feat: Phase 2 — Pattern Reliability, Contre-thèses & Calibration probabiliste
Sprint 2.1 — Pattern Reliability Score - database.py: get_pattern_reliability() — win_rate × log(n+1) sur trades matures (≥35% horizon) - database.py: get_all_pattern_reliability_map() pour injection rapide dans les prompts - ai_analyzer.py: inject reliability_map dans suggest_patterns (patterns fiables mis en avant) - auto_cycle.py: charge reliability_map avant suggestion et le passe au suggéreur - routers/analytics.py: GET /api/analytics/reliability - PatternEditor.tsx: ReliabilityBadge sur chaque card + usePatternReliability hook - useApi.ts: usePatternReliability, useCalibration hooks Sprint 2.2 — Contre-thèses & Invalidation Triggers - database.py: migration ALTER TABLE — counter_thesis, invalidation_trigger, invalidation_probability - database.py: save_custom_pattern() persiste les 3 nouveaux champs - ai_analyzer.py: counter_thesis + invalidation_trigger + invalidation_probability dans le JSON schema - auto_cycle.py: détection automatique des triggers d'invalidation contre les news (keyword match) - routers/analytics.py: GET /api/analytics/invalidation-alerts - PatternEditor.tsx: affichage contre-thèse dans les cards + champs dans le formulaire - PatternEditor.tsx: affichage dans AiSuggestModal (suggestions IA) - routers/patterns.py: PatternRequest inclut les 3 nouveaux champs Sprint 2.3 — Calibration probabiliste & Demi-vie KB - database.py: migration — predicted_probability sur pattern_score_history - database.py: save_pattern_scores() stocke probability du pattern à chaque scoring run - database.py: get_calibration_data() — Brier score + buckets de calibration par décile - database.py: expires_at + confidence_decay_days sur knowledge_base - database.py: decay_kb_confidence() — decay automatique + archivage à 0 - auto_cycle.py: decay_kb_confidence() appelé au début de chaque cycle (non-bloquant) - routers/analytics.py: GET /api/analytics/calibration + POST /api/analytics/kb/decay - frontend/src/pages/Analytics.tsx: nouvelle page — tableau fiabilité + calibration Brier - App.tsx + Sidebar.tsx: route /analytics + entrée menu Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
OpenSquared
@@ -1,6 +1,6 @@
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from routers import market_data, geopolitical, options, backtest, ai, portfolio, config, patterns, journal, cycle as cycle_router, profiles as profiles_router, reasoning as reasoning_router, knowledge as knowledge_router, options_vol as options_vol_router
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from routers import market_data, geopolitical, options, backtest, ai, portfolio, config, patterns, journal, cycle as cycle_router, profiles as profiles_router, reasoning as reasoning_router, knowledge as knowledge_router, options_vol as options_vol_router, analytics as analytics_router
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from services.database import init_db, get_config, cleanup_stale_running_cycles
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import os
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import uvicorn
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@@ -72,6 +72,7 @@ app.include_router(profiles_router.router)
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app.include_router(reasoning_router.router)
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app.include_router(knowledge_router.router)
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app.include_router(options_vol_router.router)
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app.include_router(analytics_router.router)
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@app.get("/")
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55
backend/routers/analytics.py
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55
backend/routers/analytics.py
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@@ -0,0 +1,55 @@
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from fastapi import APIRouter, Query
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from services.database import (
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get_pattern_reliability,
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get_all_pattern_reliability_map,
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get_calibration_data,
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decay_kb_confidence,
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get_custom_patterns,
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)
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router = APIRouter(prefix="/api/analytics", tags=["analytics"])
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@router.get("/reliability")
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def reliability_all():
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"""Pattern reliability scores — all patterns with mature trade history."""
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return {"reliability": get_pattern_reliability()}
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@router.get("/reliability/{pattern_id}")
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def reliability_one(pattern_id: str):
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result = get_pattern_reliability(pattern_id=pattern_id)
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if not result:
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return {"reliability": None}
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return {"reliability": result[0]}
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@router.get("/calibration")
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def calibration(days: int = Query(default=365, ge=30, le=1000)):
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"""Predicted probability vs realized outcomes (Brier score + buckets)."""
