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/services/auto_cycle.py

@@ -1506,6 +1506,52 @@ Réponds en JSON avec ce schéma EXACT:
         except Exception:
             commentary_parsed = {"commentary": str(commentary)}
 
+    # ── Calibration report ───────────────────────────────────────────────────
+    calibration_report: Dict = {}
+    try:
+        from services.database import get_calibration_summary as _get_calib
+        calib_rows = _get_calib()
+        if calib_rows:
+            pure_ai   = [r for r in calib_rows if r["source"] == "pure_ai"]
+            early     = [r for r in calib_rows if r["source"] == "early"]
+            mixed     = [r for r in calib_rows if r["source"] == "mixed"]
+            driven    = [r for r in calib_rows if r["source"] == "data_driven"]
+            with_data = [r for r in calib_rows if (r["n_mature_trades"] or 0) > 0]
+            avg_w = (
+                round(sum(r["calibration_weight"] for r in with_data) / len(with_data), 3)
+                if with_data else 0.0
+            )
+            calibration_report = {
+                "total_patterns":           len(calib_rows),
+                "pure_ai_count":            len(pure_ai),
+                "early_count":              len(early),
+                "mixed_count":              len(mixed),
+                "data_driven_count":        len(driven),
+                "avg_calibration_weight":   avg_w,
+                "avg_calibration_weight_pct": round(avg_w * 100, 1),
+                "patterns_with_data":       len(with_data),
+                "detail": [
+                    {
+                        "name":           r["pattern_name"],
+                        "asset_class":    r["asset_class"],
+                        "source":         r["source"],
+                        "weight_pct":     r["calibration_weight_pct"],
+                        "ai_estimate":    r["ai_estimate"],
+                        "observed":       r["observed_avg_win_pct"],
+                        "calibrated":     r["calibrated_expected_move"],
+                        "n_trades":       r["n_mature_trades"],
+                        "win_rate_pct":   round((r["bayes_win_rate"] or 0) * 100, 1),
+                    }
+                    for r in calib_rows if (r["n_mature_trades"] or 0) > 0
+                ][:15],
+            }
+            logger.info(
+                f"[CycleReport] Calibration: {len(pure_ai)} pure-AI, "
+                f"{len(mixed)+len(driven)} with data, avg_weight={avg_w:.1%}"
+            )
+    except Exception as _ce:
+        logger.warning(f"[CycleReport] Calibration report failed: {_ce}")
+
     # ── Assemble full report ──────────────────────────────────────────────────
     report = {
         "run_id": run_id,
@@ -1553,6 +1599,8 @@ Réponds en JSON avec ce schéma EXACT:
             "n_alert": (options_assessment or {}).get("n_alert", 0),
             "assessments": (options_assessment or {}).get("assessments", []),
         } if options_assessment is not None else None,
+        # Calibration: AI vs observed expected_move blend
+        "calibration_report": calibration_report,
     }
     return report