Backtesting

Backtesting is the evaluation of a trading or investment strategy using historical data to estimate how it would have performed. It aims to assess viability, risk, and robustness before live use and to compare competing approaches.

The typical process involves assembling reliable historical data, defining entry and exit rules, and simulating trades under assumed execution conditions, including costs and slippage. The test usually splits data into a development (in-sample) period where parameters are chosen and a validation (out-of-sample) period where performance is observed. Some approaches use walk-forward optimization to periodically re-optimize with a rolling window.

Performance is summarized with metrics such as annualized return, drawdown, Sharpe ratio, Sortino, maximum drawdown, win rate, and profit factor. Equity curves and other visual analyses help assess risk and robustness. Backtests should address biases such as look-ahead bias, survivorship bias, data-snooping, and overfitting, and should consider data quality and instrument liquidity.

Limitations and best practices are important: backtesting relies on historical conditions and may not predict future results; execution realities, liquidity, market impact, and regime changes are often simplified. Best practices include using out-of-sample testing, multiple data sets, accounting for transaction costs and slippage, employing robust risk metrics, conducting code review, and ensuring reproducibility of the results.