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return get_calibration_data(days=days)
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@router.post("/kb/decay")
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def run_kb_decay():
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"""Manually trigger KB confidence decay (also runs at cycle start)."""
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updated = decay_kb_confidence()
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return {"updated": updated, "message": f"{updated} entrée(s) KB mise(s) à jour"}
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@router.get("/invalidation-alerts")
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def invalidation_alerts():
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"""List all active patterns that have an invalidation trigger defined."""
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patterns = get_custom_patterns()
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alerts = [
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{
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"pattern_id": p["id"],
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"pattern_name": p["name"],
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"invalidation_trigger": p.get("invalidation_trigger"),
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"invalidation_probability": p.get("invalidation_probability"),
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"counter_thesis": p.get("counter_thesis"),
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}
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for p in patterns
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if p.get("invalidation_trigger")
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]
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return {"alerts": alerts, "count": len(alerts)}
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@@ -23,6 +23,9 @@ class PatternRequest(BaseModel):
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ai_quality_score: Optional[int] = None
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ai_evaluation: Optional[Dict[str, Any]] = None
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source: Optional[str] = "custom"
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counter_thesis: Optional[str] = None
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invalidation_trigger: Optional[str] = None
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invalidation_probability: Optional[float] = None
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@router.get("/all")
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@@ -727,6 +727,7 @@ def suggest_patterns_from_market_context(
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macro_regime: Optional[Dict] = None,
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geo_score: Optional[Dict] = None,
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portfolio_lessons: Optional[Dict] = None,
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reliability_map: Optional[Dict] = None,
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) -> List[Dict]:
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"""Ask GPT-4o to propose new patterns based on current geo/market + macro regime context."""
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top_news = sorted(news, key=lambda x: x.get("impact_score", 0), reverse=True)[:12]
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@@ -810,8 +811,30 @@ Leçons clés :
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Évite les erreurs identifiées dans les pertes. Privilégie les types de thèses qui ont fonctionné.
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"""
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reliability_block = ""
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if reliability_map:
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top_reliable = sorted(reliability_map.values(), key=lambda r: -r["reliability_score"])[:5]
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bottom_reliable = [r for r in sorted(reliability_map.values(), key=lambda r: r["reliability_score"]) if r["trade_count"] >= 3][:3]
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lines = []
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for r in top_reliable:
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lines.append(
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f" ✅ {r['pattern_name']}: WR={r['win_rate_pct']}% | {r['trade_count']} trades | "
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f"avgPnL={r['avg_pnl_pct']:+.1f}% | fiabilité={r['reliability_score']:.2f}"
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)
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for r in bottom_reliable:
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lines.append(
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f" ❌ {r['pattern_name']}: WR={r['win_rate_pct']}% | {r['trade_count']} trades | "
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f"avgPnL={r['avg_pnl_pct']:+.1f}% → À ÉVITER ou reformuler"
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)
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if lines:
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reliability_block = (
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"\n## 📊 FIABILITÉ HISTORIQUE DES PATTERNS (trades matures uniquement)\n"
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+ "\n".join(lines)
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+ "\n⚠️ Inspire-toi des patterns fiables. Évite de reproduire les patterns en bas de liste.\n"
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)
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user = f"""Tu es un stratège géopolitique et financier senior.
