backtesting-frameworks

To prevent look-ahead bias in backtesting, you must enforce strict point-in-time data handling. This framework mitigates such biases by time-aligning historical data and applying modular execution models to ensure deterministic results.

Walk-forward analysis is an out-of-sample testing method that validates trading strategies across historical data. It applies modular components to evaluate strategy performance dynamically, ensuring your equity curve reflects realistic market conditions.

To include transaction costs when measuring strategy performance, this framework applies realistic cost models during execution. It accurately evaluates strategies by combining these costs with bias-aware architecture to generate transparent performance metrics.

Yes, this backtesting framework handles survivorship bias in historical data. It enforces bias-aware architecture to mitigate survivorship bias alongside look-ahead bias, ensuring reliable results for comparing trading strategies.

To run a backtest on historical OHLCV data, apply the framework to generate an equity curve and performance metrics. It uses pattern-based backtester implementations to provide deterministic results for your dataset.

Pattern-based backtester implementations rely on explicit time-aligned data and out-of-sample testing to function correctly. Limitations arise if your historical data lacks point-in-time accuracy or if execution models fail to account for realistic transaction costs.