feat: frise sub-lanes + event manager + MA indicators + absorption

Frise chronologique: - Sub-lane stacking (assignSubLanes) — overlapping events se décalent verticalement - Zone d'overlap semi-transparente sur la période commune entre 2 événements - Hauteur dynamique selon nb de sub-lanes par niveau - Événements en cours avec flèche ▶ à droite, gradient de fin - Tri par start_date pour placement greedy Event Manager (composant EventManager.tsx): - Tableau filtrable par niveau (Long/Moyen/Court) - Edit modal complet : tous les champs + absorption_pct éditable - Bouton "IA — Enrichir" par événement → POST /api/timeline/events/{id}/ai-enrich → GPT-4o-mini suggère absorption_pct + indicateurs pertinents par niveau temporel - Delete avec confirmation double-clic - Expand row pour voir description + indicateurs - Intégré Timeline page via bouton "Gérer événements" Backend: - Nouvelles colonnes market_events: absorption_pct + relevant_indicators (ALTER idempotent) - DELETE /api/timeline/events/{id} - POST /api/timeline/events/{id}/ai-enrich Snapshot Externe: - AbsorptionBar par événement dans cellule Géopolitique - MA indicators : fetch 200j history, compute MA10/MA20/MA100 per level (short/med/long) - Affichage prix vs MA + % écart dans CellMarkets - Si relevant_indicators configurés sur l'event → utilise ces symbols au lieu des défauts - Calendar : horizons exclusifs (short 0-7j, medium 8-30j, long 31-90j) — bug corrigé Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-06-24 20:31:57 +02:00
parent 43b4816596
commit aeb5233deb
6 changed files with 950 additions and 175 deletions
--- a/backend/routers/timeline.py
+++ b/backend/routers/timeline.py
@@ -2,7 +2,9 @@
 Timeline Navigator — historical market context browser.
 Prefix: /api/timeline
 """
+import json
 import logging
+import os
 from typing import Any, Dict, List, Optional

 from fastapi import APIRouter, HTTPException
@@ -23,6 +25,8 @@ class EventCreate(BaseModel):
    market_impact: str = ""
    affected_assets: List[str] = []
    impact_score: float = 0.5
+    absorption_pct: Optional[float] = None
+    relevant_indicators: List[Dict[str, Any]] = []
    parent_event_id: Optional[int] = None


@@ -52,11 +56,108 @@ def update_event(event_id: int, body: EventUpdate) -> Dict[str, Any]:
    return {"status": "updated"}


+@router.delete("/events/{event_id}")
+def delete_event(event_id: int) -> Dict[str, Any]:
+    from services.database import delete_market_event
+    delete_market_event(event_id)
+    return {"status": "deleted"}
+
+
+@router.post("/events/{event_id}/ai-enrich")
+def ai_enrich_event(event_id: int) -> Dict[str, Any]:
+    """Ask GPT-4o-mini to suggest absorption_pct + relevant_indicators for this event."""
+    from services.database import get_all_market_events, update_market_event
+    from datetime import datetime
+
+    api_key = os.environ.get("OPENAI_API_KEY", "")
+    if not api_key:
+        raise HTTPException(400, "OpenAI API key not configured")
+
+    # Fetch event
+    all_evs = get_all_market_events()
+    ev = next((e for e in all_evs if e["id"] == event_id), None)
+    if not ev:
+        raise HTTPException(404, "Event not found")
+
+    from openai import OpenAI
+    client = OpenAI(api_key=api_key)
+
+    today = datetime.utcnow().strftime("%Y-%m-%d")
+    days_elapsed = max(0, (datetime.strptime(today, "%Y-%m-%d") -
+                           datetime.strptime(ev["start_date"][:10], "%Y-%m-%d")).days)
+    is_ongoing = not ev.get("end_date")
+
+    prompt = f"""Tu es un analyste macro senior spécialisé en options géopolitiques.
+
+Événement à analyser:
+- Nom: {ev['name']}
+- Niveau: {ev['level']} (long=structurel, medium=régime, short=catalyseur)
+- Catégorie: {ev['category']}
+- Début: {ev['start_date']}
+- Fin: {ev.get('end_date') or 'en cours'}
+- Jours écoulés depuis début: {days_elapsed}j
+- Description: {ev['description']}
+- Impact marché: {ev.get('market_impact', '')}
+- Assets affectés: {ev.get('affected_assets', '[]')}
+
+Ta mission:
+1. Estime le taux d'absorption (0-100%) de cet événement par le marché à ce jour.
+   Absorption = à quel point l'impact attendu est déjà "pricé" par les marchés.
+   Exemples: 0%=pas encore pricé, 50%=à moitié absorbé, 100%=fully priced in.
+
+2. Suggère 3-5 indicateurs techniques PERTINENTS pour suivre cet événement dans le contexte d'un trader d'options.
+   Pour chaque indicateur:
+   - symbol: ticker yfinance (ex: "CL=F", "GC=F", "SPY", "^VIX")
+   - indicator: type ("MA10", "MA20", "MA50", "MA100", "price", "RSI14")
+   - label: nom affiché (ex: "Brent MA100", "Gold prix spot", "VIX 20j avg")
+   - rationale: 1 phrase pourquoi cet indicateur est pertinent POUR CET ÉVÉNEMENT PRÉCIS
+
+Règle: choisis les indicateurs selon le niveau temporel:
+- Long terme → préfère MA100, MA50 (tendances structurelles)
+- Moyen terme → MA20, MA50
+- Court terme → MA10, MA20, prix spot
+
+FORMAT JSON STRICT:
+{{
+  "absorption_pct": <0-100>,
+  "absorption_rationale": "<1 phrase expliquant pourquoi ce niveau>",
+  "relevant_indicators": [
+    {{"symbol": "...", "indicator": "...", "label": "...", "rationale": "..."}}
+  ]
+}}"""
+
+    try:
+        response = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt}],
+            response_format={"type": "json_object"},
+            temperature=0.3,
+            max_tokens=600,
+        )
+        result = json.loads(response.choices[0].message.content)
+
+        # Update event in DB
+        ev_update = dict(ev)
+        ev_update["affected_assets"] = json.loads(ev.get("affected_assets", "[]") or "[]")
+        ev_update["relevant_indicators"] = result.get("relevant_indicators", [])
+        ev_update["absorption_pct"] = result.get("absorption_pct")
+        update_market_event(event_id, ev_update)
+
+        return {
+            "event_id": event_id,
+            "absorption_pct": result.get("absorption_pct"),
+            "absorption_rationale": result.get("absorption_rationale", ""),
+            "relevant_indicators": result.get("relevant_indicators", []),
+            "status": "enriched",
+        }
+    except Exception as e:
+        logger.error(f"[Timeline] ai_enrich failed for event {event_id}: {e}")
+        raise HTTPException(500, str(e))
+
+
@router.get("/day/{ref_date}")
 def get_day_context(ref_date: str) -> Dict[str, Any]:
-    """Return cached or freshly generated timeline context for a date (YYYY-MM-DD)."""
    from services.database import get_events_for_date, get_timeline_context
-    import json

