98 lines
3.7 KiB
Python
98 lines
3.7 KiB
Python
"""Tushare 数据源（主）。日线 + 前复权。
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token 从 settings.tushare_token 读取（.env）。免费版 pro.daily 与 ts.pro_bar 实测可用。
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"""
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from __future__ import annotations
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from datetime import datetime
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from ..config import settings
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from ..domain import Bar
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from .symbols import to_ts_code
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def _parse(date_str: str) -> datetime:
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return datetime.strptime(str(date_str), "%Y%m%d")
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def get_pro():
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"""创建 pro 客户端（统一入口）。
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15000 积分档 token 只认 quicksync 镜像（直连 api.tushare.pro 会 40101 token 不对），
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官方接入方式是改写 SDK 的私有类属性 DataApi.__http_url——类属性是所有实例
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（含 pro_bar 内部自建的 DataApi）共用的请求地址，打一次补丁全局生效。
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"""
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import tushare as ts
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import tushare.pro.client as client
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if not settings.tushare_token:
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raise RuntimeError("未配置 TUSHARE_TOKEN")
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if settings.tushare_api_url:
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client.DataApi._DataApi__http_url = settings.tushare_api_url
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ts.set_token(settings.tushare_token)
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return ts.pro_api()
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def fetch_daily(code: str, start: str = "20200101", end: str | None = None,
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adjust: str = "qfq") -> list[Bar]:
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import tushare as ts # 延迟导入：未装或无 token 时该数据源不可用
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pro = get_pro() # 含镜像补丁（quicksync）+ token 检查
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ts_code = to_ts_code(code)
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end = end or datetime.now().strftime("%Y%m%d")
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# 优先 pro_bar（含复权）；积分不足则退化为 pro.daily（不复权）
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df = None
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try:
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df = ts.pro_bar(ts_code=ts_code, adj=adjust, start_date=start, end_date=end, freq="D")
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except Exception:
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df = None
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if df is None or df.empty:
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df = pro.daily(ts_code=ts_code, start_date=start, end_date=end)
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if df is None or df.empty:
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raise RuntimeError(f"Tushare 无数据: {ts_code}")
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df = df.sort_values("trade_date")
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bars: list[Bar] = []
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for _, r in df.iterrows():
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amt = r.get("amount")
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bars.append(
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Bar(
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ts=_parse(r["trade_date"]),
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open=float(r["open"]), high=float(r["high"]),
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low=float(r["low"]), close=float(r["close"]),
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volume=float(r["vol"]) * 100.0, # Tushare vol 单位为手 -> 股
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amount=float(amt) * 1000.0 if amt is not None and amt == amt else None, # 千元 -> 元
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)
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)
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return bars
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def fetch_chips(ts_code: str, end: str | None = None):
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"""拉取筹码分布截面：cyq_perf（成本分位/获利比例/平均成本）+ cyq_chips（价位→占比）。
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end 为 YYYYMMDD 参考日：取 <=end 的最近有数据交易日（周/月 K 线由前端换算成
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周期末传入，这里只需向前吸附到实际数据日）；None 取最新。
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数据自 2018 年起，早于此返回 (None, [])。返回 (perf_dict|None, [(price, percent)])。
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"""
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from datetime import timedelta
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pro = get_pro()
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e = end or datetime.now().strftime("%Y%m%d")
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if e < "20180101":
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return None, []
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# 吸附余量 60 个自然日：覆盖春节等长假 + 月初参考日（如月 K 传上月末）
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s = (datetime.strptime(e, "%Y%m%d") - timedelta(days=60)).strftime("%Y%m%d")
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s = max(s, "20180101")
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perf = pro.cyq_perf(ts_code=ts_code, start_date=s, end_date=e)
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if perf is None or perf.empty:
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return None, []
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perf = perf.sort_values("trade_date").iloc[-1] # <=end 的最近一条
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d = str(perf["trade_date"])
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chips = pro.cyq_chips(ts_code=ts_code, trade_date=d)
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rows: list[tuple[float, float]] = []
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if chips is not None and not chips.empty:
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rows = [(float(p), float(v)) for p, v in zip(chips["price"], chips["percent"])]
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return perf.to_dict(), rows
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