diff --git a/data_platform/fallback.py b/data_platform/fallback.py
index d9758390..b8df5a14 100644
--- a/data_platform/fallback.py
+++ b/data_platform/fallback.py
@@ -72,24 +72,31 @@ class FallbackManager:
             prefix = "sz"
         tq_symbol = f"{prefix}{code}"
 
-        # 计算天数
         days = (datetime.strptime(end_date, "%Y-%m-%d") - datetime.strptime(start_date, "%Y-%m-%d")).days + 10
-        url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={tq_symbol},day,,{days},"
+        url = f"https://web.ifzq.gtimg.cn/appstock/app/fqkline/get?param={tq_symbol},day,{start_date},,{days},"
         try:
             import urllib.request, json as _json
             opener = urllib.request.build_opener(urllib.request.ProxyHandler({}))
             req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"})
             with opener.open(req, timeout=10) as r:
-                data = _json.loads(r.read())
-            klines = data.get("data", {}).get(tq_symbol, {}).get("day", [])
+                resp = _json.loads(r.read())
+            d = resp.get("data")
+            if not isinstance(d, dict):
+                return None
+            klines = d.get(tq_symbol, {}).get("day", [])
             if not klines:
                 return None
-            df = pd.DataFrame(klines, columns=["date", "open", "close", "high", "low", "volume"])
-            for c in ["open", "close", "high", "low", "volume"]:
+            df = pd.DataFrame(klines)
+            ncols = len(df.columns)
+            if ncols >= 7:
+                df.columns = ["date", "open", "close", "high", "low", "volume", "amount"][:ncols]
+            else:
+                df.columns = ["date", "open", "close", "high", "low", "volume"][:ncols]
+            if "amount" not in df.columns:
+                df["amount"] = 0.0
+            for c in ["open", "close", "high", "low", "volume", "amount"]:
                 df[c] = pd.to_numeric(df[c], errors="coerce").fillna(0)
-            df["amount"] = 0.0
             df["date"] = pd.to_datetime(df["date"]).dt.strftime("%Y-%m-%d")
-            # Filter date range
             mask = (df["date"] >= start_date) & (df["date"] <= end_date)
             return df.loc[mask, ["date", "open", "high", "low", "close", "volume", "amount"]]
         except Exception as e: