feat: Phase 4+5 — price discovery status + replay historique

Phase 4 — Price Discovery Status (la pièce maîtresse) :
- price_discovery.py (nouveau) : capture_price_snapshots() sauve les prix des tickers
  liés à chaque news scorée (energy→BZ=F/NG=F, metals→GC=F/HG=F, indices→^GSPC/IWM)
- compute_absorptions() mesure combien du mouvement attendu s'est déjà produit
  (status: not_yet_priced <30% / partially_priced 30-80% / fully_priced >80%)
- build_price_discovery_block() → bloc prompt avec opportunités classées
- database.py : table news_price_snapshots + save/get/purge fonctions
- auto_cycle.py : capture après ai_score_news_batch, compute avant suggestions,
  block injecté dans suggestion + scoring prompts + context snapshot
- ai_analyzer.py : param price_discovery_block dans suggest + score

Phase 5 — Replay historique :
- cycle.py : POST /api/cycle/contexts/{run_id}/replay — recharge le snapshot historique
  et relance suggest_patterns_from_market_context avec le contexte original
- useApi.ts : hook useReplayCycle
- SystemLogs.tsx : bouton "Rejouer ce cycle" dans onglet Contexte IA avec champ
  notes, résultats inline (liste des patterns générés), section price_discovery
  ouverte par défaut en rouge

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/services/auto_cycle.py

@@ -175,6 +175,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
             add_cycle_run, update_cycle_run, save_reasoning_trace,
             get_latest_portfolio_lessons, log_system_event,
             get_last_completed_cycle_ts, save_cycle_context_snapshot,
+            purge_old_price_snapshots,
         )
         from services.data_fetcher import fetch_geo_news, get_all_quotes, get_macro_gauges, score_macro_scenarios
         from services.geo_analyzer import compute_geo_risk_score
@@ -326,6 +327,21 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
 
         quotes = get_all_quotes()
 
+        # ── Phase 4A: capture price snapshots for scored news ─────────────────
+        try:
+            from services.price_discovery import capture_price_snapshots as _cap_snap
+            _quotes_flat: Dict[str, float] = {}
+            for _qs in quotes.values():
+                for _q in _qs:
+                    if _q.get("symbol") and _q.get("price"):
+                        _quotes_flat[_q["symbol"]] = float(_q["price"])
+            _n_snaps = _cap_snap(news, _quotes_flat, run_id)
+            if _n_snaps:
+                logger.info(f"[Cycle {run_id[:16]}] Phase 4: {_n_snaps} price snapshots captured")
+            purge_old_price_snapshots(older_than_days=14)
+        except Exception as _pd_e:
+            logger.warning(f"[Cycle] Price snapshot capture failed (non-blocking): {_pd_e}")
+
         gauges = get_macro_gauges()
         scenarios = score_macro_scenarios(gauges)
         macro_regime = {"gauges": gauges, "scenarios": scenarios}
@@ -370,6 +386,19 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                 logger.info(f"[Cycle {run_id[:16]}] Reliability map: {len(_reliability_map)} patterns")
         except Exception as _re:
             logger.warning(f"[Cycle] Reliability map failed (non-blocking): {_re}")
+        # ── Phase 4B: compute price absorption from previous snapshots ───────
+        _absorptions = []
+        _price_discovery_block = ""
+        try:
+            from services.price_discovery import compute_absorptions, build_price_discovery_block
+            _absorptions = compute_absorptions(min_age_minutes=30.0, max_age_days=7)
+            _price_discovery_block = build_price_discovery_block(_absorptions)
+            opps = sum(1 for a in _absorptions if a["opportunity"])
+            if _absorptions:
+                logger.info(f"[Cycle {run_id[:16]}] Phase 4: {len(_absorptions)} absorptions, {opps} opportunities")
+        except Exception as _abs_e:
+            logger.warning(f"[Cycle] Price absorption failed (non-blocking): {_abs_e}")
+
         # ── Phase 2: FRED recent macro releases ──────────────────────────────
         _fred_releases = []
         _fred_block = ""
@@ -436,6 +465,10 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                         "older":       [{"title": n.get("title"), "source": n.get("source")} for n in _news_snap["older"][:5]],
                     },
                     "fred_releases": _fred_releases,
+                    "price_discovery": [
+                        {k: v for k, v in a.items() if k != "article_hash"}
+                        for a in _absorptions[:10]
+                    ],
                     "tech_indicators_block": _tech_block,
                     "iv_context_preview": iv_context[:500] if iv_context else "",
                     "calendar": calendar[:8] if calendar else [],
@@ -454,6 +487,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                 cycle_meta=cycle_meta,
                 tech_indicators_block=_tech_block,
                 fred_block=_fred_block,
+                price_discovery_block=_price_discovery_block,
             )
         except Exception as e:
             logger.warning(f"[Cycle] Suggestion step failed: {e}")
@@ -579,6 +613,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                 cycle_meta=cycle_meta,
                 tech_indicators_block=_tech_block,
                 fred_block=_fred_block,
+                price_discovery_block=_price_discovery_block,
             )
             scored_with_id = [s for s in scored if s.get("pattern_id")]
             scored_without_id = [s for s in scored if not s.get("pattern_id")]