feat: time-aware trade maturity classification

- Add _trade_maturity() helper: classifies trades by % of horizon elapsed
  (trop_tot <10%, debut 10-35%, mature 35-75%, fin_horizon >75%)
- Fix horizon_days fallback chain in log_trade_entries (default 30→90)
- journal.py: enrich each MTM trade with maturity dict + horizon_days
- reasoning.py: portfolio report segments trades by maturity; GPT-4o
  draws lessons only from matures (≥35% elapsed), never from trop_tot
- auto_cycle.py: 90d window, maturity-aware prompt with timing rules
- JournalDeBord.tsx: maturity badge with emoji, label, progress bar
  and day counter (Xj / Yj Z%) replacing plain days_held column

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

backend/routers/journal.py

@@ -1,7 +1,7 @@
 from fastapi import APIRouter
 from typing import Any, Dict, List
 import math
-from services.database import get_macro_regime_history, get_geo_alert_history, get_trade_entry_prices, reset_journal_history, _fetch_live_prices
+from services.database import get_macro_regime_history, get_geo_alert_history, get_trade_entry_prices, reset_journal_history, _fetch_live_prices, _trade_maturity
 
 
 def _sanitize(obj: Any) -> Any:
@@ -66,12 +66,16 @@ def trade_mtm(days: int = 30):
         except Exception:
             pass
 
+        horizon = e.get("horizon_days") or 90
+        maturity = _trade_maturity(days_held or 0, horizon)
+
         result.append({
             **e,
             "current_price": current_price,
             "pnl_pct": pnl_pct,
             "days_held": days_held,
             "direction": "bearish" if _is_bearish(e.get("strategy", "")) else "bullish",
+            "maturity": maturity,
         })
 
     return _sanitize({"trades": result, "days": days, "tickers_fetched": len(current_prices)})

backend/routers/reasoning.py

@@ -21,6 +21,7 @@ from services.database import (
     get_trade_entry_by_id,
     list_ai_reports,
     save_ai_report,
+    _trade_maturity,
 )
 
 logger = logging.getLogger(__name__)
@@ -234,36 +235,72 @@ def generate_portfolio_report(days: int = 90):
 
     from services.ai_analyzer import _chat
 
-    data = get_mtm_trades_with_traces(days=days, limit_movers=5)
-    winners = data["winners"]
-    losers  = data["losers"]
+    from datetime import date as _date
 
-    winners_block = _trade_summary_block("TOP GAINS", winners)
-    losers_block  = _trade_summary_block("TOP PERTES", losers)
+    data = get_mtm_trades_with_traces(days=days, limit_movers=10)
+    all_trades = data.get("all_trades", [])
 
-    avg_pnl = data.get("avg_pnl_pct")
+    # Classify every trade by maturity
+    def _enrich_maturity(t: dict) -> dict:
+        try:
+            dh = (_date.today() - _date.fromisoformat(t["entry_date"])).days
+        except Exception:
+            dh = 0
+        return {**t, "days_held": dh, "maturity": _trade_maturity(dh, t.get("horizon_days") or 90)}
+
+    enriched = [_enrich_maturity(t) for t in all_trades if t.get("pnl_pct") is not None]
+    trop_tot  = [t for t in enriched if t["maturity"]["status"] == "trop_tot"]
+    en_cours  = [t for t in enriched if t["maturity"]["status"] == "debut"]
+    matures   = [t for t in enriched if t["maturity"]["status"] in ("mature", "fin_horizon")]
+
+    # Sort by P&L for report
+    winners = sorted(matures, key=lambda t: t.get("pnl_pct", 0), reverse=True)[:5]
+    losers  = sorted(matures, key=lambda t: t.get("pnl_pct", 0))[:5]
+    avg_pnl = (sum(t.get("pnl_pct", 0) for t in matures) / len(matures)) if matures else None
     avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
 
+    def _tline(t: dict) -> str:
+        mat = t["maturity"]
+        return (f"  {t.get('underlying','?')} {t.get('strategy','?')} "
+                f"P&L={t.get('pnl_pct',0):+.1f}% [{mat['readable']}] "
+                f"score={t.get('score_at_entry','?')}")
+
+    mature_wins  = "\n".join(_tline(t) for t in winners) or "  Aucun"
+    mature_loss  = "\n".join(_tline(t) for t in losers)  or "  Aucun"
+    early_lines  = "\n".join(_tline(t) for t in (trop_tot + en_cours)[:8]) or "  Aucun"
+
     prompt = f"""Tu es un stratège macro-géopolitique senior. Génère un rapport synthétique sur notre portefeuille options.
 