    events = get_events_for_date(ref_date)
    cached = get_timeline_context(ref_date)
@@ -85,9 +186,7 @@ def get_day_context(ref_date: str) -> Dict[str, Any]:

@router.post("/generate/{ref_date}")
 def generate_commentary(ref_date: str) -> Dict[str, Any]:
-    """Generate (or regenerate) AI commentary for a specific date and cache it."""
    from services.timeline_service import get_or_generate_context
-    import json

    try:
        ctx = get_or_generate_context(ref_date)
@@ -96,7 +195,7 @@ def generate_commentary(ref_date: str) -> Dict[str, Any]:
            "long_commentary": ctx.get("long_commentary", ""),
            "medium_commentary": ctx.get("medium_commentary", ""),
            "short_commentary": ctx.get("short_commentary", ""),
-            "evidence_headlines": json.loads(ctx.get("evidence_headlines", "[]") or "[]") if isinstance(ctx.get("evidence_headlines"), str) else ctx.get("evidence_headlines", []),
+            "evidence_headlines": ctx.get("evidence_headlines", []),
            "events": ctx.get("events", {}),
            "has_commentary": True,
        }
@@ -107,7 +206,6 @@ def generate_commentary(ref_date: str) -> Dict[str, Any]:

@router.post("/bootstrap")
 def bootstrap_events() -> Dict[str, Any]:
-    """Seed historical market events (idempotent — only runs if table is empty)."""
    from services.timeline_service import bootstrap_events as _bootstrap
    count = _bootstrap()
    return {"seeded": count, "status": "ok" if count > 0 else "already_seeded"}
--- a/backend/services/database.py
+++ b/backend/services/database.py
@@ -746,9 +746,20 @@ def init_db():
        market_impact TEXT DEFAULT '',
        affected_assets TEXT DEFAULT '[]',
        impact_score REAL DEFAULT 0.5,
+        absorption_pct REAL DEFAULT NULL,
+        relevant_indicators TEXT DEFAULT '[]',
        parent_event_id INTEGER REFERENCES market_events(id),
        created_at TEXT DEFAULT (datetime('now'))
    )""")
+    # Add columns to existing DBs (idempotent via catch)
+    for _col, _def in [
+        ("absorption_pct",       "REAL DEFAULT NULL"),
+        ("relevant_indicators",  "TEXT DEFAULT '[]'"),
+    ]:
+        try:
+            c.execute(f"ALTER TABLE market_events ADD COLUMN {_col} {_def}")
+        except Exception:
+            pass

    c.execute("""CREATE TABLE IF NOT EXISTS timeline_context (
        ref_date TEXT PRIMARY KEY,
@@ -4487,12 +4498,25 @@ def update_market_event(event_id: int, ev: Dict[str, Any]) -> bool:
    try:
        conn.execute("""UPDATE market_events SET
            name=?, start_date=?, end_date=?, level=?, category=?, description=?,
-            market_impact=?, affected_assets=?, impact_score=?
+            market_impact=?, affected_assets=?, impact_score=?,
+            absorption_pct=?, relevant_indicators=?
            WHERE id=?""",
            (ev["name"], ev["start_date"], ev.get("end_date"),
             ev["level"], ev.get("category", "macro"), ev.get("description", ""),
             ev.get("market_impact", ""), json.dumps(ev.get("affected_assets", [])),
-             ev.get("impact_score", 0.5), event_id))
+             ev.get("impact_score", 0.5),
+             ev.get("absorption_pct"), json.dumps(ev.get("relevant_indicators", [])),
+             event_id))
+        conn.commit()
+        return True
+    finally:
+        conn.close()
+
+
+def delete_market_event(event_id: int) -> bool:
+    conn = get_conn()
+    try:
+        conn.execute("DELETE FROM market_events WHERE id=?", (event_id,))
        conn.commit()
        return True
    finally: