fix: ensure actual_json is computed for auto-generated templates

Auto-generated templates had no lag_days on edges → effective_lag_days=0 →
_drift_metrics used exact-date daily bar (often empty) → actual_json='{}' →
comprehensionScore returned null even after prediction_json was populated.

- Default effective_lag_days=5 when computed as 0 in _run_auto_analysis
- Add lag_days:3 to all auto-generated template edges in _build_graph_json_from_spec

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

backend/routers/causal_lab.py

@@ -1091,7 +1091,8 @@ def _build_graph_json_from_spec(spec: dict) -> dict:
             "formula": inp_id + " * {{" + coef_name + "}}",
         })
         edges.append({"from": inp_id, "to": inter_id,
-                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid"})
+                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid",
+                      "lag_days": 3})
 
     # Nœuds de sortie — étalés horizontalement en bas
     inter_ids = [n["id"] for n in intermediates]
@@ -1115,7 +1116,8 @@ def _build_graph_json_from_spec(spec: dict) -> dict:
             "formula": from_id + " * {{" + coef_name + "}}",
         })
         edges.append({"from": from_id, "to": out_id,
-                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid"})
+                      "sign": "positive" if coef_val >= 0 else "negative", "style": "solid",
+                      "lag_days": 3})
 
     instruments = list({out.get("instrument", "EURUSD") for out in outputs})
     return {
@@ -1252,6 +1254,8 @@ def _run_auto_analysis(event: dict, template_id: int) -> bool:
         # Effective lag from template edges (for price window and chip width)
         edges = graph.get("edges", [])
         effective_lag_days = max((e.get("lag_days", 0) or 0 for e in edges), default=0)
+        if effective_lag_days == 0:
+            effective_lag_days = 5  # Default: measure close-to-close impact over 5 trading days
 
         # Fetch actual prices to compute actual pips for scoring
         actual_moves: dict = {}