+⚠️ RÈGLE FONDAMENTALE DE TIMING :
+Nos trades sont des options de 30 à 90 jours. Un trade vieux de 3 jours n'apporte AUCUNE information sur sa performance finale.
+Tu dois tirer des leçons UNIQUEMENT des trades MATURES (≥35% de l'horizon écoulé).
+Les trades IMMATURES (< 35%) sont listés pour transparence — n'en tire aucune conclusion de performance.
+
 ═══ STATISTIQUES GLOBALES ═══
-Période     : {days} derniers jours
-Trades total: {data['total_trades']} | Pricés: {data['priced_count']} | P&L moyen: {avg_str}
+Période : {days}j | Total trades: {len(all_trades)} | Pricés: {data['priced_count']}
+MATURES (signal fiable): {len(matures)} — P&L moyen: {avg_str}
+IMMATURES (trop tôt): {len(trop_tot) + len(en_cours)}
 
-═══ {winners_block}
+═══ MATURES — TOP GAINS (signal fiable — tire des leçons ici) ═══
+{mature_wins}
 
-═══ {losers_block}
+═══ MATURES — TOP PERTES (signal fiable — tire des leçons ici) ═══
+{mature_loss}
 
-Génère un rapport JSON structuré :
+═══ EN COURS / IMMATURES (ne pas juger la performance) ═══
+{early_lines}
+
+Génère un rapport JSON structuré basé UNIQUEMENT sur les trades matures :
 {{
-  "headline": "<1 phrase résumant la performance de la période>",
-  "regime_assessment": "<le régime macro a-t-il bien servi nos thèses ? convergence ou divergence ?>",
-  "winners_analysis": "<pourquoi ces trades ont marché — pattern commun, catalyseur, régime ? 3-4 phrases>",
-  "losers_analysis": "<pourquoi ces trades ont déçu — mauvaise thèse, mauvais timing, contra-signal manqué ? 3-4 phrases>",
-  "key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
-  "blind_spots": "<ce que notre système de scoring n'a pas bien capturé cette période>",
-  "next_cycle_priorities": "<3 priorités concrètes pour améliorer les prochains cycles : patterns à surveiller, ajustements de scoring, régimes à anticiper>",
-  "risk_watch": "<1-2 risques macro-géopolitiques à surveiller de près qui pourraient impacter nos positions actuelles>"
+  "headline": "<1 phrase résumant la performance des trades matures>",
+  "regime_assessment": "<le régime macro a-t-il bien servi nos thèses matures ?>",
+  "winners_analysis": "<pourquoi les trades matures gagnants ont marché — 3-4 phrases>",
+  "losers_analysis": "<pourquoi les trades matures perdants ont déçu — 3-4 phrases>",
+  "key_lessons": ["<leçon 1 des matures>", "<leçon 2>", "<leçon 3>"],
+  "blind_spots": "<ce que le scoring n'a pas bien capturé sur les trades matures>",
+  "next_cycle_priorities": "<3 priorités concrètes pour les prochains cycles>",
+  "risk_watch": "<1-2 risques à surveiller>",
+  "timing_note": "<observation sur les trades immatures — signaux à surveiller sans jugement>"
 }}"""
 
     try:
@@ -272,7 +309,7 @@ Génère un rapport JSON structuré :
             prompt,
             model="gpt-4o",
             json_mode=True,
-            max_tokens=1200,
+            max_tokens=1400,
         )
     except Exception as e:
         logger.error(f"[PortfolioReport] GPT-4o call failed: {e}")
@@ -282,9 +319,11 @@ Génère un rapport JSON structuré :
         raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")
 
     stats = {
-        "total_trades": data["total_trades"],
+        "total_trades": len(all_trades),
         "priced_count": data["priced_count"],
         "avg_pnl_pct": avg_pnl,
+        "mature_count": len(matures),
+        "early_count": len(trop_tot) + len(en_cours),
     }
 
     report_id = save_ai_report(

backend/services/auto_cycle.py

@@ -503,60 +503,106 @@ def _auto_portfolio_snapshot(ai_key: str) -> None:
         os.environ["OPENAI_API_KEY"] = ai_key
 
         from services.database import (
-            get_mtm_trades_with_traces, save_ai_report, get_latest_portfolio_lessons,
+            get_mtm_trades_with_traces, save_ai_report, _trade_maturity,
         )
+        from datetime import date as _date
 
