feat: quant validation framework — DSR, PSR, Haircut, regimes, walk-forward
Three-module quant framework replacing 'sort by Sharpe' with proper
statistical validation:
quant/significance.py (15 tests):
- deflated_sharpe_ratio(): adjusts for N trials (Harvey & Liu 2015)
- probabilistic_sharpe_ratio(): P(True SR > benchmark) given T, skew, kurt
- sharpe_haircut(): expected OOS Sharpe after selection bias deflation
- QuantVerdict: DEPLOY / SIMULATE / DISCARD with 5-point scoring
- validate_strategy(): one-shot validation function
quant/regimes.py (8 tests):
- classify_regime(): trending_up/down, ranging, volatile
- RegimeClassifier: stateful rolling-window classifier
- conditional_performance(): per-regime trade statistics
quant/walkforward.py (5 tests):
- WalkForwardRunner: sequential IS/OOS window optimization
- WFWindow/WFReport: structured walk-forward results
- consistency score, performance decay, concatenated OOS equity
- significance_report() integration
Walk-forward results (real HL data with date-sliced windows):
grid_mm 1h: 2/4 pos, OOS S=-0.45, 74t, haircut=-22.66 → DISCARD
momentum 4h: 2/4 pos, OOS S=-1.47, 116t, haircut=-45.35 → DISCARD
composite_mm 1h: 2/4 pos, OOS S=+2.97, 6t, haircut=+43.25 → SIMULATE
28 tests total
ramseshk
543537e33f