backtest_engine

You can backtest a trading strategy by simulating trades bar-by-bar using historical price and fundamental data. The engine processes your predefined rules to calculate performance metrics like win rate and maximum drawdown.

Lookahead bias in backtesting occurs when a strategy uses future data unavailable at the time of a trade. This engine enforces anti-cheating rules to prevent lookahead bias, survivorship bias, and improper cost modeling.

Yes, the backtesting engine supports technical, fundamental, and institutional data inputs. It simulates trading scenarios using these data types to validate your quantitative strategies against historical market conditions.

Simulating trades calculates key performance indicators including win rate, Sharpe ratio, and maximum drawdown. These metrics help you evaluate the profitability and risk profile of your trading strategy before deployment.

Yes, the engine enforces proper cost modeling and calculates risk management metrics. It ensures your backtesting results reflect realistic trading conditions by accounting for transaction costs and risk exposure.

Event-driven backtesting simulates scenarios bar-by-bar, which provides accuracy but may require significant processing time for large historical datasets. Results are also constrained by the quality of historical financial data inputs.