feat: 修复报错

python-app/app/market/providers/__init__.py

@@ -0,0 +1,12 @@
+from market.providers.akshare_provider import AkshareCnProvider
+from market.providers.cmes_provider import CmesCnProvider
+
+__all__ = ["AkshareCnProvider", "CmesCnProvider"]
+
+try:
+    from market.providers.futu_provider import FutuProvider
+
+    __all__.append("FutuProvider")
+except Exception:
+    # Allow non-futu environments to keep using other providers.
+    pass

python-app/app/market/providers/akshare_provider.py

@@ -0,0 +1,63 @@
+from __future__ import annotations
+
+from datetime import datetime
+
+import akshare as ak
+import pandas as pd
+
+from market.provider_base import MarketDataProvider
+
+
+class AkshareCnProvider(MarketDataProvider):
+    @property
+    def provider_name(self) -> str:
+        return "akshare"
+
+    @property
+    def channel(self) -> str:
+        return "cn"
+
+    def fetch_spot(self) -> pd.DataFrame:
+        df = ak.stock_zh_a_spot_em()
+        if df.empty:
+            raise RuntimeError("未获取到 A 股实时行情数据")
+        return df
+
+    def search_spot(self, query: str, limit: int) -> pd.DataFrame:
+        q = query.strip().lower()
+        if not q:
+            return pd.DataFrame()
+
+        df = self.fetch_spot()
+        code = df["代码"].astype(str).str.lower()
+        name = df["名称"].astype(str).str.lower()
+
+        exact = df[(code == q) | (name == q)]
+        if not exact.empty:
+            return exact.head(limit)
+
+        starts = df[code.str.startswith(q) | name.str.startswith(q)]
+        if not starts.empty:
+            return starts.head(limit)
+
+        return df[code.str.contains(q, na=False) | name.str.contains(q, na=False)].head(limit)
+
+    def fetch_daily_kline(self, code: str, start: datetime, end: datetime) -> pd.DataFrame:
+        hist = ak.stock_zh_a_hist(
+            symbol=code,
+            period="daily",
+            start_date=start.strftime("%Y%m%d"),
+            end_date=end.strftime("%Y%m%d"),
+            adjust="qfq",
+        )
+        if hist.empty:
+            raise RuntimeError(f"未获取到 K 线数据: {code}")
+
+        frame = pd.DataFrame(
+            {
+                "date": pd.to_datetime(hist["日期"]),
+                "close": pd.to_numeric(hist["收盘"], errors="coerce"),
+                "volume": pd.to_numeric(hist["成交量"], errors="coerce"),
+            }
+        ).dropna()
+        return frame.sort_values("date").reset_index(drop=True)

python-app/app/market/providers/cmes_provider.py

@@ -0,0 +1,70 @@
+from __future__ import annotations
+
+import importlib
+import os
+from datetime import datetime
+
+import pandas as pd
+
+from market.provider_base import MarketDataProvider
+
+
+class CmesCnProvider(MarketDataProvider):
+    def __init__(self, token: str | None = None) -> None:
+        self._token = token or os.getenv("CMES_TOKEN", "").strip()
+        self._module = importlib.import_module("cmesdata")
+        self._login_once()
+
+    @property
+    def provider_name(self) -> str:
+        return "cmesdata"
+
+    @property
+    def channel(self) -> str:
+        return "cn"
+
+    def _login_once(self) -> None:
+        if not self._token:
+            raise RuntimeError("CMES_TOKEN 未配置，无法使用 cmesdata 通道")
+        self._module.login(self._token)
+
+    @staticmethod
+    def _to_prefixed_code(code: str) -> str:
+        raw = code.strip().upper().replace(".", "")
+        if raw.startswith("SH") or raw.startswith("SZ"):
+            return f"{raw[:2]}.{raw[2:]}"
+        if raw.isdigit() and len(raw) == 6:
+            if raw.startswith(("6", "9")):
+                return f"SH.{raw}"
+            return f"SZ.{raw}"
+        raise RuntimeError("cmesdata 通道仅支持 6 位 A 股代码或 SH./SZ. 前缀代码")
+
+    def fetch_spot(self) -> pd.DataFrame:
+        raise RuntimeError("cmesdata 不支持全市场快照拉取，请通过精确代码查询")
+
+    def search_spot(self, query: str, limit: int) -> pd.DataFrame:
+        _ = limit
+        code = self._to_prefixed_code(query)
+        df = self._module.get_real_hq([code])
+        if df is None or df.empty:
+            raise RuntimeError(f"未获取到实时行情: {code}")
+        return df
+
+    def fetch_daily_kline(self, code: str, start: datetime, end: datetime) -> pd.DataFrame:
+        prefixed = self._to_prefixed_code(code)
+        df = self._module.get_history_data(
+            prefixed,
+            start.strftime("%Y-%m-%d"),
+            end.strftime("%Y-%m-%d"),
+            "D",
+        )
+        if df is None or df.empty:
+            raise RuntimeError(f"未获取到历史 K 线: {prefixed}")
+        frame = pd.DataFrame(
+            {
+                "date": pd.to_datetime(df["时间"]),
+                "close": pd.to_numeric(df["收盘价"], errors="coerce"),
+                "volume": pd.to_numeric(df["成交量"], errors="coerce"),
+            }
+        ).dropna()
+        return frame.sort_values("date").reset_index(drop=True)

