CSMOM.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from scipy.stats import rankdata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data.csv\")\n",
    "df.index = pd.to_datetime(df['date'], format='%Y-%m-%d')\n",
    "df = df.drop('date', axis=1)\n",
    "\n",
    "close_columns = []\n",
    "high_columns = []\n",
    "low_columns = []\n",
    "open_columns = []\n",
    "volume_columns = []\n",
    "open_int_columns = []\n",
    "\n",
    "for i in df.columns:\n",
    "    if \"close\" in i:\n",
    "        close_columns.append(i)\n",
    "    elif \"high\" in i:\n",
    "        high_columns.append(i)\n",
    "    elif \"low\" in i:\n",
    "        low_columns.append(i)\n",
    "    elif \"open_int\" in i:\n",
    "        open_int_columns.append(i)\n",
    "    elif \"open\" in i:\n",
    "        open_columns.append(i)\n",
    "    elif \"volume\" in i:\n",
    "        volume_columns.append(i)\n",
    "        \n",
    "close_df = df[close_columns]\n",
    "high_df = df[high_columns]\n",
    "low_df = df[low_columns]\n",
    "open_df = df[open_columns]\n",
    "volume_df = df[volume_columns]\n",
    "open_int_df = df[open_int_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AN_open</th>\n",
       "      <th>AN_high</th>\n",
       "      <th>AN_low</th>\n",
       "      <th>AN_close</th>\n",
       "      <th>AN_volume</th>\n",
       "      <th>AN_open_int</th>\n",
       "      <th>BN_open</th>\n",
       "      <th>BN_high</th>\n",
       "      <th>BN_low</th>\n",
       "      <th>BN_close</th>\n",
       "      <th>...</th>\n",
       "      <th>UB_low</th>\n",
       "      <th>UB_close</th>\n",
       "      <th>UB_volume</th>\n",
       "      <th>UB_open_int</th>\n",
       "      <th>AX_open</th>\n",
       "      <th>AX_high</th>\n",
       "      <th>AX_low</th>\n",
       "      <th>AX_close</th>\n",
       "      <th>AX_volume</th>\n",
       "      <th>AX_open_int</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>2019-06-17</td>\n",
       "      <td>68.91</td>\n",
       "      <td>69.02</td>\n",
       "      <td>68.67</td>\n",
       "      <td>68.69</td>\n",
       "      <td>71826.0</td>\n",
       "      <td>202496.0</td>\n",
       "      <td>126.46</td>\n",
       "      <td>126.58</td>\n",
       "      <td>125.83</td>\n",
       "      <td>125.91</td>\n",
       "      <td>...</td>\n",
       "      <td>134.02</td>\n",
       "      <td>134.11</td>\n",
       "      <td>752301.0</td>\n",
       "      <td>1250472.0</td>\n",
       "      <td>12094.5</td>\n",
       "      <td>12112.0</td>\n",
       "      <td>12043.0</td>\n",
       "      <td>12082.0</td>\n",
       "      <td>282803.0</td>\n",
       "      <td>100579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-18</td>\n",
       "      <td>68.72</td>\n",
       "      <td>69.00</td>\n",
       "      <td>68.49</td>\n",
       "      <td>68.92</td>\n",
       "      <td>100420.0</td>\n",
       "      <td>178401.0</td>\n",
       "      <td>125.86</td>\n",
       "      <td>126.16</td>\n",
       "      <td>125.56</td>\n",
       "      <td>126.11</td>\n",
       "      <td>...</td>\n",
       "      <td>134.08</td>\n",
       "      <td>134.61</td>\n",
       "      <td>538366.0</td>\n",
       "      <td>1250617.0</td>\n",
       "      <td>12073.5</td>\n",
       "      <td>12345.5</td>\n",
       "      <td>11969.5</td>\n",
       "      <td>12321.5</td>\n",
       "      <td>177881.0</td>\n",
       "      <td>111770.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-19</td>\n",
       "      <td>68.94</td>\n",
       "      <td>69.27</td>\n",
       "      <td>68.73</td>\n",
       "      <td>69.08</td>\n",
       "      <td>127610.0</td>\n",
       "      <td>179027.0</td>\n",
       "      <td>126.08</td>\n",
       "      <td>127.25</td>\n",
       "      <td>125.94</td>\n",
       "      <td>127.19</td>\n",
       "      <td>...</td>\n",
       "      <td>134.24</td>\n",
       "      <td>134.45</td>\n",
       "      <td>472803.0</td>\n",
       "      <td>1229089.0</td>\n",
       "      <td>12340.0</td>\n",
       "      <td>12342.5</td>\n",