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{macro_block}{geo_block}{lessons_block}
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{macro_block}{geo_block}{lessons_block}{reliability_block}
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## Actualités géopolitiques du moment (triées par impact)
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{news_block}
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@@ -847,6 +870,9 @@ Retourne UNIQUEMENT ce JSON:
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"expected_move_pct": <float, RENDEMENT OPTION MOYEN en % pour ce pattern, levier inclus. Typiquement 50-300%.>,
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"probability": <float 0-1>,
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"horizon_days": <int>,
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"counter_thesis": "<1-2 phrases: principal scénario adverse qui invaliderait ce pattern — sois spécifique (ex: accord de paix inattendu, données CPI sous 3%, etc.)>",
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"invalidation_trigger": "<événement précis et mesurable à surveiller — ex: 'prix pétrole < 70$/b 3j consécutifs', 'FOMC hawkish surprise', 'cessez-le-feu Russie-Ukraine'>",
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"invalidation_probability": <float 0-1, probabilité que ce trigger d'invalidation se réalise dans l'horizon>,
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"suggested_trades": [
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{{
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"strategy": "<Long Call|Long Put|Bull Call Spread|Bear Put Spread|Long Straddle>",
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@@ -87,6 +87,15 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
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ai_score_news_batch, _chat, DEFAULT_ANALYSIS_TEMPLATE,
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)
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# KB confidence decay (non-blocking)
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try:
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from services.database import decay_kb_confidence
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_decayed = decay_kb_confidence()
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if _decayed:
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logger.info(f"[Cycle {run_id[:16]}] KB decay: {_decayed} entrée(s) mise(s) à jour")
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except Exception as _e:
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logger.warning(f"[Cycle] KB decay failed (non-blocking): {_e}")
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# Check AI key
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ai_key = get_config("openai_api_key") or ""
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if not ai_key:
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@@ -157,6 +166,37 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
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geo_score_val = int(geo_score_obj.get("score") or 0)
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summary["geo_score"] = geo_score_val
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# ── Invalidation trigger detection ────────────────────────────────────
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try:
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from services.database import get_custom_patterns as _gcp
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_active_patterns = _gcp()
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_news_headlines = " ".join(
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(n.get("title", "") + " " + (n.get("summary", "") or "")).lower()
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for n in news[:15]
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)
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_triggered = []
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for _pat in _active_patterns:
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_trigger = (_pat.get("invalidation_trigger") or "").lower().strip()
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if not _trigger:
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continue
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# Simple keyword matching: split trigger into words and check majority match
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_words = [w for w in _trigger.split() if len(w) > 3]
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if _words and sum(1 for w in _words if w in _news_headlines) >= max(1, len(_words) // 2):
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_triggered.append({
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"pattern_id": _pat["id"],
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"pattern_name": _pat["name"],
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"trigger": _pat["invalidation_trigger"],
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"probability": _pat.get("invalidation_probability"),
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})
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if _triggered:
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logger.warning(
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f"[Cycle {run_id[:16]}] ⚠ INVALIDATION TRIGGERS FIRED for "
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f"{len(_triggered)} pattern(s): {[t['pattern_name'] for t in _triggered]}"
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)
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summary["invalidation_alerts"] = _triggered
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except Exception as _ie:
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logger.debug(f"[Cycle] Invalidation check failed (non-blocking): {_ie}")
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quotes = get_all_quotes()
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gauges = get_macro_gauges()
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@@ -167,12 +207,21 @@ def run_cycle_once(trigger: str = "auto") -> Dict[str, Any]:
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# ── Step 2: Suggest new patterns ──────────────────────────────────────
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logger.info(f"[Cycle {run_id[:16]}] Step 2: suggesting patterns")
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_reliability_map = {}
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try:
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from services.database import get_all_pattern_reliability_map
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_reliability_map = get_all_pattern_reliability_map()
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if _reliability_map:
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logger.info(f"[Cycle {run_id[:16]}] Reliability map: {len(_reliability_map)} patterns")
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except Exception as _re:
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logger.warning(f"[Cycle] Reliability map failed (non-blocking): {_re}")
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try:
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from services.data_fetcher import get_economic_calendar
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calendar = get_economic_calendar()
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suggestions = suggest_patterns_from_market_context(
