PRD: M5 Learning System and Continuous Improvement
Problem
The product should improve over time, but naive reinforcement learning or always-on adaptation would create opaque behavior and unsafe incentives.
Objective
Create a controlled improvement loop using simulation, backtesting, paper trading, evaluation, and staged promotion.
Success criteria
- Recommendation policies can be evaluated offline against explicit metrics.
- Paper-trading and backtest environments exist for strategy and policy experiments.
- Changes are promoted only after measurable quality and safety gains.
Scope
- Simulation environment
- Paper trading
- Backtesting
- Experiment registry
- Quality scoring and promotion workflow
Out of scope
- Unbounded online self-modification
- Production execution driven directly by unreconciled experiment outputs
Requirements
- Version every strategy and policy
- Preserve reproducible datasets and experiment configurations
- Track benchmark comparisons and failure cases
- Require human sign-off for production promotion
Risks
- Backtest overfitting
- Metric gaming
- Confusing paper performance with production readiness
Delivery notes
Improvement should be cautious, evidence-based, and reversible.