0337t1rsj0iInKEK2uqrL+z3eEk3+nGYVgp5dgjS28QOC1Ynwmw9kXrefPj4c9vSm+/dAZ43NBvQrW10T+Gu6yU8sKcajuPiIhIfRDyGO7HHntMwbaIiNS4sqbK+jvbHU4KnR4c3h7uMsdhxzQEX03zTkMhZ7v1/Z/fQHRA1tlxo61ge8k0sNlLHebvKE4pL2taMPVwi4iIBDqkomkiIiI1paypsgKVmiqrHsl3uonwFk3DVkYSms0Osd7X/etn1mOj9tb0YAVZxdutf8MKtvvdZU0fVo18RdPcHrN4mjYVTRMRESmTAm4REalTfFNllcUXhAdNlVWPFDjd/irlZaaUQ3Glcl/AndbDCqpPHVe8zdo5NRJsAzgiiv918BdO07RgIiIiZVLALSIidY5vqqxoR/CfqdSk6NJTZdUTpmlaY7grSimH4sJpW7+2HtN7Wo9nTiruFbdH1kiwDcVF0yBgHLd6uEVERMr0twLugoL6V7BGRETqhkFd02jfJN7//LhWDfny9v71MtgGK3g1TfxVystMKQeI8/Zwe5zWY3oP63HpDGsubnskuIus5zXAYQvo4XaV7OFWwC0iIhKoygG3x+Phv//9L82aNSM+Pp7ff/8dgP/85z88//zz1d5AERE5MpmmyZa9ef7neUXueplG7lNQZAWvFVYph+IebgAMSD2muEBav7vgP7utxyXTaiTottkMfy+3v3CaAm4REZEyVTngvueee5gzZw4zZswgMrL4n4Fu3brx3HPPVWvjRETkyLU/z0l2gcv//K/9eRVsffgrcFk925GVpZTHBQTcjdrD17NKF0jrM75Gg27f1GDOUinlmhZMREQkUMjzcPu89NJLPPvss5xxxhlcf/31/uXHHHMMP/30U7U2TkREjlwZe6ze0gaxDg7kOckpcJGV7yQpppxA9DCXX2QF2lE2bxBbMqV8yX3eKuUBAXd6D2ue7TanW4+BfMF3yeXVwAq43RT6UsqjAsZwmyYY9TcTQUREpCqq3MO9bds2jj766FLLPR4PTqezWholIiKyxRtwd0xNICXOyqjatj8/nE2qUflOX8BdTkq5zW71WP+xonhZek9recaysufb7jMe+k2o9rYWz8VdYgy3x2WNHxcRERHgEALuLl26sHz58lLL33rrLXr27FktjRIREfljrxVwt2kUR/OGMUD9Tisv8AbckYY3iC2ZUu5LE9/0XvGynT/W2HzbFYksmVLuiCteqXHcIiIiflVOKZ80aRKjRo1i27ZteDwe5s+fz88//8xLL73EBx98UBNtFBGRI1CGt2Ba65Q4svNdrP8ri7+OgB7uSKOCKuV9xkPODljjLVL63cu1HmwDAUXTfB8OREBENLgKrLm4Y5NrtT0iIiJ1VZV7uIcNG8Ybb7zBRx99hGEYTJw4kU2bNrFgwQIGDBhQE20UEZEjkC+lvHVQD3f9DbgLnVbwGuULuMurUj7ofsAo3qaWg20oLprmH8MNqlQuIiJShir3cAMMHDiQgQMHVndbREREAO+UYL6AOyWOXdkFQP1OKff1cDuMSqqUfzUTMIPn267loLt4DLcZsDAO8vZC4cFabYuIiEhdVuUe7j///JO//vrL/3zVqlXcfPPNPPvss9XaMACXy8Xdd99NmzZtiImJ4aijjmLq1Kl4PJ7KdxYRkcPW3twicgqt4mGtUmJp3jAWqN893L4x3A7ftGC2MgLuWpxvuyL+acGCergTrMciBdwiIiI+Ve7hHjlyJNdeey2jRo1ix44dnHnmmXTt2pW5c+eyY8cOJk6cWG2Nmz59Ok8//TQvvvgiXbp0Yc2aNVx55ZUkJSXxf//3f9V2HhERqVt8vdvpSdFEO+xHRNE0fw83virlJQLuwGA7cL5tsJYHPq9hvqJpRW6llIuIiFSkyj3cGzZsoFevXgC8+eabdOvWjRUrVvDqq68yZ86cam3cypUrOffcczn77LNp3bo1I0aM4KyzzmLNmjXVeh4REalbtvgKpjWygrhm3oA72zsXd33km4c7gnJSyj3usguk+aqX18B82+VxRFhjyI/a8Fhx73rJgHvpDGvucBERkSNYlXu4nU4nUVFRAHz22Wecc845AHTs2JHMzMxqbdypp57K008/zebNm2nfvj3r16/nyy+/ZObMmeXuU1hYSGFhof95dna2v911dZ5wX7vqavuOBLoHNUvXN/wOp3vg9pgs27wTgKgIg4LCIhw2g4axDvbnOfljdw6d0hLC3MrQVOW65xVa20R4e7hdpoEZuN+p//YdtPTOp9xS/roaEGHzVin3AEum4Xa7MRyx2AB3fhYsvg/7svtxn34HnjC/5w6n9359ousefroH4ad7EB6hXvfaui+GaZpm5ZsVO/HEE+nXrx9nn302Z511Fl9//TXdu3fn66+/ZsSIEUHju/8u0zS58847mT59Ona7HbfbzbRp05gwYUK5+0yePJkpU6aUWv7qq68SGxtbbW0TEZHqt36vwfwtNg4UGf5lDSJNhrf2sGibjT9zDf7ZwU235Cr96TosfLjVxqfbbKyN+RfJ5n6+6DCVrNjW4W5WmZ77ycYP+21cdJSbK8x36JQ5n6zoliQVbGVnQlea5mxgU9pwNqeeF+6mioiIlCkvL4+RI0eSlZVFYmJijZ2nyj3c06dP5/zzz+eBBx5g9OjRdO/eHYD333/fn2peXd544w3mzp3Lq6++SpcuXVi3bh0333wz6enpjB49usx9JkyYwLhx4/zPs7OzadGiBWeddVaNXsi/w+l0smjRIgYMGIDDUU5VWqlRugc1S9c3/A6He/DJjzuZvXI9JUPprCKD2ZvtdG+RxJ+5WTRt25khJ7cKSxurqirXff3HP8O2P4i0Ay7ofXpfaNK5VtpZVZ/krOeH/Ttp36kLR580GPfy9iQtux+ApjkbcJ9+B0ef9m+ODnM74fB479dHuu7hp3sQfroH4RHqdfdlQte0Kgfcffv2Zc+ePWRnZ9OwYUP/8muvvbbae5Bvu+027rjjDi6++GIAunXrxh9//MF9991XbsAdFRXlT3kP5HA46vwb/XBoY32ne1CzdH3Dr67eA7fHZNrHP5cKtgFMrFmnN+/MAeDLX/fRtVlDerVJxm4zytij7gnluhd6p9jyjeF2RMZAHbxXAFEO698Hj2lYr6v/BFj+AJhuMGzY+0/AHuY2llRX3/v1na57+OkehJ/uQXhUdt1r654c0jzcdrsdl8vFl19+iWEYtG/fntatW1dz06xufpstuK6b3W7XtGAiIvXMqox9ZGYVlLveBPKKrN/9SzfvZunm3aQlRTNpWGcGdU2rpVbWrAKn9frspndMWXnzcNcBpaqUL51hBdsApicsc4OLiIjURVWuUp6bm8tVV11FWloap59+Oqeddhrp6elcffXV5OVV73Qtw4YNY9q0aXz44Yds2bKFd955h4cffpjzzz+/Ws8jIiLhtSun/GC7PDuyChgzdy0LN1Rvwc5w8c3DbfOUMy1YHeKrUu50e4qnK+t2obUyPjUsc4OLiIjURVUOuMeNG8fSpUtZsGABBw4c4MCBA7z33nssXbqUW2+9tVob9/jjjzNixAhuuOEGOnXqxL///W+uu+46/vvf/1breUREJLyaJERXeR9f+vmUBRtxew7/ImrWPNwmNtMbcNvqcMDt7eE+bsv/iucGP/lfxRv0u0tBt4iICIeQUv72228zb948+vbt6182ZMgQYmJiuPDCC3nqqaeqrXEJCQnMnDmzwmnARETk8NerTTJpSdEVppWXxQQyswpYlbGPk9um1EzjakmB040dD4bvo4Q63MMdGWEF3Gbg3OA51lRu5O6CU73FS2txbnAREZG6qMo93Hl5eTRt2rTU8iZNmlR7SrmIiBw5Lj6h5SHveygp6XVNvtPtL5gG1O2A29vDvTj16uKx2nGNwLBZY7hzd1vL+5U/jaeIiMiRoMoB98knn8ykSZMoKCj+5yY/P58pU6Zw8sknV2vjRESk/lu4IZNTpy/mkc82H/IxDiUlva4pcHpw4CpecBiklPuLpgHY7BDv/UD+4I4wtEpERKTuqXJK+aOPPsqgQYNo3rw53bt3xzAM1q1bR3R0NJ988klNtFFEROqphRsyGTN3bZnTgQH83xlH88bqv9iZXVDmNgaQmhRNrzbJNdjK2lHgdAcH3HW4h9sfcLtKzBqSkAo5mZCjgFtERAQOoYe7a9eu/PLLL9x333306NGDY445hvvvv59ffvmFLl261EQbRUSkHnJ7TKYs2FhusG0Ab675i4lDO/ufl1wPMGlY58NmPu6KFASmlBs2q8e4jvKN4Xa6Swbc3inacupH5XgREZG/65Dm4Y6JieGaa66p7raIiMgRJJS5tzOzCmgYF8lTlx3LlAUbg7ZPrWfzcOc73cT6Au46nE4OEGkPmBYskC+lXD3cIiIiQIgB9/vvvx/yAc8555xDboyIiBw5Qi10tiungHN7NGNA51SW/7KbK2avBuCdG3qTmnT4j932KXC6STJ8c3BHhrcxlShOKS+Rn+Dv4VbALSIiAiEG3Oedd15IBzMMA7dbU4CIiEjlQi105tvObjPo26EJbRrFkbEnl193Haw3AbfHY1Lg9BBheP+G2g8pAa3WlFk0Dawx3KCAW0RExCukMdwejyekLwXbIiISKt/c2+UxgLQyCqJ1aJoAwE87smuyebWq0Ft8zHG4pJT7xnCXKpqmMdwiIiKBqlw0TUREpDrYbQaThnUuc11FBdE6pvkC7pyabF6tKnBagba/SvlhklJeumiab1qwnbXcIhERkbop5IB78eLFdO7cmezs0j0KWVlZdOnShWXLllVr40REpH4b1DWN1MTSvdypSdE8ddmxZRZE65iaCNSvHu58b8AdbT88UsojI8opmubr4T64C9wuREREjnQh/0WfOXMm11xzDYmJiaXWJSUlcd111/HII49w+umnV2sDRUSkfnJ7TN5dt40d2QXYDHhm1HHkFblpkmClkZc31Vcnbw/35p0Hcbk9RNgP/2QtXw93XIS3CFkdTyn39XAXlkwpj20Ehh1MN+TugsT0MLRORESk7gj5v5T169czaNCgctefddZZfPvtt9XSKBERqd8Wbsjk1OmLufXN9QB4TJj43o9ERdg4uW1KhfNqt2gYS2yknSKXhy17c2uryTXK18MdZ/cGsHU8pTyyvJRymy1gajCN4xYREQk54N65cycOR/mfuEdERLB79+5qaZSIiNRfCzdkMmbu2lJzcO/IKmDM3LUs3FBxoGazGbRvWr/GcRc4rcA11tfDXcdTyh2+omlus/RKf6VyjeMWEREJOeBu1qwZP/zwQ7nrv//+e9LSSo+1ExER8XF7TKYs2EgZYZp/2ZQFG3F7ytqiWIfUeADeX7edlb/trXT7us6XUh5rPzxSyiP983B7Sq9UpXIRERG/kAPuIUOGMHHiRAoKCkqty8/PZ9KkSQwdOrRaGyciIvXLqox9pXq2A5lAZlYBqzL2lbvNwg2ZfPyDNc/zpxt3csn/vubU6Ysr7Rmvy/KLfAG3r2haHU8pjygnpRw0F7eIiEiAkHPW7r77bubPn0/79u258cYb6dChA4ZhsGnTJp588kncbjd33XVXTbZVREQOc7tyyg+2Q9nOl45esj/bl45eXmXzuq7A5atS7hvDXcdTyn093BUG3IfvByAiIiLVJeS/6E2bNmXFihWMGTOGCRMmYJrWvzuGYTBw4EBmzZpF06ZNa6yhIiJy+GuSUHoKsFC3qywd3cBKRx/QObXComt1ka+HO8Z2eKSUO+zlTAsGxQG35uIWEREJPeAGaNWqFR999BH79+/n119/xTRN2rVrR8OGDWuqfSIiUo/0apNMWlI0O7IKygycDaw5uHu1SS61rirp6Ce3Tam2NteGAu9Y6JjDJaVcY7hFRERCckiTlzZs2JATTjiBXr16KdgWEZGQ2W0Gk4Z1LnOdr0960rDOZfZQ/9109LqsoOjwSin3jeH2mJQuWKcx3CIiIn6HFHCLiIgcqkFd03jqsmMpGVOnJkVXOAb776Sj13W+KuXRNm/AXedTyov/fSiVVh7vDbhzd4PbWYutEhERqXvq9kfoIiJSL53ZqSm+jtF7zutK28bx9GqTXOHY67+Tjl7X5XsD7ijj8EgpDwy4i9weoh324pWxKWCLAI8LDu6CpGZhaKGIiEjdoB5uERGpdXsOFgFWivnIXi05uW1KpYXOAtPRS25ZWTp6nbbkPk7Y+hwAUTZfwO39PHzpDFhyX5gaVj5f0TQoYxy3zVbcy620chEROcIp4BYRkVrnG2fdKD4SWxUCZF86empScNp4ZenodZrNTr/t/+Mm+3yijICU8qUzYMk0sNkr3j8MDMPwF04LSilfcp/V7rKmBqujHx6IiIjUJKWUi4hIrduVXQgc2njrQV3TGNA5lc827uC6uWsB+GxcH+KiDtM/aX3G89EPmdy65wW2Zx1rLdvxA3w7G/rdBX3Gh7d95XDYDYrc4HQFJPjb7NaHBI3aW88Penu4fR8e9LurSudwe0xWZexjV04BTRKiKx12ICIiUtccpv+diIjI4WxXji/gjjqk/e02g7O6pBIbaSevyM2O7ALaNo6vzibWqgUNLmNTZja3Zs2zFmxbU6eDbQBHhA2K3BQF9nD72rtkmvWYsyM42K7C61m4IZMpCzYGTQWXlhTNpGGdD89MBhEROSIppVxERGqdL6W8SeKhBdxgpTW3aBgLwJ/78qqlXeGS73TzuHs4Ht+fZcNep4NtKC6cVmoMd5/x0KaP9f3yhw452B4zd22pedd3ZBUwZu5aFm7QHN8iInJ4UMAtIiK1ztfD3bi8lHLfWOCyBIwFbpEcA8Cf+/OrvY21qcDp5ib7fGx4g1fTXf7rryPKHMPt03OU9Wh6rIrrVQi23R6TKQs2llmJ3rdsyoKNpef/FhERqYMUcIuISK3zjeFuWl4Pt28scMmgs0QhsebeHu6/DvMe7mEH5nKrYx4HE4+2FrQ7q+zXX4dERlQQcG9dUfy9u6hKr2NVxr5SPduBTCAzq4BVGftCPqaIiEi4aAy3iIjUut2+lPLyergDxwLnZMKxl8Mvi0qlJ7dI9qaU7z+MA+6lM7g0by4POUdwZdwuyP4VupwPzU8oHgtdB9PLfVODFZUMuJfOgDUvFD8/9ZYqvQ7fcIPq2k5ERCScFHCLiEit25kdQtG0PuPBVQjLHywO4EqMBW7R0JtSvu8wTin3uHnecQmPFwzjOvfD1rLIuOLX6XGHr20VKHMMd2CBtG/nQPY26DAEHLEhB92hVq4/lAr3IiIitU0Bt4iI1CqPx2TPQW/AXVnRtPaDrIAbyiwk5uvh/usw6uEuNdVVnzt4ZsXnQCF2l/d1RHorrtfBnm2f4pTygLHUHnfxhyJ/fGUF3Ht+qdKHB73aJJOWFM2OrIIyx3EbWPOu92qT/PdfhIiISA1TwC0iIrVqX14RLo+JYUCj+EoC7i8fLv7edMPie6H/nf5FvoB7f56Tg4Uu4uv4XNzlTXWVU+AEIMKVay2MSghH86rEUVbRtH4Tir9PaQe/fwF7NlvPQ/zwwG4zmDSsM2O8c6wH8s3APWlYZ83HLSIihwUVTRMRkVrlK5iWHBvpD9rKtHQG/PxR8LJl04MKcMVHRdAw1gHU/anBKprqKt9pBa0250FrYWRcbTevyiqsUg7QqL31uOeXKh97UNc0nrrsWBKjgz9ASU2K5qnLjtU83CIicthQwC0iIjXG7TFZ+dte3lu3jZW/7cXtMf3FrhpXNH7bNxY4qaX1PK6x9ZiQFly9e+kMxke9A9TtgDuUqa4AbM4SKeV1mC+lvLDkPNw+jbwV1/dWPeAGK+gefmzzoGVf/Luvgm0RETms1O3cOxERCYtS44zbJFc5hbe89OkzOzcFoEliBUWvPG7oeyd8Pct6nrvbGsOdkwnHXWGt9wbl8clXwv7iubiro+3Vbc0f+yuc6grAjhvD5S3+dhgE3L4q5ZX2cO/7HdxOsDuqfI4te3ODnu/Pc5KaZK/ycapsyX3W1HNlpcEvneEdqz6h9DoREZESFHCLiEiQ8gLlScM6h9y76EufLtmjuyOrgJdX/gFUUqG83wTI2gZf3GsF2qf/G5ZOt9YV5RXP093vLjbkDYPtv/PnvrxqaXtN2JVTWOk2sQRsE3U4BNzelPLyergT0sERB85c2P9HcY93Ffy+Ozjg3pldQGpSLVQn972/IDjoDqzCLiIiEgKllIuIiF9544wzswq4fu5aHv1sM25PWYnRxUJNn24UH1lxY3b+6N2wHfS7E46/ynr+w5tB83G3aGgVTmu36Ul+ev3OMsdIj5m7loUbMis+Xw2q8MMFrzi8vdu2CLBXcm3qAN8Y7iK3p8yhA9hskNLW2thXOK0KCpxuf/X5dG+QHcoHF2W2par6jLfeXyWGL5ScB15ERKQy6uEWERGg4kDZ55HPfuG1VX8y+Zzye4y//n1vUNB7c8Q83KaNx93Dg7bLL3LDnKHWkys+KONA3nTypl2sx6GPFM/HDdD8BACaNYjhJvt8Ls2fx0PmiFKHMbGqW09ZsJEBnVNrLb3c7TH5JmMf3+4xONNjkpoYzc7ssqe6AogzvNcsMh6Mul+B2zeG+8ft2Zw6fXHZWQWN2sOO770B95AqHf+PvXl4TEiIjqBzeiLbswr84//LU1aGQ2piFENSjSqeHSuoduZ7g+7p4HEp2BYRkSpTD7eIiACwKmNfpeOMAXZkl99jvHBDJv96JXg6J7dp41bHPG6yzw9a3nfnHNiy3PoKqDwOWM9/X2J97wu4S27z8vms/XQuP795N7c65vGQc0SpoN7HxOqlX5Wxr9LXVx0Wbsjk1OmLueyFNbz0i53L53xLgctd4YcZqVEu65vDYEowKE4pf2/d9nKzCn7xpFoLDqFw2u+7rYrtRzWO94/391W4L0t52Rk7swt5YbONT37cWeU2kJhuPXpcVtaBgm0REakiBdwiIgJQae9hSVMWbAxK1/UFPAfynUHbPe4ezkPOEf6gO4293GSfT7/t/7N6DMtL3fVVJm/SJTid9+5dEJsCmPT86l9c73mjwmD777zGQ1Fe4JeV5yxz+6QYq5hYk2jv+sNgSjCgwkwB37vixc3e1PhDmBrs9z3W+O22jeL8Kfnl3b9QhjFM+/inqqeXfzsn4CRFpT/0ERERqYRSykVEBIAmCaEXowrsMT65bUql6ei+YPhWxzxudcwDYP+Jt9HQ12NYlGsF1F/cD6Yb+twByx+01m1ZDiufCErndY/7Gds9jTAM8JhGSMF2VV/joQh1/Hq/jo34fXcef+zN46zOTXnr279IshdZKw+DCuUAe3MrHk9tAt/mNoYoDing/s3fwx1Hcpw34C6nh7vy7AyDzKzCoPdrpZXsl86AnRus76MbwMn/KruQmoiISAUUcIuICAC92iSTlhTNjqzyxxmX5OtxDCUd/XH3cG6JeBubYeIxDWLODJhWyZe6a7qt6aM6DYOl90NUktXjW2Ls7LYF0/DO0I3NMLnJPr/CoNsAUpOswKomhZqWv2bLAXq0aMAfe/P47s8DACTavMHkYdLDXeQspzp5gAzTm1Kevw9y90JcSsjH/81bobxt43j/ePHyiqaFmrmwK6cgtEr2vowKn4IDcOot1vcKukVEpAqUUi4iIoCVIjxpWOeQg20o7jEOJeAZa7eCbbCC5MgvHyheGVgMze0sTt1t2tmqUF5iaqaW6x/hK3dnAH5wty5zjLiPr99y0rDONV4wLdTAL6fAxfJf9gDw6y6rJ9cfcB8mY7h9qfDluTliHtfYP6Qgrpm1ILBS+dIZ1lzX5TBNM3gMt/d9tjO77OsbaubClj15Zab7l6pk73FDtwuDd87bW1y93OMO6XwiIiIKuEVExG9Q1zTO6V75fNUGVvVnj2ny3rpt7Klkuqab7PMZ53g7aJlt6X388uZ/4IvpsPun4KNves/6NrBg2pL7/D2PW7vfwrueUwHYT0LQGPGSUpOieeqyY2tlHu5G8ZVP/1WeBFtAlfLDQOvGFffEe7zF8qIivdfEVzjN13tss5e5n9tj8smPO8gpsIrINW8YQ5NE6xh7DhaWOQ7bl51R0ajy1MRIXlu1tcJ0f39dgn4ToGGr4I1yd1uPfcZb60VEREKggFtERILkFVm9d33aNy5zvYEVoBS4PFz63Df83+vr+O+Hmyiv8/gm+3xudczjU/dxQcsXu3vQbuNj8MW91gKbb5RTQEiUtS04QPO4od9dNDt3EoWxVgCdZuzzF2azG8FpzvFRdpaP71crwfbCDZnc+ua6Q94/3j8t2OGRUh4VURwwl7z1BtYQgl86j8XY/7u1cM/mSuey9lV3v35ucaX7Mx9eypot+7AZ4DHLHjvuy84oi69tFx7Xgh3l9JBDGZXs/1odvEHunnL3FRERKY/GcIuIiJ9pmqzzjikee0Y7LunVotR416RYBwfynBwoUXW7vALQdsPDQ84RNDOsgOWgGU28UUAe0ax0dyLdnkUrtkPLk6FxB1j9XPHO2dtg88elAjQ7MKL/SfAJpBl7AdM/htsXYNltBgcL3fyxL4+2jWu219hXmbyKNbCDxOG9xlGHRw+3b1x1zxYN2J6Vz86Agmap3jHR7bqeDa9uhs0LrcJ3pllhsF3WNdyRVcCNr35HQnQE2QUudmUXlplCPqhrGk9ddiw3vfYdTnfxURrFRzIsPZ+WKTEhva5dOQXWBzt/fWstiG0EeXsUcIuIyCFRD7eIiPj9tT+fPQeLcNgNuqQnMqhrGl/e3p9jWzYE4IpTWhEdUXYqcHlmuqwpu3rafgXgdXc/AE6w/cQlzrv5zt3a2rDVKXD2Q9C4U/HOO74vN0A7/bgegNUznEiuf7kvhfy4VlabV9fw3NuVVWgPleG0xiwfLinlkXbro42U+Eheu+akoHVLbwvIKjhlrPVomuXOZR1KdXdf5kVF4+QHdkkl2vtBQGKM1adw7eltAJj20c+hvCwrmN+zGYpywBEHrXt7G6CAW0REqk4Bt4iI+PkqZndOSyTaYQXWdptBt2aJAOzPdVaYlhto+gXduLHf0QDEk0c7YxsAc9yDKDLtNDUO0NLYxYk27/jtVt7A5qKXiw9SToAGQGQsxFhVx18e0YxHL+7Ba9ecxJe392dQ1zROaG2t++iHTN5bt42Vv+2t+jzMIQi1MnlZAlOxDxw4AMCmfZVX/64LHHbrX4git8n+vKKgddkFAdkPv31e/H05c1lXdg1NwOW9d+VNDQawL7eInEIrML/yFCvQfm9dJi9strG/nHnQfQysauW92iQXp5M3OxbivZXWfWO4RUREqkABt4iI+K3begCAHi0aBC1Pb2Cl4247kMfNEWUXJ7s5Yh6vOO7h5oh5pCVGc9EJLel9dCMAJkW8hM0w+ctsxF9mY7432wJwgX05acY+PEYEND/BOtCP71iP9shyAzS/JKsCdvfEXM7t0YyT26ZYlciX3MdJf1mp6ct+2cP/vb6OS/73NadOX2wVaqugQnZVhVqZvCyB4X8c+QC8uGZPcbXsOsyXUl7kcrO7RNG8fbneAHzpDFj+kP+DEY652BrDXeKeVuUaljc1GEDGHivToVmDGAZ0bgrAhu3ZlR6zVCV7X8Dd/HiIs97DCrhFpKa5PSYrf9tbox8SS+3TGG4REcHtMVmVsY/FP+0E4JjmSUHrfQH3wQI3bm/1aSBo7usTjJ/obd8IblifZk1t1atNMnfGvc8/3MsA+M5j9Xiv9nTgeNtmrrAvBMBodqzVY12yqFbgfMhl9XQntYAdP0DWX0GLf9mdx6l/PstN9n1BbfzHwVdpt3Eev3QeS7tDulKlhTolVSBfAbBAcViB5EEzmikLNjKgc2qNT2P2d/h6uJ1us1TAvfdgEfwUcC93/wQb3oZG7aznJe5pVa5hRcH5796Au02jOLbuzQtYU/F1TIxx8HbHLzh674/AePhrjbWi+Qlw0PqZ4I+VIbdRRKSqFm7ILFUzJc1bD6M2Cn9KzVHALSJyBHF7TNb8tpddOQU0SbDSZxdt3FHqj/z9H/9MXFSE/4+8L+DOLnAyL34kxkG41TGPvrZ1rPZ0JJdoets38pW7M73tG0mIfBe+WIJ9y3KudS/nN08abW2ZrPMczc0R80jFGledZFhBkdG6d3Fw3fq04uDa91he0J3oneM5IOB2e0wu/60vI5zbgz4YsKYmm8fDzhG89VtfvvSY1RLQ+qak2pFVUM44bpPUxCgeurAnew4WsienkP9+uKnUVnGG1cN9kGh/teyT26b87fbVlEh/wO0pFXDvzyvyV5Snz3j46jEr4M5cBxfNtTYKmMu6smtoYAXFWfnOoOJsJfl6uO02g3+9urbc7UoaekwaRyc3sN5nrgLY5b0/zY6Hhd4pwJx55e4vIvJ3VFQ0cszctbU2taXUDAXcIiJHiPV7De57aBk7AgKWBt6K4yXtOVgY9Ee+mTfg3plTyOMX9+Rfrw4nAhdjHe9ynN0qhvak8xwedF/Io+aTnPPLk+Cddpm+d9J85TNQaPVw3xrxJr3tG/EQMK6pVW/46lHr+zanBzfGF2QHBGh+3pRysrf5F/nGAz/OcOIo4FbHPMZGvIPDcPOQ0yrgRjUGtL4pqcbMLR3g+aZQu3tIJ396/XvrtpXaDoqrlOeZVm/v30lVrw3+MdwuD7sPBo/h3ptbFDxXdXoP63H7euuxxAcnlV1DgEt6teDppb9XnFK+2wq4v9u6v0pF7HIKXKU/3GnQEta+CD9654+POPQ51kVEyhNK0cg73/mB/h2b+ofyyOFFd01E5AjwyY87eWGzLSjYBsoMtqH4j/yUBRtxe0waJ0QRYTNwe0x6tmrAU5cdy2KOD9rn6oiP+SzyNs6JKJF626QjUYV7MW0OHj1+L73tGzmQenLxHyDDBltXwpbl5VYkp8/44ADOJ7G59ZhVHMQGBqpuwzqLw3BTaEYEpZdXZ0A7qGsaj13Ss9Ty1KQormrvYWCXpv5l5aVPx3nn4c4lpsLt6gr/GO6AHu4Ib8bAvhIBOGndrcesrZBXdtV437ReDWIdQct9Ved9vTu7Kyja9/seq9J7doGrSq/loK/IW5/x0MJbcT3rLyv4PvF667mmBRORGhBK4c19uU5Ouu/zw6K+h5SmgFtEpJ5ze0zu+einKu9ngj+12W4zSE2yAsDtB/Lp37Ep/7R9YG3njc6jDSdtbcX/DPjHKL95OQBGXCNa/PA49LuLBtcvhGbH4T/A8ofKD7YrkuQNuLOLU8oDA9WLbEv8p4gyXEHF3qo7oO2Yao1bj3bYePQiq2L6knGn0z0luN/Clz5dMpnd38NNVHG17DrM4Z0WzOn2sPugFXAf1TgOgH25JXqho5Mg+Sjr+8x15R5zUNc0buhrFdTr2aJBUNX5JglWD/OunEI8ZRQScntMtuw9tLTvzbu8U7I5C6wpwQBMj1W4r+8d1vPCbHCV37suInIoQv3wd19uEWPmrlXQfRhSwC0iUs+tytjn7dk+tPHKvn8GiiuVF5D9yTSG2r8GYL7nVB52XhC0z0POEXQvfI6D3t5aAHIyg4Pqsx/yrih/fuZK+VPKt4PHmk7LF9BOjHiJRrYcAAwDZjmHWenl9vk1EtD+6g3aOjRN4NyeARXTS/ClT0PgHTH9AfdBM6a4WnYd5i+a5jLZ4+3h7pBqTR+3N7eo9A5pPazH7esqPK6vp6fXUclB17CxN+B2eUxeW7W1VAXf7QfyKXJ5sIf4n01KXCQPjjgGsOafP1jogh/nQ763B95XJf+bZ8HmHYGnXm4RqWZV/fDXl3kmhw8F3CIi9dzfTZ32/TPgG8eduu4xGq1+kB/crQHY6mmKWUYwn0MspxQ8WjwuzbAFB9W/LLIeQ5n+qzwJaYBh7e+dtsluM3ip7RdcFbEwaNMlnp487BzBOMc8Xmr7RbUHtL6Au22T+Eq39aVP+7IGYijEZlhX6t6LTzwsiuMEpZR7e7h9vfz7ygy4vWnlFfRwA2zbbxWP873ffD7ftBPfLbvr3Q3+ad58vT2+gmmtU+LKzCAIlBznYOWEMxhxfAt/z/nmnTnw+VRrg6P6wn92Wx8QfXEvRHj/Ic5TwC0i1au8rKeyBGaeyeFDRdNEROq5Q02dNrDGz/p6gtMbWMfJzS/k61bX4/ndmuqrg/EnQyJWscLdCRMDA9NfHdx3HAyblaK7dEbwdF+hTv9VHrsDElKt3vPsvyDBGivdrnEs+TFpxORn4jENbIZJW9t23oofybC26bRrHFula+GbNi2wunvJgP3X3VbAfXQIATdYQfeAzqmsythH1u6/YCGYGAzo3rZKbQsXX5XyA3lF/uED7ZtWEHD7C6etq/C42w6UDrhDqeDrq17etnE8w49txpi5a/1F63x8d+ze87sRuXw62Ox0SO3DrpxCEhf923ofGXarN37JfcV1A3zvTc3FLVK9ltwHtv9n77zjoyjzP/6emd0kpBcICb2FElAQESlCwIqeNWIvpz/7aVRAsZ96CionFsDzTj31FBU1xnZ6WAOEJk0QCCVAaCEhkJBOkt2Z+f3x7MyWbJINEJLAvF+vvDY7bWdnd595vu3zVfyP+wtnuLod+NHvOIFoSDSyPlq7qKaFN5bBbWFhYXGCM7xnLAmRwRSUVRNoWrmxlWdqs5FS/lG76wkLtvGwLIzqi2wr3OrfLtK0DNPo3j14Et2ueMZtVO/MgtxF3unljbX/aojIzsJQKs1z14WfcRvtFr4EwJbYcQw4lMmZ4QeY9sjZKPI5gR+bwHuj5ux3GdwdAjO4QUy0RvaOg+gSmA9SULjIf28D2F0RbsPYjgixkeiK2PtPKXdFuEt2CeG0UP8p/abBHSO+b40p+ErAM99s5JTOond8sF3mvOQE3rxxaJ3PLcHzc1uoQOY0butUSBbjCT+wVmwUPwCWvCa+nyC+i2s/gkM7obIogCtjYWERMLLif9z3dMqeBEwYlMgr1wxh0qdrA9q+tYtqWnhjGdwWFhYWJziKLPHkRf25b97aOuuMCGBkiM1L2TnBj0HpWcMdhINOCOPjn45LvIxtT1ZJAzntsqfFE0+juufYukZ1Q+2/GiKqC+StgtXvw4HN4jhb/ifeWeIQInufD4szOb1mOU3NIg+0N6qm6aZCdqARbi9qRTo0QWFN37eFMETTDDqEBxMXHgTAocpadF1HMpwHRhQrpocwXPPXQe/xYp1HFKuyxmkq5xsR7sYUfHWgoKyGgrJCAL5dl8+qnYd4+pJkFj9yNnkZT5KzO5/gi19gZJ94d2ZCylTYmcW43LeZqhQQW71LLN+/oa6AX+dhLoPbinBbWBxTPO8Lug7jHqmbAXWSkOS6d/hm5njim3lm0TZo9TXceXl53HjjjcTFxREaGsqQIUNYvXp1S5+WhYWFRZvigoEd+b++GqFBitfyhKgQ/nnjUN66SbT4ah8e5KUM7YlhAO09VEXtwZ3Iko5DacdL6rV14uaKpPGKYyIHr/zCO/U6ZaqYRHUb5f9E62v/1RCGUnlthZikLZwBm78Ty/pfTMeKjWIzvdyMngZCIL1RDfGavJLDVDs0ghSZbrFNS1c3zx0g+AiM9RYiyEedrH1EMLFhwuB2ajplhz1acxlRLKMW2qjjNibWsvheGp9PZIiNiBDRHuxIUicNh8hP2QV0jg3nnLIvGLn3Xe/v4sIZkLuIFQziL/ZvCUIY+m8p1zI/7ibvA4Z1EI9WDbeFxbEnZSqMfUToJTwbc1Ia2+DWoejVIQyJuvlo/jLPLNoGrdrgPnToEKNHj8Zut/O///2P7OxsZs6cSXR0dEufmoWFhUWbY3Cczpk9YszniVEhpmHtbusUXq+6tpEuXF7tJF4tAMAW15M3bzzdFP8y+DTsRpKve96/+NeRGNUNEelSKo9IFJO0zGmw7WexrGI/trVzAYiSqti8uyDgwwYSWc0vreb9JblsLRBq6D3ah2ILVCbbkxqXwd2GItyGaJpBh4hggm0K4cEiea7IszWY4Wg54GpPl7/ObxTLFEyLcTstjiR10tMh4hg9hU2JqSiLXoQfnoA9K8zXfsUxkRuqp5qt7Zy6zAuVl9ZtvRMWJx6tCLeFRfPQ7wLxaLTjO8mMbXAb3EO7xXiJahokRIWYWVUWbYtWnVL+0ksv0bVrV9577z1zWY8ePVruhCwsLCzaOHsOuSO8ZYcdpmG9v0wYlh0j6zduIkLsZup5N2k/AFJMTy/xr4ZExZoNszVYHlz9HyjcJNo7Aax8G8Y/QcWiOYSrpRTs2ACDewV02EAjq899t4mIEHE7PaJ0cnBHuIMijmz/FsDu41joEC7UvmPC7FTUOCmurKVXB48NUqZC8Q5Y9wls/FL8+USx9voRTDMUfAtKq+tNs/SH4RBZtesQRQmX0zeprzC6l80BRCR7VvWl/MP2KpIkslltksZ9SgZz1FSe/Tab85ITxPfYiHBbNdwWFs3D0jnu/42uFSeZ0W0Y3D07hJn31TOn/czBylqeu2wg15/Z3Ypst1FatcH9zTffcMEFF3DVVVexcOFCOnfuzF/+8hfuuOOOevepqamhpsbtVS8rKwPA4XDgcDia/ZyPBOO8Wuv5nQxYn0HzYl3flsfhcKDr3gZ3Za1KcflhIkJs5JdUAdAhzN7g59QpKoSy6gq6SaJeVo3uhubafli3SED0YdZUZ5NLsY8UKSwBG6CX7sVZXYWtcJOZeqcrQThHTaLi9+8JL/mdyrxsHI4LAzpuXGjgt8hyV/37wfIaqmtqzUlRoN996XApNkCzh6K2kd+Jrnubv7GhNhwOBzGhdvYUH6aw9HDd933+i9jWfSJqFGUbzlGTwGObPUXC8ZAYFey17xMX9iNt3rojOs/8kiqCgJoRD9Bu0YtmfeTMygmkKRlcZFsJwN+d16CgmmJ/s0tTWbatkDN7xiIFx4jPp6KwzXw+LY017rc8beUzkLNeRjGcpIDWbRRy5jRUVUUb89CRHXPRSyApfveXs14GXUUb+8gRn3OgNOUz2H5AZEp1jQ4xt+8QEczBylo6RQUf1/tqWyfQ6368fhut2uDesWMHb775JpMnT+bxxx9nxYoV3H///QQHB3PzzTf73eeFF17g2WefrbP8xx9/JDT0COrqjiM//fRTS5/CSY/1GTQv1vVtWcocUOPUkNAJkqFGk/j8vz+SEAprc2RApihvB99/v73eY8g1Yrvurgj3+r2V7Pr+++PzBnzol5+BLsnsihvHBIDyAnL+k0bygU0AaMjIai3b3r0ThxpFAiAd2MT3AZ6vU4Nwm0KFEwJVd1+x8xBnTvuRK3toDI5zG6SNffd7Fa7kFGDfwTJWt9D1PBJskoJTF9cmP3cL31duxlkhviMLf1uNY6e3Ud634CsGuP6XNCfb3r2TrQmXm+tXbRX7luXn8v33O7z2vbWvxLztMlVq0yI8uzavJykKdn9wN/2N1wZ+CJpKD1k4jir1YMI4jEOyMdMx0TS6f8xSKdqkE1ORw1jg8ME9/NyGPp/WgDXutzyt+TPoW/AVA/IzKA7tQ2zVNgCKiks4mJjKgEUvsjVnq9cYEfhxtzMgP6PO/sbrbUpMZWvF8fstN/YZ6Drk5CuAxN7s1Xzv0nB0VrnG02UrqchpSo6PBTR+3auqqo7LebRqg1vTNIYNG8b06dMBOO2009i4cSNvvvlmvQb3Y489xuTJk83nZWVldO3alfPPP5/IyMjjct5NxeFw8NNPP3Heeedht9tb+nROSqzPoHmxrm/L43A4+OcX4sbTObodIXaFbQcq6XfamYzuHcfH766Eg4cYN3wIF53qvz7sh4372bl6A6DS1RXhfn9/N84943QuGNjxeL0VEzkrG2XRi/RNSkKX7UiagwH5X5jr9bFTUYEBi16ktsd4KIfx+goqyhKpPHMSw7rHeKXneUY9fti4nxe+30yFs8bPKzdMaa3Ee1sVZl87mLP7xgb03ZcXb4I86NQjiY4XXdTk12wpHl/zC84aEXI5e9QwxvXtwMLqDWT/vo/OvfpxUYo7fV/Oehnl9wzUITeirJ2LLtsZkJ9B36S+ZhTq/b2/QVEp54w4jQsHJXi91kVAyvYi/vx+YMKpQs03mLtSR7L3o/voX/C11/oeciHlWggRcjVbtK7ca//Gq72dImmcO3o4sixRmR8LORBKJRe1oc+nJbHG/ZanLXwG8qL1qEmPEl24ETYLg7t97S6ib8lEXdKXvrpKn7FH8pu7CDWrLwMWvUg/eQ96z7Eg28UYNPZR+ox5iD40f7Q70M+guLKWw8sXAHDj5RcQYhdCkt+VriUnu5Be/QZy0ZndmuUcT0QCve5GJnRz06oN7sTERJKTk72WDRgwgC+++KKePSA4OJjg4OA6y+12e6sdbAzawjme6FifQfNiXd+WpahGGJfd24chSxLbDlRysNKJ3W6nsFz0Te4UE+b3M5q/IZ+0eetcNbS6mVL+R2UMX89b1zJCLmc/BoqCkjkNQqKguhTJqPId9zjKONcESlEIcvV5jaGcpO1vMnPzAR4Ov96jJ/MMWPQijH+Cn7Yc9HivR860/23h3AFjgAC++06R6i+HRCK3od9IkCJTiTC4E6LEd6eDS+SstFp1v2eP66uMfRj2/IZUlAP9/oSy6EUURYGUqexzidR1ax/h93qd1bdjQPXcbjXfgYSumMWA/AzU025G+f0DCI6AGpG6GSFXo+kwVNnmZWzPUVOJCrUzL2MjBWXVRFDFBSEg1VaSmb2X8wf3PPqLd5JgjfstT6v+DM55Ujy+Nd5cJNVWYj+0TYzxgOJvv0A4+zGoLUVe/ibkidIRxj+BkjJVHNNzXGrm69PYZ7C3VIxJnaPbERHq1lKJCRU2TUWt1no/w1ZMY9f9eF3TVq1SPnr0aLZs2eK1bOvWrXTv3r2FzsjCwsKi7VJULcyQbrGhpjja/rJqdF1vUDTNtz1WB0oJk2rQdIk9uhCTMtpjHXcM9evqUvey4XeJXq4u5sfdxNsOESGJlKp41XElU+zpXFXxMffMXUPOZ0+ZatnqmIfrbQXWFDwFuwKiDbYFA2/htA4RYmJotAYrrqx1b6ipboG0BS+6W7lVHRTLNZVap8Y1VZ/woC3dSzTNE0WWePoS4YhvKLHcS81XV9mUmIre53yxMq4PDLvN3FaW8DK2QXx+JVUOCly/i3LaUauLaf+znyzyVjC3sLA4ekr3isfQ9uJx78pjc9won6hw3mqRv93Ken3vOCAE03q09y5/jQoVBmHp4dZdh2/RMK3a4J40aRLLly9n+vTpbNu2jY8//pi33nqLe++9t6VPzcLCwqLNcdAlut01NpQEl2GdX3qYihonVbUiShkfWTdDyLc9lpFOvo84HNhM43JFbnHzvoH6SJkKkut2Jilw0QxzleEsmK5eT5UeTJCk8q02kjcdlzDZns624BtIyp6FNu5xSJnaaCuwplJYHmBKehtsCwbercHiwoWhbRjcRZ4G9/jH3JNaWYEdmYAEe36D5Mth/GNU/TSdybZ0JEmhvetY/pgwKNFvy5zYMDu3je5Rp4+8NvYRUcNp9NAO6wAXvyK+K4ADxcvY9o9EEVHidaSylnMwWViciDiqoVLcV0i+VDweK4N71b+9n2+dD3+LbVXGNngolLf3vgdEtbMM7hOBVp1SfsYZZ/Dll1/y2GOP8be//Y2ePXvy2muvccMNN7T0qVlYWFi0OcyU8tgwiquEMVRQWsP+MmEURoTYCA2qe1vwbY9lCKbt1uIb3O64sXCG6N0q20BzerWTWZFbzDWVc1EVme16IqdIOxkg7WaYIrKnFElH1WVWdL2dkc3wHuIjggmokZTZFqxtRbiDXBHu2LAgM9ptGN7FlfU4G4wJrivNn7VzISic6N/+zkzHRL6LuZEHpIaF0Y6kFZ1k9NAOi3d9Z1RQgrCrtSw6cyXv267i3SU7692/WI8gUSomTipjvcvBNLJ3XIPnaWFhEQBleeLR1g6SLoBV78KeFUd/3MwXoUjUhXPnAvj5WeHsa4W9vncWGQa39z0g0jK4TwhatcENcPHFF3PxxRe39GlYWFhYtHmMCHe32FAzMrm/rJpCV9psgm86eeYLICvEd7nNa7FRvx0pVXotj4+ov4d3s+GbFmg8B0iZSmF5NaouM8WeziatKwB/s79HnFRuHkKRNKJXvgq9nw/4PcSE2jlUVf8ESAh2hTCseww/bArggGZKedvpw61qOg5VAyA0SEbVdBRZIjZMZEkUV9TWv3PKVDiwBTakw5LXAVjU+U5mbx/HKcEK2q/TkRWb/wnxwhmgqSjjH2uawWsY3MXbhZHv8Z3pljmNBCUfuLTe3Yt0IbwaixDZaTEHk4XFiYbL4D4cmsiCki5cCFCUA1XFEBrb8L6u+1SdsWLhDFj4gvi/XQwkDIZuI13ZNbSqXt+qprN+ryiLqnWo5lgKVoT7RKFVp5RbWJyUZL4gbgL+WDhDrLewaCJVtU7KHa4a7jh3SnlBWbVZp1qnfltWIHMaZ+55h8SoELNm9nxFpPod0oVxKAGJUSLCeFzxV4Nn1HRnToOFM4iPCGG2mspMx0QGyHsAvIxtgDVqHwZsng0LZzC8Z6zXe/XFeK+/PX4uk87tW+82AE9fktxgxNWLWpfzoo2klM/fkM9ZL/1q9nXfe6ias176lfkb8onzSCn37dXtxRX/NP91oHDz9nEArM8r450lu8zP0AvjM5ebLqNkRrh3L6vzndk9eBJ3qvNIUzLq3b/I1WM+ThIGd4s4mCwsTkD+2LgBgMKSCrK/eZUdmuhQsGqJR0un+uY/rvtUnbEid5H7/x5jIOtlWDAdwl0dNeKT/e93HFA1nWXbi/h6bR6v/5zD6BfdY+lLP2wxx1LwNLidx/08LY4drT7CbWFx0mHcPMDb8+ppXFhYNJG9rpt5VDsbUe3sdIwSUciDFTXkudbFRwZ7Rwtc3z85cxo/Riax2S7hQGGgvBuAT9XxSECaksGlvTqiyOcc3zflKcTlifFcU00Dek5pKklSHpfalpmb/dd5JhfbfiPMpqKlPI6cOQ0FePqSm7hn7po6L+dpSAfZZB44N4l+CeE8+222V913QlSIqX7ucAQYlahpOynl8zfkc8/cNXWE5QpKq7ln7hpevWYIIHq+V9WqhAX7n2poWa+YXn87KmlKhllL/ULlpVQoTiZ7joVHK3JkGNwDLquz/+897+DzVXtQJK3e3Y0Id3uprGUcTBYWJyDzN+STvWw1p9qhTA9lij2djZoQR1668H8cTBzLhKIP65//eJaoOKvhnL+KsWJnFkR3h5Jd4Kxx71+wHjZ9A0NvFt0K/M23mvn9+t4zfDHG0jdvHEpilBCQLLMi3G0ay+C2sGhteN48dF2oLbcyNU2LtsfuYmFUd40RCqjtw4KxyRJOTWfDPpHK1jEypK7DZ8j1sPo/RJTlcIYrqFihhxAuVbNL78hjYd9wp5oOCS3gCBr/WP3rXL8TYUAnc8/cNTzhvI1LlGVIEtToNv7mvJmLbb/RV9+FNOIekCTQVFOU68mvNnDQIy3a05A2OJJaYr+YEe7WbXD7KtZ7oiOcEi/N30yQIlGr6hRX1vo1uHM+e4qk7FlkqoMZr6wjV0tgij0dgNlqKrrrMTzExp2Z02DBC6Lu8ijGQMkQTRt2a5118REhPFCPcNqDtnRUXabYI6XczF5wpbc3+F20sLDwizGe3C8JpYuftGH8qA0zx4LTpG1s+vIZUOc1/NtPmQr7syFrJix+TegzjJ1qlqsQ3c29/49PiWWHdsKFL4n/NbXZ3qMn9TkrfTHG0me/zWbubWcCVkp5W8cyuC0sWiMpU4WndsF0WPjiUU80LU5uVE1n8TYxoQkLUsz6sPiIYPaVVvOHq3asY0QwjPZw+BzcBtt+gsPe6uPhkvDMvzk0jy4bG5kItQIMA3r3l88gqcLYDpac3Bi0gOKgTsTW7kPdvQLF4z1MGJSIXZG57T+r6BzdjpevGlyvIa3I0tGLZ9W60txbeVuwxlTcDcX6uDA7RZUOiipr6Rrr3ebGMLZnOibykzaM8co64qVDvOZIrWN0T6+8lDtCPkXSNaEqfjTfMyPCHR5fZ5WRCeGvv7ehAfCDejoAKV0k4o3e7VbWkYXFEWOMJ53twhm2jzjS1RTipRJusv3MWfJ6xqjr2T14Et0a++0fyhWPugqyHbqPgkUzIKITXPR3kCRUTWenGk9v4NDeLURqute435w05Kz0hzGW7jggsp8qapw4VQ2bYlUDt0WsT83CopWidh8r/tE1NDkIdczDLXtCFm0So9b2oxWifvm3nYfM+jCjrZJhQJltllKmwqnXwvpPvY1tyfuW0WXjm63e2DaYUPQhd6rz2D14Erd0+Y6ZjoncL39OfrWoj/vg00/q9FY+WCFUtvt2DGdk77imR62bQhtpCxaoUFiIXaRDfL9+H8u2F5kttFRNZ+GWArPv9Ra9C0V6BGFSDVnaKcx0TPRK635YmYdkTFF19YjrLSVdFQJMIFTKfWiov7ehAXCBshqAeLncyjqysDgGGONJJ1eEO08XPbifcf4ZXRdJR7W6wu8972j4QPnrIH+t+7nmgAUviv97pYAkkfPZU7w37U6ezhLZRAf3bPWqlW5uVu06dEQtJytq3dH3smqrjrutYhncFhatkPkb8ln88TTzuazV8u9pdx+3G4PFiYGRvuZ7kzfqw3yJ9xRNs/sIQo1/Ap4+BKdc7V7Wytqq1IuHcZSddBfLtheZRtRAeRcAyY4N3DN3jddvrKBUGNx1xOSONc5aMUGEVp9SHqhQmNF//K1FuVz39nJzYrsit5jnKy83a7V1ZJZpwtAdKWczW03lNedEQGgD3Gv/xn3QkKgjFjkKcpYLw12S61U9rq+/d0SIjdlqKkuiXB1T9q2xjG0Li2OAGE90El0G9z5dZAr9RfkaozNgkKRyWu7bDR/ov5PEY0i0e9me5eKxZ4qZVVNarbFbFw63rlIh+0ur6oz7zYUxJjaVhMgQIlxlOSVVDXR+sGjVWAa3hUUrY/6GfLI/eZIUzd2D8j3n+dypziP7kycto9siIBqrtQXYUuCt1u1lWG790f+B2yd5vEhtiyi8NhmXuJo65mGe/TbbXDxbTeVd5wUADJa2E4SDZ7/NNqOx+8vrUW8/UurrQGC0BINWb3A3puJu4FC9v3mGk+fn7II62y53Gdyj5I3msjQlgyn2dLZIPd0bVpfCmClHZHQHO4SyOKFxDSqcTxiUyOJHzuaTO0bw+rVD+OSOEbyUegoA/7Jd796wrTibLCxaMcN7xtIv0kmYJIzRAj3W/O3naJ0ByOI0uq17tf7f/K/PQ57IPuGq96HfRV6rtdXvmSUss9VU9ulxOHWZEMlBe0Q5lee431zERwQ3aXvP7h9WL+62j1XDbWHRilA1nd1fPsNkezobtW6mGvTP2ukUOyKZYk/nrS9tqMn/bN70Vos2TyC1tpW13kIxHcJdE4IFL0H5PvH/8LsgrL0wcnZmiVYrRs1q7qLjrvB6RLgErVZsL/K6Jg/a0jmkR3BQj6S9VMZAKZc1pX1ZkVvMyL3/ZsSu3XzMhcfO4K6vA0HWy671NlBa923ZSL2+Z+4aJGhSPaIEfLk2r866pdpAAE6XcwimlhqCUCSNVxxXcm/4QqjBtbcOfc4FW0iTRY6CnWJiTViHRrf1rcnf6BIVHFH0lXujVtTD18KiLXNzsg3WwgE9kjuV/zLFns5Mx0RCpRqS5Dx2ONuzKfhaIZ4I4rfv2Xd7v8uJGt0d9qyAjqdAwQYoFfMnec9vprEN4MTGPj2ObtIBukv7KdRjyC+tFuP+0WpxNMCw7jH16kT44ttaMqqdnbySw63C4FY1/ehFQk9CWved3cLiJGNFbjFVNbXM1CdylrLBXJ7AIfNmoThrm/3GYNH28a21NZSWZ/tRYk5TMggPkgiy/UkYEQumixW2ELhgujACDWO751jvntdGurbxvBXje00MMawcrTPtpTKGy5tZo/YleuWrsHk2Ze1uBKBjZNMiE/Xi2YFAU+HMu2DlO7DsDbHcMx2yFWOkXvu2tokNs1NcWf+EUAeKKx3EhgVxqLIWHff3skCPIUE6xFA5h2XaQD4Nu5F5nT8jeMdBsIdB1+GwI1NMpI/gexbsdEW4AzC4fekeF0aaksFfSEcLT0CuKIBBE9vM997CojVitMcaWL6GG4Jgn94eRdJM4/gqZQEAPaUC/lx5KxWKk0sLSuiTEO392zt8CAAtpgfyguls6p/G4fEfcNpX45HQUWV7nfvebj2ebhygm1TISr0/ELg+xZHi6axsDN+OGFGtJMLtr6VZop/uHRZ1sQxuC4tWRGF5tVm/eK0t01zeURI3FOOm8Xoz3xgs2j6+tbaGcQl4TT6M9L1/264T6uWaCgNTYWMGxA9wR1y7jfJ+NPDoed3a8b0mxnUwrssweQtpZDBgczqMf4LXFw8Fao5tDben0b3QJeoz9BZY836rF0zzxF87tIKyaiZ9urbRfS8f0on3luwE3N/LbK0bCdIhRskbGXH25aTZvkReME/s0HMsdBwoDO7964/ofM2Ucj8K5Y0RvvwVM+p2W8daoiu+gcRToUM/y+i2sDgCPNtjnae4FMr1OHP+A5CrJQDQS8432wR+viOExVedjQLit1dVBLuXoiMh5y4UxvrakaStn8NQu44m21E0B2lKhtd9b7feEdjIVbYFdFIPMltNratP0Qwt/wxn5aRP13LY4RaHTIgM5rrh3ejRPsxv1NgwuFuyF3d9Lc08e4ZbRnf9WAa3hUUrwhjwbThJwK0O3VEq9rudhUV9+LY58jUuZ6up3G/LYLJNGBKzqy/hnZd+5elLbmEC74mDdBzoPmAAPa9bO/5aP81WU+koHeJG2y+cI//OucrvaOMeRzvrIQ7O/x/QDKJpZ012G2qSDAMvEwZ3cMSxfZ1mxjf1etn2ooD2Oy85geE9Y3nki/XMPuz9vbw9Yjnt7F9B5nSI6gqleyDpXNi5ROxccIQG91FEuNFUPg69kdnFF3GpfTHRAEXb4dJZ5noLixOSzBe807c9OUKD1FdfpJMpmNbea7tcXRhvnSgyS03M1G9PxyUgoZuRccOJ/IpjIrPUVB4O+Yop9s8A933QEE6LpIop9nQiQmwM7+lR+92MLf8mDErkpfmbyT1YxV1jezGuX3yjadktHeFuTBPG6Bl+XnKClV5eD5ZomoVFK8IwCBKlYhTJPbR1lEoAbxENC4uG8GxzZGAoc0+xp7Mj+Aa3se2ahBie6v05LgGajoOO92k3K/W1fnraeYvZgkaTbMjjHuFgRS26LvaJCws6tifyP4/Jq67Bmg/E/61cMK0xGhNU8xy/JgxK5MFzhQBfVqdbyRtwGwDtDheIie5Zk6HMpSNQtENkXAAUbgK16a1xmlLDXYfxj/Fbt9sB2C2JqBvFO8RjytRjGgGzsGhVGLoTvoJlhkHagABhffjqi3TyUSg3KCKSMj0UWdLpLu03l5up3ylT0V2jjdNVLmUY2zNdxjbA36svN+97aYoYRwyD+zDBvOKYyJ3qPJSsv3u/t2bqQlBa5SD3YBUAd6X0DqjlZFRoyxrcgWjCGM4QC/9YBreFRSvCMAi6SAe8lidIxXVENCwsGsNIXwsLck+K3lQvRddBlnQcuuKVZme4eBz7XFFEzwj3CYK/1k+eLWhk3QkLZ7C/TEwu4iOCkY/l723hDFj1b+9lG78Uj20opdwfDfWy9jd+lbt6yg5IjKTzNa9475X9jei7HRoHy9+AcY+LWm5nNRRvb/K5HU1KOUD32FAAttS69i9yn4Oq6SzbXsTXa/O8eo5bWLR5UqYKw9PT6D5Kg9S3Vtq3B7cbiR26cHD1lNzdWcwMv1+fF63+AJukkaZkeNWAe2I4mxVJpHHv0jsC0EM+QPJ1z7vf47Mxzd7yb93eEgC6x4USG6Azt6Uj3IHWtzd3HXxbxjK4LSxaGRMGJfL4KDHxLtHFY0fpEAlRIVaNjEWTmTAokZR+Iqo3rL3G173cxqVdUk2Pv0EYVXTBFU04wSLcBkbrp+cuH2RGRLTBrpZPtmDInEbY8pmAT2/yo8WYqCaINlMorsmW7KruKqur4N3WqK+Xtb/xy5g8Rrazuybzrj7ZAMXbxGNVkZj8jnvE7QA6grTyo0opB7q6DO51Va4oXPk+qK1i/oZ8znrpV657ezkPzFvr1XM8UCyD3aJVkzJVOLwyp8Hf4o7aII2PCOFBmzva3Ely13CD0BV50CZKTHbonQDoJRV4Z/gtnAGLREQ6T48zI9j1CYOCMLqNGvE9rgh3HCVM6BsJI/4iNtI1MR4frbHtagGpajq/5Raz+qDEb7nFqJpO0JKXedCWzpCu0QEfrqXbggVaxmiVO9aPZXBbWLRCTgkTk8PVWl8AOkglLH44xTK2LY6IAlcq2L3Klwzc94W5fIF6qleaHUBfaS8Ah0M6QuiJW7qgyBLXVn1iph8ePPtliOgEzhoYNJE+G2eRpmSQcKwUysHsB47jsHhuTPI0V4q0bD92r9WCGA6Nv4zrDUC/juEsfuTsOuOXMXk8e//77kn8X4sh+XL3Rp6T3wSXA+iIDO6jSClHKJUDZJfaTDX5xStWcM/cNXVSLY3SjECM7mNhsFtYNDt9zhGPmvOoe9AP7xlLaHAQU+zpPKCk0xEhCrtPb286QFVdmCeGcJoR4X76kmSR+p05DXqMBWCV1s+rXMrXieyPMsLMgAaHdsGXd7lXas76e34HiisV/9/T7ubGd1fxQY7Cje+u4t/T7mbErn+i6jKDu0QHfLiWjnA3pVzIwj+WwW1h0RopEf0j12m9UXUJGxolB/a18ElZtFXyS6tJUzIYX/oFFTEDzOVb9K51JikDXL3fq2P7t8i5Hk/sks67QdczW01l0/4qOOVKsUKtZXHXu1Ak7dgKpo1/DM64HYq2uZdFdHL/392lAL9whoiQtGEUWeLy0zoDsPfQYb8TtdLDQj34zJ3/dEfMJMm7lMFz8mtkXOzfUPdgDaHrRx3h7h4nItz7SqrRYoUj4bsFi+sVEQIhItRQtNpQ/T0ag93C4riQNdP9v9GD/ghRZIluVzzDTMdEJtkzUCSdGt3G9crPpgP0w+BrALdwWj/7fneGjMtxqbvqxzcooozFN228MYy0crJmwub/ulfED/Rft94E5sfdZNaGG/fWNCWDO9V5Zsr7KV2iAj6e2+Buun7FseLaM7r5He+scsfAsAxuC4tWiHZoFwA79Y4US9EA7N7V9LpFAytl8eTFqWrsL6tGkTR+75BKqOxWVO4oHaozSRkgie9eVM+hLXK+x5Xxj7G6+x0AhC2d4Y48b53PD2GX8ppzojC4j6UBnOcSpGsXA0teg+iu7nVBYUclRtTa6NU+jGCbTGWtys6iyjrrSw87UCSNzQPS3BEzz/rQ8U9AjzHi+fsXQ/4fYhvfCHdjn091KbLu+t4focHdITyYYJuMqulUhXcHIPrwnnq3b0xEqDHVX2jcYLewOC4snAFbvnc/H3X/URukEwYlsqzLbWQ4RwMQhJPJ9i94S7mWgdc9z+onz+OTO0ZQHCLGxwFBhe4MmfGP8UPMdVTtWAZAVk0f87ieaeONYaSVsyHda3mlQ0MzUuiP4D0av+1ZXiKl15vOBCPl/b6PA3eqNaktmCud3S9HcC8zsnBe/Xmr3/XxkcFWuWMAWAa3hcXxogmDoOaKcBfK8VQFi5tCYV7uEb2slbJ4clNYXoOmwxztKvYmnI9UlGOui6cEcE9SJKC/LIwIOeHErN/2ZUCiaMVVWOGEle8Ig0ytpUfhzwCk5L93bA3gvavEY9IFwqDc85t73b41zS7YczyxKTIDEiMB2LCvrM76ssMOXnNOpPC0B8QCXzGmlKlwy3/F851ZonUaQMV+qCj03qehz6dSiFDqwZFgP7KMBVmW6BrTDoDfK0WtaQ+poNH96hMRClT1d3mArdYsLJoF4/cV08O9bFBqXSG1JqJpOrsKS8yMKkkCTQ7itif+yYRBiWbLwY49RbZLUE0xHBap5/M35DP7k68Jo5oyPZStepcjOgdDqVyTxNiRq4mIt614G2OWnkZO8v1H1PLP87c9Tx0PgCy5ldQN9pfV1J/J4jNf9Eopb8xoPobK8vVl4Xjy8sTBlrEdAJbBbWFxvAh0EFSdKOUifdwR0RU1TNwEyg7UH02pDytl0SK/VERtO0YGE3s411R1BehsK/USr0mMDGJIkEu4q+PAEyK1uTEMg/B1xxViEukyzi469BFpSgaDts7xNoADuSYNOdf+EP1g6TLMLUZksGPBCWNsGwzsJK7vxn2lddYZ0RpjMmnWuPu+f0MpuccY97KC9QGrJUtVrq4PYb4qyIEzf0M+ew6J39LnuULsrofcuMFdn4hQoGq+9zYhCmZhccwxdSc8vq/l+92/yaYYpB5CYvNW7Oby2m8Z4HLwIinIWq27NZeLPl0SKNBjxJOiHWb0eJi8BYDVWhJaE02ZJ8O+4oPeC8yUciP7ZY7zChy6TLDkwF6+h/PXjGB+h1uadGzw/m3/zfa++b+hpO6Lmcnied/wmS8m/D6LB23p3Or8rHGj+RgpyzeUhePJ9oMVAR3vZMfW0idgYXHSYAxymdNEDVSX4SKiteAF70GwfB+SrlKj2wiOTsQe2gWKwXGoaQrGjaUsSoiB/rzkBKvu5gTGcLYkRoUQU+nqHdzxFNi/nm5B5aSe2p1u617lqmFd6Tz2zyizK4UozsavYaHru3kC099lcG8/UEHN6CkEV5fCsjkk6oVMsadz8IyHaO8v3bkhjMkSwKhJ7uULXoJDrs+gyxnicdwjsGiGS4zIfkIZ2wCDOos6xY15dSPcpb4Gd0P9rI2I9xtnwoHN8NFVom1YIBNII8IdFl+v6E9DGI5LYyzdabYqqt/glhDK7PWJCAWq5lty2ME9c9dYKZsWLcP4x0Rk2RjPACpc3/umjlWGkNii7bxfOYoFwZ8CkCt1oae+F3qOdb+O69iDOkeRqyWSoByCom2sqOlBfmk1w+zC4F6p9WvSKUw6N4n/s/VBXjCdAmms+y3pIXSXCrC7SquSpL3s0hOOaI5k/LbTlAwutK00lzt1mSl2kb5uRLo9S09Get43POeLO7Nol7uIM6RkRtuyqRr9KKGNXXvP/Re8GPhY6UFjWTgGS7cVccuongEf92TFMrgtLI4nnoOgge8g6Eon36fHkRAdSmR0F9gOQYcLqHVqBNkC8+YGmrL4/pJc2kcEEx8hJoeW8X1ikV8ivgMJkSHEFLp0AAZcDPvXI9WU0e1PD0NsKN0yp4Hqija0i3Eb2yeYAehLp6gQIkNslFU7ydlfwaALpqEv/weSLiZe0XkLwPmIqLcONELg8TtXNmQw1BmNnJUNi14Uy23t3BkEuYvcyr+GGNEJdM2NCPfaPYf4+vc84iPFOKPpOpW1IrJkGtyBcNqN8OOTYgIZoFqyVOGKcIc2PcLtz3FpGNzxUglhHKaSdn73bUhEyFD9DWRCC5Zz1KIFKdzs/bx8/xEdZn7cTWQ7tjCZeVwZ9B1BkkqpFkpPeS+vOCaSfPrzTOjxoZfRObBTJD/qCYwkG+fq/1A49CxA5wxXhHuV1s/sv+1Zux3dzk6JR71zYlQIT1+S7HJaPYKqw9ULp5vrd2oJ3G//imytG8nybpKkPH5imNsY7h0X8Psc3jOWx8O+4U41ncN6EO2kWqp1OyGSg2+cI+oY3eCKinvOD501Qhk+tpe4R0gyo5VsZjomknra/QRk3qZMFQEdXQUkOGuy9/rMF4QTxGMMVTWdFbnFRK98Fb3iMHB2oy+Te7CuPodFXSyD28LieJMyFTKnY8aZfSeMJcLo2at3ICEqhMgO3QDooB/i34t3MKRrTECGcaApi899t8n83/umZHEisM+VUp4YGUxMrsvg7pkCS2aBoxLKC+o6gir2nxTGNoAkSfRPiGDFzkN8smI3d/EF3XQNVZdRJA3bvlXwfDygN+2apEyFmnLkpbPoCrBoGfT7E2z5DjqdBotfdV9v47hGBN3Y/wRgp2syVlGj8sCnawExzkw+r6+5TURIE6YiRu02BO6gqDIi3E0XTPPnuCwjjCI9gjipnB7SfjbqPbzWyxLcOqoHUe2CUDW93rH62jO68urPOX7XeeIVBWvCxN/C4phwYJP384rGSyl8MRxX+WoqXaVCrrItQtchSq5ipmMic9RUEr7N5rxHHkYBkaqe+QLtZYWDwV1BBdvuJZwW8zZdpW50lEpQdYnR8gYesH/JTIe3UNob1w9FliUKy6v9BhOU8Y/w9uLt3KGKKPsgZSczHRNRkYXBLe8FV7b8km0H6j2OPxRZ4uy+cXy8/myut/1KmR7K52oKt9n+hyopfpXUzYyXlKlQlgdZL4s/A12jFhuz1VTOMRwJfgxmE8OZqxuvo8MHl8KtHuJ3RkQ9dxHc8l/mb8jn2W+zmVjxsSnwFggHymsC2u5kx6rhtrA43iycgVuHVoef/uq93hXh3qt3IDG6HauKxUDcUTrES/O3NC565qoDio8I8arPBZHi9KAtvc7/BlZt94mHEeHuE1JKiLMUXVIgcTBEuFqilHukB0quW4JUz038BGT+hnxT0Ct21Wt0W/cqb0rX0LtmLj8pRsqhLq7N2IebdvCEU7yfb/vJdTitrrEN/mvv2jDzN+TzwLy1dZYXlFbzcLpQHA8PtmFTApyKLJwBS2dBnMtY73pmYNeq0qjhbrrBXZ/j0ohyG8JpY/u254YzhaKypsO/l+ysd6x2q/42bmwHci4WFs2KEeEOFtkq5j2jCXg6rvYhnEaSBDW6MCK9VP1TpopUdpdBeEaICEKU2mLptu5V/hMsWpRV6iGmsW1Ei41+0CN6xzGydxyXDenMyN5xfo3kXzreitPV79s4jxyXAFuS5C7hm5O5ndz0J1n67lSv37PR/WXTvCfZk/GU2VFA1XQODJuCHiHGiCxtEN+pZwJwjvw7/1QvNaPxfvtXH/Q/LgThJE3JoKSqVixoTBdoZxZ4FtHsWgJf3VP3wDuzyPnsKe6Zu8bL2PaMwPvDOHLJYQdl1S3TH7wtYUW4LSyOJ54RLIMlr0NQuHvS7TK48/T2qIcOM3dRET8EC4PbwDCM/db1uQbhM8fprA8O4k51nrnKGEjTlAy/HkyrtvsEI/MFxuTvZT4XkeQQKXjEJ0NQKKiufp5GtGLhDLc3XFdPuNRmf3jW5nr+JmarlwGwV+liRjnQNfj0Jrh2rvsAC2e4RIXqqT1eNlvsimtyoromSnuWCwGwnmP9C4TBEanjtiYCaXsFEBlodNuzfj7hVPjkGtHPPOUR/1kBHtEfyTS427uP1dDn5oFvrfWDtnRUXWannsDp5JgG96qdhxi8/S0etHmntvqO1ds+fZzsP/aT72cy6y81tqFzsbA4LhgR7p5jRb/qIzC4PZ1FE2RR1+zUZYIlYUQaxp2XUyllKjn7yxmZPQsAm6OSjVp3BiqidWWkfLiOsQ2B9YOevyGf0XvfxSZrQi/HdR7faiMB6CPlIaOZgmyqUX9dAXfPTeWc/h34fU8pN1TPM+8b6Vt+5dLBiXyzLp/80mq+DFoBMoRITs6S13NAj6KDVMoIOZss7VQkxG/+0l4dUeRzxIntXCwMYxBOXl0T45im8kPoJUypSmfT74nQ7/m6mWm+WVJDb4Y1H0BEIoR3hPy1sPZj4XgMCjfHU81xmKTFr7AteDaKpDfJ2DbKsbYXVnBat5gG9znZsSLcFhbHC2MgHPZ/3ss7DPD2UpaIm8levQOfr95DvkuhM0aqIBgxYW+wT6srSiYvmM6fOh/ma+dIptjTmWJPZ5bjckAY3kvUZL+D6n1KBtdUzq23f6xFG0JWuKFqLmlKBp2rxKRJ6zRUfNdKhWOH8v0eSvmuWtoz7z5hoqz14WsQKpLmNdFIUzK4tfZj1B5uYR02fws/uFTFG2uxsnCG2Td6ee+HUIfc6F6nBIl2V/U5NIwITxsmUMGdgKPbngrmeavFhLGqCOIHeKslGyryntGfyoOAEE1ramsco9bamGAaE+8ERLuuni6l8nf0Z5hiF8a4J4YzZ++Xf6XWqfHr1iIm29PrqBUbDh/f/aGeKJiFxfHigMtZ22uceKxoeg23p5BYP3kvAHc4pph9qo3fg6dTaf6GfM5fM4LXHGJMDpNqTGMbwKErXnOYhKiQgMQF52/IJ/uTJ0mTP2OmYyL9aj4wz+NSeSk1rnrrLtIBc5/ZHj2105QMftl8wMvYnq2mck3lXIKWzCS/tJpoyhksiRKu7Woik+1foEtiFLnA5XB4LOwbJtvT6VO93n1yX/1FPEYkCmM7urs5tu2KOI2ZjokM2DzbfW/2zIp6NlY89hgjlsUni206nQa3/s+tYbFklpceyTZJVIQrkk6tzzWtD+NaG6KY2wotpfLGsCLcFhbHC2PCGNMTVr0LIVFQXQrl+6D7WaKOJmUqeskeJGCv3p6DFbWkKT/i1GVskkZH6RC7Xa0sGqzrS5kKuYvovPNrOnv8yu+zfY0s6SxRkxmtZJOmZXgNrp5RPit9se1TO/oh5vy0hSn2dJy7XL1KKw/A7/8RKvl7V0D21yLiOup+ka4rKXDecxAad8LVE3viaxB6RhU9fwcf7bmWTwecStKmOWLlsjcgfz3sXOTdpsoTjyiDLtsoCusLkR4/xBNQHM2XQMePIFuAWTSeDgjFDrWuCd6aD+CmL8X//lrfZE5DCokGQNr+M6yd26RafEWWePqSZO6ZuwZwCx0Zwkc9pALSlAxGKZv87v+x/TlGKZuYWT2RD5ftZHrlpZQrTqbY0xkpb2SFPsA04v1FlpoStbOwOOZUFbsN7J4u52PFftA0kAOP2XkKiTl0GbuksV3vRKZ2GiB+TxEhNob3vAjwdoi+pk7kftuXyJKOrotUdIeuYJdUHrR/SfnwSZybnBBQfbWq6ez+8hkm+/zePH/XhVoU8VIpfaW95nzL2CZRKmKKPZ37bV9il1SvY6geKuQ79QRkSWez1pVyl6hiPCUATAz/g7F9V9Dtj3nimuYuEmNXyW4RcJFkiOoC5flCJHLTt1DwB2dX/Jdz1YcY3ac9IzwzoFKminFPV0G2CWcuQMad4jFxiMhqu3Q2zLsO0L0EJ9tveNs8VJCkemUc+GPSuUncd3YSiiyxdHsRS7cXse2AZXA3hmVwW1gcL4wJ468uI2bAJbD5O9FyI7Yn/P6haN9QJry/+6V40mQx8T+khREjVdKRQ+ymo9dh/U5sNRWKd7if6hKSpCNLOg5d5gbHk6Rp4tjx0iHeUi/mcnmJ16TvEyt9sc2zv6yaWc5UFAkeKBcTAWXr98LgsAULg7umXDzvNlIY3DHdwRZ0wqQ210dDBqFXtLuylvN/H0XmoDJ6bPtAbLBzkeifLUl10/k01Z0SCOidhtL7wHyUPzLc7cRyF53QzgwIPP05JjSo6QdPmSrGzeX/gO2/wqFd8MendY3tlKmga0gLRN90pYnGtsGEQYm8eeNQHv9yPcWVDmarqXSUDnGj7RdOl7YyzL7VLM8xJtwAI+WNXob4ruIqAN5RL+I65RdGKZsYoW9GbiCNs31EMM9dNtASsrRoGQ646rejuolgAYiuCoeLm9TX3hASe2f9hdxu+x81uo29utBUmKOmIgGXDnDXWns6RNOUDGRJR9UlFFfA4AbHky7H6Oe8slyntOfzATmkVuQWU1VTy0y97u/NeP4nZTnxlPJn5Qd+1k4313fmAOcrqwGwS6pZ9+27/xR7Ohu17gCU6+3MeZWCxoP2DEKqD9Dtj9fqimW6HIMkDoa9K93rFTsU/EGkVgLAL/F/ZsT4ZPeJ//ys+3/N6Xbm7lsrlnUSTg32uluUmU5fIPbQH+biWl3xq6JuIAHzVu7hvrOTAOjdIRyA7VaEu1Esg9vC4nhzcKt4LN0HEZ3ExDGsg5kWJAGqLvFq8D8Ypos2EGcpGziTzSRIxfgWRfqd2OYuEkqXQC02giSnucouaTwRks706okMknO5yfYLNyq/IEmYaqFW+uKJwb4SoVD+a8TFPFAlbqK6EoSUMhX++ExsFBYnbs6r3hPPY3u7D3CCGoPQsEHor4b2uj2Xs1T+GElz/Za2/g9u/1X8bwjU5C4SEYudWRA/EAo3gmxjwN4M1LGPongagiegIrknRip2QWm13zpug+5xoUf2AhNegM3fQ8lOmHVa/X1m27nHMfO7fyQvNyiRww6NSS6l9WnOG7jBNW5qOsxTx3OAGMKo9jK6PQ3xVSUd2CB15N/2vxMjC/V2WdLrTNyf+tMAPl21h637K7g7pZdlbFu0HIUuh1F8f+GIDY0TpRzlBf4N7gaUs/skRKMd1OEA5OqJZn10QlQIyZc8Tx+P77nhEDWyjYysPH/ZeVPs6bz1pQ01+Z8BdW+pTyMBhJGpIdNf/owxygbzdUKp5p2gmbSXhMCmrlOn/tzYX0Jnsv0LAM5Qtno50x6wf4WEJqLYnveD0jxY8754vu9377GsvUskUhHOyVKPdmcsnAGLX3E/D450txUz5pqdhri3C48XnR6SL3dnYSEhoVOqhxIlVbFAPdXL6PbUlvDNrOzVPgyAtXtKWLa9yGot2wCWwW1hcbwxFCjDOsAO14Q95ye4Z7GoldqQjiLpDNOzeUu5ljnVl5IkC+M53kM4TULcqPwaxj8+CUBN/BDm5PXxmgAC3EEG3ey7uMDlrRWTRok5rpuClb54YlBQJiYtf8FlbAOS4dnuKlRTzZ6qxa6WYXF9jvNZtgyBGoQgrtvEio+R7E5TwIZ9v8M/z4JRaRDZxeyVSq4r+r3qXbFzVFc2JabSZ8xDeFUNn+AZBJ6p2BLefkLP59HtjiDCbXD2k5Bxu7snt+Yj9ldeYI6FOpL7u3+ERndCpNtJc7vyPZIkJt6yBFnBD/Af9QJuVeab26i65DUZn5L7Jl8Ei/dfrdsIcTlCjYn7HDWVhKgQbhndE1XXmf79Zr5Zt4/24cG0DxM7HqyoCbg9kYXFUWNEuDv0F4/hCcLgrigABtXd3tBOAO/fmcvBGN5+NAAHQ7rxeuqQer/L8REhXqU9iqSx3CF0Z4zsPHBHYRVnbUBt8xpytBqCiFt00W1gvxbNFHs6EjqXKsvoI+8D4LBmp53s4Nt6emqv13uZ/9d6ONPSlAxhbIOoz57/KEx4UQwihnEMXunegGlwxxzeiYTG5oIylm0v4sw97yAvmA59J8BW17hTUwZn3O5uKRbZGVa/D5nT0MY9zv6CfSRufp8CZzgdu5+FtGsxEjq19giiHOUA9JQKeMVxJYqk1SuwW1hezfwN+fz1640AHKyo5bq3l1utZRvAEk2zsDieaKpQ1gWRYj56kvh//3qRFrTRLaTjlOx0u+IZsVoXRnWCy+D2V9dntKjY8vFU2L8BgPzQJKbY0/ko9EZ3OiuggWlsG8iSzmNhXwckOmLRNthXUk2aksEFVaKmqyykK+rYR8WEaPN3YiNDcbbIMLh7+znSiYdhEAaCMenY1D8N/losogMgIthf3W2WgQhFWTsMvEJMSJVg1ItmsjXhcv8HPgHE0RrCSMVOiPKe5CZEhTDaNTGOamc/8hcwjAEQKZK7l3qL/X14Bag1aEHhSOho3ccclRig4aS532MSOq72Faq0IEIkJ3fZviNIcjtQFEnnBZuoj/xeO1PUnyIi4iGSk7WqmJhv1LqbYkzGmC67xvV1e0p5YN5abvj3b9zwzm88MG9t460hLSyOBldrUcAjwj1APDpEWYTpqPXFU8RrwYtCsNBDW2Efrqh4XFKDLbuG94wlKkTmFVd0+DWnd721Zy9rY30guhG+IoieGDXYI2TxniOlKv7huITJ9i9MY/sPtSdztfMAqCKkjugbwF9tH5jHC3I504x7iDbucegj9mf5m+LaZH8lxi4Q6eMe6d4AxPRAk2zY1WoSOMQfe8u47u3lvJu1jZzk+yGqq/cbiewEfc4V/3c6DTSVnOT7Gb1sGM/8IUR4SzcvYFTBZIraDxfPu5zD25IwqrvLhWzQezaoLbHzYBX3zF1DoU8Pbqu1bP1YBreFxfGkdA+oNaAEQ3Q3OO8ZkVYOIt3H1ZapVrdh0x1MWHUHPw5dTlWwqHVKkIRyeIeIYH4cupwJB96HzBfI+ewpznrpV657ezm7Nok6nZ10onzvZpaqA1iUcCvqmIdZ3esvHNQivX74KxlAuS5EPe5UP2VC0YfH40pYHAf6bvkHU+zpbI1JAaDGHoU25iExIVrxL7FRTSnUVrkdQSeJwQ1ugzA2rH6jz9PDX3KGy0F29X/glKs9tvKYvmkO+MFlRHcdDrYQ+hZ8hbzopWP/BtoAEwYlsviRsxncRajZ3jW2F4sfOZuIEHHNo0KP0OBeOENEcYye3IbwZM+xYnL/z7OgMBsAubaCTYmpqDd+eVR9zhVZ4oPeC5hsTzcNgV16AiNq5+DZLGKJ6nbkXGfLZJLyOZ/Yn8clUowsiW2mOYVyfVepkH8rVzHZns6Eog+ZvyGfaf/1L8JmYE1sLZoNT4V/zwj3whlwKFc8N9pJ+sMwuhe8AH/v7aWtEFomtGXsCQMaPAVFluhyxd/qFe8yjGxPAtGN8HS0+hrdc1yG/P/Z5uPUZdpJtdxi+8Fcr+oSlzqmsUg7VbxNZR2z1Su8jP+/2v5DT3k/mg7jameaBvkUezpb+t+HrGsQLuZzyDZxbTLuEs+ju8NZU+qMUfM3HWSHGg9Ab5fhDzCt8nLOXzOCoty1ABTFDgVA3/IDtHO16EocwvwOt3D+mhEiFVzrB0A/eS81pYVs3i9KW17dGse0w6msVMV4+o59pl9jWwISIoP5ZMXuBls++u2gc5JjGdwWFscTI508ro+7Lc0Z3m3Cvo6+hb41H7C2z19gZxZJ2bNI61cCQDe7qB96r1cmSdmzQFbIOVBFUvYsJlZ8DECUJDzQe9RYTnGuZ5k2kB+y93P68z9xZfZZTKh9yZwcOnWZJc4BbNGEgnVFaFfvyajRYseizaFqOgdLRZ/Sve1EOmC1PVqsTJkq0p5lV1VR2T4odk2kTpKUcoMJgxJZ/ti5xIb5T21WJI1XHBNJD7/eu3yjfZLrH1eCdGQX97ptP4vHninIWS8zID9DqL+fpCiyRHKnSABCg2woskRZtahDPKIIt6ca+diHxLLKA+J57iLocRYUuFvtqGMfdWcZGMbAEabyJ3UIJSf5fj4Pv95c9mflRzyDdMu1ZP7huMR8/oD9S+LlUgBWqUlmHerkpAKq2iUQKR3m1n4O6DkWTXXW6V+epmTwoM27LMia2Fo0G55RaqOH/ZbvxfNuok91vRFugzFT3P+76pV1XadjrWhHGdd9YKOnYWbIRAY3uF1T2+Y1lHmTfO3zvME12FwGdKgkWrGqkg1F0rlfyWCF1p/DehAJ0iGeswntk9ecE0lTMvg/l4H+izaUy+UlKJLGakm81z7x4WLet/ZjkZqvOcEW4grCBAmFcqP+3XX9tQUv8ey32WzXRWCmt+Q2uHVAR0d29UmfWjBeLN+7guqcBeK8E4d4jSeHiDTneyPlbE6ThaN9pcsQn+T8i6tMRq/Tds0Y4q4b3s0sV/OHZ523hRurhtvC4nhi1OmYk3XA4T1wFVWJAX5xp//j1C7RyAumI2d/BUA3eylpSgYDt6TD+CdQxzzMzS/9ykTHPqbY04mUKjlT3oymwxhlg5d3sqRKTHCvU35BljBbjZ0hbWaYkoOmQ3jVHjjjDnctpDGptWhTzN+Qz7PfZpNf+icAntn9PthgtzMas1hg3COw7hMRsdi7UkRmlWBvw/EkIcgmM/2KQWbrJ0/z5XVXFOVNT10D47dhtHQxHg2hNIPN/0XJX+u/hvskIyFSZNHklwohP0P4J/JIDG7PntzVpSKN/+AWGHCpWL9rqXtbJUhkdXz/vXvZ0YjUjX+MJGCxprMit5jola8yYHM6evezkHqlsCm/jCmbZzPTMZGf1dM4V/nd3PUr5ygedN4HIOpQd79FVTvxi5QLN8HBLeyNPJ380mpTqMgzrdOXBltDWlgcDSlTxb1hrXDks+jv4jcXHAm7l4mWVQ2gffuAO6Kna2iZL3Ig+c90RDieuiSdGtBpTBiUyHnJCcz5dRuv/ry1zvojbZtnHHdFbjGF5dVeteTXLfs/7s773Ixaz2t3Ldc+8i9yPnuKydmz0IHftAGMU9bRUynkJkk4WNtJNdToNoIlJyV6GFPs6SzrdhcDb/gXm96/i766Cimu7Cejzt3pmv+ptXU7LAB5ReXkl1az3SYM7l4eBjdAB0qIkSrQdDhV2sFGrTsD5V2EVAtHyRpHdyZWvIRi08yMgN+0AfST93KrbT6hUg0lehjbXAb9FfJiMxPH7tMiLMFVn13j1AK6xlZrWW8sg9vC4nhiGtyuNEgjLbLHGHJCTyNzSyF31n7MIaWWl39M5aOoYXyQfD9JpUshbxXRNXlMsafzb/t13JYylRXbi8gvrWY23r1hZZfiuG86lmd6rKE+6akACoDjsDul7Aha6Fi0LPM35HPP3DVeRmO8VALAz0UxlGzcz8VDXEZ1RKKYVO1aLJ7H9mpSb9UTCSPqIRwV7olCgq8IjKcjSlNFL25P1fGIRPdkNH+tiK6WJ3Ny5Q3UJdEVTTKurWFwH1GE27PuPSQKep8NOT+InvJn3AaLXIJBsg3UWuSsl4HA6vUDRZElRu79N2yeDeOfMNXPS7YXMXN9PlPs6bziuJJz5N+RJJGOahjbIFJiR8jZjD7sGncP7YQxU+iWNZOP7B6KzLbseluGGVgTW4tmocot0moKeW109byvqD/CnfPZUyRle5emyQtfoGBTNh2BQqk98WFRAZ+GIks8cG4S/RLCGx+fm4AiS34dVXeSbhrbAAmxEQAkXf0c2oJwpiyYTkVYd6gEuywxs1akjWeqgwmWnFTpQVxly+IVx0Q+338BmcDWhMvpM/Yi4XRNmeoKarzoftFxj9eda6VMZc3aPFixlh26eH+9fQzufrLQDynRw3nA/iXL1P7munzaE7b+Ay+H3YO2dGIQ4mjDZDEfXa31RUfmI/vzjFay+Y/zPP5s+wlNF3PKi05JpOSMSaZDYtn2ogavqyE+Fx8xou5Ko3XmCaxdUh+WwW1hcTwxUsrb9/WauM+Pu8k0ksoVp2k4zylN5fw1I/jn9bdzQcapSIBDV3iu/BLsS3d6CVb8Q72MybZ0JFf0ujFjG7z7Rv6h9uRUJRd97VzhNbaM7TaHqul10lEBOrgM7kI9hmn/28yFp3YW0YAIV0/3na6+0SdR/bY/Gop6mGj1tJ8ynjuqYcmrQo/BX3T1JMVI3yw4Fga3J5kvCKEhgE3fCLVetQbCO8Kw22BnFsqiF+mbmApcdHSv5Yuf78LwnrFMDr8eqQJGyBvN8djmUvz1HJdvcDzpVlxWa6C8gNqgaEbXZqPrMFpp3NiGwHueW1gEzK6lovUhiAwSQ8irxxixrNx/DbcwtmdxSAsjRq6kTA8lUqpisTqQswq/BqC4XTfij+CUAhqfj5Kcz55i/L63WaoOYJkmUsGn5L1NzmftSLr6OeRxj8CuxYTnLgJgiJbNn9UHsePkfvtXgEhDN3+3pdWs2nWo7guNf0wEWzSnuL7jHvF7PsZve7vmSimXvTML+kl7AFiuJ7PJ0c2rI025FkyyK9vGGENUXeYS+3KvY6zS+pnG9hI1mUNEsE3rRB95HyWR/RmweTYkRkJv4Vg+U3WSGHVmvR0+NFdWjranL/T2eF8nedbkyRnKsLBoKTxTyl2TNXXMw15GkqcCp7Fs19fTzUMYaT5//WYjczK3mctfsf3DbFNjTO48USTN7+TNeL1f9dPcKrqSDXXMw8f4zVs0Nytyi728/wbxlABQqEeTX1rjrq2KcEUFDCGck9zgBnfUo14F3fGP1e+ISpkK9hDT2HZHVy3cEe7DqJpOebVoiRUZcpQGt6zA5v+KOtH9G2CfKAsg6TxYMB16jkUd+ygD8jOO/Wfh57vgKco0StnETMdE+tTM9atmDGL8PdDe1aJv7UcE1ZYAmFFxf45To567qbWrFhb14qlMruvwxe3i/05DxXe8h0vhf9M3YnnFfrGdB6qms3BLAe87zydGrqRGt/OG8zJAzFtWqKJOuCbqyO8zjY7PR4HhLJjpmMj1jqeYraYyW03lFcdEkrJnkfPZU2LDP38rItJAsOTgQVsGf7b9aB7HsxUYUEfJG3BFep2udoaOekUcDVV1I8KdKBUTxmFzvWFwb9W7uM71SnNdXzmPrC538mHQNeYyY77nSXepwDS2b3A8iarLpip7VHkOjPiLV5mhrNjqFZ6TXK+Rk3y/aFn26c2wZ4W3sX2SBnIsg9vC4nhRVewWIInrY07W/BlJngqc9ykZ3KnO4/dEMWgaaT6eE7c0JYPLbMsA+Kd6id/JnWdbDV9mq6mouuxW0dWd/Hva3ZYCbhvDf2qpbqaUFxLjvV14R+9NTzLBtGOO56TiKSHipSx6kb4FX7X0mbU4RoS7rNrJfg/BnaOOcBsCQ7pHXWH7fvD7XHNyp415iE2JqaJf93FgQtGHTLan8w/pmjqtjPwZ3btSXsc9dXVPYRVJ5zXbHPO5kaWk6jIP2rzbiHlhiV1aNBVPZfL026AsT0Ree5zl1qsY/wQs/4fY3lkt9BM8WJFbzPOVl3NAjwZgiTaQ7zXRdmqolIPqUrHIpVOrE/oznAX+ghKzXEb3wi0F7vMe9wgVsaIP+d22b02xWoeumK3ADOIjfETf/Nwn6uucYDjwygjngC7S8HtJ7nlZX1kY3Fu0rq5zvRKnLky7Wl3hpm3jKHFlExnMVlP5Q+0JCJ/JtbYFzHRM5AbHk+b6V1xGuaSrohWcT5lhfcJzHSKCefPGoSRd/RwMvg42fQ3/Pv+kN7bBSim3sGheMl9wq04abZciu0BwuFnLUhhzc727e6aBf1gwkVlqNmOV9axQ+3qlDhn/a7rER+o57NXjvZY3lpJovM7vam9OU7azQu3HnczjlU+ccN3zVl/uNoK/1NIIDtPOpbRa6JoImdtF+HyulsF95Pjz4KdMRVVVBix6ETWrL5x98tWtGUSE2IkItlFe42RLgaghbGdXCLIdA79/ylTY97tQUgYhoOYzufOqoWxuXNlLt49+iHde+IXiSvH7M8Zhoz5UQjgihhZ9A+hCyV5XoedYDpWWEVO8lsttS+ksHWCRNtjU25itpvJQyFfcx2ewaj8M+q+7NtJzYmxhESjGbyVzmlDOBtHWcOmsuobSopdFCUTFfmgXbc5zCqNuAOA8ZRUAP2rDuFxeQrEeTqxUwUiXTsznu0J56aVfj7j2ujkwnAX1MUtNhUoY6CFQ2O78J2Hete5tHJfzinq1OZ+SgM/Dr2dY9xh+MLr81XOfANxCaj5G6YQD7/Pj0Cp2ZXemA6X0lvaxXu+FhEaytAsQEW4QczmbpJnibb5lLAZvqpfypvI6kgQ1PhF5EGPVVUk6XXd+AavfFwt9vgeeKf5pn6zhYEUtr149hNFJrl7rPc4Swqzobg2Akxgrwm1h0Zx4eo0908mNQVdWaB9ef8sLzzTwWxyfUKwL8Y5T5VyWqQNQJA1F0sjTREphpjaEK+TFPGhL90pNB3fcJNqn7+39NrdR/6V2FgClhDPTMZHJ9nR2f/lMq/NGW/jHSD/zjHfFS6J+rExvRzVBJEYFu1NQI3wi3LFWSvkRU09t9/GOrrZmjGjIlv3C4D7q6LYnV/5bpJVDy0/uXNlLhvq9hHv8NbKXjOcf9F4gUi97jjWNbXIXEXPKBHSXQ+wMJYcp9nRKOo5ktJJNmpJBB+O+sTML/nOJuJ/sXmpFkiyOnJSpQunfUM7etcT8LqmazrLtRXwddQNVYS7RTaOO2zXPGbLjLRIoYoi8A02X6EEBU+zpVOveLRe3a50oKK0m+5Mn2fbp48fxDdZPoMKDntsp+9d7rXO4YphGhHiyPZ0Pei8IXAOkvnaFskJS9ix6R4vjGL24n7B9RJCk4tRlduoJXgGafjUf1JtRA9BHygNEFDzYJyIfHWrnzRuH0vWWdzFHLtnmTiv3wEjxfzT0Gx60pbOv1J3uzh+fikdJdmsAnMRYEW4Li+bE03PZzaXYWFPuJZb2zGdr693dmJhFt7Oj1spcbl9KqRZKlFzFSGUTSx0DsaHSWRY1uaV6KFPs6czWryYhNITZZW6vpaHm6Sk6crC8hvIf3Eb9KHkDAL2lPO5QRR9NxVlrtZ1pIxjpZ0Z7K3ArlBtpfk9c2N89AfCMcAdFQPiRSNlYAA2qrh7X6GorJiEqhJzCCjPCfUwN7mVzvGrnWTijVRidDanff9B7AUnZs+pVvJdG/MWdwgtEl2/lcNcxTNmTDhWwO/F8Opb8QbBLwIncRZaxbXF0eBp8LseVu82k+P5+bA9mlAL5v8whsVcKpEwlZ385SX+8xvP20wCoIIS77f/17oACVOrBFBBLmpLBZHs6b229lp6afkxrsY+EQIUHze2MoIlHh5kpzAOEwf15+PVc0rsTSR1C8UrobkiduyFtECDGFQHvLe0jTcngdpsQtduid+UvytcNiuJ6Pq+vW42xzRvXDRVR6oUzMJtkak7h0DPGGs9zXTiDiWUfMFOfyN5Dh93Xx9z2EWF01xPBP1mwDG4Li+bGN10ob1UdZfL6MG5Bt47uwas/ew+exVq4V1p5qRZKqm2JaxC9nI9uHIwsS37VPA3j+eu1eTzndAto5GidAegu7SeYWnOAft1qO9NmmDAokempp/BYhvC+d3AJppUosfxfX40LBnZ0lzoMv9O9Y1xvodR0ErftsGheDOG0TfllwDE0uH3TNI3n0Comd/WqKy9c0bDivTFhlWThTKgqol1VlrlZt/wfqcOo+5vpXVic8DiqIcf1nXIpk+d89hT3rBnhNU8pJBqAxLwf2frpk/wv7s+8umYEM21ruNImWkxGSodNg26y9pmp4L1d70Sa8qXb4Ku+lFNagUPfyA6rT3nbKP8Y3jO2zniTBPTSdHZ/nciUda9y1bCudL7saRT5HLGzw+HniE0kZSrawW3I6z/lQnkFFykrhPK7spEtelcUSWOJmuzVzgzqlrE01q0mIsTGiN4Xud9j3wmi80NMLzEe9RzrPba6tvutx93M3jyWKw8ddu8b2xuKt0NkJxjqKp1sRePy8cYyuC0sjgcpUyFzOqCDLBTAn33p1waNbfCOSs9buYc5pakEU8t99m+IlSu8to2Sq7wG0YOVNVw2pHODx/f16h4g2mzj0UMqYIveze92Fq0bQ6SlS0wId/YKhY0wZEA/9ga7vnFGqYOug60dOA8Lg/skb9th0bwkRLUDYPsBMXZFtjsGU5BAaiJHTTr61zlK/Pb8bcyptTPL/b5++RtkzQSEVgfoyJIQ0ZRd3SkkCbSPr+a3s97zNuyz/m450Swa59sHhGJ2UAQ8tgdt4QySFkznPmWft+q2LsQ3V6lJDNs0m4XObbxtL+Q8ZbW5jadS9yvq1VyqLKWHXMhAaSen2nO95iqtoY+8Z3aY+HW5MQIfpkChn7RwRZbodsUzEBtKN00VP8pjjHzeM+jrPxW/cx1k11lu1bpgx8loWzbLHcl19vP87OrrVjNHTUUCLh0QJ8YLY0xNOk8Y3IeLhTK7Uf6SOQ0WvCAcgeOfoCDqBti8lr2HqqCD6/oYPdsjXfNQ43r5S5s/CbAMbguL48GCl/BMzcn7+lnyS89odLeXJw42BSiMm8HL6rXcZfsOu6Si6hISIEt6nVYUgRjJdb26Etv0TgyVttFH2sdWvZvbq2vRZti4T0QQz+gRx6BIV4pXREeodW3gaZCERAuDu2I/bPjCSkm1aDY6uSLcDlWMNpHHIsLdWF/0tji58+NE0JRgU3RHlsT1M/p7L1GTKdXDuci2Ajl3Ieu3pjFdFQJWj4d9w53qPMuJZtEwC2fAHyIlmlOuBEnit663s9Sx1cykUyQNVZdN8c08OrDO2Zs7XKnNBrW6zVTqNuYk05w38pb9FRRJryPS1Voc+g2Vf3gJvB1JWvix4Pe5pjNAlmCUK1W/r7THI7uxYYHc150TiQq1k2BTKCjzfo/JlzxPn0GJIgPOGHuctaAEQ3UJnDLR5dVTITdLGNuSDClT6bJLlDXuPXTYfX2WuTosRHoEfk7iuYVlcFtYNDcLZwivIAgV2rEP023hi6QpjQ+OByvd/RuNm8GuL5/BrqqmCiXgpUg5R00N2Ej259XdpnVmqLzNFNXw23bGolWzIU+0axnYKRIK94uF4R2h2GMj3yjgzsWWsW3RrPi2kDkmKeWBTH6PRUrn8cTXibBwBvKC6cx0TGSEnG3WxBrG9mglm5mOiZyqbaeLXMSd9u8oR2QT3Kmm84pjIslxNzGhpd6PRetHc0JwFNSUijRiROTZMyVZ1WWm2NP5zilafSVJeZwqbfc6TH11wf2l3aYi9pHMVY4X9ZZ/tPQcyMMJJ51yFfq/xiLVCMd6oMa2wYuppzT8Hj3HVFsQJA6GvSsgb7UYk356GjOApGvwy3N0OeNhAArKqnGqGjZnlbttXFTDmZYnC5bBbWHRnBiD5Gk3we8fQnRXGP8Yu0tqmLLuVaDhll2+nt8JRR+COo9l3e5m6faD5g1tjvNyALMVRfIlzwd8g/D16m7TOwFwSnA+b6YObTVtOywCx4hwD+wUBduFkqzua3CDuwZLc7S8srPFCU+iK6Xc4JiKpp1IeE54XfeQTf3TYH2+aVyPlDcyStnEci2Z5VoyU+zpfOg8lxukn5EleND2BYqkM9MxURg232ZzXnJCyxsOFi2HZ5tSX6qKhbFtC4GeKYB7/uE7RzHmHcnyrnpfyrMu2HAS+RrjTZ2rHC/8ln+0JH4yXqRHdsFzcaBraJIckLEdHWrnxdRTzDldwO+x8+lug/vQTljymvf6rJfpoAQRpJxKraqRX1pNV1UEbAiOhOCIAN/oiY1lcFtYNCdGpCKyszC4Y3oA0Pmyp3lrQz6Ks9bvbl4CHQYeg+5IYOTuf/KWci3l1U5TBOMt5VomMw+K+gGBG0+GV/fF/20iZ4lo+XFO+xIky9huc5RU1ZJXItLIkztFilRxEBFuvOv+vYztVqTsbHFi0iwR7hMd1z2kpMttKBseMo2W2WoqaVoGiqTxmkv4UpE05qlnc73t1zqpu/ml1Va3iZMdQ7sD3D3bDafryrfF8p5jRSqwpjI85dE6QmKz1VRipTJutbkF+/K1GD5WhUCYZ1R7tppqGtv/Uq5ldvWl5rqIENsRzVVOSvyVzWS97ErpVpB1td5+256Y6uNNpfPp4nHTt1CWB+37wcEtEN0NSnZDaBzyguk8GnY9fyu7mL2HDtMVl8EdaUW3DSyD28KiOTEiFb8+Lx5jegJugY27Pdo3GdQR6DDwHHRdNTa3jXmYFbnFbFqZyNVBEp0u/xtk9T6iukVFlhjVpz3zF4sIt1S03X1TtmgzZLui211j2wmDplwY3HqYj8HdypWdLU48IkNshAYpVNWK8ckyuAPAdQ8ZrulMDruRAo/aUs8JtvH/azZRN6npmKm7rUmcyqIF8SwjcvV7Z2eWeIzqCqV73O2bxj9hlpz5zlNmOK/lFuVHJAlUXWJk7Rte6z2N7pV6f+KTz+X2iX/jVK8U5ouOeK5y0uFbNuNz79YWvMSUBdORgFl+jG4jgDPiSJ1tnYeKx/ICGPMQLH9TPO8xBrK/hqoiGHwd0bkylEHEb68ArlIDK53cxDK4LSyOB8W54jG2p7lowqBE7hvfmzmZ3jVQdQQ6DDwHXdf/Cq60oN7Pu9cdhbGUEBlCnt6BaoIIUWtE+lBc7yM+3smGquktWvulajrfr88HxGep1lSh1LjqqMI7Yt4EA1F2toxui2OMJEkkRIWw40AlYBncTcFTb6M+0pQMLrctBcCJwj8cl3kZP61FnMqiBfEd53MXQeIQKPhDPN863+u+MGFQIqd2juIPly4IwO3K90gSOHUJm6R7OXU8a74To0Lof8l0kupLYbbuMU3Hz71bHvcIOYUVTM6eBXgb3fUGcJpCbC8hrlpdAvYQcFRCUDis/Qg6DxOtbg+XsLLH46QVvc6grenQY6zYN7LTkb3mCYhlcFtYHA8OuQzumJ5ei9sFiZ/g6N5xXH1G1xYX6EiIDEFDZoeWKOqzDm61DO4Amb8hv466aWJ9zpPj8Pordx7iqplfkQFCZTQkyr3xiajsbNEmSIx0G9x7iqtQNb3V1XC2Vgy9jUe/WE/JYW8hOM/+urfbvidKquJHbRiqQzb76w7veVELnblFqyJlqtvgBshf6/7f476gajoLtx5gY77ImnohdRBK1t+5ujydL6L+zJT9F9QRRzMeJ52bxOKzk6zf9rGmnnt30tXPkfMZRG4pgEr38noDOE1BkkRa+fZfYOlsseyUq4QxbXyPts7nms4xDLGn878Ot3FhrAN2LoLILkf+uicYlsFtYXE88BPhBrea9Lh+8Y32zD4eRIfaCbLJbNM7kcwuOLAZ+l3Y0qfVLBzLaPT8DfncM3dNnb7qBaXV3DN3DW/e2Hzic6qmM+fXbbz689Y666Ty/RAMVcHtsUse762l2ppYnNTM35DPmt0l5vNnvs3mX4t2HDen1InAhEGJRITYueGd37yWe/bXHSVnM1LJJlne5d1f1zJ+LAB+fNL/csktqObPgVz2w3TuUucx0zGRTyovoXcHG7MPuMXRANLDr7d+z81JA/fupKufo5emM/BYZtkZQnuGwW0oj/e7SDhqeowRZQnoDMn7mJmOiaywXcWFZS+L7awIt4llcFtYNDfVpXDYJQ/tEk0z2LDP1b6pc+RxPin/SAte5JF2u8ip6izy1Q94GHELZ7i8qw0Ya22EYxmNVjWdZ7/NrmNsg2icIQHPNodCcOYL5Byo4qZtKRSU1XitSlOEmNJmrRsA2w+Hk6T5O0MLi+NDSzqlTjRG9IqrI2ZlCKcBZOvdGUk2ydIulnj217WwWDjDHaVMHCwyn3IXidptXYWFM5gfd5Pf3+rhmlpm6q72UxW1HKwQoq//sV/N6Z1iuLp9Ox68/GzLsdOCHHOFdUNo75Sr3cvsobB3FSx6SUTbdy8FTUVHYraaSudDh8HpEk2zarhN5JY+AQuLEx4juh3Wwas9QklVLXuKhZr0wE5R/vY8/sgKtzk+IUnaK54f3CIejbqhE0BAzZj4exrb4J74z9+QH9iBMl+AhTNYkVtc51hpSgYP2oTHX8etEHwsyTlQRVL2LK6q/KTOa0+xp6PqMvHSIQD2OiNZtevQMX19C4tAacwpBcIppVpOoYAw6rnBXaPpySZdONouTyxm8SNnn7CODFXTWba9iK/X5rFse5H1/WkM4z5uqE4rQcLY7jlWKF73HAuZ09j95TN+f6uvOf33ei6vdnLrjvFs7PsXy9g+0UiZKozq9Z+5l0V1cRvbYJagSYh6/oKyavQyS6XcFyvCbWHR3NRTv230Su4WG9p6xINSpvLdH/lcWvSueH5gKyx4CRZM91/z28Y4ptFol+c3un8ZMNJc7FlL6UlTFYIbSnlXNZ2bt49jomMfU+zpjJQ3skwbCGC+9mw1lYdsnwKQKBWxo7yGtu8usWiL+HNKeeLplLLaVgWGUc/tL1Nnx+FeAESVbkE5Qe2fQLOUWlrIslVh1P9mfy2e713p3aVCU9kVMZQ7/3iNcsUZUG9naOZMLouWx1do7+BWt7GdOQ1SHhW9uZ3VTLGnE+RwItWI+e3yg8Hsz8uzfntYBreFRfPjinAfsHdi6Vr3wGPUbw9qJenkBut630lOQQkP2jOgtrxNGNuBTqqO6cTfdT0GZE5jirKbn7RhpMjrvAxeTw6W1wQsEFXfZPLDXr/QJyGaFV1uI7+0mtmkEs5h7rJ/xyhlE4DXa4+T1gFQobcjPiKYokZf2cLi2BOos8lqW9U0JgxK5LzkhDpj359e1aktUwiqLRWtnqK7tfSpHlMCLU9oaSHLVsf4x+BwCWROF89HP+DVpWL+hnweXbSemxx7USStSYe2nGYnOClTYeFLoDlFZgR4q6Vv/wX2ruR79UzS7F8BUKaHcu1/NpqHOKl/e1gGt4VFs7NnRzZdgY9yZF7btBaA2DA7Ue3EoJXcqXUZ3B0jQ3hOncgD9gyRrijbWrWx3ZRJ1bGa+JsGftQNDD6lkrT1r5GGiBr4M7YBnvtuE+8szm30htPQZPKbP/YzedMbZlS9t5THxbblXtudLf/Ol9pZXCEvZpCyE4CFweOY0j2GHzY1+tYtLI45gbajstpWNR1/NZsxEeFsK+1CsrQLCtY3q8Ht6exsHxYMEhysqCE+IoTTukQ0foAjeL1AspQ0De792NIMqMOeFYAOsb3hvL+Ziz3vO7MJLLLtD8tpdoKycIbb2FZrRSmCRyBmV0g/urOSfD2WdOcYJtqy2Kd7j0sn+2/PMrgtLJqR+RvyidiWTVcFdmkdzeXFlQ6KK0Vbl3ezdtKnQ3irGYASo0JIUzLctYGaUwy2rdDobqoQ07GY+Psa+DLD2B4sOmdoOsxRL69338ZuOI1NJmerqYSH2Lhz82xesG1ggrKSGKlCrNfFOZymbCdLfhBJgkItini5lAvOHIwiS/Qt+Ap50Xo4px6VWguLZmB4z9g6Il+eSIj2NcN7xh7vUzshaR8RTLbeXXSaKFgP/f9UdyNDfdjfuB6gQKY/Z6cnCZHBXJQgcSybkQWapfTk1xuOv5BlW2C36NNOd1EGpWo6y7cX8egX6/1er6ZiOc1OQHx7fxvPe4pe26qm8/7OWJ4GTpW387mawkSyKNC9x/OT/bdniaZZWDQThvHUXd4PwG493u92xVW1TRPramYG73iLKfZ05imXuBdmThODbCviSISYjIl/fcO8hHA4eE38XeJoqqbz+s853O0huJamZPCl/a8YHbdkCV61/aPec9Zdf49l/EHW1gN1xH4CmUxOr7yUDM7mOlumaWz/pvajV81cPneKG6AkCQM8VBZOnWGDBiBnvcyA/AzR+sXC4jjSkMiX8fzpS5JPuglYc9E+PIhNrg4FFKz3v5GhPuw7rgcokFmf+KQn+8tqeHerzA8b9zfl9Bsk0AhqcWVtveuaS8iyTbBrmXjsNpL5G/I566VfueHfv9Xp695U/N47Ldo+vsY2uIXUXOPHitxiFlV2BWCQtJMu0gEA9ul1vwsn82/PinBbWDQTK3KLOVhaQWKwqJzdrXdscPtW4fVbOIPOv7/CTMdE/um4kmt6HUDasxx6n+0WzGglke4jqcc2Jv53z11TZ3t/E39V08krqaHbulf514JtvHr4MnN7QxzNQNOFwX25bSnb9U5mWrnRosuzbc+hKic3vbvCfG6kwNc4G6+bk9Doq+0w3aVOXeYax9OAt1NHkiCcKvFkQzrKktfZlJhKnzEPWeJpFsed+kS+Ek7yur7moH14MIv17uKJp8HtGdX2FUICkSa6M6tRzY6GnJ2eGOun/W8zF57aGUWW/Kagx62cSWhIMJ0ve7ru/c8n2t5QBPVBm+jO4K+kx984fKKkPzeU1n9G7pv0LdgOXASOatgn7n2LavqQnf4k10garzGx4RdwERqkUFWrIoHXZ285zU5gDKE93/HAeK6pFJZXs0NPpExvR6R0mLGyGHPy9fpr+U+U315TsAxuC4tmorC8mi7SARRJp1IP5gD1t/5qNYIjmoqa8jhzfhyErutU9EslYs9yqDggBl1X+4fWwJHWY08YlMjtY3ryTlau13Lfib87XfIM0pSJTLF/ymFFZbaa6mVsb9E600/OQ5bAocvYJc3LEPenWO6LkWr+4LlJjb6fC+RVDJJ3AsLYtkkaaUqG+VpvKddybq9Qem37j+irCrDkddSxj7K1PJk+jb6ChUXzUJ/IlzVJP7aM2/cOSK62PCW7hFBWu2h3VDt3EdzyXxj9IOSv8za6AxDIbMzZ6Y1EfmkNK3KLKT1c6zcFPU0pFGPXhny6XfGM2/niGV1zMbxnLE+GfUVZtcYsH8Na1WWza8P1jqc8ju+/c8SJkP7cWFr/Y2F7uEvNwLGoLxvaDWGQWkttSAc2/PAukwO4N3ny9k3DKK9xWE6zk4mGykpc40T89iJ0ZDZoPRmlZDNE3g5AAfVnO5wIv72m0qYM7hdeeIHHH3+cBx54gNdee62lT8fCokHiI0LoLnmmkzc+qWxxr9/4x1CAuCU/c2P1R1QfSCRCtsP+9ZD6L+goWk8FWuPXnAQ6YLcPC66zLMQmYrz9EyLYXFCOTZb4eXIKYcFiSPStDTciJlPs6Txgy8AmaSxVB7BMG8jNth8BmOccx7W2BQAc0sO4SllAN/lgHRE1f5EWo7bpkxW7SYgMYX+ZqHX1jdjIaDxv+zcAu7X2ZGhjzUkmCMG2ETdMo1dSeyhOg1mniReQFLQxD8H33wd0zSwsmgt/Il8Wx5Z2wUHca/+GCimUcL0K9m+EHqPdG+zMgnk3Qt5qKN/nXi4pYlyvT7PDNe4Xxtzc5HP6KbuA95bs9BsVN8dX5rHs03XkZJ9HUscId52ocS4LZ6BoKin9EkjKnsUIH8PaYJSyiTQtw8s56jkOnyiaAfVpmHjyYuWlVChOpmS9yCF1ICiQWxXMX+RP6xX49MW4XiN6x6HIkuU0s/DCKNX7o7I3o8g2l/uKpsGJ89s7EtpMDffKlSt56623OPXUU1v6VCwsGsZV8zu8ZyynhIo6FSOdPE3J4EFber27thavX2JUCKou02HdGxDr6h/+x2fiMcAav+amsXpsgymfr6tTH7+5QPSIvPaMrsRHBOPUdP7YK9q01Zcu+aF6HroONknDoStc73iK77QRdJDKqNHt/NV5K287LwQgRqqkm3yQJWpyHWN7il0Y0b7oQEFZDdcNdysKG8a0EcGebZtFe7kchy7TTT5oGuNL1QHmPgcra8Q/613fM0kBXUXOermRK2VhYXEiUDliCjMdE4WxDSKt3DNafObdsPlbb2MbREbMriWN1nYfyX3qq7X7zDH1QZt7TDOYrabytXMkI5VNJGXPchvbuYvEa3u8flJHoX4+StnEA0o6NpxehvVMx0Sm2NPZEXxDvW0azfRn1/3aLwtniPWtkFqnxuNf+heG80SIbV7BTMdExiiiRVM/eW+TjG3wThc3nGaXDenMSJcRbnHyYpTq/aH18lruK5p2spcetAmDu6KightuuIG3336bmJiYlj4dC4uGcaXtKVl/58oeQohkl96xQWOrtQmOdIwMYbaayro+98LBrWLh+s9hwUt1BTRaCGOQb2zCUVBWzd1z1/D6z1tNcbLNBeUA9E+MNKNtn6/ew9dr83h/Sa7f9LznbO+Z4mh2SSVNyWC4vBmAtXpvarEzzXkTqi420nUYrWTzoCIMX3+RFn/0aB/KmzcOJViRmK2mmpPHB5QvGOWaMNklzes41zueYqZjIoqkicmw5+T66WIY/wTKohfpW/BV4xfWwsKiTdM+IojZairLNZcj7ofHvMftsA7eO4x9GNq57j07s4ShmzkN3r/Y29h17T+8ZyyPh33ToPPYjU5smJ2baz42jWxfR2KaksFb9pe5RFnmvWtMDzhrsnjtzGnQdYRY7jqX/4X8iUn2DHJCbvYaW+eol6PrIEs6Tp+abkWWvLtEHKV4XEswf0M+I174uUFhOG8kFmhD0F03y1pdCcjYBhGNPFnbOFkEzoQD7/PwwFKvZUYNtxFoOtm/S20ipfzee+/lT3/6E+eeey7PP/98S5+OhUXDeIjR9IgTNbl9bQWMY7VfY6s1ev0SokQa9s/xf2Zwp3BY9BKU5cGC6XWN7RZML58wKJHx/TqQueVAo9u++nMOn6zYw9QJ/dh76DAgUsrDXWnkGWvyyFiT53ffNCWDSzz6XWu6SC/PdikB/6b1N7dTJB1Vl1AkMbt50J7BfbYvsUl6QOnl8REhjOwdR//EbazbW+aVzm4wy3F5ne/RHDWVhKgQ7t/zTt3PKWUqqqoyYNGLqFl94eyWKwWwsLBoXuJcZTT/dF7CiKBNoGuif64xHqx422v70i1Z1Ib2psPhYvSIRKTcRe7o8s4ssZHHeKJk/Z071XnM1AOr/x3WLQZ1q7v0xXNMGyFnM1rJ9treaHHImg+8D7Rnufgb+wjEJzOuWmTtSICqS+ZxX7C9YzpHDY0LY52q6Qzr4eHY9hWP82x71Aocy56oms6cX7fx6s9b66xrSDDufuULbrd9jySJ6xTkchg3ZHRHt7Pzxg1DGdHLimBbBICs0DPnfXR7KJKjilp7JG/fkEKX9bPp/kc6uwdPIu2ys0/q71KrN7jnzZvHmjVrWLlyZUDb19TUUFNTYz4vKxOpow6HA4fj6NoeNBfGebXW8zsZOOafwahJyLVVKEteBTCN7YUJtxB76DDFVe7XSYgK5okL+3NOv/at5jvQISwIgH0lVVRf9hDBi2YguWLJDikIXOcpZ72MsuhF1LGPojVw7s3xHTeUWVfvPhTwPgVl1Uz+bB0gesQWfv00sRsKgfpVbY1ojKHAWaRHECeVs0Lty3BFTHxWaAPMCPZbyrV0uuQpLtzyOMqmrwGwmUa4e/LnK+Rzv5JBZIjMaV3Ow+FwsL/MPY7NVlOZZEtHloRQ2ivq1V7natzCnriwH3rRMvF5jJpkfk4AjhEPsCtnK32cjgY/K4tjizW+tzwn22cgAVHtbAyu3e5eqNai/voCVJeiVBSgA9uTbuOXHZXctX8e4BJ9LM9nd7fL6eLY55UCqf0+F4p3Qcku5F1ZqGMfZeHGsyBPzLH8OQ9NI3BTKj/i7TiU0CnUouoY2zMdE5mrnsuihFlElIh1RhaTMc7py99Aqq2gncd+iqSbEXNDSwPczlEJ+Id+JU5N518LtjEgMYL4iGCGdY9BGTUJ+XApSuY09MzpSOh1xlA562XQVbSxjzTlozhm/LBxP899t4n95f6j2p5aHsZ9y7jPTLZ/AYjI9rvOCzlV3l7H+eGJBDx/WTLDu0ehqc7WpJXaJjkpxp9Rk5BVFWXRiwDYY7owYs/bKH+8hjr2URLHPHTcv0uBXvfj9blIuq43lpHZYuzZs4dhw4bx448/MnjwYADGjRvHkCFD6hVNe+aZZ3j22WfrLP/4448JDQ1tztO1OInpl5+BLslsTbjcXDZ493v0KMoEQEWmd/Vcbuyjcnp7ne1lEmUOiLRD70id1ub0W7ZfYt4Oheggjdv0r0hT0tF0CdkVtc2KvJT2oTYGFGSwKTHV630fD9YVSWTslCmp9X/hAmkPMz80lUuqv+JeuW6at6cxrEgaUVRwq+1HnLrMfPUMLrb9RqUeTJhUgwYsj7qMUaVfsztkAKv7P2Z+nqO3Pk/7Snc0okCLJkEuYYmazGgl23xd4/Uyo66krNdlHHbCoysNf6jO32zvcbPtZzPy43u+0UE6qT00Bse12uHcwsLiOFLw+zfcRTp7gvvStWYr5cEJRNQUmOt3Bg9gXOmTAKQpX3pl0BhjG7gFHT05ED6Ad9o/zjtbZFRd4l/tv+CCii/Iir6Sj5TLmZ8nex3Xc7x6WJnHvfZv/J6z2O4KQm1wr/wl98reKesqMgrerRNXq304XdlmtmX0PP/DehDtpFq+cw7nT7YVrJaSufLwk173h+ggndei0xl+eBGhjiLzPX8z5H2QxPvoW/AVA/Jb5l4H4n737lbD/VH/ZMG4j6xVezFE2cFSdQCjlE3m+kXqIMYqG0wnr78Sp3CbztW9rHuJxZExKmc6HSo2oyMhobfYb6YpVFVVcf3111NaWkpkZGSzvU6rjnCvXr2awsJCTj/9dHOZqqosWrSIOXPmUFNTg6J419c89thjTJ482XxeVlZG165dOf/885v1Qh4NDoeDn376ifPOOw+73d7Sp3NScrSfgZyVLWpkk/qijXkIKXchtt8zzfUKIrI5/tyXOKVz/e3BWgM/bNzPD+uyAQc3qV+RZt6Ur2BB0CR6yIWMKfsGykAd+yh9xjzUaKspf9dX1XRW7TpEYXmNO9LQiOdB1XTeXLiDd7dub3g7vW76IrgnJEvUZJbHxfH33FSqFSMCoqMjMVLeyChlk9dE5BZlPgB79fZcbPuNEi2MaLlSHLTTUEbYDkIpdB5+GYljLgJc0f/KrWjdRlJTmEu76gIS5BI0V233erUHb6kXm+e0pf99nHXlMwCs3nUIVq4kITKY//TKpN/mnwGYr51BttZdpGL2imX/kLSAr501zrQM1nVveU62z0DOehkFMW6fMv5mumZeSnhtIVrnM5DzRLbgF85RGMabMc6Nl39nqLLdNLbVuH5ody9B/ukplBVvmsfvULGJyEOfo+rXkqZkcEHFF2zpfx9nXPE0D89cBNR4HXeKPZ1oqYJ21HKVstA8juFA9CzBAYnb9AzTEeqZcv6aI5XRPSIYkfceILJ9rnT8jXnSc4yQN7FL60B3+QBhUg1FegSfq+O42/Ytss1ObsTpnF6+mjQlw+v+IKkwruxLr+snAZdsfxw1bR3y4pkov2cEfK871qiazgse17QhZqupxEuHZdS4kQAAVL9JREFUuMn2CyBE5Wp1mSBJo1gLN41tr5p2ye3AiA2zk/VQCkG2NiHv1GY4qcafkoHobwwT8ykliD7/91aLtSIN9LobmdDNTas2uM855xzWr1/vtezWW2+lf//+PPLII3WMbYDg4GCCg+u2AbLb7a3+i94WzvFE54g/g7MfA0VByZyGojthxVvmquqzHuWNzG1MsadTs70P9h6tt352/oZ80uatQ8e/yNe42tfYEXwDsqSLNL+xD2O3BT6MGNfXX+/QxEZ6ec7fkM8z32ykoCywiQeIiZ6CRro2livkxaaxPVrJZl/lJyxnArPVKzhd3mqm3UHdCPIYWYxDXynnMySxHeP2uWsgZcUOu5cIYbKUqSggagAXvQjjn0BOmUq7mgr0T65F2pllRmFOUXaySb4VWdLZN3Qy/S592jzmtoOixvzhdt/Qb/N/0O1hSI5KYkf9mVF9L0Lb05fRC6ZDnw5wetNqDK1xpmWwrnvLc9J8BhL8N+5WZuedR8bO7yCqK1LpHqTOQ8FlcH9eOcQrDXy2mspsNZUdshjfVV1ixUX/Y6TdDmF1xTzvtX/DHbbvCJJUXnFMZPbaUTzYfifXVn2Eqrizi2arqXSSirjNNt9rf8PIXqImc4PjSfN+M0rZxEh5oxmFNTKBQIznS3Yng2vqZ5M0PrY/R4keJo6Jwga1O4OUXXyvnkkUFQCca1/P+Jp3uMLxWR0l8zr0vwQ2f4tcvg95ejyge4/tx5lV24sCuucBSGgMlHd5LQtyGdSxckWd+5pnmzSA6VecQli7uvNni2PDSTH+ZGcAOihBSGot9qWvtrgOQmPX/Xh9Jq3a4I6IiGDQoEFey8LCwoiLi6uz3MKixfEVXwEY8xCbku5m9s9LCQ+xcVfWi2BTWnwA8odvOyzFRwkbhBEuS7oZmSieeysdbvmwSa9TX+/QgtJq7pm7xq+KZSD9Rn3xNLofRNT2mSncWgZTyj6gi30FcVI5fWW3WFqtbvN6z0E4GCmLCEvaHXehdDoVfo6Axa+IDfb8VldcR1O9lwWHI3Ub6RIgkjCqEmVJp1a38UPczdzqce5b9wsV9fgwG3S/BumPTyE0jjPPuwZsQdD7EfEBWMV1FhYWvox/jFXlGyFvJ5UOHUr3iOUr/gVAccypXFW40EtDAtzju1OXsUka0Stfhb2RkDmNXac+yH//yGe0upIhyg4AgiSVGt3GLDUVCXhvyU5u8skuGiZtrqM+bjg9jcf/DVzGvXk3QTGmU3S2msqDNm+HrxHtNs7ZyEYCUZ/cSy7AoQuzOAgH19gWcphg2jnK6Fyzntl6Kr2kfKbY0/FXTPkP6Rp6DXqWCX3Ohv9OAnSRVt5C92tV01my7WC9631LpyYqixgqbxP76jKKpJmlYDU+9zVPEhpxdltYBISv2KDxHFrlnPd406oNbguLNofRwgRAtsE5T7F99V4AFibcyl19e7daI2lFbrFXxNlT/Aa8I97tpFr+YvuGDju/EYNqgINpfT2uwS2M8/iX6zm7f0czra2hfRpjs97N63knqQgFlX+ol3Gh/Bsjlc119gmSnHxsf45l2kBmq6kMlXMIlWooIprojgPF+1WCxOerOb3Vfw18FdsXzoBFM9w3ov9cIhSAXa/XYc3rMPo1sW3mCwzMyQMu5MCwybDjr2L5oImw5DW3Irx1A7OwsKiHDhEiUvlN1I2M6RkJWS+b6+SQyDrZS74ZTWlKBlM2z4bN8JZyLdNXDAfg71zG5zzDGS7ByGDP8fJwKrM9xNG6Sge4XFlMkOR9z5Nwd2xYNHQlA9a9yp8c+ea5GGnOnvegNCXDS/cChEH/ftdMxu1723wNu6RSrodwjW0hS9RkevRKovOurzlbWUOlGswERUT4JZ8KnBrdxozqy5DmruHHobkkGSt0DX58Cs5/zr3xcejM4S8LzBfP1Pj/qOfzrO19AHaTQDepwEyxd+oywZLTFOyMCbUx69qhFFfVEh8h2pGezOrRFscAf8r+/joAnMS0OYN7wYIFLX0KFhb18/Nf3f9rTlg4gx2HLwWgV4ewVj3gFJbXf2P3nYw9YfvQXXfnO5j6TEZUTee33GJWH5TYv3xXgxMIgOJKByNe+IXpVwxiwqBElu8oanQfXyYO7cyXa3bzok2kfhuCOtfZMukn76GvtJdwyfuY3zpHcIltOVV6EKOUTWbkpJ0kVGEXqIM445vn6LbuVdEyxzC21dqGnQ7+vL65iyAiEcrzydNiubjoPVjYCVKmoksy11R8yF7lMP1jToXN34njaA73cSwsLCwaoH246DRxsKIGrnoKtv0M+WsBiM5fzFvKtcyuFvcm3/FdAtLDr+eWiN3EHVxBebXTPG6aksEZylb2abF0kovZq8V5jZcfBl3DPw5fzhnyZq62ueu192jt+UwdBwhjfLk2kMSoEBIu+StvbchHcYpxtr4orL+MK4B9g9PIiW6Hvukb+uo7AYiQqlkpDWK0sgHC+wHwJ+U3LleWmOO5Z914jW4zDVIJSMpOR+t+FvKuxeJFls6CfWvhlm+9x3NoFuM70Iwuzyyum+y/Ekot2EPp5iiok0WwRE021dqTU59nTN8ODR/cwqIp+Gb1GRjPW2mg6XjS5gxuC4tWy8IZsOwN8X9MDxhyA2ROo19MHnA+vTuEt+TZNUp8REi963wnO+V6mFvkJj4ZNBVV08n7+lm6rXuV3YMn0VnT+Sm7wMNLr0BO3f6h/iiurOXuuWu4+NREsnICS6l70JaOpst8Hn49L00czDO7byG8opwqPYh3nRdyjZJJB7nMTLmr1RWvyEt7qZQSLZRouYqf1aGcq6xhij2d/Vo0AKdKO+i2brG7P22gaVOeNyLPyVpsL/jiNmySJtRkM6exq6iS9b3vYotjK1Ps6TiXVoKzGuztYNW7ra4vrIWFReukfbiIcB+scLWRuuQ1eGscALoSxOGRD4Grn7Pn+G7EOZ/60wAu/u5RJjo+NiPOnoZ5pFTFrdJ8ushFfO4cw1W2LKbY0xmfGET83h/oIheZ5+LUZcbUzjKfS8BkezqX9O7E6l1DmF55aaPvxzfjysCmyCRd/Ryq+gz6c7FIgCbbGPrkYsj6O2ROQ5dkuuC+jyxX+7NEG2RGh+c4LwfcLctmOiYyauwMRm550a3HsnMRvHYqlOzyP54fI2qdGo9/uSHgjK7Zairnd3Fyyv6vxAJHFfQcy+jcRSIzofpS0jRRq79ROZXJzIOifoB1H7E4hjTkcLLmLIBlcFtYHBuMG++AS2HTNxDV1RxkLsucxjalgt4dhrfwSTbM8J6xJEaFUFBaXedm7zvZmaWmck5XncEFX0DhJhYMe4Ot0+7mTnWemLj9dgbR63+ipOro+hv+94/8Btd7ptQZ/1/SuxPKf2YSXpELwG/aAO6zf81Mx0QelL5AkXQ0XdQfLlUHsEwbyJk9Yzlrr1vorpe0j9cdV5CqLKKra+KYJO+jJGEk0Z7GNjSeNuV5I/I0vktF3Xh7SvlMS2GUsom833/gvhXDgVQ6SKXcnPM/sZ/jsGVsW1hYBIyRUn6g3CW4lfMTAJpsR1ZrcWa+CK70b8/xPSYsiOlXDCKqXRD5pdVmijh4G+ZpSgY2lyHeQ97P644rmGhfwtB9H+PZwNuoBzfSmQE+D7+eS3p3IqlDKNkNZFYFQtcY0e5VWTxTLJDtyJpDGNuu8VLy0FVZpSaZxrZveyzPNlqLcw5Q2OlBxnTOITYvU7RHK9mFHpEonBL+UmibEO1WNZ0VucUUllfTPiwYJPhl034+X73XK6PAF08nc2JUCM9d0IVTFq5xbyAp0G0U9BjDbWMeJnlbIT9mqZw/5kxG9omHrNZb1mZhcSJjGdwnAZ4Du1Wv00wYhpQkCYM7WtQOO896iDk/b0WRVHrHt+4ItyJLPH1JMvfMXeMh61U/l+28kiUhWXSmkDHfnc04SfeKgh+tsR0Inil17yhXs6vH9SRluyMpDnsk41lnTqwUl/qupzouwIiUM3lrXhl3qvMA6CUXcI20gATpkHmsWmxE9h0LA84+8rQpj4nY/D0yA7R4usuFrNT7M9NhZ4o9nTQtg216Z65VfjW31WQ7smVsW1hYBIgR4S6qrEFf8BLSgunkJN/P+WtGcJ8rUg11U7hvGtGdCYMS+XptXp1jehrms9VUwqjmbvt/OUPeSnepkHhKvLbfdcoD/LtkGP8XtZIpG2Zx0SmJlJwxyTUHOQeA+O1FHCl2RWJ4z9jGxZoWvgSaEw2ZO4Omc3PNJ37bY13veMqMBs9eYLSevJ3twQvMjC6pPN88tjbucfe4HEC025iL/ZRdwFdr91FcWdvk92w4ls/sFcvIW15CSf8zlO52b6CrIAthVgU4s2csRZt0zjTmfdZ9xMKiRbAM7hOcI2m/ZNEAmS+YNzMvxj8mbrjZ34rnUV1RNZ1v1+3jNccV2BWJe8Nbf7uNCYMSefPGoXW+M+HBChU1dY3JSTV38WnQcyguZVvfyZtn25nmYraayqjesdy++y3wac9td5TxlnItksPJZFcUQ5E0zpA2M1rJ5n4tg8/Dr2dE7zjKr3iGVz5xcrWSSRe5yMvYNmr8UGz1T1iaMJExhOCm6P3pTiHD5c287LwGoE6rmlrdRpDmQFvwEvK4RwJ+DQsLi5OXOFcN9918gbQgHW3c49y8bBg61V6OSvA2ulfvKgYaLjEyeFG9nuHyZoYq24iXSuqs7xLTjtNtOp0v+it0iGBA5jRIjITe7rGyocyqxkiKj0BaNAMWTK8/62hnlqm3Iau1rDjrd4Yvvt7L2PXXKssgTfkSRdLNEiSjQwdAzsJPyA89j3GHf64b7fYhEBG0QDA/uz1vwad7YOv/3CsNY98SqbKwaHVY3e1PYAzhDd8B3mi/NH9Dw+m6Fn6QFXEzWzjDe7nh3a4pA2B9ZQRnvfQrkz5bB4BD1Rn798w2cc0nDEpk8SNn88kdI3j92iF8dNuZhAf771N4przJnHzYJI1ptnfMdUa9n6o33zATHWrnnzcOZeTF/+exVBKtXACUIMb3i2eyPV30ilVTec05kRscT/KKYyKT7el80HsBiiwxYVAiydc9zx0hL6Pp7gyQt5Rrybxqg5jM+PvsjwBDEf43rT8Aw2Whlv6rdppXu5pXHVfSt+YDZjomIi+Yfkxe28LC4sQn2KYQGWJDkTSKhj/Eb11v95oLzFZTTQekJ6t2HULVdE7vHkNsWFCDrxHdzo7jmk/QJY8xvscYMVaOfwJl0Yv0LfhKLE+ZKpb7ZAEZmVXg7gdtYDyPDvV//8nOL+PdrG3kJN/vP+vIU2/jqQMw/glsC6fzab9FdV7LH541631rPmSJmiy6MbrG6H56LinfjXcb25paZ4xWNZ3Xf85h4ydPck3l3ABetS4P2tJJU0RrS0PQTht6S11jO2Wq+zofo3uVhYXFscGKcJ+gNNZ+SQKe/Tab85ITrPTypuDpOS/Ng3GPwO9z3Tfc9Z8D8MLSKvI1/44Of32mWxuKLDGydxwAy7YXUVBW1ytvTEZecVzJpcoy+sj7uMH2K6V6GIcJrtN2pjl447qhjE5qD58aky1XMryumwriSVW/k5N8P59vHwceE07POkKDCYMSOf9gDvIC9y/n9jG9kAclYorMHIPogaEIv1ITCrqDpe0EU8s/7a96tavRXNPC2WoqF52SKCJER/naFhYWJwftI4J57cBEzuw3wm8XCn9jc7VDY8pna1mUc7DelGdjiHrxylM4s+hD0TrLoOdYc3xSVZUBi15EzeoLZ9ffyrC+zCqjP/R5yQnM+XUbr/5cV3RzWuXlTFsDbybne99XjW4QfiLfSZnT+HEoXLNlDMWV/kuf/LVJ81T9/kPtyalKrjDAkdDHPIziEmkDQFPJOVDFTdtSuKryE6+a8aZmfnlqlcxRU3l1VA3y0gz3BpJP1p2lDG1h0eqwDO4TFN+eyr7oQH5pNStyi03DyiJAUqZCxX5Y+Q6s+Q+gi5v62IfRs15BAvL0ute0rTo6/E3UfCcj76kXsjDoQWLlCv5iF2n1S9UB9R7zaFPNJcRkbETvODGx2vSNWNH5dMhbJf4f+7B4zJxG0vixLJ54th8tg3O8D7xwhogk9xgjJo6AnDlN5BAa0QM46olMfESIKX5TqEcTL5Xwif15usoHUXVYoyXhwOaV8llyxiSRjmlNoiwsLAIgLiyIHQcqmb8hn26xoY3v4OKrtfsaXG8YwhOKPhQGpsd46emQ1MY8xNacrfTVGx+zJgxK5LzkBL96M6qmM2/lbr/71XtfbaRNUZKm8lTSQCZ9utbvcX0F4ryMby2DkfJGc1sZnfx595F4/RvmNdgbOZSksjXMVH9mtD3b77Hqw1MYDby1Sm7vkE3UwhzRJhKEsa2rdVtTGv87ml9LxcLConEsg/sEpaGeykeynYUPHfq7/nFFUlOmQuVBJOdhAPL9GNyurduco8NfLV+dNmGEcpvjYb4MfhoQHvll2kC/NYL+JhyBiLR5bgvw9CXJ7ohCdHfRriVvlXcdmyu1kcxpKMDIhiLD/lRnDTwjy8cgujy8Zyzrg4O4U53HFq0L8VIJQxXRrmyFNoCRyiZmOiaa1zAixMbwnhd51T5aWFhY1Mf8DfmszysF4D/LdgEge6RDHymxYXYWPjyeoCUvNz5ejprE1oTL6TP2IpQAju2ZWeXJEQUQAmhTlNCAYJunM9hfD/BRrjH6T8py+st7Sdw6FxZ2Z37cTQRLPzO+7Dd0HUYr2axTezFbvaKO4V4fnhFtY7uPQq5hUuxGog5kuzfsPhpu/b7x1pQWFhYtjmVwn6AEInjSlO0sfPj9Q/f/aq244fU5F4D9ejS1+K85M2hLjg5/ojb+ItNnyevN/426wJmOiV4TB98Jh2E8v3H9aeQUVvpNGfQlwVP0L1OFcY+7+5+fcbv3hMOzRUtjkeFGIiLHMrKsyBLdXCJtkz1E0ir0ENPYNq6PBFw6IK7NZERYWFi0LIZ+i69tfbTGNkBxpYPVuw4x8jiOl80VQAhUsM3zfud5D1MkjZVaP/rLe9GRkDKnUexcypXKaiEl4hqyBys72C7fiOLTyaM+PCPaEvCeOoHMiJnIBza5N5IUYWxD460pLSwsWhzL4D5BaexGYqTkDu8Ze7xPre2zcAbkr3M/H3CpuNEVipthnt6+0UO0JUdHIO3CjEnIcrU/I5TN5GidzEnJq44rmWJPZ5ItHVnCa8KREBXCB70XkFT0B5z7GP0SwuvW8UUGc93wbvRoH1a3rd34x6A4V6jUKkEw4UX3SflLr2uIACIix5IJgxLhuudJz6hgojYfgHCpus71Sb7kefq08pp/CwuL1kFD+i3HisLy6sDGy2OUztxcAYSmtsKEuqnmN9l/YQ/xdKWQbVJ3LlWWio4WYLagFPsJpfNANU08je5J9nS8ROD9pZFbddsWFq0ay+A+QfG8kfjilZJrRc2ahpG6ZdzwAGJ6uFVBadjgbquOjvpEbcDb4/+jNowflEfpJh1gtuMyptjT+UE9HXClMwKjb3iSPrVBxEeEcOaed5AXzDLTwBuq46sXw/kRnwxKw5kFrY0JgxJRB3yM/lx7JDQ02c6IW1+iT0VNYO/dwsLCwoPG0q8NzhnQgUsHdyZnfwVzMrc16TWOt8O4OQMIDd3b/OHbixzc7dX66LvMCdYytT/XOf7KR/bnGa2INPAgSSVNyWjU6I4Ns3PFkM6MSp6BPvcLJM/WFfX1Gvd8tLCwaHVYBncbR9X0eo0T40Yy5bN1VNa6vZ4JVh/uI0dT4Yw7YOXb7mX7NzL/tDfooH7K6co28vQOfndt644Owxh+f0kuz33nTm3zrm/T2aZ1oo+8j+16Z153XMFdNiGipuvC6B6WMRrbpPWw6t91+6dSfx1fvRT8IR4TBx+Lt3ncURbPBDSzT+zovHetiZOFhcUREWha9YrcQ1x1eldG92kfsMHdUg7jhiLRx+K+6uvobR8WDBIcrKgx//9l036+WruvjnK7r9ENsEpN4jrHX01lc0PRXNPFdiPkbG5wPGka1mf37wgSxK56jfAgiU6X/028l8zpePWJ9FCAt9LILSzaFpbB3YaZvyG/jlc20ceYnjAokXkrdrNg60Fzm49uP5NeHcKP+/meEIx/DNbNEwZ3UATUlqMXZvPst9k8QzQAe+uJcJ8Ijg5FlrhldE/eWZxrRhs8Pf4P2r7ggB5FH/bxJ2U5NlRCJCdleju+U0dwlbIAm7MS/t5L7OCvBrCpGBHuxFOP7jgtga9QmyV+Y2FhcRQEGn0ur3Zyz9w1vHH9aQHVMbe0w7ix1mFHe19tzNE7uk97Hr1wACNe+MWv0X2/7UvskoqqS0x0PFun1rubtp9ouYp9WiyjlWwWxb5C5wd+FsKfe1WQFdg8W9wLZEncCxa+JF7AHiY6cOQustLILSzaKJbB3UapTxTFX6/nrfsrALDJEk5NJ/dgpWVwHw35IqJa2PUC4renI5XnU1V9kM5Bwqmxrx6F8pcnDhY9o9s4DUUbVF1mpE1Ev8fLa7G5xNOWa8lcZ8vkPef53GL70TV5kxo2KjNfEJMQf9ssnCEmGeMe9TC4hxz9mzue+FNFt6IWFhYWR0GgQmAGz323iaf+lMy9Hzdcx9waHMZHVHJ0DFm965Df3uRpSgZ2SaVGtxEsOc22l5613tFyFQCxcgXFcafTrWgVzDoVSnaLyLVnz3Dj3hDZBcr2QvKlcMU/rTRyC4s2jNzSJ2DRdBoSRTGWPfttNqqmU1btYJ/LG2x4b7cfqDg+J3qCUrRd9Hn+++ZY9mgifby/tIfOkjC466vhPlhZc3xO8DhgRBsSoryjKW9oqWa7L8PYLtbCOV9ZzUzHRBzBsbinRjr8d5L3gRfOEIY2CGM7c5pYVmebaWJ9eQFUHhA19R0HHts32dw0pPI7/gkramFhYdFkDIdoIBjttGLCgvyO57Fhdm4b3YNP7hjB4kfObhXZWUYk+rIhnRnZ+/h2b/CXru8Zye5X84HZmcO3j7ZxXwyhltjoaIhIFMY2eBvbxnMQHVAAep8jHq17g4VFm8WKcLdBmtKT0q6Im9GTYV+RLMWQRQrbCn0MbiNa2JDqqAUA89fvY2ThepAgW+vBZrkrXTnA6fJWYiRxXeszuNuSMnkg+Is2HKqs5d6PYYScbQrFxMoVvOKabNypznN78wFWvQvhHUWk2jPiC/6jvb5R4a0/iHXt+4K93fF668eG46yKbmFhcXJgOEQf/WI9JYcbVwovLK/msiGdWzR63BbwvYf766vtW9M9W00lMSqEgZc8D5uBDemw/Ze6B9+5GEbeB8vmwM4sGPUALH0dkKD3ePd21r3BwqJNYhncbZCm9KQsrxbtKWLC2zFq979IU4pYUvh/7o18jRyLelE1nX99u5AJUhW1usJWvQtb9K6cxxrGK78DUKqHUkGo135tVZk8EPzVvb0pD+WVb+5jVM1fkCSo0W2Eh9i4U52HOvZRlLMfgx//6ppMAAtegF1L6nr5DUfQiL+4It0vgeaEHmPcL2amkw/23sdyHllYWJzETBiUSESInRve+a3RbQ1DssmClScZvun63oKhbt5QUxnVO44/tVMYNXyE23Ex8B3Y+KWrw4mRwO96zF0I010ZBOOfgKiu4v/EwRDW9kvRLCxOdqyU8jZIoJHS9mHBbN1fDsDWfvdwYNgUptjTObfwPfSyff5rSC3qZUVuMe0rtgKwTe+CAxubtW4ADJVyAOoolLe00ExLMGFQIp+P2oskgSrbCZac3NZ5D5sSU9H+v707j4uq3P8A/jnnzLApLrjhgoqGC2bu5gYqWtnikqHmmtYtLdPKpbrqLVvUm4W3NK/d7FaaWqk/y+vt5k2vCGq57yJoChoKIqCoKDAz5/n9McyBQVBExgeGz/v16hWcOTPz8P2OM/M9zxYyzX7Sw+8CzR7Nu1NhxbZj2HiqPebQrfZtvwJD84aa518wLf99iIgquC5NaqBuVS8U9cmjwL7QqjteDHaF/MP1FdgXDC1siy8dwIgTPZHQerLzsPfoD+3FtqIBEPbPsrfSgZYD8+6sqEDo9Lxe8Pv6uPRvIqJ7gwV3OeS4ynq78m3qmkP49VQaAKC5vy98H5mJBdZwvIQ1UBa0ZLF9h1KuZqGVmgAAOKY3AgDECvtVaE2xz54vOJzcv6qX0wJ2FULUfKi5231pb6Xa/392x83nPb0KyP8qPrUFsOY4Xwhq0An4fXPeObbc4ZGOfc/jt9l/P/QtX89ERPkULBDzq4gXg0uDY7h+nSqetz3XsZYOgLzPtcBQe9HtmFq17SPA//68Owkd+H60/fMQsM/fzr+2CRGVSyy4y6HiLopy4UqWMV+7WR1feJk1rK86Kt+2jgrg6HGkokXOA6Lmo7avF4KVMwCAGGEvuB9Xd8Iq8r6snMu3QvlrfYPKzEIz90wRK2/bQt9Ey6R1ULd9lHfuto8ACPsVfQA4+xvwfq28+4dMBda9YL/NlG9+tmNOd5vhQHaG/efkIyy2iYgKKGqBywp5MbiU9Lu/LiKGtr3lOfnX0nH6XGzYzf7/ZzbkXTh2FOIOsRuA62n2rUfjozlyi8gNcA53OdXv/rr4+Om2eOW7g0Wek38V84Z+9nnFM9XlUJR8Z/wwHnhqqaua6R5yV8t+sJfABc1ecB/TG2OV+T10044jRa+K2oq98HP0cE/W1qHSbyoQ9rm0ZktRxMrbesg0nDh5As1E7uqqBQvzNeOAY+vy7tCoG7D6GSAzxT78znrDPqct4w9A88gruh00DxbbRESFkL2dljtKvVa8XUdSrmYVvSOFQ+MQewEe+VcgKl9PtskDiPorLyYTuQEW3OWYo4gujl4fbcX3zaPx8NV1zjccWQ3UDOKb+a3kxkaNnANHX0CoehjdNPt+06moitrIK7gnaeswxbwWEVnh2B2fXrEWobnFYmUn/AfhvtDHoBXWC167JXAs38lfPwGYcofsidwvK90mAwtaAjfSnR9Y87BvnxI1n69jIqJCcEG00lXctXRq+3oBbYv4XCxYiPd+0/75tvsf9t+vp7HYJnITHFJejh1Pulrsc0dmfYegmIVI9gwEAJxUm0B3pL/gXscVbL6QTRfYFZ+OfakKdsWn5825yr3tt1NpWF91JFKaPAXAPnLgZfN6RFjCEWEJR7B61ji/u3rUaZuQ4q4oX6EU/JKRvwAPnQ6YfQAIwJobu9A37OeavYDh3zk/Vu+ZwF8u5g3NK7hnNxERUSm73Vo6xVqQrvefby6mH5sPqLl9YRy5ReQ22MMtS+Q8+1Dlwt5Mi7m1UUxSRrGfTlN0LLA8hWfEL4AC7MtpiCDTaQDA1SpB8NULGerr7iLn4eTF6xhzqlfuvuYalp/ci7pVvbC86VYowobRp/sYe56/ZhJ4xWT/IM0WJmN10sZKMp4ybQcAjDRtcdomxN323i4V+V/XhfV2954JvFMdxvzusBl558dHFf6Yhe3ZTURE5AKOtXReXLHf2ODL4a4WpIuan7sjB0duEbkT9nDLkjsvGFHzoUZ/gGbJP9qPF9za6Ba9zY4e7mre5tuuWP6xNRyb9Q6ooVzFNeGFWdZnsdz6EACgUsZJRHn3qXDbhJ28eB1BMQsRfm2V0/Hwa6sQFLMQ6w9fMIptAHhata8aahUqPBUrJpvsw/PnWUcaC9E5CnFut1JMhc1ti/4Q9mJbs6/Y6ui1drw+G4fY71OwV7vn6/ZjjotHRERELlLqC9Ll/w7GkVtEboU93LLk65FTGoWgZdI26CsuAme25RUgt+ht1nWB2KQrAIDJfYLw3r9jbvuU3dWjAIDdegtYYcJb1nEIUQ8jUL2AHhsfBiDsxUxRitnzXh7YdIExp3oh3HIeU81rYYIN39rCMESLchoS7vBnbSXqqJehC+DB7MUYof0PU81rjUJbUQCL0OyFuLYOi2yDud1KcRR8LRW86OP4PWHbzXt1O+Tv1a4AF4qIiKhsKLUF6YrY4QMAR24RuQEW3DLlW4xLAFDPbAOqBwK1g4GtHwC5exnnvenmDUP/49J1ZObY4GFSMaZrI4Sc/xJbjidh3o0ni3y67qp9Vaodet6ej7Osz2KlxzxoENBVM9TA0Lw3d92WN+y9YPFfzovv3fHpSMrIwiLYi+qp5rWYbPoBioKbiu1J2jqMN/8EADgggpCGqsbtU81rAeTdZ5K2DlPNa9G/TT0E3f/4Pf6ryrnbfeEIDL35C4fjd/ZqExGRBKWyIF1RK5nzM47ILbDglq3n6xCRc/KGhF+KB74faf/ZMTzWMYfHMQwdgCX5MlaaI3GmchuYvlmMoIRtCOw5A//Y7oGRWd9BU3QAQCclFntECyy2DkJnNRaAveCepK2DpuiwibxZBapuyXveyDlA41AgIfrm3sVyOs/bpgvjKvSJ5LwF51bY+mKKaS0UBdCF4lRsA/b576f0umiqJuF/tnY3Pe6vtpao1m8mPvH1RG3fLtD/aIagrXOBKF9ekb4TJf3CwRgTEVF5dqvOC37GEZV7LLhli5oPBYAOFSp059ua9QNObHTec7hxCBA5B9XN/rhPS0ZQTjqQkAwAMKkKNlWfjxqpu7HAEo4H1Rh012IAG9BOOQkfJRuZwgMPq3swxfx/2GELRndTDH6ydsbjpt2wmrxhchTSHcYC+762P2d8NFCrBdDlpXI7z3vj0SS8syHGaU62w3zz58be5KoiMNv0NWZbxxq3f2btjwmeGwAA/9PbG8c1RTd6tj/x9cTAtvXtNzR9wz7GnFek7wy/cBARERGRm2HBLVNu8WoLfRP/vhqM/ulL7cPKHVY8BbyWuzlxbtGdVqsLPOGNGpZkCAHUtiXjtNIA5kadERA5B45BTSEesegkYuxFtRaDhkoKAOC68MortrUYRFjCYVasuAof+FqvA80etT+X2du5rRdjgXkNAIhyWWy/uGK/0yqiDpO0dXhI2w8AuCE84K3kYKzpF6SJKkZPd3f1KLwUCxJFTcSJAOO+H1vDjZ9vWo28HMWHiIiIiIhcg6uUy5Kvp1gPmYZmyT/ai+3A0LxzMlOAFU8CF08Yh2pc3InKuAEARq9sE5GIgIR1Tg/fSRzFjcoB8OkwHBazLwLUVABALfUKIizh2CNaGL2zVmGCL67b75gaB3hXByw3Cml07jZNjmHugH1eef6fl/XPW1U9/wrrkvb2tukC72yIKbLYnmpeC8e221tsbQHYVyGfal6Lleb3AQCvaPbY2oeTK5ikrcOrJvvcba5GTkRERERERWEPtyz55quqW+ahZdI62ELfhBb2ZyBqPsSRtVBS44CE7cZdhLAX2TahQFMELEKDWbEZx3UAChQoueWl97U/0O7gX5yeNgcmpznKvp4mBA95H7jQGNj2EZB+2rmdvWdCj98GNSE6txE69BP/hZp0wP67Y155fLS9EfHR0AWQ+MNsNDz0N5xt8xrqRX4AU9RcHG8xCZdPpZVsBc8SciyOVhhN0bHF1hZh2kGc1/3wuGk3kvTqqKtewhm9FrprMViJ9/GAFg/APpzcUaRHWMLvbq9NIiIiIiJye+zhlqX3n/OGHQsbjtcdDD1kGgBgY43R6HZlLmzCXsQJAey2NYei2HtfNUVghy0YQdnfYIct2DiuAvZiW/Mo8GS5xaDmAQ9YEf3gHgxub59v3L5RNftekX3+AjTs6nSvs21eQ2RcCtSEaJzRaxnH1aQDSKvZGYicg2uH1tsP5i6sluQdBDUhGg0P/Q2fWJ7Emr1/wBQ1FxGWcDx6sCuGL92JTnM24b0Nx/DbqTTY9ML6nktPylXnYvtV01pMyu2x/tgajiqKvWd/qe1x7LAFo656CQDgo2TjiK2xfQ48gCzFC909TzttGVbivTaJiIiIiKhCYMFdBuihb+CE/yAAefONwzO/NXqxFQXorMVhhy0YJkU35l+vNL+P7lqMcdwQOj3f0HQFgLD//peLQO+ZaHjob3jD+18AgANnL0N3FL0j18JRnOfAhDV7/0Dv80sRYQlHz5xPsMvW3HiKGqm7YREaKqcfdfpb6t44afw82fRDoXtap2da8M8dCRi+dCd6fLAFG48m3TpA+YetF3Sboeq1fb2cimxb7nDxSdo61EMqOqonIARQB5fQXYvBr7aWAIBayhW01hKMx/FCDsaLNTjb5jXcN+RdfPt8F2x/I4zFNhERERERFYkFdxnimG/8cr5hywuteftqKxCIsIRjpGWWUXTvsAVjpx4MwL491dk2r+UN8a7WCICw/z8+Om97sd4zUWdfBKZ4/IgrWVacunjN/gQ7/w5AIFuY4AEruqrHnIrlYZa38bO1k9Eex3B2wL6dlp6vR94xzF0I4DPbgCL/5uSMLLy4Yn+RRbdNFzh7ORuInIOzP8x27hF3zINXtZvu89upNKw/eA5Wqw4Ps9koshfZBiPCEm6fo+1hn6N9RfhggvnfiLCEY4r3e9AV++MJAKJ+R/u8daEDmgcaPjkbA9vWR9emNTiMnIiIiIiIbolzuMuQvWcuIfzaKqde4VdN9p8BYKp5LX7TWwEA9ogWgM3+f03RscASjjWVR2BH9b32B2scAjTqDpz9FWjYzWkPb8dQ9np7EoBUYN+ZSwiKXQJEzsHn2tOYmznAmKvseD6HF62v4pQ2CpoijLnk2cIET8UKAE4/O4ruJea/4U+W6YX+zQL2PvV3NsTgoWB/aKpi7Je9KSYZPx48j/TMTpikhWPqob/h86NJaPjkbPRL+6bQ7ckK3/5rIG5oNkw12xc6+4/+IIbqkQhU7Su3V1WvY4ElHItsT+K/TbZCjbUBmgcUWw7g4WMU27Dl5F20ICIiIiIiug0W3GVIytVsp72dAeetpwD7Ql+FHVcALOkfDPXi7ltv2+XYG7rn64jPigUiT8G840MgYzn2NXkRc2NCAMB4fkeR6vh9kvYDNEXAKlRjePtOPdg4b6/ezJj3HC/80URJRl/tAKboq7HANtRoxiRtHTRFx8fWcAgASRlZ2B2fjowbOYXul220B99BX/MdoAB6rxlQ8/2dJ1f/BbGHE5FUIDYF/56pWOt0W44wYU3l4fjKaxWax/5fXvyW9bePDAgMBZ7ZkNejnhs/IiIiIiKiW2HBLZlNF9gVn459qQrqV8m+qZDOL/886PzqVvXC2/2Dc+cT/7noJyuiSPwj7RoiRDgW5RbbBZ/PUeQ7er3zD2fvrsUY+3l3UWOchrk7inAAmGz+EZbcFdLzr/T9qmktbELFIttgLP8tHj8fvWDcJ39RDgBLbAMwxbQWau5Q9c+3nkTqlUPo06oBOp39AkExC2ETRcdvr8ibg67DPp/CpprhoVuw3f9jaGe22VeK7/m6vbh2FNv5h+MDLLqJiIiIiKhYWHBL5Dz8WQNOnrjtfQp6rW8QXg4LuuP5xBuPJuHvkacA3Nxbnl9ez3ZekawpOnZagrHINhir8B66accBOA9zd9yvv/YbmqnnYBNAiHoYAJyGzDset6t6DCOO5m1hlv/5HD4xLTbmhSsKMAFrsGvvEezfFYSu5g03Lc5W0GzTMgCALgBVAS77d0W1CRuBqPnQIufgYuWWqBYyDRrgtG0bouY7jQyA43YiIiIiIqJbYMEtiWM18pJuiuXcq31nHIuz3clzFxzq7jDC8hdM0p17oh0W2QbjU9sg7PaYiFpqBjprJ9BZO1Ho43TTjmOSvu6mHvD8Bf/jpl0AgAXWcDyh/obm2jk8qMbiQTX2tsX2G9q3aK4mArAX2ztsweie/JvRc22z2VAr+q+wbfsICPuzfds2h4I92ezZJiIiIiKiYmDBLUFJCl7/Kp4Y3rkhGteshNq+Xugc6FfiVbJ3x6ffNEf6dkoy1B0ABFS8bH0F35nfhZLb3JHaZnghB1nwMAprM6yYal6L10z/B1URxvZcQF5vNwBkCTO8kY2fxYO4T9jnkwthXwgt//D0/CZp6/CieQMA4Lzww7fWMCyyDUZ0+z1omDs8XA+ZhhMnT6CZYM81ERERERGVDhbcEpSk4I0Y0hbdg2qWyvOnXL2z5waAyp4armUXvxj1q2TGk23ro2+wPzqdPQolCtChQIWAv3oZE1X7PuARlnB8bnsCX5g/AgCouauf/6a3MopsTdFxUq+HIPU8TugN8JJ5A3bYgqEpwhge/ql5If5je9DpPo7iu5t6zGjXR5ahaKCkYlalH1F/4OeAn48xPPyE/yDcF/oYnDcZIyIiIiIiKhkW3BKUpOBNzcwuteev7et1x/f5x6iOuJptuWkFcUdhHdaiDqAAqdeynXvgo+YDUXOB3jOh9nwd+o8vQT240rh/A+UiPjUvRIh21DimKfa+f8d+2V9YH0VTxb5P9wNavLFYW4QlHP5KOkaatqCl+gd26i2N++y0tUAXUyyGaFvRUE0FAFwWldBIuYBXzD/gZPPJ9vY5hodbLHccEyIiIiIiolthwS1BSQrektynKJ0D/VC3qheSM7KKPaw9NTMbA9vWx0PB/tgdn46Uq1m3H9ru2EYr/zZl1Ro5nTLMFGX8nKRXR131EtL1ysZQc0cB7ZC/2HbM93YYZ/oFn1oG4JCtCbposQCAhmoqrupe8FWz8Ltez15sB09G0ND3ivmXExERERERlYwquwEVkaPgLc4MbAX2BdI6B/qV2vNrqoK3+wff0X0cBb+mKujatAYGtq2Prk1r3Hoeef6VvgEgaj7UrXMRYQnHQssgpOuVjVNtQsFDOR8iS5jhp17DN9a+mGpei0rIgsi9KmARGvaIFk4LpC2yDcY31r72pxPAs6aNaKOddmqGr2rvke+onYTeawaLbSIiIiIiuidYcEuQv+C9VdHtuO3t/sElXiCtKP3ur4slo9rDv4rnLc+7q4K/95+dim1EzsHxFpOwyDYYC2xD8bXtEeNUTREYp21EpN4WAHAVPoiwhGNkpV32rcCgwKzYCl0U7S/WZ3FSrwdVAXyUHOO4RdhnYxu9+JoH1F5v3PnfQUREREREVAIsuCUxCt6qRQ8V96/qhSWj2pdo66/itmHHm33wWt9mhd5eqgV/bm/35U6vAbCvHD7F/H/GsPFfbS0x1bwWgbDP1R5o2onBwb7wzblob0vbEdAb9cBU81p81SQSfpU8nB7+Wct06CKvjRGWcARlf4MdtmD736FogC3HXvgTERERERHdA5zDLVG/++vioWB//PZ7Cn7Ztgt9u3eGZjLdvPCYC2mqglf6BqG5f+WbFkTzv4u9vm+Su691Z11gRqV/4QXb2puGhq/E++iuxcAKBfWRApz8yn7fao2Agyuh9p4JNOmJ3pFzsLdXLSzIHoRPI38HAAxSd0BVnGekT9LWobsWg4s1H0St1F1AYKh9TjnAvbSJiIiIiMjlWHBLpqkKHgz0Q9pxga5Na8BsNktph6P4L/aCaCWkqQrCmtXAgsPh+LTA0PBRlllYkVt0O7l8xnkuOABVt6H7fTXxaeTvxj7dv9pa4je9FQAYC61FWMLR7dH5qJX4T3sve+MQFt1ERERERHRPsOAmg2NBNFe7b9hcBLdKgn8hPepX+68DYmcBR/NWJi9YbDt+Lqq3HAC6qsfQTTsOXy+Tff550wLFtV78PcWJiIiIiIhKggU3SXHLHvXmi/MKbtVcZE/0rXrLR1r+gkn6OgxoWchK6uzZJiIiIiKie4AFN0lTZI/6rwvt/1fNgG6xL3RWRJF8q97y4P7v4z4XLThHRERERER0Oyy4qWzJ3T7MGEbu+B0osui+V/PPiYiIiIiI7gQLbio7ChbbQN7/b1N036v550RERERERMXFgpvKjty9um8qqh2/c6EzIiIiIiIqR1hwU9mRu1d3objQGRERERERlTOq7AYQERERERERuSMW3EREREREREQuwIKbiIiIiIiIyAVYcBMRERERERG5QJkuuOfNm4dOnTrB19cXtWvXxqBBgxAXFye7WURERERERES3VaYL7qioKEycOBE7d+7Epk2bYLVa8fDDDyMzM1N204iIiIiIiIhuqUxvC7Zx40an37/66ivUrl0b+/btQ2hoqKRWEREREREREd1emS64C8rIyAAA+Pn5FXlOdnY2srOzjd+vXLkCALBYLLBYLK5tYAk52lVW21cRMAeuxfjKxxzIwbjLxxzIwbjLxxzIxxzIUdy436u8KEIIcU+e6S4JITBw4EBcunQJ27ZtK/K82bNn45133rnp+KpVq+Dj4+PKJhIREREREVE5cP36dYwYMQIZGRmoUqWKy56n3BTcEydOxE8//YTt27ejQYMGRZ5XWA93QEAAUlNTXRrIu2GxWLBp0yY89NBDMJvNsptTITEHrsX4ysccyMG4y8ccyMG4y8ccyMccyFHcuF+5cgU1a9Z0ecFdLoaUT5o0Cf/6178QHR19y2IbADw9PeHp6XnTcbPZXOZf6OWhje6OOXAtxlc+5kAOxl0+5kAOxl0+5kA+5kCO28X9XuWkTBfcQghMmjQJP/zwA7Zu3YrAwEDZTSIiIiIiIiIqljJdcE+cOBGrVq3C+vXr4evri+TkZABA1apV4e3tLbl1REREREREREUr0/twL1myBBkZGejVqxfq1q1r/Pf999/LbhoRERERERHRLZXpHu5ysp4bERERERER0U3KdMFdGhxFu2M/7rLIYrHg+vXruHLlChdUkIQ5cC3GVz7mQA7GXT7mQA7GXT7mQD7mQI7ixt1RH7q6k9ftC+6rV68CAAICAiS3hIiIiIiIiMqSq1evomrVqi57/HKzD3dJ6bqO8+fPw9fXF4qiyG5OoRx7hf/xxx9ldq9wd8ccuBbjKx9zIAfjLh9zIAfjLh9zIB9zIEdx4y6EwNWrV1GvXj2oquuWNnP7Hm5VVW+7d3dZUaVKFf5jlIw5cC3GVz7mQA7GXT7mQA7GXT7mQD7mQI7ixN2VPdsOZXqVciIiIiIiIqLyigU3ERERERERkQuw4C4DPD098fbbb8PT01N2Uyos5sC1GF/5mAM5GHf5mAM5GHf5mAP5mAM5ylrc3X7RNCIiIiIiIiIZ2MNNRERERERE5AIsuImIiIiIiIhcgAU3ERERERERkQuw4CYiIiIiIiJyARbcRERULFxjkyqa7Oxs2U2okFJSUmQ3gfLRdV12Eyo8fv7KUVpxZ8HtBvhGKJfNZkNOTo7sZrila9euIT09HZcuXZLdlAorPj4ee/bsAQAoiiK5NRXHgQMHsHjxYtnNqNBiY2Px9ttvY//+/bKbUqHExsaiTZs2+OSTT2Q3pcJKSUnB4cOH8euvvwIAVFVlwXePZWVl4dq1a7BarQDsn7/8vu96169fR0ZGhvG9vrTizoK7nEpISMDy5cths9mgqir/EUoSGxuL8ePHIywsDC+++CI2bdoku0lu49ixYxgyZAi6d++O8PBwLF26VHaTKpyUlBQEBQVhwIAB2Lx5s+zmVBiHDx9Ghw4dcObMGdlNqbCOHDmCLl26IDs7G9WrV3e6jYWH6xw8eBAdO3bEhQsXeKFDkiNHjuChhx7CkCFD8NRTT+HZZ58FwAuu99LRo0cxZMgQhISEYMiQIZg1axYA+4UPcp2jR4/iySefRJcuXTBw4EDMnDkTQOnEnZkrh06cOIH27dvj3XffxRdffMGiW5Jjx44hNDQUVqsV3bt3x+7du/Hxxx9zKFwpOHr0KEJCQtC8eXPMmDEDderUwerVq3H16lXZTatwAgMD0bt3b0yZMoUXlO6BQ4cOoWvXrpg+fTrmz58vuzkVUlpaGp577jn86U9/wt/+9jcEBgYiIyMD586dA8DCw1UOHTqE7t27Y/bs2YiMjMSKFSvwyy+/yG5WhXLy5EmEhYVh4MCBWLFiBd5//33s3LkTZ8+eNc7hBSfXiouLQ8+ePREUFIQpU6YgODgYn332GQYNGoSMjAwAzIErnDp1Cj179kTz5s3x6quvolWrVli+fDl69eqFK1euALjLuAsqV9LT08Wjjz4qBg8eLMLDw0W3bt3EkiVLhNVqFUIIYbPZJLewYkhOThadOnUSU6ZMMY6dPn1aVK5cWaxZs0Ziy8q/c+fOieDgYPHGG28Yx6Kjo8Ujjzwi4uPjRXJyssTWVSxZWVmiY8eOYvHixWLkyJGiVatWIioqSgghxO+//873m1J25swZoSiKePPNN4UQQuTk5IgPPvhAjB49Wrz44ovin//8p+QWVgwnT54Ubdu2FYmJiSInJ0eMHj1atG/fXgQFBYkBAwaIy5cvCyGE0HVdckvdx+HDh4WqqmLGjBlCCCFSUlJEWFiYmDBhgsjJyeF7zT2g67qYPXu2GDp0qHHs/PnzomfPnmL79u3iv//9L/PgYlarVUyZMkW88MILxrHr16+LQYMGCUVRRFhYmHGc7z+la8mSJaJXr14iOztbCCGExWIRv/32mwgKChLdunUzzivpvwH2cJczVqsVTZs2xfPPP4+lS5eicePG+Oabb7B06VKjp1vwypfLHTp0CA0aNMDYsWMBABaLBYGBgQgNDTXmGzMPJZOYmIgBAwbghRdeMI798ssvOHDgAHr06IH+/ftj1KhREltYMVitVpjNZtSvXx+dO3fGjBkz0K5dO7zyyitGD2xWVpbsZrqVxMREVKtWzehJ7devH9atW4cbN24gNjYW8+fPx8svvyy5le7v3LlzuHbtGurXr4/Ro0cjLS0N06ZNw6xZsxAXF4c+ffoAYE93abFYLFi0aBFmz56NOXPmAABq1aqF3r1749tvv8Xly5f53eYeUBQF8fHx+OOPP4xjy5Ytw549ezB+/Hg899xz6NChgzHSjPkofZqm4ffff0dmZiYA+xpN3t7e6NmzJ1544QWcOHEC48aNA8D3n9J25swZJCYmwsPDAwBgMpnQpUsXrF69GufOncOTTz4J4C6Gl9/1JQG6ZxxXsy5cuGD8nJaWJkaMGCG6desm/v73vxtXXnJycqS1syL4/fffxeLFi286/thjj4m333773jfIjWRmZoqEhATj97/+9a/C29tbLFu2TERFRYlvvvlGNGrUSHz22WcSW+m+Cl41nzx5spg3b54QQohjx46JJk2aCA8PD7Fw4UIZzXNrVqtVREdHC39/f6EoinjqqafEuXPnhBBCXLt2TURERIjmzZuLbdu2SW6pe3K89jMyMkSTJk3Eyy+/LB599FFx8OBB45yzZ8+KBg0a8H2+lKWmpho/O77H3LhxQ7Rq1UpMmjSJPasu5hgluWbNGhEYGCj69Okjnn32WeHp6Sn+/e9/izNnzojz58+LZs2aiWeeeUZuY92U1WoVFotFTJs2TfTv31/s379fCCFEfHy88PPzE59//rlYtGiRaNu2LUf6uUB0dLRo3LixWL16tdNxm80m1q1bJ1q0aCG2bt1a4sdnD3c5UHBudo0aNaAoCiwWC/z8/PDpp5+iUaNGWLFiBT7//HPcuHED06dPx/Tp0yW12D058qDrOpo2bYoJEyY4HQfsVyfzr1i+ZMkSrFix4t42tBxyxFAIAR8fHzRo0MC4LTAwEOvXr8eYMWMQGhqK/v37w8fHB0lJSbKa65YKvs9YLBYAgK+vL06cOAEA+Oijj3DlyhWEhYXhq6++wn/+85973k53k/+1r2kaunTpgm+//RbDhg3Dyy+/jHr16kEIgUqVKmHo0KFISEjA77//LrnV7qXga99sNmPEiBGIjo7GgQMHEBAQAMA+6qN+/fpo3749d04oBfk/U2vUqAGbzQYgrwfJZDKhZ8+e2LVrF65fvw6AvaqlzZEDR8x79OiBBQsWoGvXrtB1HdOmTcPjjz+OgIAA1K1bF7169UJaWprMJrud/DkwmUwYPHgw4uPjMW7cOPTt2xfBwcEYMmQInn/+eTz++OM4duwY4uPjJbfaPeR/PwkMDETLli3x7bffYufOncZxVVXRrVs3pKenIy4ursTPZbqrlpLLxcXF4YsvvsClS5fQsGFDjB8/HnXq1AFg/1Jgs9lQvXp1/P3vf8fEiROxcuVKfP311zh8+DC2b98uufXuI38eAgICMGHCBCMPjgXrVFWFn58fqlWrBgCYMWMGIiIicPDgQXkNLwdu9RoHgKFDhxo/CyFgMpkQGBiIwMBA4xiHVt2dW+Vg4MCB+PLLLzF8+HBs3boVUVFRyMrKwttvv405c+agZ8+e8PHxYQ5KoGDcX3jhBfj7+6NHjx5o3Lgx6tata5zr+GLQpk0bNG7cWFKL3U9RORg5ciT27NmDI0eOICIiAnPmzIHJZP/K5OPjA19fXwB8/ymp273vO97rp02bhvvvvx+ff/45pkyZwliXooLfa8aPHw9/f38MGjQIgwYNwtChQ3HhwgUAecOXMzMz4e/vD5vNBk3TZDbfLRTMwQsvvICuXbti1apV2LRpE9LS0vDMM89g9OjREEIgNTUVwcHBqFevnuyml2tJSUmw2Wxo0KABdF2Hoiho0KAB3nrrLYwZMwYLFizASy+9hF69egEAateujeDgYHh5eZX8SUvcN04ud+zYMVG1alUxbNgw0adPH9G5c2dRs2ZN8fPPPzsN+3QMtUpOThb16tUT1atXF4cOHZLVbLdT3DwIIcTQoUPF/PnzxXvvvSe8vb3F3r17JbW6fChObAvGeNasWaJJkyZOw86p5G6VAyGEOHjwoFAURfj7+4t9+/YZ99uzZ49ITEyU1exyr7C416hRw4h7YWbNmiVatmxpDDOnu1NUDn766SchhBCxsbHiySefFDVr1hSjRo0SX375pXjppZeEn5+fiIuLk9z68utWr/2C322sVquYPHmy6NmzJ4fRlqJb5cAxvPzjjz8WYWFhYvny5SImJka88cYbombNmuL48eOSW+8eCsuBn5+f8f5TmOnTp4t27do5TcGgO3P8+HEREBAgHnvsMXHq1CkhhH04v6OWio6OFu3atROhoaHirbfeEps3bxaTJ08W1atXN84vCRbcZZTVahVPP/20GD58uBDCXnQkJyeLZ599Vvj4+Ii1a9cax4Wwryb8/PPPi8qVK4sjR45Ia7e7KW4eHIYNGyZMJpPw8fFhsX0bdxrbPXv2iFdeeUVUr15dHDhwQEKL3c+tcuDt7W3MZdq8ebMRc66MevduF/eCr/1du3aJiRMnimrVqjnNJ6aSu1UOvLy8jNf+6dOnxWeffSYeeOAB0blzZ9G3b1/m4C7c6XcbIYRYtmyZqF27tkhLS5PSZndT3BwcPHhQDB48WNSqVUs0a9ZMtG7dmp+9paS4nwGOInD//v3imWeeEdWqVWMO7kJiYqLo3r27aNu2rejVq5cYNmxYoUX3oUOHxPTp00XDhg1Fq1atRPv27e867hxSXkYpioKLFy+iR48exrE6dergn//8J7y8vDB27Fg0adIE7dq1g67r8PT0xLlz57Bp0ybcf//9ElvuXu4kD1arFX5+fqhRowb+97//oVWrVhJbXvbdSWwvXLiA//znPzh9+jSioqLQunVriS13H7fLwbhx49C0aVP06dPHGNLMIZ13705e+8nJyfjxxx8RFxeHqKgoPPDAAxJb7j6K89pv0qQJOnTogPHjx2P8+PHIycmBrut3N6ywgrvTz1STyYQxY8bg0UcfhZ+fn8SWu4/b5eCZZ55B48aN0aFDByxatAhJSUnIyclB06ZNUbt2bYktdx938u8gOzsbJpMJnp6eiI6O5vefu3Do0CGYTCZ89tlnOHbsGJYtW4YZM2Zg7ty5aNKkCaxWKwDggQcewAcffIDZs2fj2rVr8PLyQpUqVe7quRUhuAJFWTVy5EjExcVhz549UBTFmDOj6zqeeuopnD17Ftu3b4e3t7fsprq14uRh27Zt8PHxwbFjx+Dt7Y0mTZrIbna5cCexTUtLg6Zpxhx5Kh23y8GZM2ewY8cOvs+Usjt57V+8eBGaprHgKGXFee1v374dPj4+spvqVvjdRr7b5SAhIQHbt29HpUqVZDfVbd3JZwBgX8jUbDZLbnX5t3XrVmNu9hdffIFvvvkGdevWxZw5c9C0aVNjTreiKMb6TKWBq5SXQY5rICNHjoSu63j//fdhsVigaRqsVitUVcXzzz+P9PR0nD17VnJr3VdJ8tCqVSsW28VQktjWqFGDxXYpKm4OLl26xPeZUlSS136tWrVYbJeiO3nt59+TmO4Ov9vIV9wcXL58GYmJiZJb657u5N9B/vcfFtulw1FsA8Cf/vQnjBkzBufPn8fMmTNx+vRpqKqKd955BxcvXiy1YhvgKuVlkmPIZlhYGHr06IENGzbAx8cHEydONIayNWrUCACQnZ0trZ3u7k7ykH8rMLo9xlY+vs/Iwde+fHzty8G4y8ccyMcclA2O3uvnnnsOiqJg2bJlmDlzJsxmM1asWIEhQ4agVq1apfZ87OEuo3JycuDl5YV58+ahQ4cOWL16NSZPnoyMjAycP38eq1atgoeHh9O2MVT6mAfXYWzlYw7kYNzlYw7kYNzlYw7kYw7ksNlsxs+qqhpztp999lmMGjUKGzZswIYNG7B///7SX4fprpZcI5dwbMmQkJAg1qxZI7Kzs8W8efNE27ZthaZponXr1qJu3bpOW/RQ6WMeXIexlY85kINxl485kINxl485kI85kMMR98TERLF06VLjuGNl8smTJ4sqVaqIo0ePuuT5uWhaGSCEMIaYOIY4nDlzBt27d8fw4cPx4Ycfwmaz4caNG9i8eTNq1qyJRo0aISAgQHLL3Qvz4DqMrXzMgRyMu3zMgRyMu3zMgXzMgRy3ivvo0aMxb94849wtW7Zg0KBB2Lp1K9q3b++yBpEEcXFx4l//+pfxe/49J5OTk0WdOnXEhAkTuOetizEPrsPYysccyMG4y8ccyMG4y8ccyMccyHE3cU9JSXFp21hwS3DixAlRpUoVoSiKWL58uXHc8QK4cOGC+PDDD/kP0cWYB9dhbOVjDuRg3OVjDuRg3OVjDuRjDuQoadwdQ8pdnQ8OKb/H0tPT8fzzz8NqtaJRo0b4+uuvsXDhQowdOxYA99m7V5gH12Fs5WMO5GDc5WMO5GDc5WMO5GMO5CgPcee2YPdYRkYGqlWrhvDwcDzwwAPw8fHB5MmTAQBjx46F2Wx2mndArsE8uA5jKx9zIAfjLh9zIAfjLh9zIB9zIEe5iLtL+8+pUKdPnzZ+Pnv2rHj99deFr6+v+Oqrr4zjFotF3LhxQ0LrKg7mwXUYW/mYAzkYd/mYAzkYd/mYA/mYAznKetxZcEvgmC/gkJiYeNMLY9KkSWLhwoU3nUulh3lwHcZWPuZADsZdPuZADsZdPuZAPuZAjrIed87hdrGEhASsX78ely5dwn333YdRo0YBwE1DG86dO4eFCxdi6dKlaN26NbZt24Z9+/ahXbt2spruVpgH12Fs5WMO5GDc5WMO5GDc5WMO5GMO5CiXcb/nJX4FcvjwYVG/fn3Rt29f0alTJ+Hp6SnefffdIs8/ffq0aNGihfDz8xOHDh26hy11b8yD6zC28jEHcjDu8jEHcjDu8jEH8jEHcpTXuLPgdpGEhATRtGlT8frrrwtd18WVK1fEP/7xDxEcHOw0z8DBZrOJadOmCZPJJA4fPiyhxe6JeXAdxlY+5kAOxl0+5kAOxl0+5kA+5kCO8hx39d73qbs/Xdfx/fffIygoCDNnzoSiKPD19UWHDh1w8eJFZGVl3XSf8+fP49y5c9izZw9at24todXuh3lwHcZWPuZADsZdPuZADsZdPuZAPuZAjvIed24L5gKqqqJjx47QdR1VqlQBYJ9X8MADD8DX1xeXLl266T4NGjTAl19+CS8vr3vdXLfFPLgOYysfcyAH4y4fcyAH4y4fcyAfcyBHeY87e7hdJCQkBG+++SaAvEn8ZrMZiqLgxo0bxnmbN2+G1WoFgDLxgnA3zIPrMLbyMQdyMO7yMQdyMO7yMQfyMQdylOe4s+AuJWfPnsVPP/2EpUuXIikpCTk5OQAAm80GRVFgtVqRmZkJq9UKb29vAMCsWbPw8MMPIyUlRWbT3Qrz4DqMrXzMgRyMu3zMgRyMu3zMgXzMgRxuFfd7O2XcPR06dEjUqVNHtGvXTlSrVk0EBASIadOmGRP4dV0XFotFZGZmikaNGokDBw6IuXPnisqVK4s9e/ZIbr37YB5ch7GVjzmQg3GXjzmQg3GXjzmQjzmQw93izoL7Ll26dEl06NBBTJ8+XaSnpwshhHjnnXdESEiIGDBggDh58qTT+e3btxedOnUSHh4eZfIFUV4xD67D2MrHHMjBuMvHHMjBuMvHHMjHHMjhjnFnwX2Xzpw5Ixo1aiT++9//Oh1ftmyZCA0NFSNGjBBJSUlCCCHS09NF1apVy8Ty9O6GeXAdxlY+5kAOxl0+5kAOxl0+5kA+5kAOd4w753DfJU3T4O3tjfPnzwOAMUl/zJgxGDlyJI4ePYpffvkFAFC9enUsXrwYR44ckb48vbthHlyHsZWPOZCDcZePOZCDcZePOZCPOZDDHeOuCCGE7EaUdwMGDMAff/yByMhIVKtWDVarFSaTfce1IUOG4Ny5c/j1118B2PeRU1Ve53AF5sF1GFv5mAM5GHf5mAM5GHf5mAP5mAM53C3uZbt1ZVBmZiauXr2KK1euGMe+/PJLZGRkYOjQocjJyTFeEADwyCOPQAiB7OxsACjzL4jygnlwHcZWPuZADsZdPuZADsZdPuZAPuZAjooQ97LfwjIkJiYGgwcPRs+ePdGyZUusXLkSuq6jZs2aWLVqFWJjY/Hwww8jLi4OWVlZAIDdu3fD19dXcsvdC/PgOoytfMyBHIy7fMyBHIy7fMyBfMyBHBUm7nKmjpc/x44dEzVq1BCvvfaaWLVqlZgyZYowm81i//79xjlHjhwRrVu3Fk2bNhUdO3YU/fv3F76+vuLgwYMSW+5emAfXYWzlYw7kYNzlYw7kYNzlYw7kYw7kqEhx5xzuYkhPT8fw4cPRokULfPLJJ8bxsLAwtG7dGp988gmEEFAUBQCwePFiJCYmwtvbG8OGDUPz5s1lNd2tMA+uw9jKxxzIwbjLxxzIwbjLxxzIxxzIUdHibrr9KWSxWHD58mWEh4cDyJuc36RJE6SlpQEAFEWBzWaDpmmYOHGizOa6LebBdRhb+ZgDORh3+ZgDORh3+ZgD+ZgDOSpa3DmHuxjq1KmDFStWICQkBABgs9kAAPXr13eaqK9pGq5evWr8zsEDpYt5cB3GVj7mQA7GXT7mQA7GXT7mQD7mQI6KFncW3MUUFBQEwH4Fxmw2A7C/OC5cuGCcM2/ePCxdutTYL84xDIJKD/PgOoytfMyBHIy7fMyBHIy7fMyBfMyBHBUp7hxSfodUVTXmFCiKAk3TAABvvfUW3n//fRw4cMBp6XpyDebBdRhb+ZgDORh3+ZgDORh3+ZgD+ZgDOSpC3NnDXQKO4QyapiEgIAAfffQR5s+fj71796JNmzaSW1dxMA+uw9jKxxzIwbjLxxzIwbjLxxzIxxzI4e5xL9+XCyRxzC0wm81YunQpqlSpgu3bt6N9+/aSW1axMA+uw9jKxxzIwbjLxxzIwbjLxxzIxxzI4e5xZw/3XXjkkUcAAL/++is6duwouTUVF/PgOoytfMyBHIy7fMyBHIy7fMyBfMyBHO4ad+7DfZcyMzNRqVIl2c2o8JgH12Fs5WMO5GDc5WMO5GDc5WMO5GMO5HDHuLPgJiIiIiIiInIBDiknIiIiIiIicgEW3EREREREREQuwIKbiIiIiIiIyAVYcBMRERERERG5AAtuIiIiIiIiIhdgwU1ERERERETkAiy4iYiIiIiIiFyABTcREVE5M3bsWCiKAkVRYDabUadOHTz00EP48ssvoet6sR/n66+/RrVq1VzXUCIiogqOBTcREVE51K9fPyQlJSEhIQE///wzevfujVdeeQVPPPEErFar7OYRERERWHATERGVS56envD390f9+vXRvn17zJgxA+vXr8fPP/+Mr7/+GgCwYMECtG7dGpUqVUJAQABeeuklXLt2DQCwdetWjBs3DhkZGUZv+ezZswEAOTk5eP3111G/fn1UqlQJDz74ILZu3SrnDyUiIirHWHATERG5ibCwMLRp0wbr1q0DAKiqioULF+Lo0aNYtmwZtmzZgtdffx0A0K1bN3z88ceoUqUKkpKSkJSUhGnTpgEAxo0bhx07duC7777D4cOHMWTIEPTr1w8nT56U9rcRERGVR4oQQshuBBERERXf2LFjcfnyZfz444833fb000/j8OHDiImJuem2NWvW4MUXX0RqaioA+xzuV199FZcvXzbOOXXqFIKCgpCYmIh69eoZx/v27YvOnTtj7ty5pf73EBERuSuT7AYQERFR6RFCQFEUAEBkZCTmzp2LmJgYXLlyBVarFVlZWcjMzESlSpUKvf/+/fshhECzZs2cjmdnZ6NGjRoubz8REZE7YcFNRETkRo4fP47AwECcOXMGjz32GCZMmID33nsPfn5+2L59O5577jlYLJYi76/rOjRNw759+6BpmtNtlStXdnXziYiI3AoLbiIiIjexZcsWHDlyBK+99hr27t0Lq9WKiIgIqKp9yZbVq1c7ne/h4QGbzeZ0rF27drDZbEhJSUFISMg9azsREZE7YsFNRERUDmVnZyM5ORk2mw0XLlzAxo0bMW/ePDzxxBMYM2YMjhw5AqvVikWLFqF///7YsWMHPvvsM6fHaNy4Ma5du4b//e9/aNOmDXx8fNCsWTOMHDkSY8aMQUREBNq1a4fU1FRs2bIFrVu3xmOPPSbpLyYiIip/uEo5ERFRObRx40bUrVsXjRs3Rr9+/RAZGYmFCxdi/fr10DQNbdu2xYIFC/DBBx/g/vvvx8qVKzFv3jynx+jWrRsmTJiAYcOGoVatWpg/fz4A4KuvvsKYMWMwdepUNG/eHAMGDMCuXbsQEBAg408lIiIqt7hKOREREREREZELsIebiIiIiIiIyAVYcBMRERERERG5AAtuIiIiIiIiIhdgwU1ERERERETkAiy4iYiIiIiIiFyABTcRERERERGRC7DgJiIiIiIiInIBFtxERERERERELsCCm4iIiIiIiMgFWHATERERERERuQALbiIiIiIiIiIXYMFNRERERERE5AL/D9ZdwQk0bU8pAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter the detailed predictions DataFrame for Asset_ID = 5\n",
    "asset_5_predictions = final_predictions_df[final_predictions_df['Asset_ID'] == 5]\n",
    "\n",
    "# Plot predicted vs actual close values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Actual_Close'], label='Actual Close', marker='o')\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Predicted_Close'], label='Predicted Close', marker='x')\n",
    "plt.title('Actual vs Predicted Close Values for Asset_ID = 5')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Value')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8c6e68",
   "metadata": {},
   "source": [
    "## Stacked LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c04906d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Asset ID: 0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "33/33 [==============================] - 2s 2ms/step - loss: 371.8239\n",
      "Epoch 2/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 325.8504\n",
      "Epoch 3/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 197.2780\n",
      "Epoch 4/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 102.0857\n",
      "Epoch 5/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 47.3876\n",
      "Epoch 6/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 35.3848\n",
      "Epoch 7/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 29.5365\n",
      "Epoch 8/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 25.3670\n",
      "Epoch 9/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 21.8978\n",
      "Epoch 10/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 19.3882\n",
      "Epoch 11/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 16.8566\n",
      "Epoch 12/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 15.0047\n",
      "Epoch 13/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 13.5575\n",
      "Epoch 14/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 11.7670\n",
      "Epoch 15/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 10.4546\n",
      "Epoch 16/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 9.8407\n",
      "Epoch 17/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.4389\n",
      "Epoch 18/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 8.1208\n",
      "Epoch 19/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 7.4653\n",
      "Epoch 20/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 7.0155\n",
      "Epoch 21/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 6.5694\n",
      "Epoch 22/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 6.6360\n",
      "Epoch 23/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.8106\n",
      "Epoch 24/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.8501\n",
      "Epoch 25/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.4225\n",
      "Epoch 26/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.1576\n",
      "Epoch 27/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.2868\n",
      "Epoch 28/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.8188\n",
      "Epoch 29/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.8460\n",
      "Epoch 30/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.5813\n",
      "Epoch 31/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.5022\n",
      "Epoch 32/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.0026\n",
      "Epoch 33/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.2967\n",
      "Epoch 34/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.4361\n",
      "Epoch 35/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.2528\n",
      "Epoch 36/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.9230\n",
      "Epoch 37/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.0149\n",
      "Epoch 38/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.3413\n",
      "Epoch 39/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.1671\n",
      "Epoch 40/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 4.1141\n",
      "Epoch 41/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.5216\n",
      "Epoch 42/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.8238\n",
      "Epoch 43/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.7376\n",
      "Epoch 44/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.7495\n",
      "Epoch 45/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6566\n",
      "Epoch 46/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6711\n",
      "Epoch 47/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4692\n",
      "Epoch 48/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4892\n",
      "Epoch 49/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.5625\n",
      "Epoch 50/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4406\n",
      "9/9 [==============================] - 0s 855us/step\n",
      "Processing Asset ID: 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 1s 1ms/step - loss: 88685312.0000\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88662656.0000\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88573592.0000\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88455680.0000\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88358080.0000\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88304016.0000\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88262016.0000\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88221104.0000\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88183448.0000\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88145648.0000\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88111416.0000\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88077984.0000\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88042336.0000\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88011192.0000\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87975952.0000\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87946056.0000\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87913040.0000\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87881688.0000\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87846280.0000\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87818152.0000\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87783600.0000\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87755192.0000\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87721928.0000\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87692176.0000\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87662312.0000\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87624848.0000\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87598184.0000\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87568712.0000\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87534640.0000\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87501720.0000\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87474616.0000\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87442816.0000\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87414384.0000\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87383544.0000\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87354848.0000\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87327160.0000\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87289120.0000\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87267136.0000\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87234104.0000\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87200000.0000\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87167936.0000\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87140072.0000\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87109904.0000\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87078136.0000\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 87050208.0000\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87021008.0000\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86989704.0000\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86955592.0000\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86932152.0000\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86898120.0000\n",
      "9/9 [==============================] - 0s 698us/step\n",
      "Processing Asset ID: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 1s 1ms/step - loss: 392642.9688\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 390887.3125\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 385686.5625\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 381659.4375\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 378070.3438\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 374295.8438\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 371544.7812\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 369549.3125\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 367689.2812\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 365843.1875\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 364126.2812\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 362492.2188\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 360853.9375\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 359138.8750\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 357632.2500\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 356056.0938\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 354699.9688\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 353034.2812\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 351733.7500\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 350308.5000\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 348761.8125\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 347280.1250\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 345938.3438\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 344519.6875\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 342947.5625\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 341722.1875\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 340123.3125\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 338885.5938\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 337709.8438\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 336278.6562\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 335149.9375\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 333353.0938\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 332235.5938\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 330929.0938\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 329877.6562\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 328360.5312\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 327095.7188\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 325830.9688\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 324548.0625\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 323237.9688\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 322311.8750\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 320915.3438\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 319604.2188\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 318570.3125\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 317352.5938\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 316080.6250\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 315110.1250\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 313728.3750\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 312373.5938\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 311645.4062\n",
      "9/9 [==============================] - 0s 608us/step\n",
      "Processing Asset ID: 3\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 1s 2ms/step - loss: 0.0037\n",
      "Epoch 2/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 2.8489e-04\n",
      "Epoch 3/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 2.2931e-04\n",
      "Epoch 4/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.9384e-04\n",
      "Epoch 5/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.6474e-04\n",
      "Epoch 6/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.6458e-04\n",
      "Epoch 7/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3382e-04\n",
      "Epoch 8/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.7162e-04\n",
      "Epoch 9/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3262e-04\n",
      "Epoch 10/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3079e-04\n",
      "Epoch 11/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.6459e-04\n",
      "Epoch 12/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.1652e-04\n",
      "Epoch 13/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3975e-04\n",
      "Epoch 14/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.6540e-04\n",
      "Epoch 15/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1901e-04\n",
      "Epoch 16/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.1736e-04\n",
      "Epoch 17/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.8299e-04\n",
      "Epoch 18/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.7180e-04\n",
      "Epoch 19/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3709e-04\n",
      "Epoch 20/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.3488e-04\n",
      "Epoch 21/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.5840e-04\n",
      "Epoch 22/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 1.4729e-04\n",
      "Epoch 23/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3165e-04\n",
      "Epoch 24/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.9551e-04\n",
      "Epoch 25/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.4094e-04\n",
      "Epoch 26/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2862e-04\n",
      "Epoch 27/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2467e-04\n",
      "Epoch 28/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2353e-04\n",
      "Epoch 29/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1746e-04\n",
      "Epoch 30/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1015e-04\n",
      "Epoch 31/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2703e-04\n",
      "Epoch 32/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3910e-04\n",
      "Epoch 33/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2667e-04\n",
      "Epoch 34/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1344e-04\n",
      "Epoch 35/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.4199e-04\n",
      "Epoch 36/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0350e-04\n",
      "Epoch 37/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1725e-04\n",
      "Epoch 38/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3037e-04\n",
      "Epoch 39/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3916e-04\n",
      "Epoch 40/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1008e-04\n",
      "Epoch 41/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3985e-04\n",
      "Epoch 42/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1426e-04\n",
      "Epoch 43/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.4306e-04\n",
      "Epoch 44/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2715e-04\n",
      "Epoch 45/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0972e-04\n",
      "Epoch 46/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0190e-04\n",
      "Epoch 47/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3074e-04\n",
      "Epoch 48/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0638e-04\n",
      "Epoch 49/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.4509e-04\n",
      "Epoch 50/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0918e-04\n",
      "9/9 [==============================] - 0s 669us/step\n",
      "Processing Asset ID: 4\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 1ms/step - loss: 3.2574e-06\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2817e-06\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 9.3549e-07\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 7.0673e-07\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.6520e-07\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.0997e-07\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.7712e-07\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.9831e-07\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.3139e-07\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.7525e-07\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.6818e-07\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 3.1771e-07\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 2ms/step - loss: 2.8224e-07\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7646e-07\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7313e-07\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.2125e-07\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7257e-07\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.1774e-07\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.6754e-07\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.5913e-07\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.1216e-07\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7797e-07\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4133e-07\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.3758e-07\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7257e-07\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.9367e-07\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.0272e-07\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0838e-07\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4531e-07\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.5319e-07\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.1902e-07\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4895e-07\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0509e-07\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.3088e-07\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2699e-07\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4429e-07\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 9.4354e-08\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0600e-07\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2884e-07\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0968e-07\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0494e-07\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 8.3411e-08\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.6379e-07\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4425e-07\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.1220e-07\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0506e-07\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 8.3224e-08\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.6911e-08\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0689e-07\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2664e-07\n",
      "9/9 [==============================] - 0s 607us/step\n",
