diff --git a/backend/services/auto_cycle.py b/backend/services/auto_cycle.py
index d58002f..7d5276e 100644
--- a/backend/services/auto_cycle.py
+++ b/backend/services/auto_cycle.py
@@ -905,7 +905,7 @@ NOUVELLES IDÉES GÉNÉRÉES CE CYCLE ({len(new_pattern_names)} patterns ajouté
 {_json.dumps(new_pattern_names, ensure_ascii=False)}
 
 TOP 5 PATTERNS LES MIEUX SCORÉS MAINTENANT:
-{_json.dumps([{{"name": s.get("geo_trigger","?"), "score": s.get("score"), "catalyst": s.get("key_catalyst","")[:80]}} for s in top_scored], ensure_ascii=False)}
+{_json.dumps([{"name": s.get("geo_trigger","?"), "score": s.get("score"), "catalyst": s.get("key_catalyst","")[:80]} for s in top_scored], ensure_ascii=False)}
 
 Ta tâche: Explique le RAISONNEMENT de ce cycle.
 Pour chaque nouvelle idée générée:
diff --git a/backend/services/var_service.py b/backend/services/var_service.py
index 3b87584..9ffef75 100644
--- a/backend/services/var_service.py
+++ b/backend/services/var_service.py
@@ -53,17 +53,24 @@ def _bs_delta(S: float, K: float, T_days: float, sigma: float, opt_type: str, di
 
 def _fetch_returns(tickers: List[str], lookback: int) -> pd.DataFrame:
     """Download historical daily returns via yfinance. Returns {} on failure."""
+    from .database import _normalize_ticker
     valid = [t for t in tickers if ":" not in t]
     if not valid:
         return pd.DataFrame()
+    # Map raw → yfinance ticker; keep reverse map for column rename
+    yf_map = {t: _normalize_ticker(t) for t in valid}
+    yf_tickers = list(yf_map.values())
+    reverse = {v: k for k, v in yf_map.items()}
     try:
         import yfinance as yf
         end = datetime.now()
         start = end - timedelta(days=lookback + 60)
-        raw = yf.download(valid, start=start, end=end, progress=False, auto_adjust=True)
+        raw = yf.download(yf_tickers, start=start, end=end, progress=False, auto_adjust=True)
         if raw.empty:
             return pd.DataFrame()
-        close = raw["Close"] if len(valid) > 1 else raw[["Close"]].rename(columns={"Close": valid[0]})
+        close = raw["Close"] if len(yf_tickers) > 1 else raw[["Close"]].rename(columns={"Close": yf_tickers[0]})
+        # Rename yfinance tickers back to original
+        close = close.rename(columns=reverse)
         return close.pct_change().dropna().tail(lookback)
     except Exception:
         return pd.DataFrame()