backtest-runner

Walk-forward optimization backtests trading strategies by dividing historical data into sequential training and testing windows. It validates parameter robustness and calculates Probability of Backtest Overfitting (PBO) to prevent overfitted results.

Yes, full-pipeline backtesting supports both PyBroker and VectorBT engines to evaluate trading strategies across multiple tickers and time ranges. This enables end-to-end strategy assessment and rapid parameter sweeps.

Lookahead bias occurs when a backtest uses future data unavailable at the time of trading. The backtest pipeline includes validation checks to detect lookahead bias and performance degradation across out-of-sample periods.

MLflow integrates with the backtesting workflow to log experiments, track parameter sets, and ensure result reproducibility. This allows you to compare historical strategy validation runs and maintain consistent tracking.

Historical strategy validation calculates configurable risk metrics alongside backtest execution. These metrics are applied across multiple tickers and time ranges to assess strategy performance and robustness.

Vectorized backtesting supports parameter sweeps to rapidly compare different parameter sets. This approach accelerates experimentation and evaluates strategy robustness across varied configurations.