Algorithmic Trading Strategy Project

Objective

This project implements an end-to-end machine learning-based algorithmic trading strategy. It uses historical stock data to predict future trends and execute trades based on these predictions and technical indicators.

Features

• Data retrieval using yfinance
• Feature engineering including SMA, trend decomposition, and other technical indicators
• Machine learning prediction using Random Forest Classifier
• Realistic trading simulation with transaction costs
• Performance visualization and metrics calculation

How to Use

1. Install dependencies : pip install -r requirements.txt
2. Run the main script : python main.py
3. The script will analyze the stock (default is GOOGL), display visualizations, and print performance metrics.

Data Sources

This project uses Yahoo Finance (yfinance) to retrieve historical stock data.

Methodology

1. Data Retrieval: Download historical stock data.
2. Feature Engineering: Calculate technical indicators and decompose time series.
3. Machine Learning: Train a Random Forest Classifier using walking forward validation.
4. Trading Simulation: Implement a realistic trading strategy based on ML predictions and technical indicators.
5. Performance Analysis: Calculate and visualize various performance metrics.

Results

The project generates visualizations for:

• Stock price, SMAs, and trade points
• Machine learning predicted trend
• Buy/Sell signals over time
• Cumulative returns

It also calculates and prints performance metrics including total return, annualized return, Sharpe ratio, and maximum drawdown.

Future Improvements

• Implement additional machine learning models
• Incorporate more data sources
• Optimize hyperparameters
• Implement portfolio management for multiple stocks