python-app/app/market/providers/futu_provider.py

@@ -0,0 +1,207 @@
+from __future__ import annotations
+
+from datetime import datetime
+
+import pandas as pd
+from futu import AuType, KLType, Market, OpenQuoteContext, RET_OK, SecurityType
+
+from market.provider_base import MarketDataProvider
+
+
+class FutuProvider(MarketDataProvider):
+    def __init__(
+        self,
+        channel: str,
+        host: str = "127.0.0.1",
+        port: int = 11111,
+        is_encrypt: bool | None = None,
+        market: str = "",
+    ) -> None:
+        self._channel = channel.strip().lower()
+        self._host = host
+        self._port = int(port)
+        self._is_encrypt = is_encrypt
+        self._market = (market or self._channel).strip().lower()
+        self._ctx = OpenQuoteContext(host=self._host, port=self._port, is_encrypt=self._is_encrypt)
+        self._basicinfo_cache: pd.DataFrame | None = None
+
+    @property
+    def provider_name(self) -> str:
+        return "futu"
+
+    @property
+    def channel(self) -> str:
+        return self._channel
+
+    def __del__(self) -> None:
+        try:
+            self._ctx.close()
+        except Exception:
+            pass
+
+    def _require_market(self) -> Market:
+        if self._market == "cn":
+            return Market.SH
+        if self._market == "hk":
+            return Market.HK
+        if self._market == "us":
+            return Market.US
+        raise RuntimeError(f"不支持的 FUTU_MARKET: {self._market}，可选: cn/hk/us")
+
+    def _normalize_code(self, query: str) -> str:
+        q = query.strip().upper()
+        if not q:
+            return ""
+        if "." in q:
+            return q
+        if self._market == "cn":
+            if q.isdigit() and len(q) == 6:
+                prefix = "SH" if q.startswith(("5", "6", "9")) else "SZ"
+                return f"{prefix}.{q}"
+            return q
+        if self._market == "hk":
+            if q.isdigit():
+                return f"HK.{q.zfill(5)}"
+            return f"HK.{q}"
+        if self._market == "us":
+            return f"US.{q}"
+        return q
+
+    def _snapshot_to_unified(self, df: pd.DataFrame) -> pd.DataFrame:
+        frame = df.copy()
+        if "code" not in frame.columns:
+            raise RuntimeError("Futu 快照返回缺少 code 字段")
+        if "name" not in frame.columns:
+            frame["name"] = ""
+        frame["涨跌额"] = pd.to_numeric(frame.get("last_price"), errors="coerce") - pd.to_numeric(
+            frame.get("prev_close_price"), errors="coerce"
+        )
+        frame["市盈率-动态"] = frame.get("pe_ttm_ratio", frame.get("pe_ratio"))
+        frame["市净率"] = frame.get("pb_ratio")
+        frame["总市值"] = frame.get("total_market_val")
+        frame["流通市值"] = frame.get("circular_market_val")
+        frame["代码"] = frame["code"].astype(str)
+        frame["名称"] = frame["name"]
+        frame["最新价"] = frame.get("last_price")
+        frame["涨跌幅"] = frame.get("change_rate")
+        frame["成交量"] = frame.get("volume")
+        frame["成交额"] = frame.get("turnover")
+        frame["振幅"] = frame.get("amplitude")
+        frame["最高"] = frame.get("high_price")
+        frame["最低"] = frame.get("low_price")
+        frame["今开"] = frame.get("open_price")
+        frame["昨收"] = frame.get("prev_close_price")
+        columns = [
+            "代码",
+            "名称",
+            "最新价",
+            "涨跌幅",
+            "涨跌额",
+            "成交量",
+            "成交额",
+            "振幅",
+            "最高",
+            "最低",
+            "今开",
+            "昨收",
+            "市盈率-动态",
+            "市净率",
+            "总市值",
+            "流通市值",
+        ]
+        return frame[columns]
+