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news, quotes, calendar, macro_regime=macro_regime, geo_score=geo_score_obj,
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portfolio_lessons=portfolio_lessons,
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reliability_map=_reliability_map or None,
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)
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except Exception as e:
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logger.warning(f"[Cycle] Suggestion step failed: {e}")
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@@ -63,11 +63,20 @@ def init_db():
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created_at TEXT DEFAULT (datetime('now')),
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updated_at TEXT DEFAULT (datetime('now'))
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)""")
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# Migration: add source column if not present
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try:
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c.execute("ALTER TABLE custom_patterns ADD COLUMN source TEXT DEFAULT 'custom'")
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except Exception:
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pass
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# Migrations: add columns if not present
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for _sql in [
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"ALTER TABLE custom_patterns ADD COLUMN source TEXT DEFAULT 'custom'",
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"ALTER TABLE custom_patterns ADD COLUMN counter_thesis TEXT",
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"ALTER TABLE custom_patterns ADD COLUMN invalidation_trigger TEXT",
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"ALTER TABLE custom_patterns ADD COLUMN invalidation_probability REAL",
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"ALTER TABLE pattern_score_history ADD COLUMN predicted_probability REAL",
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"ALTER TABLE knowledge_base ADD COLUMN expires_at TEXT",
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"ALTER TABLE knowledge_base ADD COLUMN confidence_decay_days INTEGER DEFAULT 90",
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]:
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try:
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c.execute(_sql)
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except Exception:
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pass
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c.execute("""CREATE TABLE IF NOT EXISTS config (
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key TEXT PRIMARY KEY,
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@@ -358,9 +367,12 @@ def save_pattern_scores(scores: List[Dict[str, Any]], meta: Dict[str, Any] = Non
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for sp in scores:
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pid = sp.get("pattern_id", "")
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if pid:
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# Look up predicted_probability from custom_patterns
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row = conn.execute("SELECT probability FROM custom_patterns WHERE id=?", (pid,)).fetchone()
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predicted_prob = float(row["probability"]) if row and row["probability"] is not None else None
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conn.execute(
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"INSERT INTO pattern_score_history (run_id, pattern_id, score, confidence, summary, scored_at) VALUES (?,?,?,?,?,?)",
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(run_id, pid, sp.get("score"), sp.get("confidence"), sp.get("summary", ""), run_id),
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"INSERT INTO pattern_score_history (run_id, pattern_id, score, confidence, summary, scored_at, predicted_probability) VALUES (?,?,?,?,?,?,?)",
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(run_id, pid, sp.get("score"), sp.get("confidence"), sp.get("summary", ""), run_id, predicted_prob),
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)
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# Keep only the last 30 runs
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conn.execute("""DELETE FROM pattern_score_history WHERE run_id NOT IN (
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@@ -575,8 +587,10 @@ def save_custom_pattern(pattern: Dict[str, Any]) -> str:
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conn.execute("""INSERT OR REPLACE INTO custom_patterns (
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id, name, description, triggers, keywords, historical_instances,
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suggested_trades, asset_class, expected_move_pct, probability,
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horizon_days, ai_quality_score, ai_evaluation, source, is_active, updated_at
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) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,1,datetime('now'))""", (
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horizon_days, ai_quality_score, ai_evaluation, source,
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counter_thesis, invalidation_trigger, invalidation_probability,
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is_active, updated_at
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) VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,1,datetime('now'))""", (
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pat_id,
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pattern.get("name", ""),
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pattern.get("description", ""),
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@@ -591,6 +605,9 @@ def save_custom_pattern(pattern: Dict[str, Any]) -> str:
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pattern.get("ai_quality_score"),
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json.dumps(pattern.get("ai_evaluation", {})),
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source,
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pattern.get("counter_thesis"),
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pattern.get("invalidation_trigger"),
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pattern.get("invalidation_probability"),
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))
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conn.commit()
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conn.close()
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@@ -1573,3 +1590,208 @@ def get_iv_history(ticker: str, days: int = 90) -> List[Dict]:
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conn.close()
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return [dict(r) for r in rows]
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# ── Knowledge Base Decay ──────────────────────────────────────────────────────
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def decay_kb_confidence() -> int:
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"""
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Decrease confidence on KB entries past their expires_at or older than
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confidence_decay_days since last_confirmed_at. Archives entries at 0.
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Returns number of entries updated.