      "Processing Asset ID: 5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 1s 1ms/step - loss: 33.1045\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 21.0088\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.2345\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.9561\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5884\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3429\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.1880\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.0846\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.9432\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.8428\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.9605\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.9465\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7542\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7043\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7046\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7053\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7368\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6772\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7375\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7121\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6459\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.7003\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6348\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6198\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6144\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5743\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6460\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5589\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6363\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6162\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6052\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5546\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5771\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5929\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6036\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5937\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5485\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4987\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5197\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5341\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6100\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5136\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5839\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5354\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5904\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5170\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5847\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4541\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5025\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5138\n",
      "9/9 [==============================] - 0s 781us/step\n",
      "Processing Asset ID: 6\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 1s 2ms/step - loss: 156534.3281\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 155477.3281\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 152190.5625\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 148548.4062\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 145537.6406\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 143675.3594\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 142262.8125\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 140882.9844\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 139623.2656\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 138517.7031\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 137320.8750\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 136240.5781\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 135179.7031\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 134108.7500\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 133070.6094\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 132011.6094\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 130987.2422\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 129934.4062\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 129113.6797\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 128047.6094\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 127047.3984\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 126086.5703\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 125243.0391\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 124260.7031\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 123335.6797\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 122591.5938\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 121657.2344\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 120744.0469\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 119866.3906\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 118998.7422\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 118195.6250\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 117327.2266\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 116634.2188\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 115788.0312\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 115037.3359\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 114027.2422\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 113370.1250\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 112435.0469\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 111758.2266\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 111007.1250\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 110540.5312\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 109439.3828\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 108473.6328\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 108147.6641\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 107320.7734\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 106674.4141\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 105760.5781\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 105254.5234\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 104551.0391\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 103601.4297\n",
      "9/9 [==============================] - 0s 622us/step\n",
      "Processing Asset ID: 7\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 2s 1ms/step - loss: 136.5459\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 108.7439\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 47.3291\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 24.3420\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 17.1329\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 14.8857\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 13.3810\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 12.4490\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10.6556\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9.5913\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9.0556\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7.8784\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6.8326\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6.7310\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6.5952\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5.7684\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5.4721\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.7024\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.4941\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.3822\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.7137\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.9432\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.9237\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.5244\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.2916\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.2258\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.1863\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.4141\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.2596\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.5698\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.8578\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.9136\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.9087\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.9633\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.6180\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.7419\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.5548\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.7162\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2.5741\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.5004\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.2045\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.5324\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.6473\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.1951\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2.2236\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2.1962\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.1222\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.1638\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.0001\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2.2536\n",
      "9/9 [==============================] - 0s 865us/step\n",
      "Processing Asset ID: 8\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "31/31 [==============================] - 1s 1ms/step - loss: 0.2022\n",
      "Epoch 2/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0309\n",
      "Epoch 3/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0086\n",
      "Epoch 4/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0074\n",
      "Epoch 5/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0072\n",
      "Epoch 6/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0075\n",
      "Epoch 7/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0059\n",
      "Epoch 8/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0048\n",
      "Epoch 9/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0059\n",
      "Epoch 10/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0054\n",
      "Epoch 11/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0053\n",
      "Epoch 12/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0050\n",
      "Epoch 13/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0044\n",
      "Epoch 14/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0041\n",
      "Epoch 15/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0051\n",
      "Epoch 16/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0060\n",
      "Epoch 17/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0040\n",
      "Epoch 18/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0053\n",
      "Epoch 19/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0051\n",
      "Epoch 20/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0040\n",
      "Epoch 21/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0044\n",
      "Epoch 22/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0043\n",
      "Epoch 23/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0045\n",
      "Epoch 24/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0045\n",
      "Epoch 25/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0038\n",
      "Epoch 26/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0042\n",
      "Epoch 27/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0050\n",
      "Epoch 28/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0036\n",
      "Epoch 29/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0039\n",
      "Epoch 30/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0042\n",
      "Epoch 31/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0049\n",
      "Epoch 32/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0044\n",
      "Epoch 33/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0035\n",
      "Epoch 34/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0046\n",
      "Epoch 35/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0037\n",
      "Epoch 36/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0043\n",
      "Epoch 37/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0041\n",
      "Epoch 38/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0049\n",
      "Epoch 39/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0040\n",
      "Epoch 40/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0039\n",
      "Epoch 41/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0038\n",
      "Epoch 42/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0043\n",
      "Epoch 43/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0034\n",
      "Epoch 44/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0036\n",
      "Epoch 45/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0037\n",
      "Epoch 46/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0041\n",
      "Epoch 47/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0038\n",
      "Epoch 48/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0040\n",
      "Epoch 49/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0042\n",
      "Epoch 50/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0028\n",
      "9/9 [==============================] - 0s 625us/step\n",
      "Processing Asset ID: 9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 1s 2ms/step - loss: 7945.1216\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7721.0376\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6957.5972\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6098.1953\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 5473.0181\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5163.4482\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 4914.6113\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 4700.1768\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 4519.0073\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4330.7964\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4162.1597\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 4024.3079\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 3872.1785\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 3752.2268\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3607.0623\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3516.1372\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3391.2947\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3298.9888\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3201.9705\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3111.7551\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3026.5847\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2940.2231\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2889.7576\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2806.5901\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2762.3804\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2684.3057\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2666.7393\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2596.5598\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2530.1960\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2498.3391\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2442.6697\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2416.4629\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2302.7581\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2226.4924\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2192.6733\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 2129.3589\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2100.0403\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2035.4348\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1973.0026\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 1911.6758\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1893.4780\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1836.3768\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1797.8724\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1772.6313\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1721.9951\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1679.1128\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1627.2433\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1596.3840\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1557.2959\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1540.7645\n",
      "9/9 [==============================] - 0s 609us/step\n",
      "Processing Asset ID: 10\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 2s 2ms/step - loss: 346707.1250\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 346601.0625\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 346363.6250\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 345597.0000\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 343498.0312\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 340264.2812\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 336655.5000\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 332395.3125\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 328962.7188\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 326861.5625\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 325286.9062\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 323856.8438\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 322538.8750\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 321450.7812\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 320219.2188\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 319003.5938\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 317989.1875\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 317106.4062\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 316020.1250\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 314941.8750\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 314016.4688\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 313114.5938\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 312305.0938\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 311232.7500\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 310112.6562\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 309285.5625\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 308428.7500\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 307643.2500\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 306574.5625\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 305838.1562\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 304995.6562\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 304067.6250\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 302941.0312\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 302146.4062\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 301520.1875\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 300384.9375\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 299764.3438\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 299112.7188\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 298034.1562\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 297364.1875\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 296252.6250\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 295690.1250\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 294697.4375\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 293957.0625\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 293383.4375\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 292508.8438\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 291595.7812\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 290610.1250\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 290041.8750\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 289245.9062\n",
      "9/9 [==============================] - 0s 879us/step\n",
      "Processing Asset ID: 11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "35/35 [==============================] - 1s 2ms/step - loss: 16196.1152\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 15874.5088\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 14875.2402\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 13747.6982\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 12721.3262\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 12198.6064\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 11822.1152\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 11455.0283\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 11137.0322\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10840.9766\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10539.9893\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10322.5625\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10046.7637\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9824.7607\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9614.5186\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9385.3770\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9169.1875\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 8979.2715\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 8763.6914\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 8611.1533\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 8428.4902\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 8241.6357\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 8082.4199\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7918.3740\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7787.8223\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7679.8994\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7546.5083\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7394.5059\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7244.5977\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7130.4507\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7017.2271\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6879.6270\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6800.6870\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6726.2847\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6609.7178\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6491.5244\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6485.7251\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6357.3188\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6286.0435\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6203.0684\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6134.8940\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6076.1924\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 5986.3242\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5948.4546\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5860.2915\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5811.0591\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5760.0522\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5738.7065\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5696.2915\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 5607.9077\n",
      "9/9 [==============================] - 0s 772us/step\n",
      "Processing Asset ID: 12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "33/33 [==============================] - 1s 1ms/step - loss: 0.0105\n",
      "Epoch 2/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 8.0459e-04\n",
      "Epoch 3/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.9712e-04\n",
      "Epoch 4/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6765e-04\n",
      "Epoch 5/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.0919e-04\n",
      "Epoch 6/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.1564e-04\n",
      "Epoch 7/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.1315e-04\n",
      "Epoch 8/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6889e-04\n",
      "Epoch 9/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.1461e-04\n",
      "Epoch 10/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6553e-04\n",
      "Epoch 11/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4444e-04\n",
      "Epoch 12/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.8688e-04\n",
      "Epoch 13/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.3184e-04\n",
      "Epoch 14/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6748e-04\n",
      "Epoch 15/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4828e-04\n",
      "Epoch 16/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.3174e-04\n",
      "Epoch 17/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.5205e-04\n",
      "Epoch 18/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.2455e-04\n",
      "Epoch 19/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9276e-04\n",
      "Epoch 20/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4004e-04\n",
      "Epoch 21/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.5872e-04\n",
      "Epoch 22/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9040e-04\n",
      "Epoch 23/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4931e-04\n",
      "Epoch 24/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.2016e-04\n",
      "Epoch 25/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6736e-04\n",
      "Epoch 26/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8697e-04\n",
      "Epoch 27/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6606e-04\n",
      "Epoch 28/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7230e-04\n",
      "Epoch 29/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.0531e-04\n",
      "Epoch 30/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7706e-04\n",
      "Epoch 31/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4493e-04\n",
      "Epoch 32/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6275e-04\n",
      "Epoch 33/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7375e-04\n",
      "Epoch 34/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4693e-04\n",
      "Epoch 35/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.0861e-04\n",
      "Epoch 36/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9360e-04\n",
      "Epoch 37/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4347e-04\n",
      "Epoch 38/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.0267e-04\n",
      "Epoch 39/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8512e-04\n",
      "Epoch 40/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6734e-04\n",
      "Epoch 41/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6764e-04\n",
      "Epoch 42/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4302e-04\n",
      "Epoch 43/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5662e-04\n",
      "Epoch 44/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7392e-04\n",
      "Epoch 45/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5648e-04\n",
      "Epoch 46/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3655e-04\n",
      "Epoch 47/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5157e-04\n",
      "Epoch 48/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5874e-04\n",
      "Epoch 49/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.6069e-04\n",
      "Epoch 50/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6455e-04\n",
      "9/9 [==============================] - 0s 722us/step\n",
      "Processing Asset ID: 13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/2106183392.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "34/34 [==============================] - 1s 1ms/step - loss: 1.4919e-04\n",
      "Epoch 2/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 2.0175e-05\n",
      "Epoch 3/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.5861e-05\n",
      "Epoch 4/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.6794e-05\n",
      "Epoch 5/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.6071e-05\n",
      "Epoch 6/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.3466e-05\n",
      "Epoch 7/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.3210e-05\n",
      "Epoch 8/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.2602e-05\n",
      "Epoch 9/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1483e-05\n",
      "Epoch 10/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.2295e-05\n",
      "Epoch 11/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1324e-05\n",
      "Epoch 12/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1232e-05\n",
      "Epoch 13/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.0763e-05\n",
      "Epoch 14/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 8.9562e-06\n",
      "Epoch 15/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.2840e-05\n",
      "Epoch 16/50\n",
      "34/34 [==============================] - 0s 3ms/step - loss: 1.0908e-05\n",
      "Epoch 17/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1585e-05\n",
      "Epoch 18/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.0915e-05\n",
      "Epoch 19/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.8307e-06\n",
      "Epoch 20/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0237e-05\n",
      "Epoch 21/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.2221e-06\n",
      "Epoch 22/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.3351e-05\n",
      "Epoch 23/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1900e-05\n",
      "Epoch 24/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.1266e-05\n",
      "Epoch 25/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 9.6571e-06\n",
      "Epoch 26/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.0330e-05\n",
      "Epoch 27/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.0822e-05\n",
      "Epoch 28/50\n",
      "34/34 [==============================] - 0s 2ms/step - loss: 1.0462e-05\n",
      "Epoch 29/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.5225e-06\n",
      "Epoch 30/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0757e-05\n",
      "Epoch 31/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.7223e-06\n",
      "Epoch 32/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1393e-05\n",
      "Epoch 33/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.6584e-06\n",
      "Epoch 34/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.5934e-06\n",
      "Epoch 35/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1090e-05\n",
      "Epoch 36/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.2050e-05\n",
      "Epoch 37/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.7536e-06\n",
      "Epoch 38/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0829e-05\n",
      "Epoch 39/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.8025e-06\n",
      "Epoch 40/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.8079e-06\n",
      "Epoch 41/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.4313e-06\n",
      "Epoch 42/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.5695e-06\n",
      "Epoch 43/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.0245e-06\n",
      "Epoch 44/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.1673e-06\n",
      "Epoch 45/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.5284e-06\n",
      "Epoch 46/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 7.8891e-06\n",
      "Epoch 47/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.3389e-06\n",
      "Epoch 48/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.5629e-06\n",
      "Epoch 49/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.4580e-06\n",
      "Epoch 50/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.6108e-06\n",
      "9/9 [==============================] - 0s 763us/step\n",
      "Evaluation Metrics:\n",
      "    Asset_ID       MAPE           MAE          RMSE\n",
      "0          0  80.773629    290.463659    331.970305\n",
      "1          1  99.755609  44514.108889  45452.847850\n",
      "2          2  82.865795    528.206175    569.047568\n",
      "3          3  56.295696      0.882118      1.058431\n",
      "4          4  89.204168      0.191260      0.240765\n",
      "5          5   9.709562      0.626105      1.299839\n",
      "6          6  95.283232   2215.147012   2341.769363\n",
      "7          7  27.713996     15.945122     24.547631\n",
      "8          8  12.649296      0.168377      0.262496\n",
      "9          9  55.473747    108.687027    120.945620\n",
      "10        10  97.805799   2751.688917   2923.082784\n",
      "11        11  64.519082    168.475152    183.689487\n",
      "12        12  14.730318      0.062520      0.093136\n",
      "13        13  14.768154      0.014859      0.025469\n",
      "\n",
      "Detailed Predictions:\n",
      "           Date  Asset_ID  Actual_Close  Predicted_Close\n",
      "0    2021-01-01         0     37.751100        32.498714\n",
      "1    2021-01-02         0     38.216500        32.502804\n",
      "2    2021-01-03         0     41.236100        32.504166\n",
      "3    2021-01-04         0     41.117650        32.501816\n",
      "4    2021-01-05         0     41.775950        32.504894\n",
      "...         ...       ...           ...              ...\n",
      "3691 2021-09-17        13      0.105388         0.077304\n",
      "3692 2021-09-18        13      0.105905         0.088516\n",
      "3693 2021-09-19        13      0.103692         0.087821\n",
      "3694 2021-09-20        13      0.091326         0.074941\n",
      "3695 2021-09-21        13      0.091349         0.094713\n",
      "\n",
      "[3696 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Bidirectional, Dropout\n",
    "from math import sqrt\n",
    "\n",
    "daily_df.sort_values('Date', inplace=True)\n",
    "\n",
    "# Function to calculate MAPE\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "# Manually calculate SMA\n",
    "def calculate_sma(data, window):\n",
    "    return data.rolling(window=window).mean()\n",
    "\n",
    "# Manually calculate RSI\n",
    "def calculate_rsi(data, window=14):\n",
