quant_engine

Backtest a multi-factor trading strategy by defining a state schema and factor weights, then validating momentum and mean reversion signals using Sharpe ratio, Calmar, and maximum drawdown checks. Quant Engine handles this end-to-end pipeline across stocks and crypto.

Game-theoretic risk management in quantitative trading applies mathematical constraints to position sizing and stop-loss frameworks to prevent outsized drawdowns. Quant Engine uses these controls alongside portfolio-level constraints to enforce verifiable, risk-adjusted execution.

Calculate position sizing using a Kelly-based framework by processing momentum and volatility factors through explicit risk checks. Quant Engine generates these sizing outputs after validating backtest performance metrics like win rate and drawdown.

Yes, quant_engine supports both trend-following and mean-reversion strategies. It provides explicit guidance for cross-asset applicability across stocks and crypto, scoring multi-factor signals to generate robust trade ideas for either market context.

The best way to validate mean reversion signals before execution is running an end-to-end pipeline from data collection to risk checks. Quant Engine tests signals against backtest-driven validation metrics like Sharpe ratio and maximum drawdown.

Limitations of multi-factor models for crypto trading include potential outsized drawdowns if stop-loss and portfolio-level risk constraints are not strictly defined. Quant Engine mitigates this by requiring a documented state schema and explicit risk framework.