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>
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
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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