+    def _get_snapshot(self, codes: list[str]) -> pd.DataFrame:
+        ret, data = self._ctx.get_market_snapshot(codes)
+        if ret != RET_OK:
+            raise RuntimeError(f"Futu 获取快照失败: {data}")
+        if data is None or data.empty:
+            return pd.DataFrame()
+        return self._snapshot_to_unified(data)
+
+    def _load_basicinfo(self) -> pd.DataFrame:
+        if self._basicinfo_cache is not None:
+            return self._basicinfo_cache
+        market = self._require_market()
+        ret, data = self._ctx.get_stock_basicinfo(market=market, stock_type=SecurityType.STOCK)
+        if ret != RET_OK:
+            raise RuntimeError(f"Futu 获取股票基础信息失败: {data}")
+        if data is None:
+            self._basicinfo_cache = pd.DataFrame(columns=["code", "name"])
+            return self._basicinfo_cache
+        frame = data.copy()
+        frame["code"] = frame["code"].astype(str)
+        frame["name"] = frame["name"].astype(str)
+        self._basicinfo_cache = frame
+        return frame
+
+    def fetch_spot(self) -> pd.DataFrame:
+        raise RuntimeError("Futu 不支持直接拉取全市场实时快照，请使用 search_spot")
+
+    def search_spot(self, query: str, limit: int) -> pd.DataFrame:
+        q = query.strip()
+        if not q:
+            return pd.DataFrame()
+
+        code = self._normalize_code(q)
+        if code:
+            exact = self._get_snapshot([code])
+            if not exact.empty:
+                return exact.head(limit)
+
+        basic = self._load_basicinfo()
+        q_lower = q.lower()
+        code_col = basic["code"].astype(str)
+        name_col = basic["name"].astype(str)
+        mask = (
+            code_col.str.lower().eq(q_lower)
+            | name_col.str.lower().eq(q_lower)
+            | code_col.str.lower().str.startswith(q_lower)
+            | name_col.str.lower().str.startswith(q_lower)
+            | code_col.str.lower().str.contains(q_lower, na=False)
+            | name_col.str.lower().str.contains(q_lower, na=False)
+        )
+        candidates = basic.loc[mask, "code"].drop_duplicates().head(max(limit * 2, 20)).tolist()
+        if not candidates:
+            return pd.DataFrame()
+        snap = self._get_snapshot(candidates)
+        if snap.empty:
+            return pd.DataFrame()
+        return snap.head(limit)
+
+    def fetch_daily_kline(self, code: str, start: datetime, end: datetime) -> pd.DataFrame:
+        normalized = self._normalize_code(code)
+        page_key = None
+        frames: list[pd.DataFrame] = []
+
+        while True:
+            ret, data, page_key = self._ctx.request_history_kline(
+                normalized,
+                start=start.strftime("%Y-%m-%d"),
+                end=end.strftime("%Y-%m-%d"),
+                ktype=KLType.K_DAY,
+                autype=AuType.QFQ,
+                max_count=1000,
+                page_req_key=page_key,
+            )
+            if ret != RET_OK:
+                raise RuntimeError(f"Futu 获取历史 K 线失败: {data}")
+            if data is not None and not data.empty:
+                frames.append(data.copy())
+            if page_key is None:
+                break
+
+        if not frames:
+            raise RuntimeError(f"未获取到 K 线数据: {normalized}")
+
+        full = pd.concat(frames, ignore_index=True)
+        frame = pd.DataFrame(
+            {
+                "date": pd.to_datetime(full["time_key"]),
+                "close": pd.to_numeric(full["close"], errors="coerce"),
+                "volume": pd.to_numeric(full["volume"], errors="coerce"),
+            }
+        ).dropna()
+        if frame.empty:
+            raise RuntimeError(f"K 线数据为空: {normalized}")
+        return frame.sort_values("date").reset_index(drop=True)