知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
357 lines
12 KiB
Python
357 lines
12 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/3/11 17:00
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Desc: 东方财富-LOF 行情
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https://quote.eastmoney.com/center/gridlist.html#fund_lof
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https://quote.eastmoney.com/sz166009.html
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"""
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from functools import lru_cache
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import pandas as pd
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import requests
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from akshare.utils.func import fetch_paginated_data
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@lru_cache()
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def _fund_lof_code_id_map_em() -> dict:
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"""
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东方财富-LOF 代码和市场标识映射
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https://quote.eastmoney.com/center/gridlist.html#fund_lof
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:return: LOF 代码和市场标识映射
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:rtype: pandas.DataFrame
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"""
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url = "https://2.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "100",
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"po": "1",
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"np": "1",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"invt": "2",
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"wbp2u": "|0|0|0|web",
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"fid": "f12",
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"fs": "b:MK0404,b:MK0405,b:MK0406,b:MK0407",
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"fields": "f3,f12,f13",
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}
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temp_df = fetch_paginated_data(url, params)
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temp_dict = dict(zip(temp_df["f12"], temp_df["f13"]))
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return temp_dict
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def fund_lof_spot_em() -> pd.DataFrame:
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"""
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东方财富-LOF 实时行情
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https://quote.eastmoney.com/center/gridlist.html#fund_lof
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:return: LOF 实时行情
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:rtype: pandas.DataFrame
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"""
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url = "https://88.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "100",
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"po": "1",
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"np": "1",
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"ut": "bd1d9ddb04089700cf9c27f6f7426281",
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"fltt": "2",
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"invt": "2",
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"wbp2u": "|0|0|0|web",
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"fid": "f3",
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"fs": "b:MK0404,b:MK0405,b:MK0406,b:MK0407",
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"fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,"
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"f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152",
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}
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temp_df = fetch_paginated_data(url, params)
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temp_df.rename(
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columns={
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"f12": "代码",
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"f14": "名称",
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"f2": "最新价",
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"f4": "涨跌额",
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"f3": "涨跌幅",
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"f5": "成交量",
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"f6": "成交额",
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"f17": "开盘价",
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"f15": "最高价",
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"f16": "最低价",
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"f18": "昨收",
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"f8": "换手率",
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"f21": "流通市值",
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"f20": "总市值",
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},
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inplace=True,
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)
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temp_df = temp_df[
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[
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"代码",
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"名称",
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"最新价",
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"涨跌额",
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"涨跌幅",
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"成交量",
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"成交额",
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"开盘价",
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"最高价",
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"最低价",
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"昨收",
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"换手率",
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"流通市值",
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"总市值",
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]
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]
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
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temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
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temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
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temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
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temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
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temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
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return temp_df
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def fund_lof_hist_em(
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symbol: str = "166009",
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period: str = "daily",
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start_date: str = "19700101",
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end_date: str = "20500101",
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adjust: str = "",
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) -> pd.DataFrame:
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"""
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东方财富-LOF 行情
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https://quote.eastmoney.com/sz166009.html
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:param symbol: LOF 代码
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:type symbol: str
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:param period: choice of {'daily', 'weekly', 'monthly'}
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:type period: str
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:param start_date: 开始日期
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:type start_date: str
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:param end_date: 结束日期
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:type end_date: str
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:param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"}
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:type adjust: str
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:return: 每日行情
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:rtype: pandas.DataFrame
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"""
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code_id_dict = _fund_lof_code_id_map_em()
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adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
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period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
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url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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params = {
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"fields1": "f1,f2,f3,f4,f5,f6",
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"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f116",
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"ut": "7eea3edcaed734bea9cbfc24409ed989",
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"klt": period_dict[period],
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"fqt": adjust_dict[adjust],
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"secid": f"{code_id_dict[symbol]}.{symbol}",
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"beg": start_date,
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"end": end_date,
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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if not (data_json["data"] and data_json["data"]["klines"]):
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return pd.DataFrame()
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temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
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temp_df.columns = [
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"日期",
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"开盘",
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"收盘",
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"最高",
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"最低",
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"成交量",
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"成交额",
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"振幅",
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"涨跌幅",
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"涨跌额",
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"换手率",
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]
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temp_df.index = pd.to_datetime(temp_df["日期"])
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temp_df.reset_index(inplace=True, drop=True)
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temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
