Research to production for RL trading systems
A practical path from reinforcement learning trading research to production: reproducible runs, promotion to live, and monitoring to stay honest.
1) Make runs reproducible
If you can’t reproduce, you can’t improve.
Treat an RL trading experiment like a product artifact: configuration, data source, reward, algorithm settings, and evaluation windows should be versioned and repeatable. This keeps iteration honest and makes results comparable.
2) Promote a checkpoint, don’t “keep learning” live
Live trading should be auditable and stable.
A common failure mode is to blur training and live execution. Instead: train in backtests, pick a checkpoint, and deploy a fixed policy. That makes behaviour interpretable and makes rollbacks possible.
3) Connect execution and monitoring
The job isn’t done when it trades—it’s done when you can trust it.
- Action drift — does the live action distribution deviate from training?
- Exposure drift — are position sizes and holdings staying within expected ranges?
- Performance drift — do returns/drawdown change regime?
How Kabu supports this workflow
Kabu is built around this research-to-production loop: configure experiments, run and compare training, then promote a checkpoint to a live deployment that runs on schedule and executes through your broker. Runs, metrics, and live executions stay connected so you can iterate without rebuilding pipelines.