baktesting

Backtesting is the process of evaluating a trading or investment strategy by applying it to historical market data to estimate how it would have performed. It is used by traders, quantitative researchers, and asset managers to gauge feasibility, compare alternatives, and refine rules before committing capital.

A backtest requires representative historical data, including price series and, when relevant, dividends, corporate actions, and trading costs. The strategy's rules are encoded: entry and exit conditions, position sizing, risk limits, stop losses, and profit targets. The simulation proceeds through time, assuming trades are executed according to specified fill assumptions and accounting for commissions, slippage, and latency when possible.

Backtesting can be conducted in-sample and out-of-sample, with walk-forward analysis to simulate adapting rules over time. It is important to guard against biases such as survivorship bias, look-ahead bias, and data-snooping (overfitting), which can produce overly optimistic results.

Common performance metrics include total return, annualized return, volatility, maximum drawdown, Sharpe and Sortino ratios, win rate, and profit factor. Realistic evaluation also considers risk controls, transaction costs, and market impact. Robustness tests, such as sensitivity analysis and stress testing, are recommended.

Limitations of backtesting include the assumption that future conditions resemble the past, changes in liquidity, market structure, and regime shifts. Overfitting can yield strategies that perform well on historical data but poorly in live trading. Data quality, gaps, and survivorship can distort results.

Best practices emphasize transparent methodology, out-of-sample validation, walk-forward testing, and clear documentation of parameters and assumptions. When possible, backtests should be complemented by phased live testing with small capital and ongoing monitoring.