Building and Testing a Moving Average Crossover Backtest in Python
Summary
This example builds a basic Python backtest for a single equity using a moving average crossover. It calculates short and long simple moving averages, sets the position to invested when the short average is above the long average, and uses changes in that signal to mark buys and exits. A market-on-close portfolio model holds a fixed share quantity, tracks cash and marked-to-market holdings, and derives a portfolio value series and period returns.
The script retrieves historical daily bars, plots the price with the moving averages and trade markers, and displays the resulting equity curve. These plots illustrate the workflow for connecting signals to portfolio accounting; the document does not report performance metrics or establish that the strategy is profitable. The example is deliberately narrow and omits transaction costs, slippage, short positions, and broader portfolio allocation. Its code uses older data and pandas interfaces, so it serves mainly as a conceptual backtesting example rather than a ready-to-run contemporary implementation.
Key ideas
- A moving average crossover can define invested and uninvested signals from price history.
- Signal changes identify entry and exit events for portfolio accounting.
- The example tracks fixed share holdings, cash, total equity, and periodic returns.
- Price and equity curve charts help inspect trades and backtest behavior.
- The illustration omits trading costs and other assumptions needed for realistic performance evaluation.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.