819 lines
29 KiB
Python
819 lines
29 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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import time
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from functools import lru_cache
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def fetch_with_retries_em(url, params, max_retries=3, delay=2):
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"""带重试机制的 GET 请求"""
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for attempt in range(max_retries):
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try:
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response = requests.get(url, params=params, timeout=5)
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response.raise_for_status()
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return response.json()
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except requests.RequestException as e:
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print(f"请求失败，第 {attempt + 1} 次重试: {e}")
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time.sleep(delay)
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return None
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def stock_zh_a_spot_em(fs='m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048', pz=200) -> 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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"""
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url = "http://82.push2.eastmoney.com/api/qt/clist/get"
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pn = 1 # 初始页数
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pn_max = 10000 # 设定初始最大页数
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all_data = []
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while pn <= pn_max:
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params = {
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"pn": str(pn),
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"pz": str(pz),
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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": fs,
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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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data_json = fetch_with_retries_em(url, params)
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if not data_json or "data" not in data_json or "diff" not in data_json["data"]:
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break
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diff_data = data_json["data"]["diff"]
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if not diff_data:
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break
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all_data.extend(diff_data)
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# 获取 total 数据来更新 pn_max
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if pn == 1:
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pn_max = (data_json["data"].get("total", 0) + pz - 1) // pz
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pn += 1
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time.sleep(0.5) # 防止请求过快
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if not all_data:
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return pd.DataFrame()
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temp_df = pd.DataFrame(all_data)
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column_map = {
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"f2": "最新价", "f3": "涨跌幅", "f4": "涨跌额", "f5": "成交量", "f6": "成交额", "f7": "振幅", "f8": "换手率",
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"f9": "市盈率动", "f10": "量比", "f11": "5分钟涨跌", "f12": "代码", "f14": "名称", "f15": "最高", "f16": "最低",
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"f17": "今开", "f18": "昨收", "f20": "总市值", "f21": "流通市值", "f22": "涨速", "f23": "市净率", "f24": "60日涨跌幅",
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"f25": "年初至今涨跌幅", "f26": "上市时间", "f37": "加权净资产收益率", "f38": "总股本", "f39": "已流通股份",
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"f40": "营业收入", "f41": "营业收入同比增长", "f45": "归属净利润", "f46": "归属净利润同比增长", "f48": "每股未分配利润",
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"f49": "毛利率", "f57": "资产负债率", "f61": "每股公积金", "f100": "所处行业", "f112": "每股收益", "f113": "每股净资产",
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"f114": "市盈率静", "f115": "市盈率TTM", "f221": "报告期"
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}
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temp_df.rename(columns=column_map, inplace=True)
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numeric_columns = [
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"最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "量比", "今开", "最高", "最低", "昨收", "涨速", "5分钟涨跌", "60日涨跌幅",
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"年初至今涨跌幅", "市盈率动", "市盈率TTM", "市盈率静", "市净率", "每股收益", "每股净资产", "每股公积金", "每股未分配利润",
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"加权净资产收益率", "毛利率", "资产负债率", "营业收入", "营业收入同比增长", "归属净利润", "归属净利润同比增长", "总股本", "已流通股份",