    "    delta = data.diff()\n",
    "    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()\n",
    "    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()\n",
    "    rs = gain / loss\n",
    "    rsi = 100 - (100 / (1 + rs))\n",
    "    return rsi\n",
    "\n",
    "# Placeholder for results and detailed predictions\n",
    "results = []\n",
    "detailed_predictions = []\n",
    "\n",
    "# Temporal split settings\n",
    "split_date = '2021-01-01'  # Adjust this date to your dataset and needs\n",
    "\n",
    "# Split your dataset based on the split_date\n",
    "train_data = daily_df[daily_df['Date'] < split_date]\n",
    "test_data = daily_df[daily_df['Date'] >= split_date]\n",
    "\n",
    "# Iterate through each Asset_ID\n",
    "for asset_id, asset_train_data in train_data.groupby('Asset_ID'):\n",
    "    print(f\"Processing Asset ID: {asset_id}\")\n",
    "    asset_test_data = test_data[test_data['Asset_ID'] == asset_id]\n",
    "    \n",
    "    # Calculate Moving Averages and RSI for the training part\n",
    "    asset_train_data['SMA_10'] = calculate_sma(asset_train_data['Close'], window=10)\n",
    "    asset_train_data['SMA_50'] = calculate_sma(asset_train_data['Close'], window=50)\n",
    "    asset_train_data['RSI'] = calculate_rsi(asset_train_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values generated by SMA and RSI calculations in the training set\n",
    "    asset_train_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Repeat calculation for the test part (important to avoid data leakage)\n",
    "    asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
    "    asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
    "    asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values for the test set\n",
    "    asset_test_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Prepare data for model training and testing\n",
    "    X_train = asset_train_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_train = asset_train_data['Close'].values\n",
    "    X_test = asset_test_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_test = asset_test_data['Close'].values\n",
    "    \n",
    "     # Scale the features\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "    # Reshape for LSTM [samples, timesteps, features]\n",
    "    # Assuming each sample is one timestep\n",
    "    X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], 1, X_train_scaled.shape[1]))\n",
    "    X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], 1, X_test_scaled.shape[1]))\n",
    "    \n",
    "    # Define the Stacked LSTM model\n",
    "    model = Sequential([\n",
    "        LSTM(units=64, return_sequences=True, input_shape=(X_train_reshaped.shape[1], X_train_reshaped.shape[2])),\n",
    "        Dropout(0.2),\n",
    "        LSTM(units=64, return_sequences=True),  # Second LSTM layer\n",
    "        Dropout(0.2),\n",
    "        LSTM(units=64, return_sequences=False),  # Add more LSTM layers as needed\n",
    "        Dropout(0.2),\n",
    "        Dense(1)\n",
    "    ])\n",
    "\n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "    # Train the model\n",
    "    model.fit(X_train_reshaped, y_train, epochs=50, batch_size=32, verbose=1)\n",
    "  \n",
    "    # Predict the Close values for the test set\n",
    "    y_pred = model.predict(X_test_reshaped).flatten()\n",
    "    \n",
    "    # Calculate evaluation metrics for the test set\n",
    "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    # Store the evaluation metrics\n",
    "    results.append({\n",
    "        'Asset_ID': asset_id,\n",
    "        'MAPE': mape,\n",
    "        'MAE': mae,\n",
    "        'RMSE': rmse\n",
    "    })\n",
    "    \n",
    "    # Store detailed predictions\n",
    "    detailed_predictions.append(pd.DataFrame({\n",
    "        'Date': asset_test_data['Date'],\n",
    "        'Asset_ID': asset_id,\n",
    "        'Actual_Close': y_test,\n",
    "        'Predicted_Close': y_pred\n",
    "    }))\n",
    "\n",
    "# Convert results to DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "print(\"Evaluation Metrics:\")\n",
    "print(results_df)\n",
    "\n",
    "# Concatenate detailed predictions into a single DataFrame\n",
    "final_predictions_df = pd.concat(detailed_predictions, ignore_index=True)\n",
    "print(\"\\nDetailed Predictions:\")\n",
    "print(final_predictions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "30b62d68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter the detailed predictions DataFrame for Asset_ID = 5\n",
    "asset_5_predictions = final_predictions_df[final_predictions_df['Asset_ID'] == 5]\n",
    "\n",
    "# Plot predicted vs actual close values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Actual_Close'], label='Actual Close', marker='o')\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Predicted_Close'], label='Predicted Close', marker='x')\n",
    "plt.title('Actual vs Predicted Close Values for Asset_ID = 5')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Value')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3f7e0ab",
   "metadata": {},
   "source": [
    "## Bidirectional LSTM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "098fba3e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing Asset ID: 0\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33/33 [==============================] - 2s 2ms/step - loss: 367.5023\n",
      "Epoch 2/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 305.2332\n",
      "Epoch 3/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 175.2235\n",
      "Epoch 4/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 89.8802\n",
      "Epoch 5/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 30.6035\n",
      "Epoch 6/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 9.5514\n",
      "Epoch 7/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 7.5789\n",
      "Epoch 8/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 6.1116\n",
      "Epoch 9/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.7283\n",
      "Epoch 10/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 5.4120\n",
      "Epoch 11/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.6190\n",
      "Epoch 12/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.2275\n",
      "Epoch 13/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 4.0646\n",
      "Epoch 14/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.5853\n",
      "Epoch 15/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.8968\n",
      "Epoch 16/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.4831\n",
      "Epoch 17/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.2639\n",
      "Epoch 18/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.3312\n",
      "Epoch 19/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.1016\n",
      "Epoch 20/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9830\n",
      "Epoch 21/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9736\n",
      "Epoch 22/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8324\n",
      "Epoch 23/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8309\n",
      "Epoch 24/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9212\n",
      "Epoch 25/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6095\n",
      "Epoch 26/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9081\n",
      "Epoch 27/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6852\n",
      "Epoch 28/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5812\n",
      "Epoch 29/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6670\n",
      "Epoch 30/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4981\n",
      "Epoch 31/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5837\n",
      "Epoch 32/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5586\n",
      "Epoch 33/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6286\n",
      "Epoch 34/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4377\n",
      "Epoch 35/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6767\n",
      "Epoch 36/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3685\n",
      "Epoch 37/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6365\n",
      "Epoch 38/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3689\n",
      "Epoch 39/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4097\n",
      "Epoch 40/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4906\n",
      "Epoch 41/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2780\n",
      "Epoch 42/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5343\n",
      "Epoch 43/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2465\n",
      "Epoch 44/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2021\n",
      "Epoch 45/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3798\n",
      "Epoch 46/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1769\n",
      "Epoch 47/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3202\n",
      "Epoch 48/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2747\n",
      "Epoch 49/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3130\n",
      "Epoch 50/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.0757\n",
      "9/9 [==============================] - 0s 631us/step\n",
      "Processing Asset ID: 1\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 2ms/step - loss: 88683232.0000\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88650400.0000\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 88558848.0000\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88434848.0000\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88282144.0000\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88110472.0000\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87972704.0000\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87868080.0000\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87781608.0000\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87700760.0000\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87625816.0000\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87549280.0000\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87472528.0000\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87401152.0000\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87337968.0000\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87269480.0000\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87201976.0000\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87133904.0000\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87067920.0000\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 87009648.0000\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86947000.0000\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86879272.0000\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86815048.0000\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86755328.0000\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86691256.0000\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86630248.0000\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86564448.0000\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86511544.0000\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86443960.0000\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86384896.0000\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86319784.0000\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86257240.0000\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86206384.0000\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86132960.0000\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86079400.0000\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 86016192.0000\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85943656.0000\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85900024.0000\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85838712.0000\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85768592.0000\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85716016.0000\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85657168.0000\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85608144.0000\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85541032.0000\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85478104.0000\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85429664.0000\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85374608.0000\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85312976.0000\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85246816.0000\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85197000.0000\n",
      "9/9 [==============================] - 0s 1ms/step\n",
      "Processing Asset ID: 2\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 2ms/step - loss: 392459.5625\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 389735.5625\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 383638.4688\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 378181.4688\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 372961.1562\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 367588.4375\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 361780.5625\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 356009.0625\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 351099.4062\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 346983.7812\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 343594.9688\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 340225.3750\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 336962.3438\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 334238.3125\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 330815.9062\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 328137.6250\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 325351.0625\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 322601.7500\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 319903.2812\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 317513.1250\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 314547.8438\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 312084.6562\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 309694.5312\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 307375.6250\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 305192.0312\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 302611.4062\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 300170.3438\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 297920.9688\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 296082.3438\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 293650.4688\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 291448.8438\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 289595.3438\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 287511.5000\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 285290.5938\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 283764.0938\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 281251.6875\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 279268.8438\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 277417.1562\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 275400.4688\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 273733.9688\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 272404.9688\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 270119.0312\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 268525.0938\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 266697.8438\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 264809.5625\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 263437.2812\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 261866.3281\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 260004.1094\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 258469.7500\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 256527.5000\n",
      "9/9 [==============================] - 0s 754us/step\n",
      "Processing Asset ID: 3\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 2s 1ms/step - loss: 0.0023\n",
      "Epoch 2/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 2.3594e-04\n",
      "Epoch 3/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.9969e-04\n",
      "Epoch 4/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.8979e-04\n",
      "Epoch 5/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.7035e-04\n",
      "Epoch 6/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.5362e-04\n",
      "Epoch 7/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3383e-04\n",
      "Epoch 8/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.5257e-04\n",
      "Epoch 9/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2840e-04\n",
      "Epoch 10/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2505e-04\n",
      "Epoch 11/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3554e-04\n",
      "Epoch 12/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1228e-04\n",
      "Epoch 13/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2272e-04\n",
      "Epoch 14/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.3766e-04\n",
      "Epoch 15/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2090e-04\n",
      "Epoch 16/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.9461e-05\n",
      "Epoch 17/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.4240e-04\n",
      "Epoch 18/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0736e-04\n",
      "Epoch 19/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1220e-04\n",
      "Epoch 20/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.2545e-05\n",
      "Epoch 21/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1443e-04\n",
      "Epoch 22/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0961e-04\n",
      "Epoch 23/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0007e-04\n",
      "Epoch 24/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.0878e-05\n",
      "Epoch 25/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1290e-04\n",
      "Epoch 26/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.6771e-05\n",
      "Epoch 27/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1480e-04\n",
      "Epoch 28/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.2237e-04\n",
      "Epoch 29/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0216e-04\n",
      "Epoch 30/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.6219e-05\n",
      "Epoch 31/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.1141e-05\n",
      "Epoch 32/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.6546e-05\n",
      "Epoch 33/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.1356e-04\n",
      "Epoch 34/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 1.0748e-04\n",
      "Epoch 35/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.7159e-05\n",
      "Epoch 36/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.4862e-05\n",
      "Epoch 37/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.1582e-05\n",
      "Epoch 38/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.5770e-05\n",
      "Epoch 39/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.6488e-05\n",
      "Epoch 40/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.8629e-05\n",
      "Epoch 41/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.7209e-05\n",
      "Epoch 42/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.8984e-05\n",
      "Epoch 43/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.2398e-05\n",
      "Epoch 44/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.8254e-05\n",
      "Epoch 45/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.1714e-05\n",
      "Epoch 46/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.9174e-05\n",
      "Epoch 47/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 8.8608e-05\n",
      "Epoch 48/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.7802e-05\n",
      "Epoch 49/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.5466e-05\n",
      "Epoch 50/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 9.4073e-05\n",
      "9/9 [==============================] - 0s 645us/step\n",
      "Processing Asset ID: 4\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 2s 2ms/step - loss: 1.2162e-04\n",
      "Epoch 2/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 8.9592e-05\n",
      "Epoch 3/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.9509e-05\n",
      "Epoch 4/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.6222e-05\n",
      "Epoch 5/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.1586e-05\n",
      "Epoch 6/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.3858e-05\n",
      "Epoch 7/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.0204e-05\n",
      "Epoch 8/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.5656e-05\n",
      "Epoch 9/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.5038e-05\n",
      "Epoch 10/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.9401e-05\n",
      "Epoch 11/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.0773e-05\n",
      "Epoch 12/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2036e-05\n",
      "Epoch 13/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.6873e-05\n",
      "Epoch 14/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.8274e-05\n",
      "Epoch 15/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.4068e-05\n",
      "Epoch 16/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.2073e-05\n",
      "Epoch 17/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 7.7701e-06\n",
      "Epoch 18/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.7845e-06\n",
      "Epoch 19/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.7100e-06\n",
      "Epoch 20/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.3824e-06\n",
      "Epoch 21/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.4194e-06\n",
      "Epoch 22/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.8075e-06\n",
      "Epoch 23/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.3371e-06\n",
      "Epoch 24/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.0209e-06\n",
      "Epoch 25/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.4396e-06\n",
      "Epoch 26/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.3681e-06\n",
      "Epoch 27/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.9540e-06\n",
      "Epoch 28/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.7804e-06\n",
      "Epoch 29/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.3345e-06\n",
      "Epoch 30/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.0463e-06\n",
      "Epoch 31/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.7012e-06\n",
      "Epoch 32/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 5.4572e-06\n",
      "Epoch 33/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.8669e-06\n",
      "Epoch 34/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 7.6529e-06\n",
      "Epoch 35/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 6.1771e-06\n",
      "Epoch 36/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.7870e-06\n",
      "Epoch 37/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 4.3067e-06\n",
      "Epoch 38/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.3135e-06\n",
      "Epoch 39/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.0879e-06\n",
      "Epoch 40/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.5633e-06\n",
      "Epoch 41/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.3124e-06\n",
      "Epoch 42/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.0636e-06\n",
      "Epoch 43/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 3.0425e-06\n",
      "Epoch 44/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.0246e-06\n",
      "Epoch 45/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.0659e-06\n",
      "Epoch 46/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.1332e-06\n",
      "Epoch 47/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.0756e-06\n",
      "Epoch 48/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7785e-06\n",
      "Epoch 49/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 2.3566e-06\n",
      "Epoch 50/50\n",
      "20/20 [==============================] - 0s 1ms/step - loss: 1.7265e-06\n",
      "9/9 [==============================] - 0s 661us/step\n",
      "Processing Asset ID: 5\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 1ms/step - loss: 31.9899\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 17.1242\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.8502\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6188\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.0822\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.8061\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6800\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.6716\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5337\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5346\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5364\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5141\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5169\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4503\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4577\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.5018\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4149\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4543\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4698\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4541\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4155\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4510\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3756\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4185\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4309\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4356\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3982\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3971\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4339\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3909\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4453\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3801\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4054\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3929\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4148\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3939\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3712\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4622\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4000\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3973\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3442\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4165\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4043\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3777\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.4233\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3922\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3661\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3490\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3269\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 0.3321\n",
      "9/9 [==============================] - 0s 678us/step\n",
      "Processing Asset ID: 6\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 1ms/step - loss: 156408.9688\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 154883.1250\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 150954.7812\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 146767.4219\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 141917.3594\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 136532.9844\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 132560.5000\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 129480.1641\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 126819.4297\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 124470.1875\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 122274.7188\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 120307.6406\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 118218.1641\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 116359.6641\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 114492.1797\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 112643.9766\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 111003.7188\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 109267.8672\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 107745.6016\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 106160.7734\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 104775.2812\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 103031.7266\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 101660.9766\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 100320.6016\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 99039.2578\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 97535.4141\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 96447.9922\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 94995.3906\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 93654.2969\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 92530.9688\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 91450.6016\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 90138.1016\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 89072.8594\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 88004.8125\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 86875.5547\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 85669.0312\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 84768.0391\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 83706.9141\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 82873.7891\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 81557.4922\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 81099.4141\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 80052.8281\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 78863.7578\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 78140.8594\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 77278.0625\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 76658.6641\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 75737.5625\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 74951.1172\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 74378.0938\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 73295.5859\n",
      "9/9 [==============================] - 0s 636us/step\n",
      "Processing Asset ID: 7\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 1ms/step - loss: 133.2975\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 94.9560\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 40.5224\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 15.3203\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 8.2752\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7.2172\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5.7712\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.8709\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4.3526\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.5806\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3.1378\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.9683\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.6468\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.5242\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.1841\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.1980\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.2817\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.9908\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.8800\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.0194\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.8523\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.8622\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2.0238\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.7023\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6490\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6662\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.7622\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6322\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5290\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5196\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6605\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6756\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6920\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6845\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5884\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.4994\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5154\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.4856\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5307\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5292\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.5946\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3888\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3989\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.2162\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3088\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.2840\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3932\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.6005\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3435\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1.3351\n",
      "9/9 [==============================] - 0s 636us/step\n",
