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>

backend/routers/patterns.py

@@ -5,7 +5,7 @@ from pydantic import BaseModel
 from typing import Optional, List, Dict, Any
 from services.database import (
     save_custom_pattern, get_custom_patterns, delete_custom_pattern, toggle_pattern_active,
-    get_config,
+    get_config, get_calibration_summary,
 )
 from services.geo_analyzer import PATTERN_TAXONOMY
 
@@ -141,6 +141,14 @@ def get_by_instrument(ticker: str = Query(..., description="Ticker / underlying
     return result
 
 
+# ── Calibration summary ───────────────────────────────────────────────────────
+
+@router.get("/calibration")
+def get_calibration():
+    """Per-pattern calibration state: AI estimate vs observed blend."""
+    return get_calibration_summary()
+
+
 # ── Find Similar ──────────────────────────────────────────────────────────────
 
 class FindSimilarRequest(BaseModel):