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"总市值", "流通市值"
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]
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for col in numeric_columns:
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temp_df[col] = pd.to_numeric(temp_df[col], 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["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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def stock_zh_a_spot_em_old(fs='m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048') -> pd.DataFrame:
|
||
"""
|
||
东方财富网-沪深京 A 股-实时行情
|
||
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": fs,
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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",
|
||
"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",
|
||
"5分钟涨跌",
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||
"60日涨跌幅",
|
||
"年初至今涨跌幅",
|
||
"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",
|
||
"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",
|
||
"报告期",
|
||
"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",
|
||
"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")
|
||
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")
|
||
temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce")
|
||
temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
|
||
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], 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["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce")
|
||
temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce")
|
||
temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
|
||
temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce")
|
||
temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce")
|
||
temp_df["年初至今涨跌幅"] = pd.to_numeric(temp_df["年初至今涨跌幅"], errors="coerce")
|
||
temp_df["市盈率动"] = pd.to_numeric(temp_df["市盈率动"], errors="coerce")
|
||
temp_df["市盈率TTM"] = pd.to_numeric(temp_df["市盈率TTM"], errors="coerce")
|
||
temp_df["市盈率静"] = pd.to_numeric(temp_df["市盈率静"], errors="coerce")
|
||
temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce")
|
||
temp_df["每股收益"] = pd.to_numeric(temp_df["每股收益"], errors="coerce")
|
||
temp_df["每股净资产"] = pd.to_numeric(temp_df["每股净资产"], errors="coerce")
|
||
temp_df["每股公积金"] = pd.to_numeric(temp_df["每股公积金"], errors="coerce")
|
||
temp_df["每股未分配利润"] = pd.to_numeric(temp_df["每股未分配利润"], errors="coerce")
|
||
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")
|
||
temp_df["毛利率"] = pd.to_numeric(temp_df["毛利率"], errors="coerce")
|
||
temp_df["资产负债率"] = pd.to_numeric(temp_df["资产负债率"], errors="coerce")
|
||
temp_df["营业收入"] = pd.to_numeric(temp_df["营业收入"], errors="coerce")
|
||
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")
|
||
temp_df["归属净利润"] = pd.to_numeric(temp_df["归属净利润"], errors="coerce")
|
||
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")
|
||
temp_df["报告期"] = pd.to_datetime(temp_df["报告期"], format='%Y%m%d', errors="coerce")
|
||
temp_df["总股本"] = pd.to_numeric(temp_df["总股本"], errors="coerce")
|
||
temp_df["已流通股份"] = pd.to_numeric(temp_df["已流通股份"], errors="coerce")
|
||
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
|
||
temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
|
||
temp_df["上市时间"] = pd.to_datetime(temp_df["上市时间"], format='%Y%m%d', errors="coerce")
|
||
|
||
return temp_df
|
||
|
||
#原有版本，实现的比较繁琐，后面有个简化版本替代它。
|
||
#@lru_cache()
|
||
def code_id_map_em_older() -> dict:
|
||
"""
|
||
东方财富-股票和市场代码
|
||
http://quote.eastmoney.com/center/gridlist.html#hs_a_board
|
||
:return: 股票和市场代码
|
||
:rtype: dict
|
||
"""
|
||
url = "http://80.push2.eastmoney.com/api/qt/clist/get"
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:1 t:2,m:1 t:23",
|
||
"fields": "f12",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df["market_id"] = 1
|
||
temp_df.columns = ["sh_code", "sh_id"]
|
||
code_id_dict = dict(zip(temp_df["sh_code"], temp_df["sh_id"]))
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:0 t:6,m:0 t:80",
|
||
"fields": "f12",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df_sz["sz_id"] = 0
|
||
code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["sz_id"])))
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:0 t:81 s:2048",
|
||
"fields": "f12",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df_sz["bj_id"] = 0
|
||
code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["bj_id"])))
|
||
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:128 t:3",
|
||
"fields": "f12",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df_sz["hk_main"] = 116
|
||
code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["hk_main"])))
|
||
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:128 t:4",
|
||
"fields": "f12",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df_sz["hk_cyb"] = 116
|
||
code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["hk_cyb"])))
|
||
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:105,m:106,m:107",
|
||
"fields": "f12,f13",
|
||
"_": "1623833739532",
|
||
}
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df_sz = pd.DataFrame(data_json["data"]["diff"])
|
||
# 把数据保存到字典中。按照f13的值分别存储
|
