quant-researcher

Run batch backtests across specified parameter ranges to execute parameter optimization and identify optimal strategy settings. The system autonomously drives grid searches, aggregates results, and generates a structured research memo documenting the most effective configurations.

Grid search discovers effective parameter settings for alpha generation by sweeping parameter ranges via batch experimentation. This systematic testing identifies robust strategy improvements by evaluating performance variations across different market regimes.

Batch backtesting supports option contract optimization by comparing strikes, DTE, and Greeks to select favorable configurations. It evaluates options strategies to identify optimal contract parameters that generate alpha under various market conditions.

Regime analysis and risk assessment require access to config/strategy_params.yaml and the batch runner script. These components drive backtesting, enabling evaluation of strategy robustness and failure modes across different market environments.

Document backtest results by generating structured research memos that aggregate batch experimentation outcomes. The system produces dashboards and summarized results from grid searches, ensuring research workflow discipline informs strategy decisions.

Regime analysis identifies failure modes by stress-testing risk and evaluating strategy performance across different market regimes. This approach isolates specific market environments where parameter settings underperform, exposing robustness weaknesses.