Simple Moving Average Crossover Trading Algorithm

Project Description

This project implements a basic algorithmic trading strategy using Python.

The algorithm uses a Moving Average Crossover strategy, one of the fundamental techniques in technical analysis and algorithmic trading. This project serves as a starting point to understand the basics of strategy development, backtesting, and performance analysis in algorithmic trading.

Algorithm Description

Strategy: Moving Average Crossover

The core of this algorithm is a Moving Average Crossover strategy using two Simple Moving Averages (SMA):

1. Short-term SMA: 25-day Simple Moving Average
2. Long-term SMA: 50-day Simple Moving Average

Trading Signals:

• Buy Signal: Generated when the 25-day SMA crosses above the 50-day SMA
• Sell Signal: Generated when the 25-day SMA crosses below the 50-day SMA

This strategy aims to capture trends in the market while filtering out some of the short-term price noise.

Implementation Details:

1. Data Acquisition: Historical price data is fetched using the yfinance library.
2. Indicator Calculation: 25-day and 50-day SMAs are calculated using pandas.
3. Signal Generation: Buy/sell signals are generated based on SMA crossovers.
4. Backtesting: The strategy is backtested on historical data to evaluate its performance.
5. Performance Analysis: Uses the QuantStats library to generate comprehensive performance metrics and visualizations.

Key Features

• Fetches real market data using yfinance
• Implements a simple yet widely-used trading strategy
• Backtests the strategy on historical data
• Generates performance metrics including Sharpe Ratio, Max Drawdown, and Calmar Ratio
• Creates visualizations like Monthly Returns Heatmap and Drawdown Periods

Disclaimer

This project is for educational purposes only. It is not financial advice and should not be used for actual trading without proper risk management and further development. Real-world trading involves significant risk and requires in-depth knowledge, which is beyond the scope of this educational project.

Future Enhancements

• Implement more sophisticated strategies
• Add risk management techniques
• Incorporate fundamental analysis
• Explore machine learning approaches to strategy development

We welcome contributions and suggestions to improve this educational tool!