goldigger: Stock Price Prediction and Analysis Tool

Overview

goldigger is a sophisticated Python-based tool designed for stock price prediction and analysis. It leverages machine learning techniques, including LSTM, GRU, Random Forest, and XGBoost models, to forecast future stock prices based on historical data and technical indicators.

Disclaimer: This project was created for fun and educational purposes, and was developed quickly as an experiment. It does not provide reliable financial advice or predictions. The results should not be used for making real investment decisions. Always consult with a qualified financial advisor before making any investment choices.

Features

• Data Fetching: Automatically retrieves historical stock data from Yahoo Finance.
• Technical Indicators: Calculates and incorporates various technical indicators for enhanced analysis.
• Multiple ML Models: Utilizes LSTM, GRU, Random Forest, and XGBoost models for prediction.
• Ensemble Prediction: Combines predictions from multiple models using a weighted approach for improved accuracy.
• Hyperparameter Tuning: Implements randomized search for optimizing Random Forest and XGBoost models.
• Time Series Cross-Validation: Ensures robust model evaluation respecting temporal order of data.
• Risk Metrics: Calculates Sharpe ratio and maximum drawdown for risk assessment.
• Future Price Prediction: Forecasts stock prices for a specified number of future days.
• Visualization: Generates plots showing actual prices, predictions, future forecasts, and trading strategy performance.
• Performance Summary: Provides a detailed table of model performance metrics.
• Trading Strategy: Implements a simple trading strategy based on predictions and calculates its performance.

Key Components

1. Data Preparation: Fetches stock data, adds technical indicators, and prepares sequences for model input.
2. Model Creation: Implements LSTM, GRU, Random Forest, and XGBoost models with optimized architectures.
3. Model Training and Evaluation: Uses time series cross-validation for robust performance assessment, including one-step-ahead predictions for LSTM and GRU models.
4. Hyperparameter Tuning: Optimizes Random Forest and XGBoost models using randomized search.
5. Ensemble Prediction: Combines predictions from all models using a weighted approach for final forecast.
6. Risk Analysis: Calculates key risk metrics for informed decision-making.
7. Trading Strategy: Implements a simple trading strategy based on model predictions.
8. Visualization: Plots results, generates a performance summary table, and displays trading strategy performance.

Recent Enhancements

• Improved LSTM and GRU architectures for better efficiency.
• Implemented early stopping for neural networks to prevent overfitting.
• Added more technical indicators for enhanced feature engineering.
• Introduced a weighted ensemble method for combining model predictions.
• Implemented a simple trading strategy based on predictions.
• Enhanced visualization to include trading strategy performance.

Customization

The tool allows for customization through command-line arguments, including:

• Stock symbol
• Start date for historical data
• Number of future days to predict
• Quick test mode for faster execution
• Option to suppress warnings

Output

• Console output with model performance metrics and risk analysis
• A plot showing actual prices, predictions, and future forecasts
• A PNG file with the plot and a detailed model performance summary table

Note

This tool is designed for educational and research purposes. Stock market prediction is inherently uncertain, and past performance does not guarantee future results. Always consult with a financial advisor before making investment decisions.

Installation and Usage

Using uv

Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

or

pip install uv

Run goldigger

uv run goldigger.py

Using pip

pip install -r requirements.txt
python goldigger.py

Arguments

Note: Use

The tool supports the following command-line arguments:

• --symbol: Stock symbol to analyze (default: 'MSFT')
• --start_date: Start date for historical data (default: '2018-01-01')
• --future_days: Number of days to predict into the future (default: 30)
• --quick_test: Run a quick test with reduced data and iterations (default: False)
• --suppress_warnings: Suppress warning messages (default: False)

Example usage:

python goldigger.py --symbol GOOGL --start_date 2015-01-01 --future_days 60 --quick_test

Contributing

Contributions to improve goldigger are welcome. Please feel free to submit pull requests or open issues to discuss potential enhancements.

License

WTFPL License