Automated Stock Scanner

This project is an Automated Stock Scanner designed to analyze historical stock data, identify significant price levels, and evaluate opportunities for potential price appreciation. It leverages Python, Pandas, and yFinance for efficient data handling and stock market analysis.

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Key Features

1. Stock Data Retrieval:

   • Fetches historical stock data for a specified number of years using the yfinance library.

2. Lifetime High Detection:

   • Identifies the highest price ever recorded for a stock within the specified period.

3. Support Level Detection:

   • Detects if the lifetime high acts as a support level, based on price staying within a threshold percentage for consecutive trading days.

4. Price Appreciation Analysis:

   • Checks for significant price appreciation from the support level after the lifetime high is tested.

5. Batch Processing:

   • Scans multiple stocks and returns the ones meeting the predefined criteria.

6. Results Analysis:

   • Analyzes results to determine total instances, annual opportunities, and success rate.

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Prerequisites

• Python 3.7+
• Required Libraries:
  • yfinance
  • pandas

Install the required libraries using pip:

pip install yfinance pandas

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How It Works

Functions

1. get_stock_data(stock_symbol, years=10)

   • Fetches historical stock data for the given symbol over the past years.

2. find_lifetime_high(stock_data)

   • Identifies the lifetime high price from the stock data.

3. check_support(stock_data, lifetime_high, threshold=0.02, days=5)

   • Checks if the lifetime high price acts as a support level by analyzing the stock’s low price over consecutive days within a specified threshold.

4. check_price_appreciation(stock_data, support_date, appreciation_threshold=0.10)

   • Verifies if the stock’s price appreciated by a defined percentage from the support level.

5. stock_scanner(stocks, years=10, threshold=0.02, days=5, appreciation_threshold=0.10)

   • Scans a list of stocks and identifies those meeting the lifetime high support and price appreciation criteria.

6. analyze_results(results)

   • Provides an analysis of the scanning results, including total instances found, annual opportunities, and success rate.

Example Workflow

1. Fetch Stock Data

data = get_stock_data('AAPL', 10)
print(data.head())

2. Scan Multiple Stocks

stocks_to_scan = ['AAPL', 'MSFT', 'GOOGL']
results = stock_scanner(stocks_to_scan)
print(f'Stocks meeting criteria: {results}')

3. Analyze Results

if not results:
    print("NO Stocks Meeting the Criteria, hence can't perform analysis")
else:
    analyze_results(results)

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Output

• Results Format: A list of tuples, each containing the stock symbol and the date when the lifetime high was tested as support.

  [('AAPL', Timestamp('2023-06-15 00:00:00')), ('MSFT', Timestamp('2023-08-22 00:00:00'))]
  

• Analysis: Summary statistics, such as total instances, annual opportunities, and success rate.

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Customization

• Parameters:
  • years: Number of years of historical data to analyze (default: 10).
  • threshold: Percentage range for detecting support levels (default: 2%).
  • days: Minimum consecutive days for validating support levels (default: 5).
  • appreciation_threshold: Price appreciation percentage to check after support (default: 10%).

Modify these parameters in the function calls as per your requirements.

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Limitations

1. Historical Data Only:

   • Relies on historical data, which may not accurately predict future trends.

2. Market Conditions:

   • Does not account for market news, earnings reports, or macroeconomic factors.

3. Static Criteria:

   • Thresholds and days are static; advanced users can enhance the logic for dynamic thresholds.

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Future Enhancements

• Visualization:
  • Add support for plotting stock data and highlighting support levels and appreciations.
• Backtesting:
  • Include backtesting capabilities to simulate trading strategies based on detected patterns.
• Real-Time Scanning:
  • Integrate real-time data feeds for up-to-date scanning.

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License

This project is licensed under the MIT License. You are free to use, modify, and distribute the code with attribution.

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Author

Daniyal Khan Data Scientist and Stock Market Enthusiast