Stock Monitoring and Backtesting Application

This project provides a framework for fetching financial market data, applying technical indicators, defining trading strategies, and backtesting these strategies against historical data.

Features

• Data Fetching:
  • Supports fetching stock data from Polygon.io.
  • Supports fetching cryptocurrency data from CoinGecko.
• Technical Indicators:
  • Average True Range (ATR)
  • Bollinger Bands
  • Breakout Detection
  • Moving Averages (SMA, EMA)
  • Moving Average Convergence Divergence (MACD)
  • Relative Strength Index (RSI)
  • Volume Spike Detection
• Strategy Framework:
  • A BaseStrategy class that allows combining multiple indicators to generate trading signals (BUY, SELL, HOLD).
• Backtesting Engine:
  • Performs backtests using historical data and a defined strategy.
  • Includes placeholder logic for indicator parameter optimization.
  • Evaluates strategy performance with various metrics (e.g., P&L, win rate, max drawdown - metrics are indicative).
  • Saves backtest results (data with signals) and performance metrics to CSV and JSON files respectively in the backtest_outputs directory.

Project Structure

A brief overview of the main directories:

my-pricer/
├── stock_monitoring_app/ # Core application logic
│   ├── backtest/         # Backtesting engine
│   ├── fetchers/         # Data fetching modules (Polygon, CoinGecko)
│   ├── indicators/       # Technical indicator implementations
│   ├── strategies/       # Trading strategy logic
│   ├── config.py         # Application configuration (API keys, etc.)
│   └── __init__.py
├── examples/             # Example scripts demonstrating usage
│   ├── run_fetcher_example.py
│   ├── run_indicators_example.py
│   ├── run_strategy_example.py
│   ├── run_backtest_example.py
│   └── __init__.py
├── tests/                # Automated tests
│   ├── indicators/       # Tests for technical indicators
│   ├── fetchers/         # Tests for data fetchers
│   ├── test_backtest.py  # Tests for the backtesting engine
│   └── conftest.py       # Pytest fixtures and hooks (if created)
├── backtest_outputs/     # (Generated) Directory for storing backtest results
├── LICENSE               # Project license
├── pytest.ini            # Pytest configuration
├── requirements.txt      # Python package dependencies
└── README.md             # This file

Setup

1. Clone the repository (if applicable):

   git clone <repository_url>
   cd my-pricer

2. Install dependencies: It's recommended to use a virtual environment. This project likely has dependencies such as pandas, pandas-ta, requests, numpy. If a requirements.txt file is present or created, use:

   pip install -r requirements.txt

   The requirements.txt file lists the necessary Python packages.

3. Environment Variables: The application requires API keys for data fetchers. These are typically loaded from environment variables via a config.py (not provided in context, but inferred). You would need to set:

   • POLYGON_API_KEY: Your API key for Polygon.io.
   • COINGECKO_API_KEY: Your API key for CoinGecko (optional, for higher rate limits or Pro features).

   Refer to stock_monitoring_app/config.py (or create one) for how these are managed.

Usage

The primary way to use this application is likely by running the backtester. This typically involves:

1. Configuring a BackTest instance with a ticker, period, and interval.
2. Calling the run_backtest() method.

Example (conceptual, based on stock_monitoring_app/backtest/backtest.py):

# main.py or a script
from stock_monitoring_app.backtest.backtest import BackTest

if __name__ == "__main__":
    # Example for a stock
    # stock_backtest = BackTest(ticker="AAPL", period="1y", interval="1d")
    # stock_results = stock_backtest.run_backtest()
    # if stock_results is not None:    #     print("Stock Backtest Performance:", stock_backtest.get_performance_metrics())

    # Example for crypto
    crypto_backtest = BackTest(ticker="bitcoin", period="6mo", interval="1d")
    crypto_results = crypto_backtest.run_backtest()
    if crypto_results is not None:
        print("\nCrypto Backtest Performance:", crypto_backtest.get_performance_metrics())

The backtest results and performance metrics will be saved to the backtest_outputs directory at the project root.

Testing

This project uses pytest for running automated tests. The tests are located in the tests/ directory, with specific configurations managed in pytest.ini.

To run the tests:

1. Ensure you have pytest and any other testing-specific dependencies installed. If they are part of a requirements.txt or a dev-requirements.txt (not provided in context), install them. Otherwise, install pytest: ```bash pip install pytest

2. Navigate to the project root directory (my-pricer/).
3. Run pytest:

   pytest
   ```The `pytest.ini` file ([pytest.ini](pytest.ini)) contains configurations for `pytest`, such as marker definitions (e.g., `integration` tests) and warning filters. Refer to this file for more details on test execution options.
   

Examples

The examples/ directory contains scripts to demonstrate the core functionalities of this application:

• run_fetcher_example.py: Shows how to use the PolygonFetcher and CoinGeckoFetcher to retrieve market data.
• run_indicators_example.py: Demonstrates applying various technical indicators (RSI, MACD, Bollinger Bands, etc.) to a DataFrame.
• run_strategy_example.py: Illustrates how to configure BaseStrategy with a set of indicators and generate trading signals.
• run_backtest_example.py: Provides a full example of running the BackTest engine for both a stock and a cryptocurrency, including data fetching, (placeholder) optimization, strategy execution, performance evaluation, and saving results.

To run the examples:

1. Ensure you have followed the "Setup" instructions above, especially installing dependencies and configuring API keys.
2. Navigate to the project root directory (my-pricer/).
3. Execute the desired example script using Python. For example:

   python examples/run_backtest_example.py

   Or, if your PYTHONPATH is set up correctly to include the project root:

   python -m examples.run_backtest_example

Note: The example scripts might require API keys (especially POLYGON_API_KEY for stock data) to be set in your environment or a .env file for full functionality. They are designed to fall back to placeholder data or skip parts if keys are missing, but live data fetching will provide a more complete demonstration. The backtesting example, in particular, can take a few moments to run as it involves fetching data and multiple processing steps.

License

This project is licensed under the MIT License. See the LICENSE file for details.