Backtesting Strategies

Backtesting is the process of evaluating a trading strategy using historical data. It is the closest thing to a laboratory experiment in trading.

Why Backtest?

A strategy that looks good in theory often performs poorly in practice. Backtesting reveals weaknesses before real money is at stake. It quantifies key metrics: win rate, average risk-reward, maximum drawdown, Sharpe ratio, and consistency across different market conditions.

Data Quality Matters

The quality of your backtest depends entirely on the quality of your data. Use high-resolution data (ideally tick-level or 1-minute) and account for fees, slippage, and liquidity constraints. Many strategies look profitable on daily data but fail when tested on hourly or minute data.

Common Pitfalls

Survivorship bias occurs when you only test against coins that still exist today. Look-ahead bias happens when future information leaks into the backtest. Overfitting is the most dangerous — optimizing parameters until the strategy works perfectly on historical data but fails forward. Keep strategies simple and test on out-of-sample data.

Minimum Sample Size

A backtest should include at least 100 trades before drawing conclusions. Fewer than that and the results are statistically insignificant. Also test across multiple market regimes — bull, bear, and sideways — to ensure the strategy adapts to changing conditions.