quant-ml-trading

Automating backtesting and validation requires running your strategy against historical time-series data to generate performance tearsheets. This toolkit automates that workflow, providing strategy validation and auditable performance reports for robust assessment.

Genetic optimization for trading strategies involves evolving strategy parameters over time to find optimal configurations. This toolkit applies genetic algorithms to automate parameter sweeps, while also monitoring strategy decay to track performance degradation.

Reinforcement learning can be applied to time-series trading by training agents to make market decisions. This toolkit supports RL trading agents and AI signal aggregation, providing end-to-end decision support for both simulated and live market environments.

Generating a performance tearsheet involves invoking the backtesting toolkit on your market data to assess strategy validity. The toolkit outputs a full performance report, enabling faster and repeatable quantitative research without manual calculation.

This backtesting toolkit operates with no external dependencies, using modular components like references for knowledge and scripts for deterministic tasks. This design ensures safe, auditable results with clear validation references for your quantitative workflows.