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>
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OpenSquared
@@ -503,60 +503,106 @@ def _auto_portfolio_snapshot(ai_key: str) -> None:
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os.environ["OPENAI_API_KEY"] = ai_key
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from services.database import (
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get_mtm_trades_with_traces, save_ai_report, get_latest_portfolio_lessons,
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get_mtm_trades_with_traces, save_ai_report, _trade_maturity,
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)
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from datetime import date as _date
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data = get_mtm_trades_with_traces(days=30, limit_movers=5)
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winners = data.get("winners", [])
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losers = data.get("losers", [])
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data = get_mtm_trades_with_traces(days=90, limit_movers=10)
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all_trades = data.get("all_trades", [])
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priced = data.get("priced_count", 0)
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# Need at least 3 priced trades with actual movement to make analysis meaningful
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meaningful = [
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t for t in (winners + losers)
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if t.get("pnl_pct") is not None and abs(t.get("pnl_pct", 0)) > 0.05
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]
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if len(meaningful) < 3:
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# Classify each trade by maturity
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def _with_maturity(t: Dict) -> Dict:
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try:
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dh = (_date.today() - _date.fromisoformat(t["entry_date"])).days
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except Exception:
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dh = 0
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mat = _trade_maturity(dh, t.get("horizon_days") or 90)
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return {**t, "days_held": dh, "maturity": mat}
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enriched = [_with_maturity(t) for t in all_trades if t.get("pnl_pct") is not None]
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trop_tot = [t for t in enriched if t["maturity"]["status"] == "trop_tot"]
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en_cours = [t for t in enriched if t["maturity"]["status"] == "debut"]
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matures = [t for t in enriched if t["maturity"]["status"] in ("mature", "fin_horizon")]
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# Only learn lessons from mature trades (weight > 0.9)
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meaningful_mature = [t for t in matures if abs(t.get("pnl_pct", 0)) > 0.1]
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if len(meaningful_mature) < 2:
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logger.info(
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f"[AutoSnapshot] Skipping GPT-4o report: only {len(meaningful)} trades "
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f"with meaningful P&L movement (need ≥ 3)"
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f"[AutoSnapshot] Skipping GPT-4o report: only {len(meaningful_mature)} mature trades "
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f"with meaningful P&L (need ≥ 2). "
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f"Immatures={len(trop_tot)}, en cours={len(en_cours)}, matures={len(matures)}"
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)
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return
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avg_pnl = data.get("avg_pnl_pct")
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# Sort matures by P&L for the report
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winners = sorted(matures, key=lambda t: t.get("pnl_pct", 0), reverse=True)[:5]
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losers = sorted(matures, key=lambda t: t.get("pnl_pct", 0))[:5]
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avg_pnl = (sum(t.get("pnl_pct", 0) for t in matures) / len(matures)) if matures else None
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stats = {
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"total_trades": data["total_trades"],
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"total_trades": len(all_trades),
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"priced_count": priced,
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"avg_pnl_pct": avg_pnl,
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"mature_count": len(matures),
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"early_count": len(trop_tot) + len(en_cours),
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}
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# Build prompt (reuse same logic as reasoning.py generate endpoint)
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from routers.reasoning import _trade_summary_block, _bucket_summary, _rankings_summary
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# Build maturity context blocks for the prompt
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def _trade_line(t: Dict) -> str:
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mat = t["maturity"]
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return (
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f" {t.get('underlying','?')} {t.get('strategy','?')} "
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f"P&L={t.get('pnl_pct',0):+.1f}% "
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f"[{mat['readable']}] "
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f"score_entrée={t.get('score_at_entry','?')}"
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)
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mature_wins_block = "\n".join(_trade_line(t) for t in winners) or " Aucun"
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mature_losses_block = "\n".join(_trade_line(t) for t in losers) or " Aucun"
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early_block = "\n".join(
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f" {t.get('underlying','?')} {t.get('strategy','?')} "
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f"P&L={t.get('pnl_pct',0):+.1f}% [{t['maturity']['readable']}]"
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for t in (trop_tot + en_cours)[:6]
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) or " Aucun"
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avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
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from services.ai_analyzer import _chat
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winners_block = _trade_summary_block("TOP GAINS", winners)
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losers_block = _trade_summary_block("TOP PERTES", losers)
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avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
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prompt = f"""Tu es un stratège macro-géopolitique senior. Rapport post-cycle automatique.
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prompt = f"""Tu es un stratège macro-géopolitique senior. Rapport synthétique post-cycle automatique.
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⚠️ RÈGLE FONDAMENTALE DE TIMING :
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Les options ont des horizons de 30-90 jours. Un trade de 3 jours ne dit RIEN sur sa performance finale.
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Tu dois UNIQUEMENT tirer des leçons des trades MATURES (≥35% de l'horizon écoulé).
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Les trades immatures sont listés pour information seulement — n'en tire AUCUNE conclusion de performance.
