strategy-generate

You can backtest a quantitative trading strategy across multiple markets by parsing your intent into configuration files and signal engine code, executing the integrated engine across China A-shares, US stocks, Hong Kong stocks, and Crypto.

Generating quantitative trading strategy code involves parsing user intent into configuration files and signal engine code. The process guides you through data requirements, signal logic, and position management to ensure robust strategy construction.

Yes, the backtesting tool supports walk-forward analysis and Monte Carlo simulations. These statistical validation tools are integrated to ensure the reliability of your quantitative trading strategies alongside signal consistency and risk management checks.

Yes, you can use pandas and numpy for signal logic in your trading backtests. The strategy engine relies on these dependencies for automated data normalization, signal generation, and quantitative performance metric calculations.

To design a dual moving average crossover strategy for China A-shares, you specify the ticker, moving average periods, and target year. The engine parses this intent to generate and validate the configuration and signal code for backtesting.

During quantitative strategy backtesting, rigorous quality gates validate signal consistency, trade execution, and risk management parameters. This automated validation ensures your strategies maintain robust construction before analyzing performance metrics.