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fix: ensure actual_json is computed for auto-generated templates

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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>
This commit is contained in:
OpenSquared
2026-06-29 23:05:10 +02:00
parent c93eba7a7f
commit af73b77cba
1 changed files with 6 additions and 2 deletions
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8
backend/routers/causal_lab.py
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@@ -1091,7 +1091,8 @@ def _build_graph_json_from_spec(spec: dict) -> dict:
"formula": inp_id + " * {{" + coef_name + "}}", "formula": inp_id + " * {{" + coef_name + "}}",
}) })
edges.append({"from": inp_id, "to": inter_id, 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 # Nœuds de sortie — étalés horizontalement en bas
inter_ids = [n["id"] for n in intermediates] 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 + "}}", "formula": from_id + " * {{" + coef_name + "}}",
}) })
edges.append({"from": from_id, "to": out_id, 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}) instruments = list({out.get("instrument", "EURUSD") for out in outputs})
return { 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) # Effective lag from template edges (for price window and chip width)
edges = graph.get("edges", []) edges = graph.get("edges", [])
effective_lag_days = max((e.get("lag_days", 0) or 0 for e in edges), default=0) 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 # Fetch actual prices to compute actual pips for scoring
actual_moves: dict = {} actual_moves: dict = {}