quant-researcher

Pareto optimization uses the NSGA-II evolutionary search to balance risk and return simultaneously across diverse market regimes. This multiobjective approach preserves strategy diversity and eliminates manual trial-and-error.

You start an evolutionary search by constructing a genome using the block DSL and configuring block-level parameterization. The system then applies fitness evaluation and generates reproducible run artifacts identified by a unique run_id for deterministic results.

Yes, the block DSL enables flexible and composable genome design for trading strategies. You can configure block-level parameterization to easily experiment with different strategy structures before running fitness evaluations.

Yes, evolutionary search supports reproducible backtesting by generating deterministic run artifacts per run_id. This ensures that strategy discovery, fitness evaluation, and campaign setup results can be exactly repeated for validation.

The best way to validate trading strategies across market regimes is applying NSGA-II driven Pareto optimization with diversity preservation. This disciplined handoff approach enables repeatable research and robust risk analysis across campaigns.