555 lines
19 KiB
Python
555 lines
19 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2022/6/19 15:26
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Desc: 东方财富网-行情首页-沪深京 A 股
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"""
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import requests
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import pandas as pd
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from functools import lru_cache
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def stock_zh_a_spot_em() -> pd.DataFrame:
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"""
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东方财富网-沪深京 A 股-实时行情
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https://quote.eastmoney.com/center/gridlist.html#hs_a_board
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:return: 实时行情
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:rtype: pandas.DataFrame
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"""
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url = "http://82.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "50000",
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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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"fid": "f3",
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"fs": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
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"fields": "f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f14,f15,f16,f17,f18,f20,f21,f22,f23,f24,f25,f26,f37,f38,f39,f40,f41,f45,f46,f48,f49,f57,f61,f100,f112,f113,f114,f115,f221",
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"_": "1623833739532",
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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"]["diff"]:
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return pd.DataFrame()
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temp_df = pd.DataFrame(data_json["data"]["diff"])
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temp_df.columns = [
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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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"换手率",
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"市盈率动",
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"量比",
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"5分钟涨跌",
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"代码",
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"名称",
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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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"总市值",
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"流通市值",
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"涨速",
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"市净率",
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"60日涨跌幅",
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"年初至今涨跌幅",
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"上市时间",
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"加权净资产收益率",
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"总股本",
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"已流通股份",
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"营业收入",
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"营业收入同比增长",
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"归属净利润",
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"归属净利润同比增长",
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"每股未分配利润",
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"毛利率",
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"资产负债率",
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"每股公积金",
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"所处行业",
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"每股收益",
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"每股净资产",
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"市盈率静",
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"市盈率TTM",
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"报告期"
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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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"量比",
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"今开",
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"最高",
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"最低",
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"昨收",
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"涨速",
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"5分钟涨跌",
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"60日涨跌幅",
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"年初至今涨跌幅",
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"市盈率动",
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"市盈率TTM",
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"市盈率静",
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"市净率",
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"每股收益",
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"每股净资产",
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"每股公积金",
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"每股未分配利润",
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"加权净资产收益率",
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"毛利率",
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"资产负债率",
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"营业收入",
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"营业收入同比增长",
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"归属净利润",
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"归属净利润同比增长",
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"报告期",
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"总股本",
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"已流通股份",
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"总市值",
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"流通市值",
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"所处行业",
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"上市时间"
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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["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["量比"] = pd.to_numeric(temp_df["量比"], 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["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["今开"] = pd.to_numeric(temp_df["今开"], 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["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
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temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
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temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
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temp_df["年初至今涨跌幅"] = pd.to_numeric(temp_df["年初至今涨跌幅"], errors="coerce")
