Build robust, production-grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates.

Backtesting Frameworks Build robust, production grade backtesting systems that avoid common pitfalls and produce reliable strategy performance estimates. Use this skill when Developing trading strategy backtests Building backtesting infrastructure Validating strategy performance and robustness Avoiding common backtesting biases Implementing walk forward analysis Do not use this skill when You need live trading execution or investment advice Historical data quality is unknown or incomplete The task is only a quick performance summary Instructions Define hypothesis, universe, timeframe, and evaluation criteria. Build point in time data pipelines and realistic cost models. Implement event driven simulation and execution logic. Use train/validation/test splits and walk forward testing. If detailed examples are required, open resources/implementation playbook.md . Safety Do not present backtests as guarantees of future performance. Avoid providing financial or investment advice. Resources resources/implementation playbook.md for detailed patterns and examples. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing. Simulation boundaries The examples are incomplete educational engines, not production grade or validated performance estimates. Event execution is single symbol long only and needs sorted unique timestamps, finite positive OHLC prices, valid cash/order quantities and explicit unsupported limit/stop handling; do not silently discard unsupported or final pending orders. State persists across run calls: create a fresh engine per experiment. Corporate actions, dividends, delistings, missing bars, borrow/funding/margin, latency, liquidity/partial fills and capacity remain unmodeled. A one period signal shift does not cure a signal function that reads future data, and close to close attribution requires a precisely defined execution clock. Annualization must match sampling frequency; 252 assumes trading day data and simple IID scaling may fail under autocorrelation. Nonzero return days are not trades, geometric annual return divided by volatility is not the standard arithmetic excess return Sharpe, and a curve without initial capital can miss first period drawdown. Preserve initial wealth in each metric and report undefined ratios rather than invented zero. Walk forward needs train only warm up for test indicators, no overlapping test windows and consistent costs/capital chaining; repeated parameter search adds selection bias. IID bootstrap ignores serial dependence/regime shifts and produces conditional simulation quantiles, not a calibrated future loss probability. Record data versions, all trials, seeds, cost assumptions, benchmarks and untouched holdout results; no live execution or investment recommendation is authorized.

backtesting-frameworks