246deaf631
Sprint 4.1 — Bayesian Updating - database.py: update_bayesian_posteriors() — Beta(α,β) posteriors sur trades matures - database.py: get_bayesian_posteriors() — posteriors + IC 95% + dérive prior GPT vs posterior - Colonnes Bayésiennes ajoutées : bayesian_alpha, bayesian_beta, bayesian_win_rate, bayesian_sample_size - auto_cycle.py: appel update_bayesian_posteriors() en Step 5.5 (après scoring) Sprint 4.2 — Détection Automatique de Régimes (K-Means numpy pur) - database.py: detect_and_save_regime_clusters() — K-Means sur 7 gauges macro (VIX, slope, DXY…) - database.py: get_regime_cluster_history() — timeline des clusters - database.py: get_regime_transition_matrix() — P(cluster j | cluster i) sur N transitions - Table regime_clusters avec anomaly_flag (points > 3σ) - auto_cycle.py: appel detect_and_save_regime_clusters() en Step 5.6 Sprint 4.3 — Embeddings Sémantiques (remplace Jaccard) - database.py: get_or_create_pattern_embedding() — OpenAI text-embedding-3-small, stocké en DB - database.py: max_cosine_similarity_vs_existing() — similarité cosinus vs patterns existants - Table pattern_embeddings avec vecteur JSON + model_version - auto_cycle.py: _is_duplicate_pattern() — cosinus seuil 0.75 avec fallback Jaccard automatique Sprint 4.4 — Tableau de Bord Analytique Avancé - AnalyticsAdvanced.tsx: nouvelle page /analytics-advanced • BayesianTable : prior GPT vs WR bayésien ± IC 95%, dérive, niveau de confiance • ClusterTimeline : timeline colorée des clusters + anomalies • TransitionMatrix : heatmap P(j|i) avec diagonale auto-transition • EmbeddingsSummary : liste des patterns vectorisés • Boutons "Bayesian update" et "Détecter régime" avec mutation React Query - analytics.py router : 5 nouveaux endpoints (bayesian, regime-clusters, transitions, detect, embeddings) - useApi.ts : 4 nouveaux hooks (useBayesianPosteriors, useRegimeClusters, useRegimeTransitions, usePatternEmbeddings) - App.tsx + Sidebar.tsx : route /analytics-advanced + entrée menu Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
103 lines
3.4 KiB
Python
103 lines
3.4 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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# Phase 4
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get_bayesian_posteriors,
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update_bayesian_posteriors,
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detect_and_save_regime_clusters,
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get_regime_cluster_history,
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get_regime_transition_matrix,
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get_all_pattern_embeddings_summary,
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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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# ── Phase 4 endpoints ────────────────────────────────────────────────────────
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@router.get("/bayesian")
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def bayesian_posteriors():
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"""Posteriors bayésiens Beta(α,β) par pattern avec IC 95%."""
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return {"posteriors": get_bayesian_posteriors()}
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@router.post("/bayesian/update")
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def trigger_bayesian_update():
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"""Force la mise à jour des posteriors bayésiens."""
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n = update_bayesian_posteriors()
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return {"updated": n, "message": f"{n} pattern(s) mis à jour"}
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@router.get("/regime-clusters")
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def regime_cluster_history(days: int = Query(default=90, ge=7, le=365)):
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"""Historique des assignations de clusters de régimes macro."""
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return {"clusters": get_regime_cluster_history(days=days)}
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@router.get("/regime-transitions")
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def regime_transition_matrix(days: int = Query(default=180, ge=30, le=730)):
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"""Matrice de transition entre clusters de régimes."""
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return get_regime_transition_matrix(days=days)
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@router.post("/regime-detect")
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def trigger_regime_detect(n_clusters: int = Query(default=4, ge=2, le=6)):
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"""Force la détection et sauvegarde du cluster de régime courant."""
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result = detect_and_save_regime_clusters(n_clusters=n_clusters)
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return result
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@router.get("/embeddings")
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def pattern_embeddings_summary():
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"""Liste des patterns avec embedding vectoriel disponible."""
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return {"embeddings": get_all_pattern_embeddings_summary()}
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