windows-desktop: C端基线（Windows 本地实时分析器 + 采集器 bug 修复）

src/fetcher/lhb_fetcher.py

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+"""
+龙虎榜采集器
+主方案：AkShare stock_lhb_detail_daily_sina（已实测可用，含上榜原因）
+备选：东财 datacenter 接口（报表名不确定，暂时注释备用）
+"""
+
+import sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent.parent.parent))
+
+import akshare as ak
+import pandas as pd
+from datetime import datetime, timedelta
+from src.storage.db import upsert_df, upsert_log, get_last_fetch
+
+
+def fetch_lhb_date(trade_date: str) -> pd.DataFrame:
+    """
+    获取单日龙虎榜
+    trade_date: "YYYY-MM-DD" 或 "YYYYMMDD"
+    """
+    # 统一为 YYYYMMDD 格式
+    ds = trade_date.replace("-", "")
+    try:
+        df = ak.stock_lhb_detail_daily_sina(date=ds)
+    except Exception as e:
+        print(f"  [WARN] LHB Sina failed for {trade_date}: {e}")
+        return pd.DataFrame()
+
+    if df is None or df.empty:
+        return pd.DataFrame()
+
+    # Sina 返回列（实测）：序号, 股票代码, 股票名称, 收盘价, 涨跌幅, 成交额, 成交额, 上榜原因
+    # 编码问题导致中文列名显示乱码，用位置索引映射
+    # 0=序号, 1=代码, 2=名称, 3=收盘, 4=涨跌幅, 5=成交额, 6=??? 7=上榜原因
+    df.columns = ["no", "ts_code", "name", "close", "pct_change", "amount", "amount2", "reason"]
+
+    df["trade_date"] = trade_date.replace("-", "")
+    # 格式化为 YYYY-MM-DD
+    df["trade_date"] = pd.to_datetime(df["trade_date"]).dt.strftime("%Y-%m-%d")
+
+    # 保留有用字段
+    keep = ["trade_date", "ts_code", "name", "close", "pct_change", "amount", "reason"]
+    df = df[[c for c in keep if c in df.columns]]
+    return df
+
+
+def fetch_lhb_range(start_date: str, end_date: str = None) -> pd.DataFrame:
+    """抓取日期区间的龙虎榜（逐日）"""
+    end_date = end_date or datetime.today().strftime("%Y-%m-%d")
+    all_days = []
+    cur  = datetime.strptime(start_date.replace("-", ""), "%Y%m%d")
+    end  = datetime.strptime(end_date.replace("-", ""),   "%Y%m%d")
+    while cur <= end:
+        ds = cur.strftime("%Y-%m-%d")
+        df = fetch_lhb_date(ds)
+        if df is not None and not df.empty:
+            all_days.append(df)
+        cur += timedelta(days=1)
+    if all_days:
+        return pd.concat(all_days, ignore_index=True)
+    return pd.DataFrame()
+
+
+def incremental_lhb(source: str = "lhb_daily") -> pd.DataFrame:
+    """增量采集：只抓上次之后的新交易日"""
+    last = get_last_fetch(source)
+    start = last.get("last_date") or (datetime.today() - timedelta(days=7)).strftime("%Y-%m-%d")
+    end   = datetime.today().strftime("%Y-%m-%d")
+    df = fetch_lhb_range(start, end)
+    if df is not None and not df.empty:
+        upsert_df(df, "lhb_daily")
+        upsert_log(source, param="", last_date=end)
+    return df
+
+
+if __name__ == "__main__":
+    import fire
+    fire.Fire({
+        "date":  lambda d: upsert_df(fetch_lhb_date(d), "lhb_daily"),
+        "range": lambda s, e=None: upsert_df(fetch_lhb_range(s, e), "lhb_daily"),
+        "incr":  lambda: incremental_lhb(),
+    })