Stock Trading Back-test using Simple Moving Average

Overview

This blog post discusses a Python script for backtesting a simple moving average (SMA) crossover trading strategy using historical stock data.

The Strategy

The strategy uses two Simple Moving Averages (short-term and long-term):

• A "buy" signal occurs when the short-term SMA crosses above the long-term SMA
• A "sell" signal occurs when the short-term SMA crosses below the long-term SMA

Implementation Details

Key Technologies

• Used yfinance library to fetch historical stock price data
• Utilized Alpaca API for executing trades
• Implemented backtesting to analyze strategy performance

Trading Mechanism

• Generates trading signals based on SMA crossovers
• Trades 100 shares per signal (customizable)
• Provides visual representation of strategy performance

Future Potential

The author suggests several potential improvements:

• Adding short and long trading capabilities
• Exploring more advanced technical analysis techniques
• Investigating other algorithms like:
  • Bollinger Bands
  • MACD
  • RSI

Important Disclaimer

"Backtesting is not a guarantee of future performance. It's a method to assess the viability of a strategy based on historical data."

Code

The full implementation is available on GitHub