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>
This commit is contained in:
OpenSquared
@@ -1,7 +1,7 @@
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from fastapi import APIRouter
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from typing import Any, Dict, List
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import math
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from services.database import get_macro_regime_history, get_geo_alert_history, get_trade_entry_prices, reset_journal_history, _fetch_live_prices
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from services.database import get_macro_regime_history, get_geo_alert_history, get_trade_entry_prices, reset_journal_history, _fetch_live_prices, _trade_maturity
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def _sanitize(obj: Any) -> Any:
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@@ -66,12 +66,16 @@ def trade_mtm(days: int = 30):
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except Exception:
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pass
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horizon = e.get("horizon_days") or 90
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maturity = _trade_maturity(days_held or 0, horizon)
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result.append({
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**e,
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"current_price": current_price,
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"pnl_pct": pnl_pct,
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"days_held": days_held,
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"direction": "bearish" if _is_bearish(e.get("strategy", "")) else "bullish",
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"maturity": maturity,
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})
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return _sanitize({"trades": result, "days": days, "tickers_fetched": len(current_prices)})
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@@ -21,6 +21,7 @@ from services.database import (
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get_trade_entry_by_id,
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list_ai_reports,
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save_ai_report,
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_trade_maturity,
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)
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logger = logging.getLogger(__name__)
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@@ -234,36 +235,72 @@ def generate_portfolio_report(days: int = 90):
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from services.ai_analyzer import _chat
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data = get_mtm_trades_with_traces(days=days, limit_movers=5)
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winners = data["winners"]
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losers = data["losers"]
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from datetime import date as _date
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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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data = get_mtm_trades_with_traces(days=days, limit_movers=10)
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all_trades = data.get("all_trades", [])
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avg_pnl = data.get("avg_pnl_pct")
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# Classify every trade by maturity
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def _enrich_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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return {**t, "days_held": dh, "maturity": _trade_maturity(dh, t.get("horizon_days") or 90)}
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enriched = [_enrich_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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# Sort by P&L for 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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avg_str = f"{avg_pnl:+.1f}%" if avg_pnl is not None else "N/A"
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def _tline(t: dict) -> str:
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mat = t["maturity"]
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return (f" {t.get('underlying','?')} {t.get('strategy','?')} "
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f"P&L={t.get('pnl_pct',0):+.1f}% [{mat['readable']}] "
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f"score={t.get('score_at_entry','?')}")
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mature_wins = "\n".join(_tline(t) for t in winners) or " Aucun"
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mature_loss = "\n".join(_tline(t) for t in losers) or " Aucun"
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early_lines = "\n".join(_tline(t) for t in (trop_tot + en_cours)[:8]) or " Aucun"
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prompt = f"""Tu es un stratège macro-géopolitique senior. Génère un rapport synthétique sur notre portefeuille options.
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⚠️ RÈGLE FONDAMENTALE DE TIMING :
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Nos trades sont des options de 30 à 90 jours. Un trade vieux de 3 jours n'apporte AUCUNE information sur sa performance finale.
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Tu dois tirer des leçons UNIQUEMENT des trades MATURES (≥35% de l'horizon écoulé).
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Les trades IMMATURES (< 35%) sont listés pour transparence — n'en tire aucune conclusion de performance.
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═══ STATISTIQUES GLOBALES ═══
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Période : {days} derniers jours
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Trades total: {data['total_trades']} | Pricés: {data['priced_count']} | P&L moyen: {avg_str}
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Période : {days}j | Total trades: {len(all_trades)} | Pricés: {data['priced_count']}
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MATURES (signal fiable): {len(matures)} — P&L moyen: {avg_str}
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IMMATURES (trop tôt): {len(trop_tot) + len(en_cours)}
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═══ {winners_block}
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═══ MATURES — TOP GAINS (signal fiable — tire des leçons ici) ═══
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{mature_wins}
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═══ {losers_block}
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═══ MATURES — TOP PERTES (signal fiable — tire des leçons ici) ═══
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{mature_loss}
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Génère un rapport JSON structuré :
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═══ EN COURS / IMMATURES (ne pas juger la performance) ═══
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{early_lines}
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Génère un rapport JSON structuré basé UNIQUEMENT sur les trades matures :
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{{
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"headline": "<1 phrase résumant la performance de la période>",
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"regime_assessment": "<le régime macro a-t-il bien servi nos thèses ? convergence ou divergence ?>",
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"winners_analysis": "<pourquoi ces trades ont marché — pattern commun, catalyseur, régime ? 3-4 phrases>",
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"losers_analysis": "<pourquoi ces trades ont déçu — mauvaise thèse, mauvais timing, contra-signal manqué ? 3-4 phrases>",
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"key_lessons": ["<leçon 1>", "<leçon 2>", "<leçon 3>"],
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"blind_spots": "<ce que notre système de scoring n'a pas bien capturé cette période>",
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"next_cycle_priorities": "<3 priorités concrètes pour améliorer les prochains cycles : patterns à surveiller, ajustements de scoring, régimes à anticiper>",
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"risk_watch": "<1-2 risques macro-géopolitiques à surveiller de près qui pourraient impacter nos positions actuelles>"
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"headline": "<1 phrase résumant la performance des trades matures>",
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"regime_assessment": "<le régime macro a-t-il bien servi nos thèses matures ?>",
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"winners_analysis": "<pourquoi les trades matures gagnants ont marché — 3-4 phrases>",
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"losers_analysis": "<pourquoi les trades matures perdants ont déçu — 3-4 phrases>",
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"key_lessons": ["<leçon 1 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 concrètes pour les prochains cycles>",
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"risk_watch": "<1-2 risques à surveiller>",
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"timing_note": "<observation sur les trades immatures — signaux à surveiller sans jugement>"
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}}"""
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try:
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@@ -272,7 +309,7 @@ Génère un rapport JSON structuré :
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prompt,
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model="gpt-4o",
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json_mode=True,
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max_tokens=1200,
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max_tokens=1400,
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)
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except Exception as e:
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logger.error(f"[PortfolioReport] GPT-4o call failed: {e}")
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@@ -282,9 +319,11 @@ Génère un rapport JSON structuré :
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raise HTTPException(503, "GPT-4o n'a pas retourné de réponse")
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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": data["priced_count"],
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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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report_id = save_ai_report(
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