strategy-generate

To generate and backtest a quantitative trading strategy, this workflow parses your goals to produce a run directory containing config.json and code/signal_engine.py, then evaluates metrics against artifacts to guide iterative design refinements.

Automating signal engine scaffolding involves generating code/signal_engine.py alongside a config.json file, enforcing an end-to-end workflow that includes syntax checks, backtesting, and result validation for reproducible quantitative trading runs.

Yes, you can run multi-asset backtests using a single config.json file, as the strategy generation workflow explicitly supports both single-asset and multi-asset configurations for validating quantitative trading strategies against artifacts.

Yes, the strategy generation workflow supports iterative design by evaluating backtesting metrics in artifacts to guide refinements, allowing you to continuously optimize and validate your quantitative trading strategies for reproducible backtests.

You need to define your user goals to start prototyping quantitative trading ideas, which triggers the workflow to parse your intent, write config.json, and scaffold signal_engine.py code for automated backtesting and validation.