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temp_df["市盈率动"] = pd.to_numeric(temp_df["市盈率动"], errors="coerce")
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temp_df["市盈率TTM"] = pd.to_numeric(temp_df["市盈率TTM"], errors="coerce")
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temp_df["市盈率静"] = pd.to_numeric(temp_df["市盈率静"], errors="coerce")
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temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
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temp_df["每股收益"] = pd.to_numeric(temp_df["每股收益"], errors="coerce")
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temp_df["每股净资产"] = pd.to_numeric(temp_df["每股净资产"], errors="coerce")
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temp_df["每股公积金"] = pd.to_numeric(temp_df["每股公积金"], errors="coerce")
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temp_df["每股未分配利润"] = pd.to_numeric(temp_df["每股未分配利润"], errors="coerce")
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temp_df["加权净资产收益率"] = pd.to_numeric(temp_df["加权净资产收益率"], errors="coerce")
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temp_df["毛利率"] = pd.to_numeric(temp_df["毛利率"], errors="coerce")
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temp_df["资产负债率"] = pd.to_numeric(temp_df["资产负债率"], errors="coerce")
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temp_df["营业收入"] = pd.to_numeric(temp_df["营业收入"], errors="coerce")
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temp_df["营业收入同比增长"] = pd.to_numeric(temp_df["营业收入同比增长"], errors="coerce")
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temp_df["归属净利润"] = pd.to_numeric(temp_df["归属净利润"], errors="coerce")
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temp_df["归属净利润同比增长"] = pd.to_numeric(temp_df["归属净利润同比增长"], errors="coerce")
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temp_df["报告期"] = pd.to_datetime(temp_df["报告期"], format='%Y%m%d', errors="coerce")
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temp_df["总股本"] = pd.to_numeric(temp_df["总股本"], errors="coerce")
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temp_df["已流通股份"] = pd.to_numeric(temp_df["已流通股份"], 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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temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
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temp_df["上市时间"] = pd.to_datetime(temp_df["上市时间"], format='%Y%m%d', errors="coerce")
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return temp_df
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@lru_cache()
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def code_id_map_em() -> dict:
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"""
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东方财富-股票和市场代码
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http://quote.eastmoney.com/center/gridlist.html#hs_a_board
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:return: 股票和市场代码
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:rtype: dict
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"""
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url = "http://80.push2.eastmoney.com/api/qt/clist/get"
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params = {
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"pn": "1",
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"pz": "50000",
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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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"fid": "f3",
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"fs": "m:1 t:2,m:1 t:23",
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"fields": "f12",
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"_": "1623833739532",
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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"]["diff"]:
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return dict()
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temp_df = pd.DataFrame(data_json["data"]["diff"])
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temp_df["market_id"] = 1
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temp_df.columns = ["sh_code", "sh_id"]
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code_id_dict = dict(zip(temp_df["sh_code"], temp_df["sh_id"]))
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params = {
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"pn": "1",
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"pz": "50000",
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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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"fid": "f3",
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"fs": "m:0 t:6,m:0 t:80",
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"fields": "f12",
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"_": "1623833739532",
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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"]["diff"]:
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return dict()
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temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
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temp_df_sz["sz_id"] = 0
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code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["sz_id"])))
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params = {
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"pn": "1",
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"pz": "50000",
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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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"fid": "f3",
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"fs": "m:0 t:81 s:2048",
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"fields": "f12",
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"_": "1623833739532",
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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"]["diff"]:
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return dict()
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temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
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temp_df_sz["bj_id"] = 0
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code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["bj_id"])))
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return code_id_dict
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def stock_zh_a_hist(
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symbol: str = "000001",
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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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东方财富网-行情首页-沪深京 A 股-每日行情
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https://quote.eastmoney.com/concept/sh603777.html?from=classic
