Quantitative trading relies on testable hypotheses rather than market prediction. Researchers define market phenomena using numerical rules and verify results against historical data. These strategies can range from simple price momentum models to complex portfolio adjustments.
A common pitfall in backtesting is data leakage, where future information inadvertently influences the model. Examples include using future prices, misaligned reporting dates, or look-ahead bias in time-series data. Additionally, survivorship bias occurs when researchers only include currently active companies, ignoring those that went bankrupt or were delisted.
Overfitting is a significant risk when testing thousands of configurations. In a simulation using 2000 random return series, the best-performing strategy achieved a 57.47% return and a Sharpe ratio of 3.02 on the first segment, but lost 5.22% on the second segment.
To ensure validity, researchers should use independent datasets for model selection and final verification. Techniques like walk-forward validation help assess the entire procedure, including periodic retraining. Proper testing must account for transaction costs, slippage, and the specific mechanics of the trading platform.
