Quant Engine

To backtest a quantitative trading strategy, you simulate and evaluate it across multiple assets using historical data. This engine supports momentum, mean reversion, factor models, and statistical arbitrage with walk-forward analysis and risk metrics.

Yes, statistical arbitrage backtesting using cointegration is fully supported in Python. The engine utilizes numpy, pandas, scipy, and statsmodels to simulate cointegration-based pairs trading strategies before live deployment.

You need Python libraries like numpy, pandas, scipy, statsmodels, and scikit-learn for full backtesting functionality. The engine provides fallback mechanisms to operate with reduced capabilities when these dependencies are unavailable.

Walk-forward analysis improves momentum strategy backtesting by validating performance across sequential time periods. It prevents overfitting by continuously optimizing parameters on rolling data windows and generating deterministic, reproducible risk metrics.

The best way to compare mean reversion and factor models is using Monte Carlo simulations and comprehensive backtesting. This engine generates performance metrics and risk evaluations to ethically compare strategies before risking capital.

Backtesting experiments are fully reproducible when testing quantitative strategies. The engine provides a unified framework that prototypes and simulates trading ideas with deterministic results, ensuring consistent outputs across multiple runs.