feat(ml): add Differential Sharpe Ratio (DSR) struct and config fields
Implement the Moody & Saffell (2001) DSR for incremental reward shaping
that directly optimizes risk-adjusted returns. This replaces the broken
double-normalization pipeline (EMA normalizer + risk-adjusted division)
that was producing random noise and preventing DQN learning.
- DifferentialSharpeRatio struct: step(), reset(), eta clamping, +/-5 bounds
- RewardConfig: use_dsr (default false), dsr_eta (default 0.01)
- RewardConfigBuilder: use_dsr() and dsr_eta() builder methods
- RewardFunction: dsr field, reset_dsr(), reset_epoch_state()
- 4 new unit tests (basic, bounded, reset, config roundtrip)
- All 40 reward tests pass
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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