       "      <td>12271.0</td>\n",
       "      <td>12298.0</td>\n",
       "      <td>167701.0</td>\n",
       "      <td>112906.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-20</td>\n",
       "      <td>69.01</td>\n",
       "      <td>69.53</td>\n",
       "      <td>68.97</td>\n",
       "      <td>69.40</td>\n",
       "      <td>91477.0</td>\n",
       "      <td>173103.0</td>\n",
       "      <td>126.96</td>\n",
       "      <td>127.76</td>\n",
       "      <td>126.93</td>\n",
       "      <td>127.48</td>\n",
       "      <td>...</td>\n",
       "      <td>134.33</td>\n",
       "      <td>134.43</td>\n",
       "      <td>442713.0</td>\n",
       "      <td>1214201.0</td>\n",
       "      <td>12333.0</td>\n",
       "      <td>12425.5</td>\n",
       "      <td>12305.5</td>\n",
       "      <td>12361.5</td>\n",
       "      <td>131392.0</td>\n",
       "      <td>114006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2019-06-21</td>\n",
       "      <td>69.43</td>\n",
       "      <td>69.56</td>\n",
       "      <td>69.21</td>\n",
       "      <td>69.44</td>\n",
       "      <td>91477.0</td>\n",
       "      <td>173103.0</td>\n",
       "      <td>127.52</td>\n",
       "      <td>127.95</td>\n",
       "      <td>126.89</td>\n",
       "      <td>127.85</td>\n",
       "      <td>...</td>\n",
       "      <td>134.20</td>\n",
       "      <td>134.26</td>\n",
       "      <td>442713.0</td>\n",
       "      <td>1214201.0</td>\n",
       "      <td>12347.0</td>\n",
       "      <td>12410.5</td>\n",
       "      <td>12253.5</td>\n",
       "      <td>12270.0</td>\n",
       "      <td>131392.0</td>\n",
       "      <td>114006.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 324 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AN_open  AN_high  AN_low  AN_close  AN_volume  AN_open_int  \\\n",
       "date                                                                     \n",
       "2019-06-17    68.91    69.02   68.67     68.69    71826.0     202496.0   \n",
       "2019-06-18    68.72    69.00   68.49     68.92   100420.0     178401.0   \n",
       "2019-06-19    68.94    69.27   68.73     69.08   127610.0     179027.0   \n",
       "2019-06-20    69.01    69.53   68.97     69.40    91477.0     173103.0   \n",
       "2019-06-21    69.43    69.56   69.21     69.44    91477.0     173103.0   \n",
       "\n",
       "            BN_open  BN_high  BN_low  BN_close  ...  UB_low  UB_close  \\\n",
       "date                                            ...                     \n",
       "2019-06-17   126.46   126.58  125.83    125.91  ...  134.02    134.11   \n",
       "2019-06-18   125.86   126.16  125.56    126.11  ...  134.08    134.61   \n",
       "2019-06-19   126.08   127.25  125.94    127.19  ...  134.24    134.45   \n",
       "2019-06-20   126.96   127.76  126.93    127.48  ...  134.33    134.43   \n",
       "2019-06-21   127.52   127.95  126.89    127.85  ...  134.20    134.26   \n",
       "\n",
       "            UB_volume  UB_open_int  AX_open  AX_high   AX_low  AX_close  \\\n",
       "date                                                                      \n",
       "2019-06-17   752301.0    1250472.0  12094.5  12112.0  12043.0   12082.0   \n",
       "2019-06-18   538366.0    1250617.0  12073.5  12345.5  11969.5   12321.5   \n",
       "2019-06-19   472803.0    1229089.0  12340.0  12342.5  12271.0   12298.0   \n",
       "2019-06-20   442713.0    1214201.0  12333.0  12425.5  12305.5   12361.5   \n",
       "2019-06-21   442713.0    1214201.0  12347.0  12410.5  12253.5   12270.0   \n",
       "\n",
       "            AX_volume  AX_open_int  \n",
       "date                                \n",
       "2019-06-17   282803.0     100579.0  \n",
       "2019-06-18   177881.0     111770.0  \n",
       "2019-06-19   167701.0     112906.0  \n",
       "2019-06-20   131392.0     114006.0  \n",
       "2019-06-21   131392.0     114006.0  \n",
       "\n",
       "[5 rows x 324 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# daily data\n",
    "returns_daily = close_df.pct_change().dropna()\n",
    "vol_daily = returns_daily.ewm(adjust=True, com=60, min_periods=0).std().dropna()\n",