-        data = get_mtm_trades_with_traces(days=30, limit_movers=5)
-        winners = data.get("winners", [])
-        losers  = data.get("losers",  [])
+        data = get_mtm_trades_with_traces(days=90, limit_movers=10)
+        all_trades = data.get("all_trades", [])
         priced  = data.get("priced_count", 0)
 
-        # Need at least 3 priced trades with actual movement to make analysis meaningful
-        meaningful = [
-            t for t in (winners + losers)
-            if t.get("pnl_pct") is not None and abs(t.get("pnl_pct", 0)) > 0.05
-        ]
-        if len(meaningful) < 3:
+        # Classify each trade by maturity
+        def _with_maturity(t: Dict) -> Dict:
+            try:
+                dh = (_date.today() - _date.fromisoformat(t["entry_date"])).days
+            except Exception:
+                dh = 0
+            mat = _trade_maturity(dh, t.get("horizon_days") or 90)
+            return {**t, "days_held": dh, "maturity": mat}
+
+        enriched = [_with_maturity(t) for t in all_trades if t.get("pnl_pct") is not None]
+
+        trop_tot   = [t for t in enriched if t["maturity"]["status"] == "trop_tot"]
+        en_cours   = [t for t in enriched if t["maturity"]["status"] == "debut"]
+        matures    = [t for t in enriched if t["maturity"]["status"] in ("mature", "fin_horizon")]
+
+        # Only learn lessons from mature trades (weight > 0.9)
+        meaningful_mature = [t for t in matures if abs(t.get("pnl_pct", 0)) > 0.1]
+
+        if len(meaningful_mature) < 2:
             logger.info(
-                f"[AutoSnapshot] Skipping GPT-4o report: only {len(meaningful)} trades "
-                f"with meaningful P&L movement (need ≥ 3)"
+                f"[AutoSnapshot] Skipping GPT-4o report: only {len(meaningful_mature)} mature trades "
+                f"with meaningful P&L (need ≥ 2). "
+                f"Immatures={len(trop_tot)}, en cours={len(en_cours)}, matures={len(matures)}"
             )
             return
 
-        avg_pnl = data.get("avg_pnl_pct")
+        # Sort matures by P&L for the report
+        winners = sorted(matures, key=lambda t: t.get("pnl_pct", 0), reverse=True)[:5]
+        losers  = sorted(matures, key=lambda t: t.get("pnl_pct", 0))[:5]
+        avg_pnl = (sum(t.get("pnl_pct", 0) for t in matures) / len(matures)) if matures else None
+
         stats = {
-            "total_trades": data["total_trades"],
+            "total_trades": len(all_trades),
             "priced_count": priced,
             "avg_pnl_pct": avg_pnl,
+            "mature_count": len(matures),
+            "early_count": len(trop_tot) + len(en_cours),
         }
 
-        # Build prompt (reuse same logic as reasoning.py generate endpoint)
-        from routers.reasoning import _trade_summary_block, _bucket_summary, _rankings_summary
+        # Build maturity context blocks for the prompt
+        def _trade_line(t: Dict) -> str:
+            mat = t["maturity"]
+            return (
+                f"  {t.get('underlying','?')} {t.get('strategy','?')} "
+                f"P&L={t.get('pnl_pct',0):+.1f}% "
+                f"[{mat['readable']}] "
+                f"score_entrée={t.get('score_at_entry','?')}"
+            )
+
+        mature_wins_block   = "\n".join(_trade_line(t) for t in winners) or "  Aucun"
+        mature_losses_block = "\n".join(_trade_line(t) for t in losers)  or "  Aucun"
+        early_block = "\n".join(
+            f"  {t.get('underlying','?')} {t.get('strategy','?')} "
+            f"P&L={t.get('pnl_pct',0):+.1f}% [{t['maturity']['readable']}]"
+            for t in (trop_tot + en_cours)[:6]
+        ) or "  Aucun"
+
+        avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
         from services.ai_analyzer import _chat
 
-        winners_block = _trade_summary_block("TOP GAINS", winners)
-        losers_block  = _trade_summary_block("TOP PERTES", losers)
-        avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
+        prompt = f"""Tu es un stratège macro-géopolitique senior. Rapport post-cycle automatique.
 