      "Processing Asset ID: 8\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "31/31 [==============================] - 2s 1ms/step - loss: 0.1470\n",
      "Epoch 2/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0136\n",
      "Epoch 3/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0063\n",
      "Epoch 4/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0047\n",
      "Epoch 5/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0047\n",
      "Epoch 6/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0044\n",
      "Epoch 7/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0051\n",
      "Epoch 8/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0038\n",
      "Epoch 9/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0034\n",
      "Epoch 10/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0042\n",
      "Epoch 11/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0039\n",
      "Epoch 12/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0034\n",
      "Epoch 13/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0033\n",
      "Epoch 14/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0036\n",
      "Epoch 15/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0035\n",
      "Epoch 16/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0041\n",
      "Epoch 17/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0028\n",
      "Epoch 18/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 19/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0034\n",
      "Epoch 20/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0037\n",
      "Epoch 21/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0035\n",
      "Epoch 22/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0038\n",
      "Epoch 23/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0040\n",
      "Epoch 24/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 25/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0026\n",
      "Epoch 26/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 27/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0033\n",
      "Epoch 28/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0029\n",
      "Epoch 29/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0027\n",
      "Epoch 30/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0031\n",
      "Epoch 31/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 32/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 33/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0038\n",
      "Epoch 34/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0037\n",
      "Epoch 35/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0028\n",
      "Epoch 36/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0037\n",
      "Epoch 37/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0032\n",
      "Epoch 38/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0030\n",
      "Epoch 39/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0032\n",
      "Epoch 40/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0028\n",
      "Epoch 41/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0032\n",
      "Epoch 42/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0029\n",
      "Epoch 43/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0035\n",
      "Epoch 44/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0031\n",
      "Epoch 45/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0031\n",
      "Epoch 46/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 47/50\n",
      "31/31 [==============================] - 0s 2ms/step - loss: 0.0030\n",
      "Epoch 48/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0035\n",
      "Epoch 49/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0032\n",
      "Epoch 50/50\n",
      "31/31 [==============================] - 0s 1ms/step - loss: 0.0028\n",
      "9/9 [==============================] - 0s 894us/step\n",
      "Processing Asset ID: 9\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 2ms/step - loss: 7917.4697\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 7595.5615\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 6770.4858\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 5890.5854\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 4845.6299\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3966.5430\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3480.8113\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3164.7717\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2940.1060\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2740.3958\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2572.9343\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2431.2180\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2307.6960\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2167.5542\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2054.3713\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1943.2513\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1855.3365\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1783.3215\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1711.0338\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1639.9458\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1568.2302\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1504.2345\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1428.3585\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1374.4749\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1329.0074\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1263.5359\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1214.4883\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1161.2611\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1102.9120\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1058.2084\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 1017.6758\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 971.4072\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 947.5820\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 922.9537\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 876.0121\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 840.7385\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 811.7372\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 789.7665\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 763.3279\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 732.6419\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 692.7056\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 674.6070\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 651.3826\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 622.8892\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 609.1871\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 609.2183\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 560.9073\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 551.5245\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 541.1290\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 532.1688\n",
      "9/9 [==============================] - 0s 715us/step\n",
      "Processing Asset ID: 10\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15/15 [==============================] - 3s 2ms/step - loss: 346682.1875\n",
      "Epoch 2/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 346488.6250\n",
      "Epoch 3/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 346069.8750\n",
      "Epoch 4/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 345018.1875\n",
      "Epoch 5/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 342764.7812\n",
      "Epoch 6/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 339481.3438\n",
      "Epoch 7/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 335629.4688\n",
      "Epoch 8/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 331881.9375\n",
      "Epoch 9/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 327424.3125\n",
      "Epoch 10/50\n",
      "15/15 [==============================] - 0s 3ms/step - loss: 322198.1250\n",
      "Epoch 11/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 316664.8750\n",
      "Epoch 12/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 311410.2188\n",
      "Epoch 13/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 307002.9375\n",
      "Epoch 14/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 303294.6562\n",
      "Epoch 15/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 300171.5625\n",
      "Epoch 16/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 297425.7500\n",
      "Epoch 17/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 294903.8125\n",
      "Epoch 18/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 292409.5625\n",
      "Epoch 19/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 290451.1875\n",
      "Epoch 20/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 288271.4375\n",
      "Epoch 21/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 285815.7500\n",
      "Epoch 22/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 284179.2812\n",
      "Epoch 23/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 282131.5000\n",
      "Epoch 24/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 280285.0625\n",
      "Epoch 25/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 278182.0938\n",
      "Epoch 26/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 276622.8438\n",
      "Epoch 27/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 274602.9062\n",
      "Epoch 28/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 272893.1250\n",
      "Epoch 29/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 271167.5312\n",
      "Epoch 30/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 268975.1562\n",
      "Epoch 31/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 267669.8438\n",
      "Epoch 32/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 266239.2188\n",
      "Epoch 33/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 264250.9688\n",
      "Epoch 34/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 262497.3438\n",
      "Epoch 35/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 261136.1562\n",
      "Epoch 36/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 259822.1719\n",
      "Epoch 37/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 257931.4531\n",
      "Epoch 38/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 256543.8438\n",
      "Epoch 39/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 255026.6250\n",
      "Epoch 40/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 253342.4531\n",
      "Epoch 41/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 251978.0938\n",
      "Epoch 42/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 250092.3125\n",
      "Epoch 43/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 248674.8125\n",
      "Epoch 44/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 247776.1250\n",
      "Epoch 45/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 245576.0781\n",
      "Epoch 46/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 244627.7656\n",
      "Epoch 47/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 242760.0000\n",
      "Epoch 48/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 241741.0938\n",
      "Epoch 49/50\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 240152.2812\n",
      "Epoch 50/50\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 238638.7969\n",
      "9/9 [==============================] - 0s 742us/step\n",
      "Processing Asset ID: 11\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 2s 2ms/step - loss: 16148.5127\n",
      "Epoch 2/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 15599.9014\n",
      "Epoch 3/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 14432.4541\n",
      "Epoch 4/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 13254.1699\n",
      "Epoch 5/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 11767.9395\n",
      "Epoch 6/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 10368.1992\n",
      "Epoch 7/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 9430.8037\n",
      "Epoch 8/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 8798.9414\n",
      "Epoch 9/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 8318.9551\n",
      "Epoch 10/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7876.3496\n",
      "Epoch 11/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7508.8535\n",
      "Epoch 12/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 7218.6899\n",
      "Epoch 13/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6912.8833\n",
      "Epoch 14/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6686.6396\n",
      "Epoch 15/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6476.1030\n",
      "Epoch 16/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6283.2061\n",
      "Epoch 17/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 6093.9629\n",
      "Epoch 18/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5925.2646\n",
      "Epoch 19/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5747.4414\n",
      "Epoch 20/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5551.9287\n",
      "Epoch 21/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5354.1880\n",
      "Epoch 22/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5251.8896\n",
      "Epoch 23/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 5060.9058\n",
      "Epoch 24/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4892.4106\n",
      "Epoch 25/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4744.9551\n",
      "Epoch 26/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4644.7026\n",
      "Epoch 27/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4522.6001\n",
      "Epoch 28/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4382.0063\n",
      "Epoch 29/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4249.6978\n",
      "Epoch 30/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 4143.5303\n",
      "Epoch 31/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3991.0374\n",
      "Epoch 32/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 3893.3115\n",
      "Epoch 33/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3839.1172\n",
      "Epoch 34/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3731.6292\n",
      "Epoch 35/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3615.1211\n",
      "Epoch 36/50\n",
      "35/35 [==============================] - 0s 2ms/step - loss: 3546.3926\n",
      "Epoch 37/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3436.7000\n",
      "Epoch 38/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3342.6921\n",
      "Epoch 39/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3325.1709\n",
      "Epoch 40/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3229.4473\n",
      "Epoch 41/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3107.9414\n",
      "Epoch 42/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 3062.5378\n",
      "Epoch 43/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2976.2244\n",
      "Epoch 44/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2926.3977\n",
      "Epoch 45/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2860.9785\n",
      "Epoch 46/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2817.9431\n",
      "Epoch 47/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2738.1970\n",
      "Epoch 48/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2662.2161\n",
      "Epoch 49/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2585.6604\n",
      "Epoch 50/50\n",
      "35/35 [==============================] - 0s 1ms/step - loss: 2561.7263\n",
      "9/9 [==============================] - 0s 902us/step\n",
      "Processing Asset ID: 12\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33/33 [==============================] - 2s 2ms/step - loss: 0.0057\n",
      "Epoch 2/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 6.2168e-04\n",
      "Epoch 3/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 3.9669e-04\n",
      "Epoch 4/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.5042e-04\n",
      "Epoch 5/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7935e-04\n",
      "Epoch 6/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.1642e-04\n",
      "Epoch 7/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 3.6617e-04\n",
      "Epoch 8/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9698e-04\n",
      "Epoch 9/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.9069e-04\n",
      "Epoch 10/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8986e-04\n",
      "Epoch 11/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6060e-04\n",
      "Epoch 12/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5912e-04\n",
      "Epoch 13/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6503e-04\n",
      "Epoch 14/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.6379e-04\n",
      "Epoch 15/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7258e-04\n",
      "Epoch 16/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7330e-04\n",
      "Epoch 17/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.8322e-04\n",
      "Epoch 18/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4385e-04\n",
      "Epoch 19/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4704e-04\n",
      "Epoch 20/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.7050e-04\n",
      "Epoch 21/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2425e-04\n",
      "Epoch 22/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.5156e-04\n",
      "Epoch 23/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2188e-04\n",
      "Epoch 24/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1467e-04\n",
      "Epoch 25/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 1.9840e-04\n",
      "Epoch 26/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1431e-04\n",
      "Epoch 27/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2272e-04\n",
      "Epoch 28/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2404e-04\n",
      "Epoch 29/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1114e-04\n",
      "Epoch 30/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.4978e-04\n",
      "Epoch 31/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1489e-04\n",
      "Epoch 32/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0220e-04\n",
      "Epoch 33/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1665e-04\n",
      "Epoch 34/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2456e-04\n",
      "Epoch 35/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.2613e-04\n",
      "Epoch 36/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1647e-04\n",
      "Epoch 37/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2055e-04\n",
      "Epoch 38/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.1669e-04\n",
      "Epoch 39/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.4309e-04\n",
      "Epoch 40/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.2474e-04\n",
      "Epoch 41/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1436e-04\n",
      "Epoch 42/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.9318e-04\n",
      "Epoch 43/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.0601e-04\n",
      "Epoch 44/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.3113e-04\n",
      "Epoch 45/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 2.4145e-04\n",
      "Epoch 46/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8695e-04\n",
      "Epoch 47/50\n",
      "33/33 [==============================] - 0s 2ms/step - loss: 1.8484e-04\n",
      "Epoch 48/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.1238e-04\n",
      "Epoch 49/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 1.9215e-04\n",
      "Epoch 50/50\n",
      "33/33 [==============================] - 0s 1ms/step - loss: 2.0251e-04\n",
      "9/9 [==============================] - 0s 916us/step\n",
      "Processing Asset ID: 13\n",
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:54: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:55: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:56: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
      "/var/folders/48/3359jr0n34v6fd5jftvl2dn00000gn/T/ipykernel_63160/1633733053.py:59: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  asset_test_data.fillna(method='bfill', inplace=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 2s 1ms/step - loss: 2.1419e-04\n",
      "Epoch 2/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 7.9259e-05\n",
      "Epoch 3/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 7.5238e-05\n",
      "Epoch 4/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 4.5707e-05\n",
      "Epoch 5/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 4.1457e-05\n",
      "Epoch 6/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 4.3011e-05\n",
      "Epoch 7/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 2.7490e-05\n",
      "Epoch 8/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 2.4873e-05\n",
      "Epoch 9/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 2.5140e-05\n",
      "Epoch 10/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 2.4307e-05\n",
      "Epoch 11/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.8928e-05\n",
      "Epoch 12/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.9581e-05\n",
      "Epoch 13/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.7900e-05\n",
      "Epoch 14/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.7052e-05\n",
      "Epoch 15/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.9924e-05\n",
      "Epoch 16/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.4482e-05\n",
      "Epoch 17/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.6808e-05\n",
      "Epoch 18/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.4598e-05\n",
      "Epoch 19/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.2445e-05\n",
      "Epoch 20/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.4570e-05\n",
      "Epoch 21/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.5876e-05\n",
      "Epoch 22/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.3814e-05\n",
      "Epoch 23/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.2940e-05\n",
      "Epoch 24/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1279e-05\n",
      "Epoch 25/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1561e-05\n",
      "Epoch 26/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0810e-05\n",
      "Epoch 27/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1278e-05\n",
      "Epoch 28/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1395e-05\n",
      "Epoch 29/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.4558e-05\n",
      "Epoch 30/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.2339e-05\n",
      "Epoch 31/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0928e-05\n",
      "Epoch 32/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0807e-05\n",
      "Epoch 33/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.3208e-05\n",
      "Epoch 34/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.0128e-05\n",
      "Epoch 35/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.1315e-05\n",
      "Epoch 36/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.1698e-06\n",
      "Epoch 37/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.4368e-06\n",
      "Epoch 38/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 1.3219e-05\n",
      "Epoch 39/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.4769e-06\n",
      "Epoch 40/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.0098e-06\n",
      "Epoch 41/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.5822e-06\n",
      "Epoch 42/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.2906e-06\n",
      "Epoch 43/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.5965e-06\n",
      "Epoch 44/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.3379e-06\n",
      "Epoch 45/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.5820e-06\n",
      "Epoch 46/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.1138e-06\n",
      "Epoch 47/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 9.5377e-06\n",
      "Epoch 48/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.9987e-06\n",
      "Epoch 49/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.4747e-06\n",
      "Epoch 50/50\n",
      "34/34 [==============================] - 0s 1ms/step - loss: 8.5744e-06\n",
      "9/9 [==============================] - 0s 692us/step\n",
      "Evaluation Metrics:\n",
      "    Asset_ID       MAPE           MAE          RMSE\n",
      "0          0  78.619795    286.429398    328.332153\n",
      "1          1  99.518507  44412.775736  45353.612161\n",
      "2          2  68.076648    442.550445    490.569871\n",
      "3          3  49.083589      0.775075      0.953451\n",
      "4          4  91.376463      0.186933      0.235798\n",
      "5          5   8.108732      0.510206      1.023755\n",
      "6          6  91.326044   2133.721335   2264.900486\n",
      "7          7  24.052719     14.113329     22.693376\n",
      "8          8  19.058271      0.266179      0.379377\n",
      "9          9  29.185176     62.498073     81.283128\n",
      "10        10  95.675679   2700.447324   2874.894172\n",
      "11        11  39.320918    110.018636    131.580162\n",
      "12        12  13.935807      0.057552      0.083919\n",
      "13        13  13.084239      0.012990      0.024111\n",
      "\n",
      "Detailed Predictions:\n",
      "           Date  Asset_ID  Actual_Close  Predicted_Close\n",
      "0    2021-01-01         0     37.751100        35.635456\n",
      "1    2021-01-02         0     38.216500        35.715515\n",
      "2    2021-01-03         0     41.236100        35.805416\n",
      "3    2021-01-04         0     41.117650        35.791111\n",
      "4    2021-01-05         0     41.775950        35.760994\n",
      "...         ...       ...           ...              ...\n",
      "3691 2021-09-17        13      0.105388         0.083216\n",
      "3692 2021-09-18        13      0.105905         0.096484\n",
      "3693 2021-09-19        13      0.103692         0.095859\n",
      "3694 2021-09-20        13      0.091326         0.081017\n",
      "3695 2021-09-21        13      0.091349         0.104703\n",
      "\n",
      "[3696 rows x 4 columns]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, LSTM, Bidirectional, Dropout\n",
    "from math import sqrt\n",
    "\n",
    "daily_df.sort_values('Date', inplace=True)\n",
    "\n",
    "# Function to calculate MAPE\n",
    "def mean_absolute_percentage_error(y_true, y_pred): \n",
    "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
    "\n",
    "# Manually calculate SMA\n",
    "def calculate_sma(data, window):\n",
    "    return data.rolling(window=window).mean()\n",
    "\n",
    "# Manually calculate RSI\n",
    "def calculate_rsi(data, window=14):\n",
    "    delta = data.diff()\n",
    "    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()\n",
    "    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()\n",
    "    rs = gain / loss\n",
    "    rsi = 100 - (100 / (1 + rs))\n",
    "    return rsi\n",
    "\n",
    "# Placeholder for results and detailed predictions\n",
    "results = []\n",
    "detailed_predictions = []\n",
    "\n",
    "# Temporal split settings\n",
    "split_date = '2021-01-01'  # Adjust this date to your dataset and needs\n",
    "\n",
    "# Split your dataset based on the split_date\n",
    "train_data = daily_df[daily_df['Date'] < split_date]\n",
    "test_data = daily_df[daily_df['Date'] >= split_date]\n",
    "\n",
    "# Iterate through each Asset_ID\n",
    "for asset_id, asset_train_data in train_data.groupby('Asset_ID'):\n",
    "    print(f\"Processing Asset ID: {asset_id}\")\n",
    "    asset_test_data = test_data[test_data['Asset_ID'] == asset_id]\n",
    "    \n",
    "    # Calculate Moving Averages and RSI for the training part\n",
    "    asset_train_data['SMA_10'] = calculate_sma(asset_train_data['Close'], window=10)\n",
    "    asset_train_data['SMA_50'] = calculate_sma(asset_train_data['Close'], window=50)\n",
    "    asset_train_data['RSI'] = calculate_rsi(asset_train_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values generated by SMA and RSI calculations in the training set\n",
    "    asset_train_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Repeat calculation for the test part (important to avoid data leakage)\n",
    "    asset_test_data['SMA_10'] = calculate_sma(asset_test_data['Close'], window=10)\n",
    "    asset_test_data['SMA_50'] = calculate_sma(asset_test_data['Close'], window=50)\n",
    "    asset_test_data['RSI'] = calculate_rsi(asset_test_data['Close'], window=14)\n",
    "    \n",
    "    # Fill NaN values for the test set\n",
    "    asset_test_data.fillna(method='bfill', inplace=True)\n",
    "    \n",
    "    # Prepare data for model training and testing\n",
    "    X_train = asset_train_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_train = asset_train_data['Close'].values\n",
    "    X_test = asset_test_data[['Volume', 'Count', 'SMA_10', 'SMA_50', 'RSI']].values\n",
    "    y_test = asset_test_data['Close'].values\n",
    "    \n",
    "     # Scale the features\n",
    "    scaler = StandardScaler()\n",
    "    X_train_scaled = scaler.fit_transform(X_train)\n",
    "    X_test_scaled = scaler.transform(X_test)\n",
    "    \n",
    "    # Reshape for LSTM [samples, timesteps, features]\n",
    "    # Assuming each sample is one timestep\n",
    "    X_train_reshaped = X_train_scaled.reshape((X_train_scaled.shape[0], 1, X_train_scaled.shape[1]))\n",
    "    X_test_reshaped = X_test_scaled.reshape((X_test_scaled.shape[0], 1, X_test_scaled.shape[1]))\n",
    "    \n",
    "    # Define the Bidirectional LSTM model\n",
    "    model = Sequential([\n",
    "        Bidirectional(LSTM(units=64, return_sequences=True, input_shape=(X_train_reshaped.shape[1], X_train_reshaped.shape[2]))),\n",
    "        Dropout(0.2),\n",
    "        Bidirectional(LSTM(units=64, return_sequences=False)),\n",
    "        Dropout(0.2),\n",
    "        Dense(1)\n",
    "    ])\n",
    "    \n",
    "    # Compile the model\n",
    "    model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "    \n",
    "    # Train the model\n",
    "    model.fit(X_train_reshaped, y_train, epochs=50, batch_size=32, verbose=1)\n",
    "    \n",
    "    # Predict the Close values for the test set\n",
    "    y_pred = model.predict(X_test_reshaped).flatten()\n",
    "    \n",
    "    # Calculate evaluation metrics for the test set\n",
    "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    rmse = sqrt(mean_squared_error(y_test, y_pred))\n",
    "    \n",
    "    # Store the evaluation metrics\n",
    "    results.append({\n",
    "        'Asset_ID': asset_id,\n",
    "        'MAPE': mape,\n",
    "        'MAE': mae,\n",
    "        'RMSE': rmse\n",
    "    })\n",
    "    \n",
    "    # Store detailed predictions\n",
    "    detailed_predictions.append(pd.DataFrame({\n",
    "        'Date': asset_test_data['Date'],\n",
    "        'Asset_ID': asset_id,\n",
    "        'Actual_Close': y_test,\n",
    "        'Predicted_Close': y_pred\n",
    "    }))\n",
    "\n",
    "# Convert results to DataFrame\n",
    "results_df = pd.DataFrame(results)\n",
    "print(\"Evaluation Metrics:\")\n",
    "print(results_df)\n",
    "\n",
    "# Concatenate detailed predictions into a single DataFrame\n",
    "final_predictions_df = pd.concat(detailed_predictions, ignore_index=True)\n",
    "print(\"\\nDetailed Predictions:\")\n",
    "print(final_predictions_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "aefaaa1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Filter the detailed predictions DataFrame for Asset_ID = 5\n",
    "asset_5_predictions = final_predictions_df[final_predictions_df['Asset_ID'] == 5]\n",
    "\n",
    "# Plot predicted vs actual close values\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Actual_Close'], label='Actual Close', marker='o')\n",
    "plt.plot(asset_5_predictions['Date'], asset_5_predictions['Predicted_Close'], label='Predicted Close', marker='x')\n",
    "plt.title('Actual vs Predicted Close Values for Asset_ID = 5')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Close Value')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "414b47b7",
   "metadata": {},
   "source": [
    "# Portfolio Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd8f6233",
   "metadata": {},
   "source": [
    "#### Choosing Bi-Directional LSTM Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "652fbc17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Actual_Close</th>\n",
       "      <th>Predicted_Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>37.75110</td>\n",
       "      <td>35.635456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>35.715515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>35.805416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>35.791111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>35.760994</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Asset_ID  Actual_Close  Predicted_Close\n",
       "0 2021-01-01         0      37.75110        35.635456\n",
       "1 2021-01-02         0      38.21650        35.715515\n",
       "2 2021-01-03         0      41.23610        35.805416\n",
       "3 2021-01-04         0      41.11765        35.791111\n",
       "4 2021-01-05         0      41.77595        35.760994"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "11bcf6b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Count</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6228</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>5</td>\n",
       "      <td>7.638</td>\n",
       "      <td>1.727143e+06</td>\n",
       "      <td>13640.0</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>EOS.IO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1360</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>13432.918</td>\n",
       "      <td>5.782304e+04</td>\n",
       "      <td>365257.0</td>\n",
       "      <td>6.779922</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14131</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>11</td>\n",
       "      <td>341.175</td>\n",
       "      <td>2.655136e+04</td>\n",
       "      <td>8536.0</td>\n",
       "      <td>1.609438</td>\n",
       "      <td>Monero</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11540</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>9</td>\n",
       "      <td>224.108</td>\n",
       "      <td>4.789200e+05</td>\n",
       "      <td>120933.0</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>Litecoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2720</th>\n",
       "      <td>2018-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>2321.520</td>\n",
       "      <td>2.330180e+04</td>\n",
       "      <td>59257.0</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Asset_ID      Close        Volume     Count    Weight  \\\n",
       "6228  2018-01-01         5      7.638  1.727143e+06   13640.0  1.386294   \n",
       "1360  2018-01-01         1  13432.918  5.782304e+04  365257.0  6.779922   \n",
       "14131 2018-01-01        11    341.175  2.655136e+04    8536.0  1.609438   \n",
       "11540 2018-01-01         9    224.108  4.789200e+05  120933.0  2.397895   \n",
       "2720  2018-01-01         2   2321.520  2.330180e+04   59257.0  2.397895   \n",
       "\n",
       "         Asset_Name  \n",
       "6228         EOS.IO  \n",
       "1360        Bitcoin  \n",
       "14131        Monero  \n",
       "11540      Litecoin  \n",
       "2720   Bitcoin Cash  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6b191ff7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Actual_Close</th>\n",
       "      <th>Predicted_Close</th>\n",
       "      <th>Next_Day_Close</th>\n",
       "      <th>Next_Day_Predicted_Close</th>\n",
       "      <th>Predicted_percentage_return</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>37.75110</td>\n",
       "      <td>35.635456</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>35.715515</td>\n",
       "      <td>-5.392121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>35.715515</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>35.805416</td>\n",
       "      <td>-6.309013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>35.805416</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>35.791111</td>\n",
       "      <td>-13.204423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>35.791111</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>35.760994</td>\n",
       "      <td>-13.027632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>35.760994</td>\n",
       "      <td>42.22035</td>\n",
       "      <td>35.776676</td>\n",
       "      <td>-14.360592</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Asset_ID  Actual_Close  Predicted_Close  Next_Day_Close  \\\n",
       "0 2021-01-01         0      37.75110        35.635456        38.21650   \n",
       "1 2021-01-02         0      38.21650        35.715515        41.23610   \n",
       "2 2021-01-03         0      41.23610        35.805416        41.11765   \n",
       "3 2021-01-04         0      41.11765        35.791111        41.77595   \n",
       "4 2021-01-05         0      41.77595        35.760994        42.22035   \n",
       "\n",
       "   Next_Day_Predicted_Close  Predicted_percentage_return  \n",
       "0                 35.715515                    -5.392121  \n",
       "1                 35.805416                    -6.309013  \n",
       "2                 35.791111                   -13.204423  \n",
       "3                 35.760994                   -13.027632  \n",
       "4                 35.776676                   -14.360592  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Calculate 'Next_Day_Close' as the actual close of the next day\n",
    "final_predictions_df['Next_Day_Close'] = final_predictions_df['Actual_Close'].shift(-1)\n",
    "\n",
    "# Calculate 'Next_Day_Close' as the actual close of the next day\n",
    "final_predictions_df['Next_Day_Predicted_Close'] = final_predictions_df['Predicted_Close'].shift(-1)\n",
    "\n",
    "# Calculate 'Predicted_percentage_return'\n",
    "# Formula: ((Next_Day_Predicted_Close / Actual_Close) - 1) * 100\n",
    "final_predictions_df['Predicted_percentage_return'] = ((final_predictions_df['Next_Day_Predicted_Close'] / final_predictions_df['Actual_Close']) - 1) * 100\n",
    "\n",
    "final_predictions_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "21c96ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>2.397895</td>\n",
       "      <td>Bitcoin Cash</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>4.304065</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>6.779922</td>\n",
       "      <td>Bitcoin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1.386294</td>\n",
       "      <td>EOS.IO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>2.079442</td>\n",
       "      <td>Ethereum Classic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Asset_ID    Weight        Asset_Name\n",
       "0         2  2.397895      Bitcoin Cash\n",
       "1         0  4.304065      Binance Coin\n",
       "2         1  6.779922           Bitcoin\n",
       "3         5  1.386294            EOS.IO\n",
       "4         7  2.079442  Ethereum Classic"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9390a4ba",
   "metadata": {},
   "source": [
    "#### Preparing the Dataset for Portfolio Optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "650c7ca4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Actual_Close</th>\n",
       "      <th>Predicted_Close</th>\n",
       "      <th>Next_Day_Close</th>\n",
       "      <th>Next_Day_Predicted_Close</th>\n",
       "      <th>Predicted_percentage_return</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>37.75110</td>\n",
       "      <td>35.635456</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>35.715515</td>\n",
       "      <td>-5.392121</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>35.715515</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>35.805416</td>\n",
       "      <td>-6.309013</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>35.805416</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>35.791111</td>\n",
       "      <td>-13.204423</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>35.791111</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>35.760994</td>\n",
       "      <td>-13.027632</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>35.760994</td>\n",
       "      <td>42.22035</td>\n",
       "      <td>35.776676</td>\n",
       "      <td>-14.360592</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Asset_ID  Actual_Close  Predicted_Close  Next_Day_Close  \\\n",
       "0 2021-01-01         0      37.75110        35.635456        38.21650   \n",
       "1 2021-01-02         0      38.21650        35.715515        41.23610   \n",
       "2 2021-01-03         0      41.23610        35.805416        41.11765   \n",
       "3 2021-01-04         0      41.11765        35.791111        41.77595   \n",
       "4 2021-01-05         0      41.77595        35.760994        42.22035   \n",
       "\n",
       "   Next_Day_Predicted_Close  Predicted_percentage_return    Asset_Name  \n",
       "0                 35.715515                    -5.392121  Binance Coin  \n",
       "1                 35.805416                    -6.309013  Binance Coin  \n",
       "2                 35.791111                   -13.204423  Binance Coin  \n",
       "3                 35.760994                   -13.027632  Binance Coin  \n",
       "4                 35.776676                   -14.360592  Binance Coin  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_optimization_df = final_predictions_df.merge(asset[['Asset_ID', 'Asset_Name']], on='Asset_ID', how='left')\n",
    "portfolio_optimization_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e731db73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Removing the unnecessary fields\n",
    "\n",
    "portfolio_optimization_df = portfolio_optimization_df.drop(columns=['Predicted_Close', 'Next_Day_Predicted_Close'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a13dccf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename the 'Actual_Close' column to 'Close'\n",
    "portfolio_optimization_df.rename(columns={'Actual_Close': 'Close'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e9c5a999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Asset_ID</th>\n",
       "      <th>Close</th>\n",
       "      <th>Next_Day_Close</th>\n",
       "      <th>Predicted_percentage_return</th>\n",
       "      <th>Asset_Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0</td>\n",
       "      <td>37.75110</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>-5.392121</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>38.21650</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>-6.309013</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0</td>\n",
       "      <td>41.23610</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>-13.204423</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>0</td>\n",
       "      <td>41.11765</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>-13.027632</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>0</td>\n",
       "      <td>41.77595</td>\n",
       "      <td>42.22035</td>\n",
       "      <td>-14.360592</td>\n",
       "      <td>Binance Coin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date  Asset_ID     Close  Next_Day_Close  Predicted_percentage_return  \\\n",
       "0 2021-01-01         0  37.75110        38.21650                    -5.392121   \n",
       "1 2021-01-02         0  38.21650        41.23610                    -6.309013   \n",
       "2 2021-01-03         0  41.23610        41.11765                   -13.204423   \n",
       "3 2021-01-04         0  41.11765        41.77595                   -13.027632   \n",
       "4 2021-01-05         0  41.77595        42.22035                   -14.360592   \n",
       "\n",
       "     Asset_Name  \n",
       "0  Binance Coin  \n",
       "1  Binance Coin  \n",
       "2  Binance Coin  \n",
       "3  Binance Coin  \n",
       "4  Binance Coin  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "portfolio_optimization_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d6e993",
   "metadata": {},
   "source": [
    "#### Portfolio Optmization Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f79f907",
   "metadata": {},
   "source": [
    "#### 6 Month Investment Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e0d71c28",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2021-01-01 00:00:00, Wallet Balance: 0.0, Portfolio Value: 100000.0, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-02 00:00:00, Wallet Balance: 0.0, Portfolio Value: 99750.243967633, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-03 00:00:00, Wallet Balance: 0.0, Portfolio Value: 110433.31571144963, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-04 00:00:00, Wallet Balance: 30113.248758892925, Portfolio Value: 91732.26800038698, Portfolio: {'IOTA': [68728.52233676976, 0.291], 'TRON': [744418.7206419867, 0.0268666], 'Stellar': [91766.53232120897, 0.1640753333333333], 'Cardano': [66772.97323181124, 0.2254897999999999], 'Ethereum Classic': [2137.3467081521953, 7.04454]}\n",
      "Date: 2021-01-05 00:00:00, Wallet Balance: 43428.056051787884, Portfolio Value: 82651.37030553003, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Stellar': [91766.53232120897, 0.1640753333333333], 'Cardano': [66772.97323181124, 0.2254897999999999], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517]}\n",
      "Date: 2021-01-06 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 74931.17109182033, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-07 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 72114.74170570081, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-08 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 70993.28284172132, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-09 00:00:00, Wallet Balance: 0.0, Portfolio Value: 152187.52962651063, Portfolio: {'Ethereum Classic': [2137.3467081521953, 7.04454], 'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999]}\n",
      "Date: 2021-01-10 00:00:00, Wallet Balance: 20441.940141218958, Portfolio Value: 130993.95685164409, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999]}\n",
      "Date: 2021-01-11 00:00:00, Wallet Balance: 12265.164084731376, Portfolio Value: 123331.7679589931, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285]}\n",
      "Date: 2021-01-12 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 125134.05182920709, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-13 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 138225.25245844384, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-14 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 138609.50358018113, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-15 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 137722.5256191082, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-16 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 141122.52286786906, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-17 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 145104.64839645135, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-18 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 152153.08118458683, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-19 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 149106.8120165446, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-20 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 149686.7875458066, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-21 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 133924.29549722455, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-22 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 143686.6403328576, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-23 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 144117.5522579828, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-24 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 146718.1230888554, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-25 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 141656.8338616679, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-26 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 141799.41676220874, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-27 00:00:00, Wallet Balance: 6279.764011382465, Portfolio Value: 132907.9067394976, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'Dogecoin': [211676.48723092984, 0.0074166999999999]}\n",
      "Date: 2021-01-28 00:00:00, Wallet Balance: 14011.850985480785, Portfolio Value: 138811.5551373479, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-29 00:00:00, Wallet Balance: 14011.850985480785, Portfolio Value: 146479.91247015173, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-30 00:00:00, Wallet Balance: 18953.76585168501, Portfolio Value: 143964.9684493824, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-31 00:00:00, Wallet Balance: 18953.76585168501, Portfolio Value: 138579.03195581833, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-01 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 117032.6871245752, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-02 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 119402.22164834128, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-03 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 124842.33847832907, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-04 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 120528.04605857632, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-05 00:00:00, Wallet Balance: 53838.36881542408, Portfolio Value: 131298.8058008001, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-06 00:00:00, Wallet Balance: 63201.521877997664, Portfolio Value: 126065.00156750565, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'EOS.IO': [19678.64851977708, 3.2116800000000003]}\n",
      "Date: 2021-02-07 00:00:00, Wallet Balance: 47401.141408498246, Portfolio Value: 147634.25199401652, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'EOS.IO': [19678.64851977708, 3.2116800000000003], 'IOTA': [28323.707931342506, 0.55785]}\n",
      "Date: 2021-02-08 00:00:00, Wallet Balance: 70205.79961573456, Portfolio Value: 140136.08905599813, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'EOS.IO': [19678.64851977708, 3.2116800000000003]}\n",
      "Date: 2021-02-09 00:00:00, Wallet Balance: -2.9103830456733704e-11, Portfolio Value: 231299.430936375, Portfolio: {'Ethereum Classic': [8023.67461391825, 9.609039999999998], 'EOS.IO': [18487.53754157308, 4.170366666666667], 'IOTA': [102799.74708283333, 0.75]}\n",
      "Date: 2021-02-10 00:00:00, Wallet Balance: -2.9103830456733704e-11, Portfolio Value: 250560.11485642433, Portfolio: {'Ethereum Classic': [8023.67461391825, 9.609039999999998], 'EOS.IO': [18487.53754157308, 4.170366666666667], 'IOTA': [102799.74708283333, 0.75]}\n",
      "Date: 2021-02-11 00:00:00, Wallet Balance: 107319.46014142818, Portfolio Value: 192057.82796473007, Portfolio: {'EOS.IO': [18487.53754157308, 4.170366666666667], 'Ethereum Classic': [9157.263058588022, 11.7196]}\n",
      "Date: 2021-02-12 00:00:00, Wallet Balance: 107319.46014142818, Portfolio Value: 197431.83038056572, Portfolio: {'EOS.IO': [18487.53754157308, 4.170366666666667], 'Ethereum Classic': [9157.263058588022, 11.7196]}\n",
      "Date: 2021-02-13 00:00:00, Wallet Balance: 362814.72889851755, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-02-14 00:00:00, Wallet Balance: 362814.72889851755, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-02-15 00:00:00, Wallet Balance: 0.0, Portfolio Value: 362814.72889851755, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-16 00:00:00, Wallet Balance: 0.0, Portfolio Value: 361953.91550847486, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-17 00:00:00, Wallet Balance: 0.0, Portfolio Value: 375477.5091343584, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-18 00:00:00, Wallet Balance: 0.0, Portfolio Value: 377645.60528423474, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-19 00:00:00, Wallet Balance: 0.0, Portfolio Value: 405589.4907020279, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-20 00:00:00, Wallet Balance: 0.0, Portfolio Value: 386180.04096056387, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-21 00:00:00, Wallet Balance: 0.0, Portfolio Value: 401432.25906780985, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-22 00:00:00, Wallet Balance: 0.0, Portfolio Value: 364311.611303264, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-23 00:00:00, Wallet Balance: 46898.939044556944, Portfolio Value: 252867.2425099943, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429]}\n",
      "Date: 2021-02-24 00:00:00, Wallet Balance: 46898.939044556944, Portfolio Value: 263241.8958415335, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429]}\n",
      "Date: 2021-02-25 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 223912.07762722127, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-26 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 222192.733917158, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-27 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 230888.93379452988, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-28 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 221509.62933980208, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-01 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 234201.7175386348, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-02 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 230071.43850327848, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-03 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 238649.94276317878, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-04 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 242135.61132268293, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-05 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 237639.32181065582, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-06 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 238209.15192043543, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-07 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 245645.02213631378, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-08 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 250667.89659440995, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-09 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 258527.01452328663, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-10 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 248703.50294847743, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-11 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 248461.11799405998, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-12 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 239721.07457043036, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-13 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 282333.4098939858, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-14 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 266353.59037502477, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-15 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 261003.63922347748, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-16 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 271419.6756827596, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-17 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 278550.5841028921, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-18 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 274095.2640504795, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-19 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 286950.2885220907, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-20 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 297627.8583154386, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-21 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 147217.26597436983, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-22 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 144113.63601854685, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-23 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 146535.4290698046, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-24 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 200323.02067936258, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-25 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 198783.71893598174, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-26 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 223617.1182219588, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-27 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 220493.8334351185, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-28 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 222131.40851647718, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-29 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 229843.69272152206, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-30 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 233662.7215544398, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-31 00:00:00, Wallet Balance: 168755.53246430247, Portfolio Value: 244045.62719463868, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'EOS.IO': [35129.00664968785, 4.80388]}\n",
      "Date: 2021-04-01 00:00:00, Wallet Balance: 168755.53246430247, Portfolio Value: 273577.9939777399, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'EOS.IO': [35129.00664968785, 4.80388]}\n",
      "Date: 2021-04-02 00:00:00, Wallet Balance: 388476.9303561051, Portfolio Value: 81411.40354020815, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-04-03 00:00:00, Wallet Balance: 388476.9303561051, Portfolio Value: 73641.98298097194, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-04-04 00:00:00, Wallet Balance: 388476.9303561051, Portfolio Value: 78274.58967284687, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-04-05 00:00:00, Wallet Balance: 421031.8145779519, Portfolio Value: 57682.34490393353, Portfolio: {'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-04-06 00:00:00, Wallet Balance: 0.0, Portfolio Value: 484947.80562646955, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-07 00:00:00, Wallet Balance: 0.0, Portfolio Value: 521679.11548609176, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-08 00:00:00, Wallet Balance: 0.0, Portfolio Value: 534267.5526144821, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-09 00:00:00, Wallet Balance: 0.0, Portfolio Value: 511457.83507455536, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-10 00:00:00, Wallet Balance: 0.0, Portfolio Value: 557101.253321053, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-11 00:00:00, Wallet Balance: 0.0, Portfolio Value: 554712.929642729, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-12 00:00:00, Wallet Balance: 0.0, Portfolio Value: 532738.8074315671, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-13 00:00:00, Wallet Balance: 0.0, Portfolio Value: 578421.1074785334, Portfolio: {'Ethereum Classic': [27253.598459386056, 17.7939]}\n",
      "Date: 2021-04-14 00:00:00, Wallet Balance: 0.0, Portfolio Value: 597349.3675258744, Portfolio: {'Ethereum Classic': [27253.598459386056, 21.91818333333333]}\n",
      "Date: 2021-04-15 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-16 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-17 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-18 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-19 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-20 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-21 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-22 00:00:00, Wallet Balance: 790353.9010955547, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-04-23 00:00:00, Wallet Balance: 0.0, Portfolio Value: 790353.9010955547, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-24 00:00:00, Wallet Balance: 0.0, Portfolio Value: 720819.9200046556, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-25 00:00:00, Wallet Balance: 0.0, Portfolio Value: 712396.7367511826, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-26 00:00:00, Wallet Balance: 0.0, Portfolio Value: 795742.2764532422, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-27 00:00:00, Wallet Balance: 0.0, Portfolio Value: 820715.4234571214, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-28 00:00:00, Wallet Balance: 0.0, Portfolio Value: 851810.502148944, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-29 00:00:00, Wallet Balance: 0.0, Portfolio Value: 838273.1565797627, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-04-30 00:00:00, Wallet Balance: 0.0, Portfolio Value: 882480.0890148485, Portfolio: {'Ethereum Classic': [24295.307913103385, 32.53113333333334]}\n",
      "Date: 2021-05-01 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-02 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-03 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-04 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-05 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-06 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-07 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-08 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-09 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-10 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-11 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-12 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-13 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-14 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-15 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-16 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-17 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-18 00:00:00, Wallet Balance: 1086572.0132952766, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-19 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1086572.0132952766, Portfolio: {'EOS.IO': [95437.23547195276, 5.6926], 'TRON': [6885335.323676611, 0.0789048]}\n",
      "Date: 2021-05-20 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1249664.1101494795, Portfolio: {'EOS.IO': [95437.23547195276, 5.6926], 'TRON': [6885335.323676611, 0.0789048]}\n",
      "Date: 2021-05-21 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1067741.4711899841, Portfolio: {'EOS.IO': [95437.23547195276, 5.6926], 'TRON': [6885335.323676611, 0.0789048]}\n",
      "Date: 2021-05-22 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1006037.5203645579, Portfolio: {'EOS.IO': [95437.23547195276, 5.6926], 'TRON': [6885335.323676611, 0.0789048]}\n",
      "Date: 2021-05-23 00:00:00, Wallet Balance: 5.820766091346741e-11, Portfolio Value: 843121.6206467539, Portfolio: {'EOS.IO': [49056.11842561036, 4.296719999999999], 'TRON': [3351295.570436035, 0.0628952], 'IOTA': [234892.07685038, 0.89735], 'Stellar': [580263.5560656942, 0.3632494285714285]}\n",
      "Date: 2021-05-24 00:00:00, Wallet Balance: 790004.1556393084, Portfolio Value: 249807.7237303791, Portfolio: {'IOTA': [234892.07685038, 0.89735]}\n",
      "Date: 2021-05-25 00:00:00, Wallet Balance: 790004.1556393084, Portfolio Value: 250606.3567916704, Portfolio: {'IOTA': [234892.07685038, 0.89735]}\n",
      "Date: 2021-05-26 00:00:00, Wallet Balance: 1089139.2155082673, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-27 00:00:00, Wallet Balance: 1089139.2155082673, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-28 00:00:00, Wallet Balance: 1089139.2155082673, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-05-29 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1089139.2155082673, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-05-30 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1139777.9682198176, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-05-31 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1216975.5848822596, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-01 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1206605.720304112, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-02 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1236274.9890478686, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-03 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1302541.8224889275, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-04 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1222616.4616334562, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-05 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1197268.1678617522, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-06 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1227100.6417474675, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-07 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1136026.6267286777, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-08 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1148029.3349843824, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-09 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1193508.9037909056, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-10 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1149687.795002993, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-11 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1095760.1153691886, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-12 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1078259.1367970055, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-13 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1133380.4851066275, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-14 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1141374.3680307234, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-15 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1136787.194380523, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-16 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1092645.2211144888, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-17 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1171103.056122282, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-18 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1102049.8513983667, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-19 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1082031.0769953846, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-20 00:00:00, Wallet Balance: 0.0, Portfolio Value: 1072582.6084371288, Portfolio: {'TRON': [15845159.413473338, 0.0687363999999999]}\n",
      "Date: 2021-06-21 00:00:00, Wallet Balance: 0.0, Portfolio Value: 849462.957446159, Portfolio: {'TRON': [7922579.706736669, 0.05361025], 'EOS.IO': [120523.11119438136, 3.5240666666666662]}\n",
      "Date: 2021-06-22 00:00:00, Wallet Balance: 0.0, Portfolio Value: 812404.8106565303, Portfolio: {'TRON': [7922579.706736669, 0.05361025], 'EOS.IO': [120523.11119438136, 3.5240666666666662]}\n",
      "Date: 2021-06-23 00:00:00, Wallet Balance: 0.0, Portfolio Value: 902040.6560321813, Portfolio: {'TRON': [7922579.706736669, 0.05361025], 'EOS.IO': [120523.11119438136, 3.5240666666666662]}\n",
