Backtesting in Quant Finance

Backtesting involves simulating a strategy’s performance using historical data to evaluate its behavior across different market conditions. It is a foundational step in quantitative research. A proper backtest accounts for realistic assumptions, including transaction costs, slippage, rebalancing schedules, and survivorship bias. Without these, results may be overly optimistic. Backtests help identify drawdowns, volatility, turnover, and factor sensitivities. They also reveal how strategies respond to crises and regime shifts. However, backtesting alone is not sufficient. Overfitting to historical data can produce impressive past results that fail in live markets. This is why out-of-sample testing and robustness checks are essential. Backtesting should be viewed as a risk assessment tool, not a guarantee of future performance.