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"""
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conn = get_conn()
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c = conn.cursor()
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today_str = datetime.utcnow().date().isoformat()
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# Entries past expires_at → archive
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c.execute("""
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UPDATE knowledge_base
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SET status = 'archived', confidence = 0
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WHERE expires_at IS NOT NULL AND expires_at <= ? AND status = 'active'
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""", (today_str,))
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expired = c.rowcount
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# Entries where days_since_confirmation > confidence_decay_days
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# Reduce confidence by 10 per overdue period
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rows = c.execute("""
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SELECT id, confidence, last_confirmed_at, confidence_decay_days
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FROM knowledge_base
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WHERE status = 'active' AND last_confirmed_at IS NOT NULL
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""").fetchall()
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decayed = 0
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for row in rows:
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r = dict(row)
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try:
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from datetime import date as _d
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last = _d.fromisoformat(r["last_confirmed_at"][:10])
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days_since = (_d.today() - last).days
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decay_period = r["confidence_decay_days"] or 90
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if days_since > decay_period:
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periods_overdue = days_since // decay_period
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new_conf = max(0, r["confidence"] - periods_overdue * 10)
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if new_conf != r["confidence"]:
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c.execute(
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"UPDATE knowledge_base SET confidence=? WHERE id=?",
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(new_conf, r["id"])
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)
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decayed += 1
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if new_conf == 0:
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c.execute(
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"UPDATE knowledge_base SET status='archived' WHERE id=?",
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(r["id"],)
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)
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except Exception:
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pass
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conn.commit()
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conn.close()
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return expired + decayed
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# ── Pattern Reliability ───────────────────────────────────────────────────────
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def get_pattern_reliability(pattern_id: str = None) -> List[Dict]:
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"""
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Compute win_rate, avg_pnl, trade_count, reliability_score per pattern.
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Uses MATURE trades only (days_held >= 35% of horizon_days).
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If pattern_id is provided, returns single-item list for that pattern.
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"""
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import math
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from datetime import date as _date
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conn = get_conn()
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q = "SELECT * FROM trade_entry_prices WHERE pnl_pct IS NOT NULL"
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args: list = []
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if pattern_id:
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q += " AND pattern_id = ?"
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args.append(pattern_id)
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rows = conn.execute(q, args).fetchall()
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conn.close()
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today = _date.today()
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by_pattern: Dict[str, list] = {}
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for row in rows:
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r = dict(row)
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try:
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entry = _date.fromisoformat(r["entry_date"])
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days_held = (today - entry).days
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except Exception:
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days_held = 0
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horizon = r.get("horizon_days") or 30
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ratio = days_held / horizon if horizon else 0
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# Only mature trades (≥35% of horizon elapsed)
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if ratio < 0.35:
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continue
|
||||
by_pattern.setdefault(r["pattern_id"], []).append(r)
|
||||
|
||||
result = []
|
||||
for pid, trades in by_pattern.items():
|
||||
pnls = [t["pnl_pct"] for t in trades if t.get("pnl_pct") is not None]
|
||||
if not pnls:
|
||||
continue
|
||||
wins = sum(1 for p in pnls if p > 0)
|
||||
win_rate = wins / len(pnls)
|
||||
avg_pnl = sum(pnls) / len(pnls)
|
||||
# Composite score: win_rate × log(n+1) — penalises small samples
|
||||
reliability = round(win_rate * math.log(len(pnls) + 1), 3)
|
||||
|
||||
result.append({
|
||||
"pattern_id": pid,
|
||||
"pattern_name": trades[0].get("pattern_name", pid),
|
||||
"trade_count": len(pnls),
|
||||
"win_rate": round(win_rate, 3),
|
||||
"win_rate_pct": round(win_rate * 100, 1),
|
||||
"avg_pnl_pct": round(avg_pnl, 2),
|
||||
"max_pnl_pct": round(max(pnls), 2),
|
||||
"max_loss_pct": round(min(pnls), 2),
|
||||
"reliability_score": reliability,
|
||||
})
|
||||
|
||||
result.sort(key=lambda x: -x["reliability_score"])
|
||||
return result
|
||||
|
||||
|
||||
def get_all_pattern_reliability_map() -> Dict[str, Dict]:
|
||||
"""Returns {pattern_id: reliability_dict} for fast lookup."""