      "Date: 2021-06-24 00:00:00, Wallet Balance: 523724.1121587542, Portfolio Value: 472806.1390599984, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662]}\n",
      "Date: 2021-06-25 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 688195.4719532058, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-06-26 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 698036.7997754852, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-06-27 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 730960.4806473231, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-06-28 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 742646.3290538304, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-06-29 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 786860.2932749025, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-06-30 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 790060.0537733908, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Date: 2021-07-01 00:00:00, Wallet Balance: 261862.0560793771, Portfolio Value: 749008.9295724868, Portfolio: {'EOS.IO': [120523.11119438136, 3.5240666666666662], 'TRON': [4262528.6867812695, 0.0614335]}\n",
      "Final Wallet Balance: 261862.0560793771\n",
      "Final Portfolio Value: 749008.9295724868\n",
      "Final portfolio holdings:\n",
      "EOS.IO: Quantity: 120523.11119438136, Purchase Price: 3.5240666666666662\n",
      "TRON: Quantity: 4262528.6867812695, Purchase Price: 0.0614335\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Initialize simulation parameters\n",
    "initial_balance = 100000\n",
    "portfolio = {}  # Dictionary to hold portfolio holdings: {asset_name: [quantity, purchase_price]}\n",
    "balance = initial_balance\n",
    "\n",
    "# Define strategy parameters\n",
    "dynamic_stop_loss_threshold = 0.8  # Dynamic stop-loss threshold (80% of purchase price)\n",
    "profit_target = 1.2  # Sell assets when they reach 120% of purchase price\n",
    "\n",
    "balance_portfolio_df = pd.DataFrame(columns=['Date', 'Wallet Balance', 'Portfolio Value'])\n",
    "\n",
    "stop_date = pd.to_datetime(\"2021-07-01\")  # Define stop date\n",
    "\n",
    "for date, daily_data in portfolio_optimization_df.groupby('Date'):\n",
    "    current_date = pd.to_datetime(date)  # Convert to datetime for comparison\n",
    "    if current_date > stop_date:\n",
    "        break  # Stop the simulation if the current date exceeds the stop date\n",
    "\n",
    "    # Selling assets from the portfolio if conditions are met (dynamic stop-loss and profit-taking)\n",
    "    assets_to_remove = []\n",
    "    for asset in portfolio.keys():\n",
    "        asset_daily_data = daily_data[daily_data['Asset_Name'] == asset]\n",
    "        if not asset_daily_data.empty:\n",
    "            current_price = asset_daily_data.iloc[0]['Close']\n",
    "            purchase_price = portfolio[asset][1]\n",
    "            # Check for stop-loss and profit target\n",
    "            if current_price < purchase_price * dynamic_stop_loss_threshold or current_price > purchase_price * profit_target:\n",
    "                balance += portfolio[asset][0] * current_price\n",
    "                assets_to_remove.append(asset)\n",
    "    for asset in assets_to_remove:\n",
    "        del portfolio[asset]\n",
    "\n",
    "    # Identifying potential assets to buy based on positive predicted returns\n",
    "    potential_buys = daily_data[daily_data['Predicted_percentage_return'] > 0]\n",
    "    sorted_buys = potential_buys.sort_values(by='Predicted_percentage_return', ascending=False)\n",
    "\n",
    "    # Dynamic allocation and buying logic\n",
    "    if not sorted_buys.empty:\n",
    "        investment_per_asset = balance / min(len(sorted_buys), 5)  # Limit number of assets to buy for diversification\n",
    "        for _, row in sorted_buys.head(min(len(sorted_buys), 5)).iterrows():\n",
    "            asset_name = row['Asset_Name']\n",
    "            if asset_name not in portfolio and balance > 0:\n",
    "                quantity = investment_per_asset / row['Close']\n",
    "                portfolio[asset_name] = [quantity, row['Close']]\n",
    "                balance -= investment_per_asset\n",
    "\n",
    "    # Calculate Portfolio Value\n",
    "    portfolio_value = 0\n",
    "    for asset, (quantity, _) in portfolio.items():\n",
    "        asset_daily_data = daily_data[daily_data['Asset_Name'] == asset]\n",
    "        if not asset_daily_data.empty:\n",
    "            current_price = asset_daily_data.iloc[0]['Close']\n",
    "            portfolio_value += quantity * current_price\n",
    "            \n",
    "    # Append to DataFrame\n",
    "    new_row_df = pd.DataFrame({'Date': [date], 'Wallet Balance': [balance], 'Portfolio Value': [portfolio_value]})\n",
    "    balance_portfolio_df = pd.concat([balance_portfolio_df, new_row_df], ignore_index=True)\n",
    "\n",
    "    # Print portfolio status for each date\n",
    "    print(f\"Date: {date}, Wallet Balance: {balance}, Portfolio Value: {portfolio_value}, Portfolio: {portfolio}\")\n",
    "\n",
    "# Final balance and portfolio\n",
    "print(f\"Final Wallet Balance: {balance}\")\n",
    "print(f\"Final Portfolio Value: {portfolio_value}\")\n",
    "print(\"Final portfolio holdings:\")\n",
    "for asset, (quantity, purchase_price) in portfolio.items():\n",
    "    print(f\"{asset}: Quantity: {quantity}, Purchase Price: {purchase_price}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "449fd597",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Wallet Balance</th>\n",
       "      <th>Portfolio Value</th>\n",
       "      <th>Total Net Worth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>1.000000e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99750.243968</td>\n",
       "      <td>9.975024e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110433.315711</td>\n",
       "      <td>1.104333e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>30113.248759</td>\n",
       "      <td>91732.268000</td>\n",
       "      <td>1.218455e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>43428.056052</td>\n",
       "      <td>82651.370306</td>\n",
       "      <td>1.260794e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>2021-06-27</td>\n",
       "      <td>261862.056079</td>\n",
       "      <td>730960.480647</td>\n",
       "      <td>9.928225e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>2021-06-28</td>\n",
       "      <td>261862.056079</td>\n",
       "      <td>742646.329054</td>\n",
       "      <td>1.004508e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>2021-06-29</td>\n",
       "      <td>261862.056079</td>\n",
       "      <td>786860.293275</td>\n",
       "      <td>1.048722e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>2021-06-30</td>\n",
       "      <td>261862.056079</td>\n",
       "      <td>790060.053773</td>\n",
       "      <td>1.051922e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>2021-07-01</td>\n",
       "      <td>261862.056079</td>\n",
       "      <td>749008.929572</td>\n",
       "      <td>1.010871e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>182 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Date  Wallet Balance  Portfolio Value  Total Net Worth\n",
       "0   2021-01-01        0.000000    100000.000000     1.000000e+05\n",
       "1   2021-01-02        0.000000     99750.243968     9.975024e+04\n",
       "2   2021-01-03        0.000000    110433.315711     1.104333e+05\n",
       "3   2021-01-04    30113.248759     91732.268000     1.218455e+05\n",
       "4   2021-01-05    43428.056052     82651.370306     1.260794e+05\n",
       "..         ...             ...              ...              ...\n",
       "177 2021-06-27   261862.056079    730960.480647     9.928225e+05\n",
       "178 2021-06-28   261862.056079    742646.329054     1.004508e+06\n",
       "179 2021-06-29   261862.056079    786860.293275     1.048722e+06\n",
       "180 2021-06-30   261862.056079    790060.053773     1.051922e+06\n",
       "181 2021-07-01   261862.056079    749008.929572     1.010871e+06\n",
       "\n",
       "[182 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_portfolio_df['Total Net Worth'] = balance_portfolio_df['Wallet Balance'] + balance_portfolio_df['Portfolio Value']\n",
    "balance_portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2190e724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Ensure the 'Date' column is in datetime format\n",
    "balance_portfolio_df['Date'] = pd.to_datetime(balance_portfolio_df['Date'])\n",
    "\n",
    "# Set the 'Date' column as the index\n",
    "balance_portfolio_df.set_index('Date', inplace=True)\n",
    "\n",
    "# Resample the data to get monthly averages\n",
    "monthly_data = balance_portfolio_df.resample('M').mean()\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Plot the average monthly wallet balance with black color and circle markers\n",
    "plt.plot(monthly_data.index, monthly_data['Wallet Balance'], label='Average Monthly Wallet Balance',\n",
    "         color='black', marker='o')\n",
    "\n",
    "# Plot the average monthly portfolio value with light blue color and square markers\n",
    "plt.plot(monthly_data.index, monthly_data['Portfolio Value'], label='Average Monthly Portfolio Value',\n",
    "         color='lightblue', marker='s')\n",
    "\n",
    "# Plot the average monthly net worth value with dark blue color and triangle markers\n",
    "# Note: Assuming 'Total Net Worth' is a column you've added that combines 'Wallet Balance' and 'Portfolio Value'\n",
    "plt.plot(monthly_data.index, monthly_data['Total Net Worth'], label='Average Net Worth Value',\n",
    "         color='darkblue', marker='^')\n",
    "\n",
    "plt.title('Average Monthly Wallet Balance, Portfolio Value and Net Worth Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Average Monthly Value')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da2eec9",
   "metadata": {},
   "source": [
    "#### 3 Month Investment Timeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ecbe8895",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date: 2021-01-01 00:00:00, Wallet Balance: 0.0, Portfolio Value: 100000.0, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-02 00:00:00, Wallet Balance: 0.0, Portfolio Value: 99750.243967633, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-03 00:00:00, Wallet Balance: 0.0, Portfolio Value: 110433.31571144963, Portfolio: {'Stellar': [151252.3696204574, 0.1322293333333333], 'Cardano': [114254.59351071998, 0.1750476666666666], 'IOTA': [68728.52233676976, 0.291], 'Ethereum Classic': [3506.7007206269977, 5.703366666666668], 'TRON': [744418.7206419867, 0.0268666]}\n",
      "Date: 2021-01-04 00:00:00, Wallet Balance: 30113.248758892925, Portfolio Value: 91732.26800038698, Portfolio: {'IOTA': [68728.52233676976, 0.291], 'TRON': [744418.7206419867, 0.0268666], 'Stellar': [91766.53232120897, 0.1640753333333333], 'Cardano': [66772.97323181124, 0.2254897999999999], 'Ethereum Classic': [2137.3467081521953, 7.04454]}\n",
      "Date: 2021-01-05 00:00:00, Wallet Balance: 43428.056051787884, Portfolio Value: 82651.37030553003, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Stellar': [91766.53232120897, 0.1640753333333333], 'Cardano': [66772.97323181124, 0.2254897999999999], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517]}\n",
      "Date: 2021-01-06 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 74931.17109182033, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-07 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 72114.74170570081, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-08 00:00:00, Wallet Balance: 72904.10887821246, Portfolio Value: 70993.28284172132, Portfolio: {'TRON': [744418.7206419867, 0.0268666], 'Ethereum Classic': [2137.3467081521953, 7.04454], 'IOTA': [30870.099553446034, 0.3517], 'Cardano': [72889.79332280386, 0.3333988]}\n",
      "Date: 2021-01-09 00:00:00, Wallet Balance: 0.0, Portfolio Value: 152187.52962651063, Portfolio: {'Ethereum Classic': [2137.3467081521953, 7.04454], 'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999]}\n",
      "Date: 2021-01-10 00:00:00, Wallet Balance: 20441.940141218958, Portfolio Value: 130993.95685164409, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999]}\n",
      "Date: 2021-01-11 00:00:00, Wallet Balance: 12265.164084731376, Portfolio Value: 123331.7679589931, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285]}\n",
      "Date: 2021-01-12 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 125134.05182920709, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-13 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 138225.25245844384, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-14 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 138609.50358018113, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-15 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 137722.5256191082, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-16 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 141122.52286786906, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-17 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 145104.64839645135, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-18 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 152153.08118458683, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-19 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 149106.8120165446, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-20 00:00:00, Wallet Balance: 9812.1312677851, Portfolio Value: 149686.7875458066, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295]}\n",
      "Date: 2021-01-21 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 133924.29549722455, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-22 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 143686.6403328576, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-23 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 144117.5522579828, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-24 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 146718.1230888554, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-25 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 141656.8338616679, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-26 00:00:00, Wallet Balance: 7849.7050142280805, Portfolio Value: 141799.41676220874, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-27 00:00:00, Wallet Balance: 6279.764011382465, Portfolio Value: 132907.9067394976, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'Dogecoin': [211676.48723092984, 0.0074166999999999]}\n",
      "Date: 2021-01-28 00:00:00, Wallet Balance: 14011.850985480785, Portfolio Value: 138811.5551373479, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-29 00:00:00, Wallet Balance: 14011.850985480785, Portfolio Value: 146479.91247015173, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Stellar': [15050.236069864495, 0.2716494285714285], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-30 00:00:00, Wallet Balance: 18953.76585168501, Portfolio Value: 143964.9684493824, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-01-31 00:00:00, Wallet Balance: 18953.76585168501, Portfolio Value: 138579.03195581833, Portfolio: {'Cardano': [72889.79332280386, 0.3333988], 'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-01 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 117032.6871245752, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-02 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 119402.22164834128, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-03 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 124842.33847832907, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-04 00:00:00, Wallet Balance: 48760.83686047649, Portfolio Value: 120528.04605857632, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'EOS.IO': [1531.988859002875, 2.6686799999999997], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-05 00:00:00, Wallet Balance: 53838.36881542408, Portfolio Value: 131298.8058008001, Portfolio: {'IOTA': [128992.40056985378, 0.4320000000000001], 'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336]}\n",
      "Date: 2021-02-06 00:00:00, Wallet Balance: 63201.521877997664, Portfolio Value: 126065.00156750565, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'EOS.IO': [19678.64851977708, 3.2116800000000003]}\n",
      "Date: 2021-02-07 00:00:00, Wallet Balance: 47401.141408498246, Portfolio Value: 147634.25199401652, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'Monero': [15.030454595907628, 130.56333333333336], 'EOS.IO': [19678.64851977708, 3.2116800000000003], 'IOTA': [28323.707931342506, 0.55785]}\n",
      "Date: 2021-02-08 00:00:00, Wallet Balance: 70205.79961573456, Portfolio Value: 140136.08905599813, Portfolio: {'TRON': [1645515.28871379, 0.0338645999999999], 'Ethereum Classic': [324.77810881129557, 7.55295], 'EOS.IO': [19678.64851977708, 3.2116800000000003]}\n",
      "Date: 2021-02-09 00:00:00, Wallet Balance: -2.9103830456733704e-11, Portfolio Value: 231299.430936375, Portfolio: {'Ethereum Classic': [8023.67461391825, 9.609039999999998], 'EOS.IO': [18487.53754157308, 4.170366666666667], 'IOTA': [102799.74708283333, 0.75]}\n",
      "Date: 2021-02-10 00:00:00, Wallet Balance: -2.9103830456733704e-11, Portfolio Value: 250560.11485642433, Portfolio: {'Ethereum Classic': [8023.67461391825, 9.609039999999998], 'EOS.IO': [18487.53754157308, 4.170366666666667], 'IOTA': [102799.74708283333, 0.75]}\n",
      "Date: 2021-02-11 00:00:00, Wallet Balance: 107319.46014142818, Portfolio Value: 192057.82796473007, Portfolio: {'EOS.IO': [18487.53754157308, 4.170366666666667], 'Ethereum Classic': [9157.263058588022, 11.7196]}\n",
      "Date: 2021-02-12 00:00:00, Wallet Balance: 107319.46014142818, Portfolio Value: 197431.83038056572, Portfolio: {'EOS.IO': [18487.53754157308, 4.170366666666667], 'Ethereum Classic': [9157.263058588022, 11.7196]}\n",
      "Date: 2021-02-13 00:00:00, Wallet Balance: 362814.72889851755, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-02-14 00:00:00, Wallet Balance: 362814.72889851755, Portfolio Value: 0, Portfolio: {}\n",
      "Date: 2021-02-15 00:00:00, Wallet Balance: 0.0, Portfolio Value: 362814.72889851755, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-16 00:00:00, Wallet Balance: 0.0, Portfolio Value: 361953.91550847486, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-17 00:00:00, Wallet Balance: 0.0, Portfolio Value: 375477.5091343584, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-18 00:00:00, Wallet Balance: 0.0, Portfolio Value: 377645.60528423474, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-19 00:00:00, Wallet Balance: 0.0, Portfolio Value: 405589.4907020279, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-20 00:00:00, Wallet Balance: 0.0, Portfolio Value: 386180.04096056387, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-21 00:00:00, Wallet Balance: 0.0, Portfolio Value: 401432.25906780985, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-22 00:00:00, Wallet Balance: 0.0, Portfolio Value: 364311.611303264, Portfolio: {'Ethereum Classic': [8245.691844062696, 14.66684], 'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334]}\n",
      "Date: 2021-02-23 00:00:00, Wallet Balance: 46898.939044556944, Portfolio Value: 252867.2425099943, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429]}\n",
      "Date: 2021-02-24 00:00:00, Wallet Balance: 46898.939044556944, Portfolio Value: 263241.8958415335, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'EOS.IO': [25675.637363932256, 4.710233333333334], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429]}\n",
      "Date: 2021-02-25 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 223912.07762722127, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-26 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 222192.733917158, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-27 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 230888.93379452988, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-02-28 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 221509.62933980208, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-01 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 234201.7175386348, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-02 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 230071.43850327848, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-03 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 238649.94276317878, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-04 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 242135.61132268293, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-05 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 237639.32181065582, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-06 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 238209.15192043543, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-07 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 245645.02213631378, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-08 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 250667.89659440995, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-09 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 258527.01452328663, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-10 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 248703.50294847743, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-11 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 248461.11799405998, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-12 00:00:00, Wallet Balance: 70529.81593829754, Portfolio Value: 239721.07457043036, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Ethereum Classic': [2061.422961015674, 11.37538], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667]}\n",
      "Date: 2021-03-13 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 282333.4098939858, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-14 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 266353.59037502477, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-15 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 261003.63922347748, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-16 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 271419.6756827596, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-17 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 278550.5841028921, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-18 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 274095.2640504795, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-19 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 286950.2885220907, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-20 00:00:00, Wallet Balance: 49456.087203274175, Portfolio Value: 297627.8583154386, Portfolio: {'TRON': [2379690.3438769905, 0.050821], 'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-21 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 147217.26597436983, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-22 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 144113.63601854685, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-23 00:00:00, Wallet Balance: 200318.34132111387, Portfolio Value: 146535.4290698046, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333]}\n",
      "Date: 2021-03-24 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 200323.02067936258, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-25 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 198783.71893598174, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-26 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 223617.1182219588, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-27 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 220493.8334351185, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-28 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 222131.40851647718, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-29 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 229843.69272152206, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-30 00:00:00, Wallet Balance: 133545.56088074256, Portfolio Value: 233662.7215544398, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'EOS.IO': [19231.995038597033, 3.667316666666667], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'TRON': [1199144.81997183, 0.0556836666666666]}\n",
      "Date: 2021-03-31 00:00:00, Wallet Balance: 168755.53246430247, Portfolio Value: 244045.62719463868, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'EOS.IO': [35129.00664968785, 4.80388]}\n",
      "Date: 2021-04-01 00:00:00, Wallet Balance: 168755.53246430247, Portfolio Value: 273577.9939777399, Portfolio: {'Stellar': [60646.04617108421, 0.3866611428571429], 'Ethereum Classic': [3592.0169860748706, 13.768333333333333], 'EOS.IO': [35129.00664968785, 4.80388]}\n",
      "Final Wallet Balance: 168755.53246430247\n",
      "Final Portfolio Value: 273577.9939777399\n",
      "Final portfolio holdings:\n",
      "Stellar: Quantity: 60646.04617108421, Purchase Price: 0.3866611428571429\n",
      "Ethereum Classic: Quantity: 3592.0169860748706, Purchase Price: 13.768333333333333\n",
      "EOS.IO: Quantity: 35129.00664968785, Purchase Price: 4.80388\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Initialize simulation parameters\n",
    "initial_balance = 100000\n",
    "portfolio = {}  # Dictionary to hold portfolio holdings: {asset_name: [quantity, purchase_price]}\n",
    "balance = initial_balance\n",
    "\n",
    "# Define strategy parameters\n",
    "dynamic_stop_loss_threshold = 0.8  # Dynamic stop-loss threshold (80% of purchase price)\n",
    "profit_target = 1.2  # Sell assets when they reach 120% of purchase price\n",
    "\n",
    "balance_portfolio_df = pd.DataFrame(columns=['Date', 'Wallet Balance', 'Portfolio Value'])\n",
    "\n",
    "stop_date = pd.to_datetime(\"2021-04-01\")  # Define stop date\n",
    "\n",
    "for date, daily_data in portfolio_optimization_df.groupby('Date'):\n",
    "    current_date = pd.to_datetime(date)  # Convert to datetime for comparison\n",
    "    if current_date > stop_date:\n",
    "        break  # Stop the simulation if the current date exceeds the stop date\n",
    "\n",
    "    # Selling assets from the portfolio if conditions are met (dynamic stop-loss and profit-taking)\n",
    "    assets_to_remove = []\n",
    "    for asset in portfolio.keys():\n",
    "        asset_daily_data = daily_data[daily_data['Asset_Name'] == asset]\n",
    "        if not asset_daily_data.empty:\n",
    "            current_price = asset_daily_data.iloc[0]['Close']\n",
    "            purchase_price = portfolio[asset][1]\n",
    "            # Check for stop-loss and profit target\n",
    "            if current_price < purchase_price * dynamic_stop_loss_threshold or current_price > purchase_price * profit_target:\n",
    "                balance += portfolio[asset][0] * current_price\n",
    "                assets_to_remove.append(asset)\n",
    "    for asset in assets_to_remove:\n",
    "        del portfolio[asset]\n",
    "\n",
    "    # Identifying potential assets to buy based on positive predicted returns\n",
    "    potential_buys = daily_data[daily_data['Predicted_percentage_return'] > 0]\n",
    "    sorted_buys = potential_buys.sort_values(by='Predicted_percentage_return', ascending=False)\n",
    "\n",
    "    # Dynamic allocation and buying logic\n",
    "    if not sorted_buys.empty:\n",
    "        investment_per_asset = balance / min(len(sorted_buys), 5)  # Limit number of assets to buy for diversification\n",
    "        for _, row in sorted_buys.head(min(len(sorted_buys), 5)).iterrows():\n",
    "            asset_name = row['Asset_Name']\n",
    "            if asset_name not in portfolio and balance > 0:\n",
    "                quantity = investment_per_asset / row['Close']\n",
    "                portfolio[asset_name] = [quantity, row['Close']]\n",
    "                balance -= investment_per_asset\n",
    "\n",
    "    # Calculate Portfolio Value\n",
    "    portfolio_value = 0\n",
    "    for asset, (quantity, _) in portfolio.items():\n",
    "        asset_daily_data = daily_data[daily_data['Asset_Name'] == asset]\n",
    "        if not asset_daily_data.empty:\n",
    "            current_price = asset_daily_data.iloc[0]['Close']\n",
    "            portfolio_value += quantity * current_price\n",
    "            \n",
    "    # Append to DataFrame\n",
    "    new_row_df = pd.DataFrame({'Date': [date], 'Wallet Balance': [balance], 'Portfolio Value': [portfolio_value]})\n",
    "    balance_portfolio_df = pd.concat([balance_portfolio_df, new_row_df], ignore_index=True)\n",
    "\n",
    "    # Print portfolio status for each date\n",
    "    print(f\"Date: {date}, Wallet Balance: {balance}, Portfolio Value: {portfolio_value}, Portfolio: {portfolio}\")\n",
    "\n",
    "# Final balance and portfolio\n",
    "print(f\"Final Wallet Balance: {balance}\")\n",
    "print(f\"Final Portfolio Value: {portfolio_value}\")\n",
    "print(\"Final portfolio holdings:\")\n",
    "for asset, (quantity, purchase_price) in portfolio.items():\n",
    "    print(f\"{asset}: Quantity: {quantity}, Purchase Price: {purchase_price}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b51e3a91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Wallet Balance</th>\n",
       "      <th>Portfolio Value</th>\n",
       "      <th>Total Net Worth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-01-01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-01-02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>99750.243968</td>\n",
       "      <td>99750.243968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021-01-03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>110433.315711</td>\n",
       "      <td>110433.315711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-01-04</td>\n",
       "      <td>30113.248759</td>\n",
       "      <td>91732.268000</td>\n",
       "      <td>121845.516759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>43428.056052</td>\n",
       "      <td>82651.370306</td>\n",
       "      <td>126079.426357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>2021-03-28</td>\n",
       "      <td>133545.560881</td>\n",
       "      <td>222131.408516</td>\n",
       "      <td>355676.969397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>2021-03-29</td>\n",
       "      <td>133545.560881</td>\n",
       "      <td>229843.692722</td>\n",
       "      <td>363389.253602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>2021-03-30</td>\n",
       "      <td>133545.560881</td>\n",
       "      <td>233662.721554</td>\n",
       "      <td>367208.282435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>2021-03-31</td>\n",
       "      <td>168755.532464</td>\n",
       "      <td>244045.627195</td>\n",
       "      <td>412801.159659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2021-04-01</td>\n",
       "      <td>168755.532464</td>\n",
       "      <td>273577.993978</td>\n",
       "      <td>442333.526442</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Wallet Balance  Portfolio Value  Total Net Worth\n",
       "0  2021-01-01        0.000000    100000.000000    100000.000000\n",
       "1  2021-01-02        0.000000     99750.243968     99750.243968\n",
       "2  2021-01-03        0.000000    110433.315711    110433.315711\n",
       "3  2021-01-04    30113.248759     91732.268000    121845.516759\n",
       "4  2021-01-05    43428.056052     82651.370306    126079.426357\n",
       "..        ...             ...              ...              ...\n",
       "86 2021-03-28   133545.560881    222131.408516    355676.969397\n",
       "87 2021-03-29   133545.560881    229843.692722    363389.253602\n",
       "88 2021-03-30   133545.560881    233662.721554    367208.282435\n",
       "89 2021-03-31   168755.532464    244045.627195    412801.159659\n",
       "90 2021-04-01   168755.532464    273577.993978    442333.526442\n",
       "\n",
       "[91 rows x 4 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "balance_portfolio_df['Total Net Worth'] = balance_portfolio_df['Wallet Balance'] + balance_portfolio_df['Portfolio Value']\n",
    "balance_portfolio_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5d94e328",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "\n",
    "# Ensure the 'Date' column is in datetime format\n",
    "balance_portfolio_df['Date'] = pd.to_datetime(balance_portfolio_df['Date'])\n",
    "\n",
    "# Set the 'Date' column as the index\n",
    "balance_portfolio_df.set_index('Date', inplace=True)\n",
    "\n",
    "# Resample the data to get monthly averages\n",
    "monthly_data = balance_portfolio_df.resample('M').mean()\n",
    "\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Plot the average monthly wallet balance with black color and circle markers\n",
    "plt.plot(monthly_data.index, monthly_data['Wallet Balance'], label='Average Monthly Wallet Balance',\n",
    "         color='black', marker='o')\n",
    "\n",
    "# Plot the average monthly portfolio value with light blue color and square markers\n",
    "plt.plot(monthly_data.index, monthly_data['Portfolio Value'], label='Average Monthly Portfolio Value',\n",
    "         color='lightblue', marker='s')\n",
    "\n",
    "# Plot the average monthly net worth value with dark blue color and triangle markers\n",
    "# Note: Assuming 'Total Net Worth' is a column you've added that combines 'Wallet Balance' and 'Portfolio Value'\n",
    "plt.plot(monthly_data.index, monthly_data['Total Net Worth'], label='Average Net Worth Value',\n",
    "         color='darkblue', marker='^')\n",
    "\n",
    "plt.title('Average Monthly Wallet Balance, Portfolio Value and Net Worth Over Time')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Average Monthly Value')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}