hmo/MoFin
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
fa45d8aa5fcf026f6313e37ced7287a7c99f0016
MoFin/venv/lib/python3.12/site-packages/akshare/fund/fund_etf_em.py
T
知微 fa45d8aa5f fix: 小果地址统一node122(兼容LAN+EasyTier) 
- 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
2026-06-30 02:56:35 +08:00

498 lines
17 KiB
Python
Raw Blame History

#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Date: 2025/4/27 00:00
Desc: 东方财富-ETF行情
https://quote.eastmoney.com/sh513500.html
"""
from functools import lru_cache
import pandas as pd
import requests
from akshare.utils.func import fetch_paginated_data
@lru_cache()
def _fund_etf_code_id_map_em() -> dict:
"""
东方财富-ETF代码和市场标识映射
https://quote.eastmoney.com/center/gridlist.html#fund_etf
:return: ETF 代码和市场标识映射
:rtype: dict
"""
url = "https://88.push2.eastmoney.com/api/qt/clist/get"
params = {
"pn": "1",
"pz": "100",
"po": "1",
"np": "1",
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
"fltt": "2",
"invt": "2",
"wbp2u": "|0|0|0|web",
"fid": "f3",
"fs": "b:MK0021,b:MK0022,b:MK0023,b:MK0024",
"fields": "f3,f12,f13",
}
temp_df = fetch_paginated_data(url, params)
temp_dict = dict(zip(temp_df["f12"], temp_df["f13"]))
return temp_dict
def fund_etf_spot_em() -> pd.DataFrame:
"""
东方财富-ETF 实时行情
https://quote.eastmoney.com/center/gridlist.html#fund_etf
:return: ETF 实时行情
:rtype: pandas.DataFrame
"""
url = "https://push2delay.eastmoney.com/api/qt/clist/get"
params = {
"pn": "1",
"pz": "100",
"po": "1",
"np": "1",
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
"fltt": "2",
"invt": "2",
"wbp2u": "|0|0|0|web",
"fid": "f12",
"fs": "b:MK0021,b:MK0022,b:MK0023,b:MK0024,b:MK0827",
"fields": (
"f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,"
"f12,f13,f14,f15,f16,f17,f18,f20,f21,"
"f23,f24,f25,f22,f11,f30,f31,f32,f33,"
"f34,f35,f38,f62,f63,f64,f65,f66,f69,"
"f72,f75,f78,f81,f84,f87,f115,f124,f128,"
"f136,f152,f184,f297,f402,f441"
),
}
temp_df = fetch_paginated_data(url, params)
temp_df.rename(
columns={
"f12": "代码",
"f14": "名称",
"f2": "最新价",
"f4": "涨跌额",
"f3": "涨跌幅",
"f5": "成交量",
"f6": "成交额",
"f7": "振幅",
"f17": "开盘价",
"f15": "最高价",
"f16": "最低价",
"f18": "昨收",
"f8": "换手率",
"f10": "量比",
"f30": "现手",
"f31": "买一",
"f32": "卖一",
"f33": "委比",
"f34": "外盘",
"f35": "内盘",
"f62": "主力净流入-净额",
"f184": "主力净流入-净占比",
"f66": "超大单净流入-净额",
"f69": "超大单净流入-净占比",
"f72": "大单净流入-净额",
"f75": "大单净流入-净占比",
"f78": "中单净流入-净额",
"f81": "中单净流入-净占比",
"f84": "小单净流入-净额",
"f87": "小单净流入-净占比",
"f38": "最新份额",
"f21": "流通市值",
"f20": "总市值",
"f402": "基金折价率",
"f441": "IOPV实时估值",
"f297": "数据日期",
"f124": "更新时间",
},
inplace=True,
)
temp_df = temp_df[
[
"代码",
"名称",
"最新价",
"IOPV实时估值",
"基金折价率",
"涨跌额",
"涨跌幅",
"成交量",
"成交额",
"开盘价",
"最高价",
"最低价",
"昨收",
"振幅",
"换手率",
"量比",
"委比",
"外盘",
"内盘",
"主力净流入-净额",
"主力净流入-净占比",
"超大单净流入-净额",
"超大单净流入-净占比",
"大单净流入-净额",
"大单净流入-净占比",
"中单净流入-净额",
"中单净流入-净占比",
"小单净流入-净额",
"小单净流入-净占比",
"现手",
"买一",
"卖一",
"最新份额",
"流通市值",
"总市值",
"数据日期",
"更新时间",
]
]
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
temp_df["开盘价"] = pd.to_numeric(temp_df["开盘价"], errors="coerce")
temp_df["最高价"] = pd.to_numeric(temp_df["最高价"], errors="coerce")
temp_df["最低价"] = pd.to_numeric(temp_df["最低价"], errors="coerce")
temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
temp_df["委比"] = pd.to_numeric(temp_df["委比"], errors="coerce")
temp_df["外盘"] = pd.to_numeric(temp_df["外盘"], errors="coerce")
temp_df["内盘"] = pd.to_numeric(temp_df["内盘"], errors="coerce")
temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
temp_df["现手"] = pd.to_numeric(temp_df["现手"], errors="coerce")
