提交

backend/app/data/index_global.py

@@ -13,13 +13,13 @@ from __future__ import annotations
 
 import asyncio
 import json
-import math
 import time
 from datetime import date, datetime, timedelta
 
 from .. import cache
 from ..config import settings
 from ..domain import Bar
+from .sync_utils import d8_iso, f_clean
 
 # ---- 静态元数据表（tushare index_global 支持的全部 21 个指数，展示顺序即文档顺序）----
 # region: americas 美洲 / europe 欧洲 / asia 亚太（含港股与富时A50）
@@ -82,22 +82,6 @@ class GlobalIndexError(RuntimeError):
     """全部国际指数都拉不到（token/网络故障）——接口层转 503。"""
 
 
-def _f(v) -> float | None:
-    """pandas 值 -> float；NaN/None -> None。"""
-    if v is None:
-        return None
-    try:
-        f = float(v)
-    except (TypeError, ValueError):
-        return None
-    return None if math.isnan(f) else f
-
-
-def _d(v) -> str | None:
-    """YYYYMMDD -> 'YYYY-MM-DD'（字符串便于 JSON 缓存）。"""
-    return datetime.strptime(str(v), "%Y%m%d").date().isoformat() if v else None
-
-
 def is_cn_index(code: str) -> bool:
     return "." in code
 
@@ -128,14 +112,14 @@ def _fetch_quote_sync(pro, ts_code: str) -> dict:
     tail = df.tail(_SPARK_DAYS)
     last = df.iloc[-1]
     return {
-        "close": _f(last["close"]),
-        "change": _f(last.get("change")),
-        "pct_chg": _f(last.get("pct_chg")),
-        "open": _f(last.get("open")),
-        "high": _f(last.get("high")),
-        "low": _f(last.get("low")),
-        "pre_close": _f(last.get("pre_close")),
-        "trade_date": _d(last["trade_date"]),
+        "close": f_clean(last["close"]),
+        "change": f_clean(last.get("change")),
+        "pct_chg": f_clean(last.get("pct_chg")),
+        "open": f_clean(last.get("open")),
+        "high": f_clean(last.get("high")),
+        "low": f_clean(last.get("low")),
+        "pre_close": f_clean(last.get("pre_close")),
+        "trade_date": d8_iso(last["trade_date"]),
         "spark": [round(float(c), 4) for c in tail["close"]],
         "spark_dates": [str(d) for d in tail["trade_date"]],
     }
@@ -249,8 +233,8 @@ def _fetch_global_bars_sync(ts_code: str) -> list[Bar]:
     df = pd.concat(frames).drop_duplicates(subset="trade_date").sort_values("trade_date")
     bars: list[Bar] = []
     for _, r in df.iterrows():
-        vol = _f(r.get("vol"))
-        amt = _f(r.get("amount"))
+        vol = f_clean(r.get("vol"))
+        amt = f_clean(r.get("amount"))
         bars.append(
             Bar(
                 ts=datetime.strptime(str(r["trade_date"]), "%Y%m%d"),
@@ -318,9 +302,9 @@ def _fetch_basic_sync(ts_code: str) -> dict:
         "market": r.get("market"),
         "publisher": r.get("publisher"),
         "category": r.get("category"),
-        "base_date": _d(r.get("base_date")),
-        "base_point": _f(r.get("base_point")),
-        "list_date": _d(r.get("list_date")),
+        "base_date": d8_iso(r.get("base_date")),
+        "base_point": f_clean(r.get("base_point")),
+        "list_date": d8_iso(r.get("list_date")),
     }
 
 
@@ -354,10 +338,10 @@ def _fetch_valuation_sync(ts_code: str, days: int) -> list[dict]:
     rows = []
     for _, r in df.sort_values("trade_date").iterrows():
         rows.append({
-            "trade_date": _d(r["trade_date"]),
-            "pe": _f(r.get("pe")), "pe_ttm": _f(r.get("pe_ttm")), "pb": _f(r.get("pb")),
-            "turnover_rate": _f(r.get("turnover_rate")),
-            "total_mv": _f(r.get("total_mv")), "float_mv": _f(r.get("float_mv")),
+            "trade_date": d8_iso(r["trade_date"]),
+            "pe": f_clean(r.get("pe")), "pe_ttm": f_clean(r.get("pe_ttm")), "pb": f_clean(r.get("pb")),
+            "turnover_rate": f_clean(r.get("turnover_rate")),
+            "total_mv": f_clean(r.get("total_mv")), "float_mv": f_clean(r.get("float_mv")),
         })
     return rows
 
@@ -395,7 +379,7 @@ def _fetch_weights_sync(ts_code: str) -> dict | None:
         latest_date = df.iloc[0]["trade_date"]
         rows = df[df["trade_date"] == latest_date]
         return {
-            "trade_date": _d(latest_date),
+            "trade_date": d8_iso(latest_date),
             "total": int(len(rows)),
             "items": [
                 {"con_code": str(r["con_code"]), "weight": round(float(r["weight"]), 4)}