    "\n",
    "#monthly data\n",
    "returns_monthly = close_df.pct_change(20).dropna().resample('BM').last().ffill()\n",
    "vol_monthly = (np.sqrt(261)*vol_daily).resample('BM').last().ffill()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AN_close</th>\n",
       "      <th>BN_close</th>\n",
       "      <th>CC_close</th>\n",
       "      <th>CN_close</th>\n",
       "      <th>CT_close</th>\n",
       "      <th>DA_close</th>\n",
       "      <th>DX_close</th>\n",
       "      <th>EC_close</th>\n",
       "      <th>EN_close</th>\n",
       "      <th>ES_close</th>\n",
       "      <th>...</th>\n",
       "      <th>ZU_close</th>\n",
       "      <th>ZW_close</th>\n",
       "      <th>ZZ_close</th>\n",
       "      <th>DT_close</th>\n",
       "      <th>HS_close</th>\n",
       "      <th>LX_close</th>\n",
       "      <th>NK_close</th>\n",
       "      <th>SP_close</th>\n",
       "      <th>UB_close</th>\n",
       "      <th>AX_close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</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",
       "      <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",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1999-06-30</td>\n",
       "      <td>0.105042</td>\n",
       "      <td>0.062671</td>\n",
       "      <td>0.340395</td>\n",
       "      <td>0.079590</td>\n",
       "      <td>0.208741</td>\n",
       "      <td>0.117408</td>\n",
       "      <td>0.057450</td>\n",
       "      <td>0.006805</td>\n",
       "      <td>0.273889</td>\n",
       "      <td>0.171174</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376478</td>\n",
       "      <td>0.145714</td>\n",
       "      <td>0.381138</td>\n",
       "      <td>0.073597</td>\n",
       "      <td>0.217934</td>\n",
       "      <td>0.083019</td>\n",
       "      <td>0.097903</td>\n",
       "      <td>0.171116</td>\n",
       "      <td>0.032165</td>\n",
       "      <td>0.126013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999-07-30</td>\n",
       "      <td>0.102485</td>\n",
       "      <td>0.092296</td>\n",
       "      <td>0.367361</td>\n",
       "      <td>0.053053</td>\n",
       "      <td>0.248199</td>\n",
       "      <td>0.228037</td>\n",
       "      <td>0.072208</td>\n",
       "      <td>0.004424</td>\n",
       "      <td>0.272543</td>\n",
       "      <td>0.151698</td>\n",
       "      <td>...</td>\n",
       "      <td>0.301792</td>\n",
       "      <td>0.282767</td>\n",
       "      <td>0.413881</td>\n",
       "      <td>0.073126</td>\n",
       "      <td>0.311318</td>\n",
       "      <td>0.224145</td>\n",
       "      <td>0.188760</td>\n",
       "      <td>0.151480</td>\n",
       "      <td>0.041537</td>\n",
       "      <td>0.218992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999-08-31</td>\n",
       "      <td>0.100980</td>\n",
       "      <td>0.087018</td>\n",
       "      <td>0.317635</td>\n",
       "      <td>0.065784</td>\n",
       "      <td>0.233056</td>\n",
       "      <td>0.262282</td>\n",
       "      <td>0.082101</td>\n",
       "      <td>0.005188</td>\n",
       "      <td>0.283183</td>\n",
       "      <td>0.173361</td>\n",
       "      <td>...</td>\n",
       "      <td>0.290636</td>\n",
       "      <td>0.255201</td>\n",
       "      <td>0.383358</td>\n",
       "      <td>0.071736</td>\n",
       "      <td>0.313992</td>\n",
       "      <td>0.221060</td>\n",
       "      <td>0.191588</td>\n",
       "      <td>0.173167</td>\n",
       "      <td>0.040713</td>\n",
       "      <td>0.222227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999-09-30</td>\n",
       "      <td>0.098992</td>\n",
       "      <td>0.082781</td>\n",
       "      <td>0.351501</td>\n",
       "      <td>0.061236</td>\n",
       "      <td>0.229098</td>\n",
       "      <td>0.270938</td>\n",
       "      <td>0.080132</td>\n",
       "      <td>0.005897</td>\n",
       "      <td>0.294703</td>\n",
       "      <td>0.186051</td>\n",
       "      <td>...</td>\n",
       "      <td>0.270916</td>\n",
       "      <td>0.260535</td>\n",