-        prompt = f"""Tu es un stratège macro-géopolitique senior. Rapport synthétique post-cycle automatique.
+⚠️ RÈGLE FONDAMENTALE DE TIMING :
+Les options ont des horizons de 30-90 jours. Un trade de 3 jours ne dit RIEN sur sa performance finale.
+Tu dois UNIQUEMENT tirer des leçons des trades MATURES (≥35% de l'horizon écoulé).
+Les trades immatures sont listés pour information seulement — n'en tire AUCUNE conclusion de performance.
 
 ═══ STATISTIQUES GLOBALES ═══
-Période : 30 derniers jours | Trades total: {data['total_trades']} | Pricés: {priced} | P&L moyen: {avg_str}
+Période analysée : 90 jours | Trades total: {len(all_trades)} | Pricés: {priced}
+Trades MATURES (signal fiable): {len(matures)} | P&L moyen matures: {avg_str}
+Trades IMMATURES (trop tôt pour juger): {len(trop_tot) + len(en_cours)}
 
-═══ {winners_block}
+═══ MATURES — TOP GAINS (signal fiable) ═══
+{mature_wins_block}
 
-═══ {losers_block}
+═══ MATURES — TOP PERTES (signal fiable) ═══
+{mature_losses_block}
 
-Génère un rapport JSON :
+═══ EN COURS — NE PAS ÉVALUER (trop tôt) ═══
+{early_block}
+
+Génère un rapport JSON basé UNIQUEMENT sur les trades matures :
 {{
-  "headline": "<1 phrase résumant la performance>",
-  "regime_assessment": "<alignement régime macro avec nos thèses ?>",
-  "winners_analysis": "<pourquoi ces trades ont marché — 2-3 phrases>",
-  "losers_analysis": "<pourquoi ces trades ont déçu — 2-3 phrases>",
-  "key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
-  "blind_spots": "<ce que le scoring n'a pas bien capturé>",
+  "headline": "<1 phrase résumant la performance des trades matures>",
+  "regime_assessment": "<alignement régime macro avec nos thèses matures ?>",
+  "winners_analysis": "<pourquoi les trades matures gagnants ont marché — 2-3 phrases>",
+  "losers_analysis": "<pourquoi les trades matures perdants ont déçu — 2-3 phrases>",
+  "key_lessons": ["<leçon 1 tirée des matures>", "<leçon 2>", "<leçon 3>"],
+  "blind_spots": "<ce que le scoring n'a pas bien capturé sur les trades matures>",
   "next_cycle_priorities": "<3 priorités pour le prochain cycle>",
-  "risk_watch": "<1-2 risques à surveiller>"
+  "risk_watch": "<1-2 risques à surveiller>",
+  "timing_note": "<observation sur les trades immatures — à surveiller mais pas à juger>"
 }}"""
 
         result = _chat(

backend/services/database.py

@@ -889,7 +889,13 @@ def log_trade_entries(run_id: str, scored_patterns: List[Dict[str, Any]], quotes
 
             ticker_key = underlying.upper()
             entry_price = price_map.get(ticker_key)
-            horizon = int(trade.get("horizon_days") or sp.get("horizon_days") or 30)
+            horizon = int(
+                trade.get("horizon_days") or
+                sp.get("horizon_days") or
+                sp.get("recommended_trade", {}).get("expiry_days") or
+                _orig.get("horizon_days") or
+                90
+            )
 
             existing_row = conn.execute(
                 "SELECT id FROM trade_entry_prices WHERE pattern_id=? AND underlying=? AND strategy=?",
@@ -975,6 +981,48 @@ def get_cycle_run(run_id: str) -> Optional[Dict[str, Any]]:
     return dict(row) if row else None
 