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:param symbol: 股票代码
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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 = 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 = "http://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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"_": "1623766962675",
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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(
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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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"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"])
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
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temp_df["最高"] = pd.to_numeric(temp_df["最高"])
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temp_df["最低"] = pd.to_numeric(temp_df["最低"])
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
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temp_df["振幅"] = pd.to_numeric(temp_df["振幅"])
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temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"])
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temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"])
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temp_df["换手率"] = pd.to_numeric(temp_df["换手率"])
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return temp_df
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def stock_zh_a_hist_min_em(
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symbol: str = "000001",
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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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东方财富网-行情首页-沪深京 A 股-每日分时行情
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https://quote.eastmoney.com/concept/sh603777.html?from=classic
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:param symbol: 股票代码
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:type symbol: 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 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 = 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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"_": "1623766962675",
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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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"最新价",
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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"])
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
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temp_df["最高"] = pd.to_numeric(temp_df["最高"])
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temp_df["最低"] = pd.to_numeric(temp_df["最低"])
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temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
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temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
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temp_df["最新价"] = pd.to_numeric(temp_df["最新价"])
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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 = "http://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",
|
|
"klt": period,
|
|
"fqt": adjust_map[adjust],
|
|
"secid": f"{code_id_dict[symbol]}.{symbol}",
|
|
"beg": "0",
|
|
"end": "20500000",
|
|
"_": "1630930917857",
|
|
}
|
|
r = requests.get(url, 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"])
|
|
temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
|
|
temp_df["最高"] = pd.to_numeric(temp_df["最高"])
|
|
temp_df["最低"] = pd.to_numeric(temp_df["最低"])
|
|
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
|
|
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
|
|
temp_df["振幅"] = pd.to_numeric(temp_df["振幅"])
|
|
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"])
|
|
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"])
|
|
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"])
|
|
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
|
|
|
|
|
|
def stock_zh_a_hist_pre_min_em(
|
|
symbol: str = "000001",
|
|
start_time: str = "09:00:00",
|
|
end_time: str = "15:50:00",
|
|
) -> pd.DataFrame:
|
|
"""
|
|
东方财富网-行情首页-沪深京 A 股-每日分时行情包含盘前数据
|
|
http://quote.eastmoney.com/concept/sh603777.html?from=classic
|
|
:param symbol: 股票代码
|
|
:type symbol: str
|
|
:param start_time: 开始时间
|
|
:type start_time: str
|
|
:param end_time: 结束时间
|
|
:type end_time: str
|
|
:return: 每日分时行情包含盘前数据
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
code_id_dict = code_id_map_em()
|
|
url = "https://push2.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": "fa5fd1943c7b386f172d6893dbfba10b",
|
|
"ndays": "1",
|
|
"iscr": "1",
|
|
"iscca": "0",
|
|
"secid": f"{code_id_dict[symbol]}.{symbol}",
|
|
"_": "1623766962675",
|
|
}
|
|
r = requests.get(url, 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",
|
|
"最新价",
|
|
]
|
|
temp_df.index = pd.to_datetime(temp_df["时间"])
|
|
date_format = temp_df.index[0].date().isoformat()
|
|
temp_df = temp_df[
|
|
date_format + " " + start_time : date_format + " " + end_time
|
|
]
|
|
temp_df.reset_index(drop=True, inplace=True)
|
|
temp_df["开盘"] = pd.to_numeric(temp_df["开盘"])
|
|
temp_df["收盘"] = pd.to_numeric(temp_df["收盘"])
|
|
temp_df["最高"] = pd.to_numeric(temp_df["最高"])
|
|
temp_df["最低"] = pd.to_numeric(temp_df["最低"])
|
|
temp_df["成交量"] = pd.to_numeric(temp_df["成交量"])
|
|
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"])
|
|
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"])
|
|
temp_df["时间"] = pd.to_datetime(temp_df["时间"]).astype(str)
|
|
return temp_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
|
stock_zh_a_hist_df = stock_zh_a_hist(
|
|
symbol="000858",
|
|
period="daily",
|
|
start_date="20220516",
|
|
end_date="20220722",
|
|
adjust="",
|
|
)
|
|
print(stock_zh_a_hist_df)
|
|
exit(0)
|
|
|
|
stock_zh_a_spot_em_df = stock_zh_a_spot_em()
|
|
print(stock_zh_a_spot_em_df)
|
|
|
|
code_id_map_em_df = code_id_map_em()
|
|
print(code_id_map_em_df)
|
|
|
|
stock_zh_a_hist_df = stock_zh_a_hist(
|
|
symbol="430090",
|
|
period="daily",
|
|
start_date="20220516",
|
|
end_date="20220722",
|
|
adjust="hfq",
|
|
)
|
|
print(stock_zh_a_hist_df)
|
|
|
|
stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(symbol="833454", period="1")
|
|
print(stock_zh_a_hist_min_em_df)
|
|
|
|
stock_zh_a_hist_pre_min_em_df = stock_zh_a_hist_pre_min_em(symbol="833454")
|
|
print(stock_zh_a_hist_pre_min_em_df)
|
|
|
|
stock_zh_a_spot_em_df = stock_zh_a_spot_em()
|
|
print(stock_zh_a_spot_em_df)
|
|
|
|
stock_zh_a_hist_min_em_df = stock_zh_a_hist_min_em(
|
|
symbol="000001", period='1'
|
|
)
|
|
print(stock_zh_a_hist_min_em_df)
|
|
|
|
stock_zh_a_hist_df = stock_zh_a_hist(
|
|
symbol="833454",
|
|
period="daily",
|
|
start_date="20170301",
|
|
end_date="20211115",
|
|
adjust="hfq",
|
|
)
|
|
print(stock_zh_a_hist_df)
|
|
|