||
grouped = temp_df_sz.groupby('f13')
|
||
for id, group in grouped:
|
||
temp_df_sz[f"us_all_{id}"] = id
|
||
code_id_dict.update(dict(zip(group["f12"], str(id))))
|
||
#print(f"分组 f13 = {id}:")
|
||
#print(group)
|
||
#temp_df_sz["us_all"] = 105
|
||
#code_id_dict.update(dict(zip(temp_df_sz["f12"], temp_df_sz["us_all"])))
|
||
print(code_id_dict)
|
||
return code_id_dict
|
||
|
||
@lru_cache()
|
||
def code_id_map_em() -> dict:
|
||
url = "http://80.push2.eastmoney.com/api/qt/clist/get"
|
||
pz = 200 # 固定每页 200 条
|
||
pn = 1 # 初始页码
|
||
pn_max = 10000 # 预设一个较大的初始值
|
||
|
||
params = {
|
||
"pn": str(pn),
|
||
"pz": str(pz),
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "",
|
||
"fields": "f12,f13",
|
||
"_": "1623833739532",
|
||
}
|
||
|
||
market_fs = {
|
||
"china_a": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
|
||
"hk": "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2",
|
||
"us": "m:105,m:106,m:107"
|
||
}
|
||
|
||
code_id_dict = {}
|
||
|
||
for market_id, fs in market_fs.items():
|
||
params["fs"] = fs
|
||
pn = 1 # 每个市场都从第一页开始
|
||
total = 0
|
||
fetched_cnt = 0
|
||
while pn <= pn_max:
|
||
params["pn"] = str(pn)
|
||
data_json = fetch_with_retries_em(url, params)
|
||
|
||
if not data_json or "data" not in data_json or "diff" not in data_json["data"]:
|
||
print(f"市场 {market_id} 数据获取失败或为空，跳过。")
|
||
break
|
||
|
||
temp_df = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df["market_id"] = 1
|
||
|
||
# 处理 total 以计算 pn_max
|
||
if pn == 1 and "total" in data_json["data"]:
|
||
total = int(data_json["data"]["total"])
|
||
pn_max = (total // pz) + 1 # 计算最大页数
|
||
print(f"市场 {market_id} 总数据量: {total}, 需要页数: {pn_max}")
|
||
|
||
# 按 f13 进行分组并存入字典
|
||
grouped = temp_df.groupby('f13')
|
||
for id, group in grouped:
|
||
code_id_dict.update(dict.fromkeys(group["f12"], id))
|
||
fetched_cnt += len(group)
|
||
# print(f'获取 {market_id} 股票列表，f13: {id}, 股票数: {len(group)}, 已获取总股票数: {fetched_cnt}, 总股票数: {total}')
|
||
|
||
pn += 1 # 翻页继续
|
||
|
||
print(f'获取 {market_id} 已获取总股票数: {fetched_cnt}, 总股票数: {total}')
|
||
|
||
return code_id_dict
|
||
|
||
@lru_cache()
|
||
def code_id_map_em2() -> dict:
|
||
"""
|
||
东方财富-股票和市场代码
|
||
http://quote.eastmoney.com/center/gridlist.html#hs_a_board
|
||
:return: 股票和市场代码
|
||
:rtype: dict
|
||
"""
|
||
url = "http://80.push2.eastmoney.com/api/qt/clist/get"
|
||
params = {
|
||
"pn": "1",
|
||
"pz": "50000",
|
||
"po": "1",
|
||
"np": "1",
|
||
"ut": "bd1d9ddb04089700cf9c27f6f7426281",
|
||
"fltt": "2",
|
||
"invt": "2",
|
||
"fid": "f3",
|
||
"fs": "m:1 t:2,m:1 t:23",
|
||
"fields": "f12,f13",
|
||
"_": "1623833739532",
|
||
}
|
||
market_fs = {"china_a": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048",
|
||
"hk": "m:128 t:3,m:128 t:4,m:128 t:1,m:128 t:2",
|
||
"us": "m:105,m:106,m:107"}
|
||
code_id_dict = dict()
|
||
|
||
for market_id, fs in market_fs.items():
|
||
params['fs'] = fs
|
||
r = requests.get(url, params=params)
|
||
data_json = r.json()
|
||
if not data_json["data"]["diff"]:
|
||
return dict()
|
||
temp_df = pd.DataFrame(data_json["data"]["diff"])
|
||
temp_df["market_id"] = 1
|
||
# 把数据保存到字典中。按照f13的值分别存储
|
||
grouped = temp_df.groupby('f13')
|
||
for id, group in grouped:
|
||
temp_df[f"{market_id}_{id}"] = id
|
||
#code_id_dict.update(dict(zip(group["f12"], str(id))))
|
||
code_id_dict.update(dict.fromkeys(group["f12"], id))
|
||
print(f'get {market_id} stock list. f13: {id}, stock count: {len(group)}')
|
||
|
||
return code_id_dict
|
||
|
||
def stock_zh_a_hist(
|
||
symbol: str = "000001",
|
||
period: str = "daily",
|
||
start_date: str = "19700101",
|
||
end_date: str = "20500101",
|
||
adjust: str = "",
|
||
) -> pd.DataFrame:
|
||
"""
|
||
东方财富网-行情首页-沪深京 A 股-每日行情
|
||
https://quote.eastmoney.com/concept/sh603777.html?from=classic
|
||
:param symbol: 股票代码
|
||
: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 = code_id_map_em()
|
||
adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
|
||
period_dict = {"daily": "101", "weekly": "102", "monthly": "103"}
|
||
url = "http://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],
|
||
"secid": f"{code_id_dict[symbol]}.{symbol}",
|
||
"beg": start_date,
|
||
"end": end_date,
|
||
"_": "1623766962675",
|
||
}
|
||
r = requests.get(url, 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"])
|
||
temp_df.reset_index(inplace=True, drop=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"])
|
||
|
||
return temp_df
|
||
|
||
|
||
def stock_zh_a_hist_min_em(
|
||
symbol: str = "000001",
|
||
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:
|
||
"""
|
||
东方财富网-行情首页-沪深京 A 股-每日分时行情
|
||
https://quote.eastmoney.com/concept/sh603777.html?from=classic
|
||
:param symbol: 股票代码
|
||
: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 = code_id_map_em()
|
||
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"{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"])
|
||
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["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
|
||
else:
|
||
url = "http://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"{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)
|
||
|