 
+def _trade_maturity(days_held: int, horizon_days: int) -> Dict[str, Any]:
+    """
+    Classify a trade's maturity based on elapsed time vs planned horizon.
+    Returns status, label, weight (0-1 for lesson extraction), and color hint.
+
+    Thresholds (percentage of horizon elapsed):
+      < 10%  → trop_tot    : P&L is pure noise, never evaluate
+      10-35% → debut       : early signal, very low weight
+      35-75% → mature      : reliable signal, full weight
+      > 75%  → fin_horizon : approaching expiry, full weight + watch flag
+    """
+    h = max(horizon_days or 90, 1)
+    d = max(days_held or 0, 0)
+    ratio = d / h
+    pct = round(ratio * 100, 1)
+
+    if ratio < 0.10:
+        return {
+            "status": "trop_tot", "label": "Trop tôt", "emoji": "🕐",
+            "weight": 0.0, "color": "slate", "ratio_pct": pct,
+            "readable": f"{d}j / {h}j ({pct}% écoulé — bruit statistique)",
+        }
+    elif ratio < 0.35:
+        return {
+            "status": "debut", "label": "Début", "emoji": "📊",
+            "weight": 0.25, "color": "yellow", "ratio_pct": pct,
+            "readable": f"{d}j / {h}j ({pct}% écoulé — signal précoce)",
+        }
+    elif ratio < 0.75:
+        return {
+            "status": "mature", "label": "Signal fiable", "emoji": "✅",
+            "weight": 1.0, "color": "emerald", "ratio_pct": pct,
+            "readable": f"{d}j / {h}j ({pct}% écoulé — signal fiable)",
+        }
+    else:
+        return {
+            "status": "fin_horizon", "label": "Fin d'horizon", "emoji": "⏰",
+            "weight": 1.0, "color": "orange", "ratio_pct": pct,
+            "readable": f"{d}j / {h}j ({pct}% écoulé — surveiller de près)",
+        }
+
+
 def get_trade_entry_prices(days: int = 30) -> List[Dict[str, Any]]:
     conn = get_conn()
     rows = conn.execute(

frontend/src/pages/JournalDeBord.tsx

@@ -394,7 +394,7 @@ function TradeMtmSection({ days }: { days: number }) {
                 <th className="text-right px-3 py-2 font-medium">Date</th>
                 <th className="text-right px-3 py-2 font-medium">Prix entrée</th>
                 <th className="text-right px-3 py-2 font-medium">Prix actuel</th>
-                <th className="text-right px-3 py-2 font-medium">J</th>
+                <th className="text-right px-3 py-2 font-medium">Maturité</th>
                 <th className="text-right px-3 py-2 font-medium">P&L th.</th>
                 <th className="px-3 py-2 font-medium w-8"></th>
               </tr>
@@ -451,8 +451,37 @@ function TradeMtmSection({ days }: { days: number }) {
                   <td className="px-3 py-2 text-right font-mono text-slate-300 text-[11px]">
                     {t.current_price != null ? t.current_price.toFixed(2) : '—'}
                   </td>
-                  <td className="px-3 py-2 text-right text-slate-600 text-[11px]">
-                    {t.days_held != null ? t.days_held : '—'}
+                  <td className="px-3 py-2 text-right">
+                    {t.maturity ? (
+                      <div className="flex flex-col items-end gap-0.5">
+                        <span className={clsx('text-[10px] font-semibold',
+                          t.maturity.status === 'trop_tot'   ? 'text-slate-500' :
+                          t.maturity.status === 'debut'       ? 'text-yellow-400' :
+                          t.maturity.status === 'mature'      ? 'text-emerald-400' :
+                          'text-orange-400'
+                        )}>
+                          {t.maturity.emoji} {t.maturity.label}
+                        </span>
+                        <span className="text-[9px] text-slate-600 font-mono">
+                          {t.days_held ?? 0}j / {t.horizon_days ?? 90}j ({t.maturity.ratio_pct}%)
+                        </span>
+                        <div className="w-16 h-1 bg-slate-700 rounded-full overflow-hidden">
+                          <div
+                            className={clsx('h-full rounded-full',
+                              t.maturity.status === 'trop_tot'   ? 'bg-slate-600' :
+                              t.maturity.status === 'debut'       ? 'bg-yellow-500' :
+                              t.maturity.status === 'mature'      ? 'bg-emerald-500' :
+                              'bg-orange-500'
+                            )}
+                            style={{ width: `${Math.min(t.maturity.ratio_pct, 100)}%` }}
+                          />
+                        </div>
+                      </div>
+                    ) : (
+                      <span className="text-slate-700 text-[11px]">
+                        {t.days_held != null ? `${t.days_held}j` : '—'}
+                      </span>
+                    )}
                   </td>
                   <td className="px-3 py-2 text-right">
                     <PnlBadge pnl={t.pnl_pct} />