temp_df["买一"] = pd.to_numeric(temp_df["买一"], errors="coerce")
temp_df["卖一"] = pd.to_numeric(temp_df["卖一"], errors="coerce")
temp_df["最新份额"] = pd.to_numeric(temp_df["最新份额"], errors="coerce")
temp_df["IOPV实时估值"] = pd.to_numeric(temp_df["IOPV实时估值"], errors="coerce")
temp_df["基金折价率"] = pd.to_numeric(temp_df["基金折价率"], errors="coerce")
temp_df["主力净流入-净额"] = pd.to_numeric(
temp_df["主力净流入-净额"], errors="coerce"
)
temp_df["主力净流入-净占比"] = pd.to_numeric(
temp_df["主力净流入-净占比"], errors="coerce"
)
temp_df["超大单净流入-净额"] = pd.to_numeric(
temp_df["超大单净流入-净额"], errors="coerce"
)
temp_df["超大单净流入-净占比"] = pd.to_numeric(
temp_df["超大单净流入-净占比"], errors="coerce"
)
temp_df["大单净流入-净额"] = pd.to_numeric(
temp_df["大单净流入-净额"], errors="coerce"
)
temp_df["大单净流入-净占比"] = pd.to_numeric(
temp_df["大单净流入-净占比"], errors="coerce"
)
temp_df["中单净流入-净额"] = pd.to_numeric(
temp_df["中单净流入-净额"], errors="coerce"
)
temp_df["中单净流入-净占比"] = pd.to_numeric(
temp_df["中单净流入-净占比"], errors="coerce"
)
temp_df["小单净流入-净额"] = pd.to_numeric(
temp_df["小单净流入-净额"], errors="coerce"
)
temp_df["小单净流入-净占比"] = pd.to_numeric(
temp_df["小单净流入-净占比"], errors="coerce"
)
temp_df["数据日期"] = pd.to_datetime(
temp_df["数据日期"], format="%Y%m%d", errors="coerce"
)
temp_df["更新时间"] = (
pd.to_datetime(temp_df["更新时间"], unit="s", errors="coerce")
.dt.tz_localize("UTC")
.dt.tz_convert("Asia/Shanghai")
)
return temp_df
def get_market_id(symbol: str) -> int:
"""
东方财富-ETF市场标识判断
:param symbol: ETF 代码
:type symbol: str
:return: ETF 代码和市场标识（1:上证 0:深证）
:rtype: int
"""
if symbol.startswith(("0", "1", "3", "2", "5", "6")):
if symbol.startswith(("5", "6")):
return 1
else:
return 0
else:
return 1
def fund_etf_hist_em(
symbol: str = "159707",
period: str = "daily",
start_date: str = "19700101",
end_date: str = "20500101",
adjust: str = "",
) -> pd.DataFrame:
"""
东方财富-ETF行情
https://quote.eastmoney.com/sz159707.html
:param symbol: ETF 代码
:type symbol: str
:param period: choice of {'daily', 'weekly', 'monthly'}
:type period: str
:param start_date: 开始日期
:type start_date: str
:param end_date: 结束日期
:type end_date: str
:param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"}
:type adjust: str
:return: 每日行情
:rtype: pandas.DataFrame
"""
# code_id_dict = _fund_etf_code_id_map_em()
adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
params = {
"fields1": "f1,f2,f3,f4,f5,f6",
"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61,f116",
"ut": "7eea3edcaed734bea9cbfc24409ed989",
"klt": period_dict[period],
"fqt": adjust_dict[adjust],
"beg": start_date,
"end": end_date,
}
try:
# market_id = code_id_dict[symbol]
market_id = get_market_id(symbol)
params.update({"secid": f"{market_id}.{symbol}"})
r = requests.get(url, timeout=15, params=params)
data_json = r.json()
except KeyError:
market_id = 1
params.update({"secid": f"{market_id}.{symbol}"})
r = requests.get(url, timeout=15, params=params)
data_json = r.json()
if not data_json["data"]:
market_id = 0
params.update({"secid": f"{market_id}.{symbol}"})
r = requests.get(url, timeout=15, params=params)
data_json = r.json()
if not (data_json["data"] and data_json["data"]["klines"]):
return pd.DataFrame()
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
temp_df.columns = [
"日期",
"开盘",
"收盘",
"最高",
"最低",
"成交量",
"成交额",
"振幅",
"涨跌幅",
"涨跌额",
"换手率",
]
temp_df.index = pd.to_datetime(temp_df["日期"], errors="coerce")
temp_df.reset_index(inplace=True, drop=True)
temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
return temp_df
def fund_etf_hist_min_em(
symbol: str = "159707",
start_date: str = "1979-09-01 09:32:00",
end_date: str = "2222-01-01 09:32:00",