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═══ STATISTIQUES GLOBALES ═══
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Période : 30 derniers jours | Trades total: {data['total_trades']} | Pricés: {priced} | P&L moyen: {avg_str}
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Période analysée : 90 jours | Trades total: {len(all_trades)} | Pricés: {priced}
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Trades MATURES (signal fiable): {len(matures)} | P&L moyen matures: {avg_str}
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Trades IMMATURES (trop tôt pour juger): {len(trop_tot) + len(en_cours)}
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═══ {winners_block}
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═══ MATURES — TOP GAINS (signal fiable) ═══
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{mature_wins_block}
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═══ {losers_block}
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═══ MATURES — TOP PERTES (signal fiable) ═══
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{mature_losses_block}
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Génère un rapport JSON :
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═══ EN COURS — NE PAS ÉVALUER (trop tôt) ═══
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{early_block}
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Génère un rapport JSON basé UNIQUEMENT sur les trades matures :
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{{
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"headline": "<1 phrase résumant la performance>",
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"regime_assessment": "<alignement régime macro avec nos thèses ?>",
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"winners_analysis": "<pourquoi ces trades ont marché — 2-3 phrases>",
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"losers_analysis": "<pourquoi ces trades ont déçu — 2-3 phrases>",
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"key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
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"blind_spots": "<ce que le scoring n'a pas bien capturé>",
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"headline": "<1 phrase résumant la performance des trades matures>",
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"regime_assessment": "<alignement régime macro avec nos thèses matures ?>",
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"winners_analysis": "<pourquoi les trades matures gagnants ont marché — 2-3 phrases>",
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"losers_analysis": "<pourquoi les trades matures perdants ont déçu — 2-3 phrases>",
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"key_lessons": ["<leçon 1 tirée des matures>", "<leçon 2>", "<leçon 3>"],
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"blind_spots": "<ce que le scoring n'a pas bien capturé sur les trades matures>",
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"next_cycle_priorities": "<3 priorités pour le prochain cycle>",
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"risk_watch": "<1-2 risques à surveiller>"
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"risk_watch": "<1-2 risques à surveiller>",
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"timing_note": "<observation sur les trades immatures — à surveiller mais pas à juger>"
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}}"""
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result = _chat(
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@@ -889,7 +889,13 @@ def log_trade_entries(run_id: str, scored_patterns: List[Dict[str, Any]], quotes
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ticker_key = underlying.upper()
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entry_price = price_map.get(ticker_key)
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horizon = int(trade.get("horizon_days") or sp.get("horizon_days") or 30)
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horizon = int(
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trade.get("horizon_days") or
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sp.get("horizon_days") or
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sp.get("recommended_trade", {}).get("expiry_days") or
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_orig.get("horizon_days") or
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90
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)
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existing_row = conn.execute(
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"SELECT id FROM trade_entry_prices WHERE pattern_id=? AND underlying=? AND strategy=?",
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@@ -975,6 +981,48 @@ def get_cycle_run(run_id: str) -> Optional[Dict[str, Any]]:
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return dict(row) if row else None
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def _trade_maturity(days_held: int, horizon_days: int) -> Dict[str, Any]:
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"""
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Classify a trade's maturity based on elapsed time vs planned horizon.
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Returns status, label, weight (0-1 for lesson extraction), and color hint.
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Thresholds (percentage of horizon elapsed):
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< 10% → trop_tot : P&L is pure noise, never evaluate
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10-35% → debut : early signal, very low weight
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35-75% → mature : reliable signal, full weight
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> 75% → fin_horizon : approaching expiry, full weight + watch flag
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"""
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h = max(horizon_days or 90, 1)
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d = max(days_held or 0, 0)
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ratio = d / h
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pct = round(ratio * 100, 1)
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if ratio < 0.10:
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return {
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"status": "trop_tot", "label": "Trop tôt", "emoji": "🕐",
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"weight": 0.0, "color": "slate", "ratio_pct": pct,
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"readable": f"{d}j / {h}j ({pct}% écoulé — bruit statistique)",
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}
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elif ratio < 0.35:
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return {
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"status": "debut", "label": "Début", "emoji": "📊",
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"weight": 0.25, "color": "yellow", "ratio_pct": pct,
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"readable": f"{d}j / {h}j ({pct}% écoulé — signal précoce)",
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}
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elif ratio < 0.75:
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return {
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"status": "mature", "label": "Signal fiable", "emoji": "✅",
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"weight": 1.0, "color": "emerald", "ratio_pct": pct,
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"readable": f"{d}j / {h}j ({pct}% écoulé — signal fiable)",
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}
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else:
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return {
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"status": "fin_horizon", "label": "Fin d'horizon", "emoji": "⏰",
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"weight": 1.0, "color": "orange", "ratio_pct": pct,
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"readable": f"{d}j / {h}j ({pct}% écoulé — surveiller de près)",
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}
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def get_trade_entry_prices(days: int = 30) -> List[Dict[str, Any]]:
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conn = get_conn()
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rows = conn.execute(
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