feat: Phase 2 + context log — FRED releases, cycle context snapshot, onglet Contexte IA

Phase 2 — Données macro FRED :
- fred_fetcher.py (nouveau) : 7 séries FRED (CPI, NFP, UNRATE, FEDFUNDS, GDP, ICSA,
  spread 10Y-2Y) avec détection direction bullish/bearish et block prompt formaté
- ai_analyzer.py : param fred_block dans suggest + score, injecté dans les deux prompts
- auto_cycle.py : fetch FRED non-bloquant avant la suggestion

Context log — Snapshot du contexte complet :
- database.py : table cycle_context_snapshots + save/get/list fonctions
- auto_cycle.py : sauvegarde le snapshot (meta, news partitionnées, FRED, tech, IV, quotes)
- cycle.py : GET /api/cycle/contexts + GET /api/cycle/contexts/{run_id}
- useApi.ts : hooks useCycleContextSnapshots + useCycleContextSnapshot
- SystemLogs.tsx : onglet "Contexte IA" avec liste de cycles et visualiseur JSON
  par section (cycle_meta, macro, news, FRED, tech) avec accordéon

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

backend/services/auto_cycle.py

@@ -174,7 +174,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
             save_pattern_scores, log_macro_regime, log_geo_alert, log_trade_entries,
             add_cycle_run, update_cycle_run, save_reasoning_trace,
             get_latest_portfolio_lessons, log_system_event,
-            get_last_completed_cycle_ts,
+            get_last_completed_cycle_ts, save_cycle_context_snapshot,
         )
         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
@@ -370,6 +370,18 @@ 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 2: FRED recent macro releases ──────────────────────────────
+        _fred_releases = []
+        _fred_block = ""
+        try:
+            from services.fred_fetcher import get_fred_recent_releases, build_fred_context_block
+            _fred_releases = get_fred_recent_releases()
+            _fred_block = build_fred_context_block(_fred_releases)
+            if _fred_releases:
+                logger.info(f"[Cycle {run_id[:16]}] FRED: {len(_fred_releases)} releases fetched")
+        except Exception as _fe:
+            logger.warning(f"[Cycle] FRED fetch failed (non-blocking): {_fe}")
+
         try:
             from services.data_fetcher import get_economic_calendar
             calendar = get_economic_calendar()
@@ -406,6 +418,34 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
             except Exception as _te:
                 logger.warning(f"[Cycle] Tech indicators failed (non-blocking): {_te}")
 
+            # ── Save full context snapshot ─────────────────────────────────
+            try:
+                from services.ai_analyzer import apply_news_decay as _apply_decay2, partition_news_by_age as _part
+                _news_snap = _part(_apply_decay2(news), cycle_meta.get("delta_minutes", 180))
+                _context_snapshot = {
+                    "cycle_meta": cycle_meta,
+                    "macro_regime": {
+                        "dominant": (macro_regime or {}).get("scenarios", {}).get("dominant"),
+                        "scores": (macro_regime or {}).get("scenarios", {}).get("scores"),
+                        "gauges_summary": {k: v.get("value") for k, v in ((macro_regime or {}).get("gauges", {})).items()},
+                    },
+                    "geo_score": geo_score_obj,
+                    "news_partitioned": {
+                        "inter_cycle": [{"title": n.get("title"), "source": n.get("source"), "decayed_score": n.get("decayed_score"), "age_hours": n.get("age_hours")} for n in _news_snap["inter_cycle"][:10]],
+                        "recent_24h":  [{"title": n.get("title"), "source": n.get("source"), "decayed_score": n.get("decayed_score")} for n in _news_snap["recent_24h"][:10]],
+                        "older":       [{"title": n.get("title"), "source": n.get("source")} for n in _news_snap["older"][:5]],
+                    },
+                    "fred_releases": _fred_releases,
+                    "tech_indicators_block": _tech_block,
+                    "iv_context_preview": iv_context[:500] if iv_context else "",
+                    "calendar": calendar[:8] if calendar else [],
+                    "quotes_summary": {cls: [{"symbol": q.get("symbol"), "price": q.get("price"), "change_pct": q.get("change_pct")} for q in qs[:3]] for cls, qs in quotes.items()},
+                }
+                save_cycle_context_snapshot(run_id, _context_snapshot)
+                logger.info(f"[Cycle {run_id[:16]}] Context snapshot saved")
+            except Exception as _snap_e:
+                logger.warning(f"[Cycle] Context snapshot save failed (non-blocking): {_snap_e}")
+
             suggestions = suggest_patterns_from_market_context(
                 news, quotes, calendar, macro_regime=macro_regime, geo_score=geo_score_obj,
                 portfolio_lessons=portfolio_lessons,
@@ -413,6 +453,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                 iv_context=iv_context,
                 cycle_meta=cycle_meta,
                 tech_indicators_block=_tech_block,
+                fred_block=_fred_block,
             )
         except Exception as e:
             logger.warning(f"[Cycle] Suggestion step failed: {e}")
@@ -537,6 +578,7 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
                 risk_context=risk_cluster_context,
                 cycle_meta=cycle_meta,
                 tech_indicators_block=_tech_block,
+                fred_block=_fred_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")]