feat: pattern calibration — progressive AI→observed expected_move blending
DB (database.py):
- 3 new columns on custom_patterns: calibrated_expected_move, calibration_weight, observed_avg_win_pct
- update_bayesian_posteriors() now also computes credibility blend w=n/(n+5):
calibrated = (1-w)*ai_estimate + w*observed_avg_win_pct (only when wins exist)
- log_trade_entries() prefers calibrated_expected_move when w>10%
- get_calibration_summary() returns per-pattern state (source: pure_ai/early/mixed/data_driven)
Backend (patterns.py, auto_cycle.py):
- GET /api/patterns/calibration endpoint
- calibration_report block in cycle report: counts by source, avg weight, per-pattern detail
Frontend (PatternExplorer.tsx, RapportIA.tsx, useApi.ts):
- MaturityBadge on each PatternCard: blend bar (AI→observed), win rate, AI estimate vs calibrated
- usePatternCalibration hook
- Cycle report: calibration section with global bar + per-pattern table (weight%, n_trades, WR, AI→calibrated)
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
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