strategy-generate

You can backtest a quantitative trading strategy across multiple markets by defining signals and position sizing, then executing them through a unified engine. This process requires configuration schemas for data routing and interval definition to generate performance metrics.

Automated strategy backtesting executes your predefined trading signals against historical market data to validate logic viability. It automatically generates performance metrics and equity curves, helping identify potential bugs in your quantitative models before live deployment.

Yes, you can use numpy and pandas within this framework to handle multi-asset backtesting. The strategy automation engine uses these dependencies to process historical market data and generate statistical validation tools for your trading logic.

To design a dual moving average crossover strategy, you define the short and long window parameters, such as a 5-day and 20-day interval, for a target asset like 000001.SZ. The framework automates the coding and backtesting to evaluate performance over your chosen period.

Automated quantitative strategy generation requires strict adherence to specific configuration schemas for data source routing and performance metric generation. Limitations include the necessity of valid historical data inputs and the risk of overfitting backtested equity curves to past market conditions.