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