feat: causal lab

backend/routers/causal_lab.py

@@ -431,6 +431,8 @@ def patch_template(template_id: int, body: dict):
             sets.append("instruments=?"); params.append(json.dumps(body["instruments"]))
         if "graph_json" in body:
             sets.append("graph_json=?"); params.append(json.dumps(body["graph_json"]))
+        if "calibration_json" in body:
+            sets.append("calibration_json=?"); params.append(json.dumps(body["calibration_json"]))
 
         if not sets:
             conn.close(); return {"ok": True}
@@ -1235,3 +1237,100 @@ def get_calibration():
     except Exception as e:
         logger.error(f"[causal_lab] calibration: {e}")
         raise HTTPException(500, str(e))
+
+
+@router.post("/api/causal-lab/template/{template_id}/generate-theory")
+def generate_theory(template_id: int):
+    """GPT-4o-mini génère absorption_days, decay_type et confidence pour le template parent."""
+    try:
+        from services.database import get_conn, get_config
+        from services.causal_graphs import get_template
+
+        conn = get_conn()
+        _init(conn)
+        tmpl = get_template(conn, template_id)
+        if not tmpl:
+            conn.close(); raise HTTPException(404, "Template introuvable")
+
+        stats = conn.execute("""
+            SELECT COUNT(*) as n, AVG(activation_score) as avg_act
+            FROM causal_event_analyses WHERE template_id = ?
+        """, (template_id,)).fetchone()
+        conn.close()
+
+        n_analyses = stats["n"] if stats else 0
+        avg_act    = round((stats["avg_act"] or 0.0), 2) if stats else 0.0
+
+        graph = tmpl.get("graph_json", {})
+        coefs = {k: v.get("value") for k, v in graph.get("coefficients", {}).items()}
+
+        key = get_config("openai_api_key") or ""
+        if not key:
+            raise HTTPException(400, "Clé OpenAI manquante dans la configuration")
+
+        import openai
+        prompt = (
+            f'Tu es un expert en microstructure de marché et dynamique d\'absorption des chocs de prix.\n\n'
+            f'Template causal : "{tmpl["name"]}"\n'
+            f'Catégorie : {tmpl["category"]} / {tmpl.get("sub_type", "")}\n'
+            f'Description : {tmpl.get("description", "")}\n'
+            f'Instruments : {tmpl.get("instruments", [])}\n'
+            f'Coefficients : {json.dumps(coefs, ensure_ascii=False)}\n'
+            f'Analyses historiques : {n_analyses} (activation directionnelle moy. : {avg_act:.0%})\n\n'
+            f'Propose les paramètres d\'absorption de l\'impact de marché :\n'
+            f'- absorption_days : jours calendaires avant absorption à >90% (entier 1-60)\n'
+            f'- decay_type : "step" (tout-ou-rien), "linear" (déclin linéaire), "exp" (exponentiel)\n'
+            f'- confidence : confiance 0.0-1.0\n'
+            f'- rationale : justification courte (max 120 chars)\n\n'
+            f'Références : décisions taux→3-7j/exp ; CPI/NFP→2-5j/exp ; '
+            f'géopolitique→5-21j/linear ; PMI secondaire→1-2j/step\n\n'
+            f'JSON uniquement : {{"absorption_days": N, "decay_type": "...", "confidence": 0.X, "rationale": "..."}}'
+        )
+
+        client = openai.OpenAI(api_key=key)
+        resp = client.chat.completions.create(
+            model="gpt-4o-mini",
+            messages=[{"role": "user", "content": prompt}],
+            response_format={"type": "json_object"},
+            temperature=0.2,
+            max_tokens=200,
+        )
+        raw = resp.choices[0].message.content or "{}"
+        params = json.loads(raw)
+
+        absorption_days = max(1, min(60, int(params.get("absorption_days", 7))))
+        decay_type = params.get("decay_type", "exp")
+        if decay_type not in ("step", "linear", "exp"):
+            decay_type = "exp"
+        confidence = round(max(0.0, min(1.0, float(params.get("confidence", 0.5)))), 2)
+        rationale  = str(params.get("rationale", ""))[:150]
+
+        conn2 = get_conn()
+        existing_calib = dict(tmpl.get("calibration_json") or {})
+        existing_calib.update({
+            "absorption_days": absorption_days,
+            "decay_type": decay_type,
+            "confidence": confidence,
+            "theory_rationale": rationale,
+            "theory_generated_at": datetime.utcnow().isoformat() + "Z",
+        })
+        conn2.execute(
+            "UPDATE causal_graph_templates SET calibration_json=?, updated_at=datetime('now') WHERE id=?",
+            (json.dumps(existing_calib), template_id),
+        )
+        conn2.commit()
+        conn2.close()
+
+        return {
+            "template_id": template_id,
+            "absorption_days": absorption_days,
+            "decay_type": decay_type,
+            "confidence": confidence,
+            "rationale": rationale,
+        }
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"[causal_lab] generate_theory {template_id}: {e}")
+        raise HTTPException(500, str(e))