       "      <td>0.352725</td>\n",
       "      <td>0.075146</td>\n",
       "      <td>0.293303</td>\n",
       "      <td>0.209225</td>\n",
       "      <td>0.184466</td>\n",
       "      <td>0.186178</td>\n",
       "      <td>0.043353</td>\n",
       "      <td>0.216286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1999-10-29</td>\n",
       "      <td>0.101794</td>\n",
       "      <td>0.082981</td>\n",
       "      <td>0.329411</td>\n",
       "      <td>0.055821</td>\n",
       "      <td>0.218192</td>\n",
       "      <td>0.297339</td>\n",
       "      <td>0.086932</td>\n",
       "      <td>0.006833</td>\n",
       "      <td>0.308855</td>\n",
       "      <td>0.217140</td>\n",
       "      <td>...</td>\n",
       "      <td>0.377323</td>\n",
       "      <td>0.247127</td>\n",
       "      <td>0.351677</td>\n",
       "      <td>0.075506</td>\n",
       "      <td>0.329189</td>\n",
       "      <td>0.212675</td>\n",
       "      <td>0.189809</td>\n",
       "      <td>0.217070</td>\n",
       "      <td>0.043262</td>\n",
       "      <td>0.213240</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            AN_close  BN_close  CC_close  CN_close  CT_close  DA_close  \\\n",
       "date                                                                     \n",
       "1999-06-30  0.105042  0.062671  0.340395  0.079590  0.208741  0.117408   \n",
       "1999-07-30  0.102485  0.092296  0.367361  0.053053  0.248199  0.228037   \n",
       "1999-08-31  0.100980  0.087018  0.317635  0.065784  0.233056  0.262282   \n",
       "1999-09-30  0.098992  0.082781  0.351501  0.061236  0.229098  0.270938   \n",
       "1999-10-29  0.101794  0.082981  0.329411  0.055821  0.218192  0.297339   \n",
       "\n",
       "            DX_close  EC_close  EN_close  ES_close  ...  ZU_close  ZW_close  \\\n",
       "date                                                ...                       \n",
       "1999-06-30  0.057450  0.006805  0.273889  0.171174  ...  0.376478  0.145714   \n",
       "1999-07-30  0.072208  0.004424  0.272543  0.151698  ...  0.301792  0.282767   \n",
       "1999-08-31  0.082101  0.005188  0.283183  0.173361  ...  0.290636  0.255201   \n",
       "1999-09-30  0.080132  0.005897  0.294703  0.186051  ...  0.270916  0.260535   \n",
       "1999-10-29  0.086932  0.006833  0.308855  0.217140  ...  0.377323  0.247127   \n",
       "\n",
       "            ZZ_close  DT_close  HS_close  LX_close  NK_close  SP_close  \\\n",
       "date                                                                     \n",
       "1999-06-30  0.381138  0.073597  0.217934  0.083019  0.097903  0.171116   \n",
       "1999-07-30  0.413881  0.073126  0.311318  0.224145  0.188760  0.151480   \n",
       "1999-08-31  0.383358  0.071736  0.313992  0.221060  0.191588  0.173167   \n",
       "1999-09-30  0.352725  0.075146  0.293303  0.209225  0.184466  0.186178   \n",
       "1999-10-29  0.351677  0.075506  0.329189  0.212675  0.189809  0.217070   \n",
       "\n",
       "            UB_close  AX_close  \n",
       "date                            \n",
       "1999-06-30  0.032165  0.126013  \n",
       "1999-07-30  0.041537  0.218992  \n",
       "1999-08-31  0.040713  0.222227  \n",
       "1999-09-30  0.043353  0.216286  \n",
       "1999-10-29  0.043262  0.213240  \n",
       "\n",
       "[5 rows x 54 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vol_monthly.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TSMOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signal2(df, date, passive, method):\n",
    "        \n",
    "    num_assets = len(df.iloc[-1])\n",
    "    signal = []\n",
    "    \n",
    "    if method == \"momentum\":\n",
    "        \n",
    "        returns = df.pct_change(20 * 12).resample('BM').last().ffill()[:date]\n",
    "        \n",
    "        if passive:\n",
    "            signal = np.ones(num_assets)\n",
    "        else:\n",
    "            signal = np.where(returns.iloc[-1] > 0, 1, -1)\n",
    "            \n",
    "    elif method == \"momentum_lagged\":\n",
    "        \n",
    "        returns_12 = df.pct_change(21 * 12).resample('BM').last().ffill()[:date]\n",
    "        \n",
    "        returns_6 = df.pct_change(21 * 6).resample('BM').last().ffill()[:date]\n",
    "        \n",
    "        returns_3 = df.pct_change(21 * 3).resample('BM').last().ffill()[:date]\n",
    "        \n",
    "        momentum_mean = (returns_12.iloc[-1] + returns_6.iloc[-1] + returns_3.iloc[-1]) / 3\n",
    "        \n",
    "        \n",
    "        if passive:\n",
    "            signal = np.ones(num_assets)\n",
    "        else:\n",
    "            signal = np.where(momentum_mean > 0, 1, -1)\n",
    "        \n",
    "                \n",
    "    return signal\n",
    "    \n",
    "def tsmom (df,returns_monthly, vol_monthly,date, method = 'momentum', risk=0.4, passive=False, momentum_window=12):\n",
    "    \n",
    "    position = signal2(df, date, passive, method)\n",
    "        \n",
    "    weights = (risk / vol_monthly.iloc[date-1])\n",
    "    \n",
    "    weights /= len(weights)\n",
    "    \n",
    "    portfolio = position * weights\n",
    "    \n",
    "    return (1+np.dot(portfolio, returns_monthly.iloc[date]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CSMOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "def signal(df, date):\n",
    "        \n",
    "    num_assets = len(df.iloc[-1])\n",
    "    signal = []\n",
    "        \n",
    "    returns = df.pct_change(20 * 12).resample('BM').last().ffill()[:date]\n",
    "    \n",
    "    returns_rank = rankdata(returns.iloc[-1])\n",
    "\n",
    "    signal = np.where(returns_rank > int(num_assets * 0.7), 1, np.where(returns_rank < int(num_assets * 0.3), -1, 0))\n",
    "              \n",
    "    return signal\n",
    "    \n",
    "def csmom (df,returns_monthly, vol_monthly, date):\n",
    "    \n",
    "    position = signal(df, date)\n",
    "    \n",
    "    num_assets = len(df.iloc[-1])\n",
    "    \n",
    "    weights = 1 / (int(num_assets - num_assets * 0.8) + int(num_assets * 0.2))\n",
    "    \n",
    "    print(weights,  int(num_assets * 0.8), int(num_assets * 0.2))\n",
    "    \n",
    "    portfolio = position * weights\n",
    "    \n",
    "    return (1+np.dot(portfolio, returns_monthly.iloc[date]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Classic CSMOM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "r = [] # retorno do TSMOM\n",
    "rp = [] # retorno passivo\n",
    "start = 12 -1\n",
    "years = 19\n",
    "end = 12*(int(start/12) + years)\n",
    "for i in range(start, end):\n",
    "    r.append(csmom (close_df,returns_monthly, vol_monthly, date=i))\n",
    "    rp.append(tsmom (close_df,returns_monthly, vol_monthly, date=i, risk=0.4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Passive return: 442.73%, Annualized: 9.31%\n",
      "CSMOM return: 48.27%, Annualized: 2.09%\n"
     ]
    }
   ],
   "source": [
    "from pandas.plotting import register_matplotlib_converters\n",
    "register_matplotlib_converters()\n",
    "\n",
    "r = pd.DataFrame(r)\n",
    "rp = pd.DataFrame(rp)\n",
    "\n",
    "r.index = returns_monthly.iloc[start:end].index\n",
    "rp.index = returns_monthly.iloc[start:end].index\n",
    "\n",
    "plt.figure(figsize=(16,9))\n",
    "\n",
    "plt.plot(100*r.cumprod(), label = 'CSMOM', color='blue')\n",
    "plt.plot(100*rp.cumprod(), label = 'TSMOM', color='red')\n",
    "\n",
    "plt.yscale('log')\n",
    "plt.legend()\n",
    "plt.title('Cumulative Excess Return of CSMOM and Diversified Passive Long')\n",
    "plt.show()\n",
    "\n",
    "tsmom_return = float((rp.cumprod().iloc[-1] - 1)*100)\n",
    "csmom_return = float((r.cumprod().iloc[-1] - 1)*100)\n",
    "\n",
    "print('Passive return: ', round(tsmom_return,2), '%,',\n",
    "     \" Annualized: \", round((((100+tsmom_return)/100) ** (1/years) - 1)*100 , 2), \"%\", sep='')\n",
    "\n",
    "print('CSMOM return: ', round(csmom_return,2), \"%,\",\n",
    "     \" Annualized: \", round((((100+csmom_return)/100) ** (1/years) - 1)*100 , 2), \"%\", sep='')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
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 },
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}