Designing a Modular Python Backtester with pandas
Summary
The article outlines a simple, object-oriented research backtester built around pandas data frames. It separates the workflow into strategy signals, portfolio positions and equity accounting, and performance analysis. Strategies return long, short, or hold recommendations from OHLCV bars; the portfolio turns those signals into positions and tracks holdings, cash, trades, and returns. The example uses market-on-open assumptions and a random signal generator to illustrate the process.
The article distinguishes rapid research backtests from event-based systems that more closely simulate market data and order execution. It emphasizes that historical results cannot establish future performance and that data resolution, routing, latency, fills, costs, and liquidity all affect realism. The described system is deliberately limited: it handles one instrument, assumes fills at bar open or close, and omits margin constraints, liquidity limits, sophisticated costs, and risk management. Its sample random strategy loses money, illustrating the framework rather than evidence for a viable trading edge.
Key ideas
- A research backtester can separate signal generation, portfolio accounting, and performance measurement into distinct components.
- The strategy layer can express recommendations as long, short, or hold signals derived from price bars.
- A portfolio layer converts recommendations into positions and an equity curve while tracking cash and holdings.
- Research backtests are useful for screening ideas, but execution assumptions and omitted frictions limit their realism.
- The article's single-instrument example uses random forecasts and does not demonstrate a profitable strategy.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.