Backtest

Backtesting is the process of evaluating a trading or investment strategy by applying it to historical market data to determine how it would have performed. It is used to estimate potential profitability, risk, and robustness before deploying a strategy in live markets. Backtesting differs from paper trading or live execution in that it relies on historical price series and rules rather than current market conditions.

Typical backtests proceed by selecting a data set, calibrating the strategy's parameters, and simulating trades in chronological order. The simulation traces the performance, recording metrics such as total return, annualized return, maximum drawdown, volatility, Sharpe ratio, and win rate. Realistic assumptions include transaction costs, bid-ask spread, slippage, and liquidity constraints; adjustments for corporate actions and data cleaning are also common.

Common pitfalls: overfitting, where a strategy is tuned to past data and fails in new data; look-ahead bias and survivorship bias; data-snooping bias; cherry-picking time periods. To mitigate, use out-of-sample testing, walk-forward analysis, cross-validation for time series, and emphasize robustness rather than sole performance. Use backtesting engines and generate confidence intervals via resampling.

Backtesting informs strategy development, portfolio construction, and risk management but does not guarantee future results. It should be complemented by forward testing, paper trading, and live monitoring. Transparent documentation and reproducible code are emphasized in professional environments.