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temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
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temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
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return temp_df
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def fund_lof_hist_min_em(
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symbol: str = "166009",
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start_date: str = "1979-09-01 09:32:00",
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end_date: str = "2222-01-01 09:32:00",
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period: str = "5",
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adjust: str = "",
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) -> pd.DataFrame:
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"""
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东方财富-LOF 分时行情
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https://quote.eastmoney.com/sz166009.html
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:param symbol: LOF 代码
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:type symbol: str
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:param start_date: 开始日期时间
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:type start_date: str
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:param end_date: 结束日期时间
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:type end_date: str
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:param period: choice of {"1", "5", "15", "30", "60"}
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:type period: str
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:param adjust: choice of {'', 'qfq', 'hfq'}
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:type adjust: str
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:return: 每日分时行情
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:rtype: pandas.DataFrame
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"""
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code_id_dict = _fund_lof_code_id_map_em()
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adjust_map = {
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"": "0",
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"qfq": "1",
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"hfq": "2",
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}
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if period == "1":
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url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get"
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params = {
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"fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
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"fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
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"ut": "7eea3edcaed734bea9cbfc24409ed989",
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"ndays": "5",
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"iscr": "0",
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"secid": f"{code_id_dict[symbol]}.{symbol}",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(
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[item.split(",") for item in data_json["data"]["trends"]]
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)
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temp_df.columns = [
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"时间",
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"开盘",
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"收盘",
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"最高",
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"最低",
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"成交量",
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"成交额",
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"均价",
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]
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temp_df.index = pd.to_datetime(temp_df["时间"])
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temp_df = temp_df[start_date:end_date]
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temp_df.reset_index(drop=True, inplace=True)
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temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
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temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
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temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["均价"] = pd.to_numeric(temp_df["均价"], errors="coerce")
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temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
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return temp_df
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else:
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url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
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params = {
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"fields1": "f1,f2,f3,f4,f5,f6",
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"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
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"ut": "7eea3edcaed734bea9cbfc24409ed989",
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"klt": period,
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"fqt": adjust_map[adjust],
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"secid": f"{code_id_dict[symbol]}.{symbol}",
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"beg": "0",
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"end": "20500000",
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}
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r = requests.get(url, params=params)
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data_json = r.json()
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temp_df = pd.DataFrame(
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[item.split(",") for item in data_json["data"]["klines"]]
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)
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temp_df.columns = [
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"时间",
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"开盘",
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"收盘",
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"最高",
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"最低",
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"成交量",
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"成交额",
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"振幅",
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"涨跌幅",
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"涨跌额",
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"换手率",
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]
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temp_df.index = pd.to_datetime(temp_df["时间"])
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temp_df = temp_df[start_date:end_date]
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temp_df.reset_index(drop=True, inplace=True)
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temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
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temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
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temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
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temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
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temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
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temp_df = temp_df[
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[
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"时间",
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"开盘",
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"收盘",
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"最高",
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"最低",
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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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"换手率",
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]
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]
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return temp_df
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if __name__ == "__main__":
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fund_lof_spot_em_df = fund_lof_spot_em()
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print(fund_lof_spot_em_df)
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fund_lof_hist_em_df = fund_lof_hist_em(
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symbol="166009",
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period="daily",
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start_date="20000101",
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end_date="20230703",
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adjust="",
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)
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print(fund_lof_hist_em_df)
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fund_lof_hist_qfq_em_df = fund_lof_hist_em(
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symbol="166009",
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period="daily",
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start_date="20000101",
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end_date="20230703",
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adjust="qfq",
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)
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print(fund_lof_hist_qfq_em_df)
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fund_lof_hist_em_df = fund_lof_hist_em(
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symbol="166009",
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period="daily",
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start_date="20000101",
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end_date="20250311",
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adjust="hfq",
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)
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print(fund_lof_hist_em_df)
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fund_lof_hist_min_em_df = fund_lof_hist_min_em(
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symbol="166009",
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period="1",
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adjust="",
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start_date="2025-03-11 09:30:00",
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end_date="2025-03-11 14:40:00",
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)
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print(fund_lof_hist_min_em_df)
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