GitHub hosts many open source code repositories for developing rule-based investing strategies and stock trading algorithms. As these strategies rely on predefined rules instead of machine learning models, the code is often more transparent and easier to interpret. By studying these repositories, investors can better understand how to formulate entry and exit signals, manage risk, and automate the strategy. The examples cover techniques like trend following, mean reversion, pattern recognition, pairs trading and more across stocks, forex, futures and other markets.
Backtesting framework to analyze rule-based stock strategies
One of the most popular GitHub repositories is zipline, a Python library for trading algorithm development and backtesting. It allows users to analyze the historical performance of a strategy by replaying market data tick-by-tick. This enables realistic modeling of slippage, transaction costs, and various types of orders. Users can test ideas across different timeframes without real money at stake. Another option is backtrader – an event driven framework with flexibility to define strategies as Python classes. With modular structure and emphasis on reproducibility, it serves as an excellent educational tool to learn trading system design.
Trend following strategy on stock indices futures
A trend following approach is to buy when prices are rising and sell when they are falling. An example repository implements this for e-mini S&P 500 futures contracts on 15 minute intervals. Entry signals are generated with two smoothed moving averages and exits with an ATR trailing stoploss. Walk forward optimization is performed to find robust parameters across different time periods. The strategy rules are coded in easy to understand Python functions with detailed commentary and visualizations provided.
Mean reversion strategies for pairs trading
Mean reversion assumes that stock prices fluctuate around a mean value. Pairs trading seeks to exploit when a relationship between two historically correlated stocks shows divergence. A sample implementation on GitHub computes a z-score for the price ratio between pairs. Entry signals are generated when the z-score exceeds upper and lower boundaries. Various combinations of stocks can be analyzed to unearth cointegrated pairs suitable for this technique. User functions allow customization for z-score calculation methodology, trading thresholds, position sizing etc.
Pattern recognition strategies with technical indicators
Classical chart patterns like head and shoulders, triangles, flags can cue potential reversals or continuations. A repository implements a backtester to screen stocks for formations combined with indicators like RSI, MACD historically known for enhancing performance. The pattern identification logic, buy/sell logic, stop loss rules are clearly articulated as functions. Walk forward testing provides insight on stability of returns across changing market regimes. Users can readily modify rules or add new indicators/patterns and instantly gauge their impact.
In summary, GitHub offers great learning opportunities for investors seeking to code rule-based stock strategies. The open source repositories cover multiple techniques allowing convenient experiments to discover alpha signals. And with backtesting capability built-in, the strategies can be rigorously analyzed before risking real capital.