strategy-generate

You can automate quantitative trading backtesting by defining entry and exit conditions to generate signal logic, which the skill then evaluates against historical market data. It handles data routing and calendar alignment automatically to produce performance metrics and equity curves.

Yes, backtesting supports multiple asset classes including A-shares, US stocks, Hong Kong stocks, and cryptocurrencies. The system automatically manages data routing and calendar alignment for each specific market during performance evaluation.

You validate trading strategy robustness using built-in statistical validation tools like Monte Carlo simulations and walk-forward analysis. These methods test your strategy against historical market data to ensure performance reliability before live deployment.

Yes, pandas and numpy are required dependencies to generate and backtest trading signal engines. These libraries handle the underlying quantitative data processing and numerical calculations required for performance evaluation.

The best way to build a dual moving average crossover strategy without manual coding is to specify your instrument requirements and entry/exit conditions. The system automatically generates the signal logic and adheres to the SignalEngine contract for validation.

Automated strategy generation requires strict adherence to the SignalEngine contract for validation, limiting custom logic structures. Additionally, performance evaluations rely entirely on historical market data availability and accurate parameter configuration.