Five Python Checks to Validate a Backtest Before Live Deployment
A software developer has outlined five critical checks to verify the integrity of trading strategy backtests before deploying them with real capital. The checks address common failure points including data leakage, where future information inadvertently influences historical signals, and improper train-test splitting that ignores time dependencies. The framework also requires explicit modelling of transaction costs tied to portfolio turnover, not just position size, and an examination of profit concentration across individual trades. Chronological cross-validation is recommended to reveal whether strong backtest results are driven by a single favourable period rather than consistent performance. The author has packaged the post-hoc audit checks into an open-source Python tool called Backtest Guard, which analyses exported CSV data and generates reports in HTML, text, and JSON formats.