|
||||
return {r["pattern_id"]: r for r in get_pattern_reliability()}
|
||||
|
||||
|
||||
# ── Calibration ───────────────────────────────────────────────────────────────
|
||||
|
||||
def get_calibration_data(days: int = 365) -> Dict:
|
||||
"""
|
||||
Compare predicted probability (stored at score time) vs realized outcome
|
||||
(pnl_pct > 0 at maturity) to compute Brier score and calibration buckets.
|
||||
"""
|
||||
import math
|
||||
from datetime import date as _date
|
||||
|
||||
conn = get_conn()
|
||||
# Join pattern_scores (has predicted probability) with trade_entry_prices (has realized P&L)
|
||||
rows = conn.execute("""
|
||||
SELECT
|
||||
psh.pattern_id,
|
||||
psh.score,
|
||||
tep.pnl_pct,
|
||||
tep.entry_date,
|
||||
tep.horizon_days,
|
||||
cp.probability as predicted_prob
|
||||
FROM pattern_score_history psh
|
||||
JOIN trade_entry_prices tep ON tep.pattern_id = psh.pattern_id
|
||||
LEFT JOIN custom_patterns cp ON cp.id = psh.pattern_id
|
||||
WHERE tep.pnl_pct IS NOT NULL
|
||||
AND cp.probability IS NOT NULL
|
||||
AND tep.entry_date >= date('now', ?)
|
||||
""", (f"-{days} days",)).fetchall()
|
||||
conn.close()
|
||||
|
||||
today = _date.today()
|
||||
pairs = []
|
||||
for row in rows:
|
||||
r = dict(row)
|
||||
try:
|
||||
entry = _date.fromisoformat(r["entry_date"])
|
||||
dh = (today - entry).days
|
||||
except Exception:
|
||||
dh = 0
|
||||
horizon = r.get("horizon_days") or 30
|
||||
if dh / horizon < 0.35:
|
||||
continue # only mature
|
||||
pred = float(r["predicted_prob"] or 0)
|
||||
realized = 1.0 if (r["pnl_pct"] or 0) > 0 else 0.0
|
||||
pairs.append({"predicted": pred, "realized": realized})
|
||||
|
||||
if not pairs:
|
||||
return {"pairs": [], "brier_score": None, "buckets": [], "sample_size": 0}
|
||||
|
||||
# Brier score
|
||||
brier = sum((p["predicted"] - p["realized"]) ** 2 for p in pairs) / len(pairs)
|
||||
|
||||
# Calibration buckets (deciles)
|
||||
buckets = []
|
||||
for low in [i / 10 for i in range(0, 10)]:
|
||||
high = low + 0.1
|
||||
bucket_pairs = [p for p in pairs if low <= p["predicted"] < high]
|
||||
if bucket_pairs:
|
||||
actual_rate = sum(p["realized"] for p in bucket_pairs) / len(bucket_pairs)
|
||||
buckets.append({
|
||||
"predicted_range": f"{int(low*100)}-{int(high*100)}%",
|
||||
"predicted_mid": round((low + high) / 2, 2),
|
||||
"actual_rate": round(actual_rate, 3),
|
||||
"count": len(bucket_pairs),
|
||||
"bias": round(actual_rate - (low + high) / 2, 3),
|
||||
})
|
||||
|
||||
return {
|
||||
"pairs": pairs,
|
||||
"brier_score": round(brier, 4),
|
||||
"buckets": buckets,
|
||||
"sample_size": len(pairs),
|
||||
"interpretation": (
|
||||
"Bien calibré" if brier < 0.15
|
||||
else "Modérément calibré" if brier < 0.25
|
||||
else "Surconfiant ou mal calibré"
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
Reference in New Issue
Block a user