backtesting-frameworks

Backtest trading strategies using bias-aware training, validation, and test structures that incorporate realistic costs. This approach addresses data biases and transaction expenses to reveal true historical performance.

Walk-forward optimization is a quantitative analysis technique that sequentially trains and validates strategy parameters on rolling historical data windows. It prevents overfitting and ensures reproducible results.

Run an event-driven backtester by loading historical OHLCV datasets and processing candidate strategies tick-by-tick or bar-by-tick. This simulates realistic execution order and tracks performance metrics.

Yes, the backtest framework supports both event-driven and vectorized backtesting. Vectorized processing applies strategy logic across entire historical OHLCV datasets simultaneously for faster evaluation.

Use walk-forward analysis instead of a standard backtest when validating strategy robustness over varying market conditions. It continuously optimizes parameters on training data and validates on unseen test segments.