period: str = "5",
adjust: str = "",
) -> pd.DataFrame:
"""
东方财富-ETF 行情
https://quote.eastmoney.com/sz159707.html
:param symbol: ETF 代码
:type symbol: str
:param start_date: 开始日期
:type start_date: str
:param end_date: 结束日期
:type end_date: str
:param period: choice of {"1", "5", "15", "30", "60"}
:type period: str
:param adjust: choice of {'', 'qfq', 'hfq'}
:type adjust: str
:return: 每日分时行情
:rtype: pandas.DataFrame
"""
# code_id_dict = _fund_etf_code_id_map_em()
# 商品期货类 ETF
# code_id_dict.update(
# {
# "159980": "0",
# "159981": "0",
# "159985": "0",
# "511090": "1",
# "511220": "1",
# "511380": "1",
# }
# )
adjust_map = {
"": "0",
"qfq": "1",
"hfq": "2",
}
if period == "1":
url = "https://push2his.eastmoney.com/api/qt/stock/trends2/get"
params = {
"fields1": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13",
"fields2": "f51,f52,f53,f54,f55,f56,f57,f58",
"ut": "7eea3edcaed734bea9cbfc24409ed989",
"ndays": "5",
"iscr": "0",
"secid": f"{get_market_id(symbol)}.{symbol}",
}
r = requests.get(url, timeout=15, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
[item.split(",") for item in data_json["data"]["trends"]]
)
temp_df.columns = [
"时间",
"开盘",
"收盘",
"最高",
"最低",
"成交量",
"成交额",
"均价",
]
temp_df.index = pd.to_datetime(temp_df["时间"])
temp_df = temp_df[start_date:end_date]
temp_df.reset_index(drop=True, inplace=True)
temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
temp_df["均价"] = pd.to_numeric(temp_df["均价"], errors="coerce")
temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
return temp_df
else:
url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
params = {
"fields1": "f1,f2,f3,f4,f5,f6",
"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
"ut": "7eea3edcaed734bea9cbfc24409ed989",
"klt": period,
"fqt": adjust_map[adjust],
"secid": f"{get_market_id(symbol)}.{symbol}",
"beg": "0",
"end": "20500000",
}
r = requests.get(url, timeout=15, params=params)
data_json = r.json()
temp_df = pd.DataFrame(
[item.split(",") for item in data_json["data"]["klines"]]
)
temp_df.columns = [
"时间",
"开盘",
"收盘",
"最高",
"最低",
"成交量",
"成交额",
"振幅",
"涨跌幅",
"涨跌额",
"换手率",
]
temp_df.index = pd.to_datetime(temp_df["时间"])
temp_df = temp_df[start_date:end_date]
temp_df.reset_index(drop=True, inplace=True)
temp_df["开盘"] = pd.to_numeric(temp_df["开盘"], errors="coerce")
temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], errors="coerce")
temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce")
temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce")
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce")
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
temp_df = temp_df[
[
"时间",
"开盘",
"收盘",
"最高",
"最低",
"涨跌幅",
"涨跌额",
"成交量",
"成交额",
"振幅",
"换手率",
]
]
return temp_df
if __name__ == "__main__":
fund_etf_spot_em_df = fund_etf_spot_em()
print(fund_etf_spot_em_df)
fund_etf_hist_hfq_em_df = fund_etf_hist_em(
symbol="513500",
period="daily",
start_date="20000101",
end_date="20230201",
adjust="hfq",
)
print(fund_etf_hist_hfq_em_df)
fund_etf_hist_qfq_em_df = fund_etf_hist_em(
symbol="511010",
period="daily",
start_date="20000101",
end_date="20230718",
adjust="",
)
print(fund_etf_hist_qfq_em_df)
fund_etf_hist_em_df = fund_etf_hist_em(
symbol="159985",
period="daily",
start_date="20000101",
end_date="20231211",
adjust="",
)
print(fund_etf_hist_em_df)
fund_etf_hist_min_em_df = fund_etf_hist_min_em(
symbol="511380",
period="1",
adjust="",
start_date="2025-04-10 09:30:00",
end_date="2025-04-10 17:40:00",
)
print(fund_etf_hist_min_em_df)
Reference in New Issue View Git Blame Copy Permalink