Reinforcement Learning Trading

objective

Execute reinforcement learning trading work with reproducible research, explicit controls, and deployable outputs.

workflow

1. define assumptions, governing equations, and boundary conditions.
2. estimate parameters with reproducible calibration settings.
3. validate residual structure, numerical stability, and convergence behavior.
4. stress model behavior across regime changes and parameter perturbations.
5. release only when out-of-sample accuracy and stability remain within limits.

required diagnostics

• residual diagnostics and autocorrelation by horizon.
• parameter stability across rolling and expanding windows.
• numerical convergence behavior and solver tolerance sensitivity.
• forecast calibration and distributional fit checks.
• policy collapse risk under sparse-reward regimes
• out-of-distribution action monitoring in production