214 lines
7.4 KiB
Python
214 lines
7.4 KiB
Python
import logging
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import pandas as pd
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import requests
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import math
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import time
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from typing import List, Dict
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from akshare.utils.tqdm import get_tqdm
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from loguru import logger
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def __stock_zh_main_spot_em() -> pd.DataFrame:
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"""
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东方财富网-行情中心-沪深重要指数
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https://quote.eastmoney.com/center/hszs.html
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:return: 指数的实时行情数据
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:rtype: pandas.DataFrame
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"""
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url = "https://33.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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"dect": "1",
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"wbp2u": "|0|0|0|web",
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"fid": "",
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"fs": "b:MK0010",
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"fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,"
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"f23,f24,f25,f26,f22,f11,f62,f128,f136,f115,f152",
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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(data_json["data"]["diff"])
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temp_df.reset_index(inplace=True)
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temp_df["index"] = temp_df["index"].astype(int) + 1
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temp_df.rename(
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columns={
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"index": "序号",
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"f2": "最新价",
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"f3": "涨跌幅",
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"f4": "涨跌额",
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"f5": "成交量",
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"f6": "成交额",
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"f7": "振幅",
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"f10": "量比",
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"f12": "代码",
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"f14": "名称",
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"f15": "最高",
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"f16": "最低",
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"f17": "今开",
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"f18": "昨收",
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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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"序号",
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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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"最高",
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"最低",
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"今开",
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"昨收",
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"量比",
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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["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["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
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return temp_df
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def fetch_paginated_data(url: str, base_params: Dict, timeout: int = 15*2):
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"""
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东方财富-分页获取数据并合并结果
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https://quote.eastmoney.com/f1.html?newcode=0.000001
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:param url: 股票代码
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:type url: str
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:param base_params: 基础请求参数
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:type base_params: dict
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:param timeout: 请求超时时间
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:type timeout: str
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:return: 合并后的数据
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:rtype: pandas.DataFrame
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"""
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# 复制参数以避免修改原始参数
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params = base_params.copy()
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# 获取第一页数据，用于确定分页信息
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r = requests.get(url, params=params, timeout=timeout)
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data_json = r.json()
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# 计算分页信息
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per_page_num = len(data_json["data"]["diff"])
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total_page = math.ceil(data_json["data"]["total"] / per_page_num)
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# 存储所有页面数据
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temp_list = []
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# 添加第一页数据
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temp_list.append(pd.DataFrame(data_json["data"]["diff"]))
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# 获取进度条
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tqdm = get_tqdm()
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# 获取剩余页面数据
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for page in range(2, total_page + 1):
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logger.info(f"获取第 {page}/{total_page} 页数据")
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params.update({"pn": page})
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r = requests.get(url, params=params, timeout=timeout)
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data_json = r.json()
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inner_temp_df = pd.DataFrame(data_json["data"]["diff"])
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temp_list.append(inner_temp_df)
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time.sleep(300)
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# 合并所有数据
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temp_df = pd.concat(temp_list, ignore_index=True)
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temp_df["f3"] = pd.to_numeric(temp_df["f3"], errors="coerce")
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temp_df.sort_values(by=["f3"], ascending=False, inplace=True, ignore_index=True)
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temp_df.reset_index(inplace=True)
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temp_df["index"] = temp_df["index"].astype(int) + 1
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return temp_df
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def stock_zh_index_spot_em(symbol: str = "上证系列指数") -> pd.DataFrame:
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"""
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东方财富网-行情中心-沪深京指数
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https://quote.eastmoney.com/center/gridlist.html#index_sz
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:param symbol: "上证系列指数"; choice of {"沪深重要指数", "上证系列指数", "深证系列指数", "指数成份", "中证系列指数"}
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:type symbol: str
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:return: 指数的实时行情数据
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:rtype: pandas.DataFrame
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"""
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if symbol == "沪深重要指数":
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return __stock_zh_main_spot_em()
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url = "https://48.push2.eastmoney.com/api/qt/clist/get"
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symbol_map = {
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"上证系列指数": "m:1+t:1",
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"深证系列指数": "m:0 t:5",
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"指数成份": "m:1+s:3,m:0+t:5",
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"中证系列指数": "m:2",
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}
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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": symbol_map[symbol],
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"fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,"
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"f26,f22,f33,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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"index": "序号",
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"f2": "最新价",
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"f3": "涨跌幅",
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"f4": "涨跌额",
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"f5": "成交量",
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"f6": "成交额",
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"f7": "振幅",
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"f10": "量比",
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"f12": "代码",
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"f14": "名称",
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"f15": "最高",
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"f16": "最低",
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"f17": "今开",
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"f18": "昨收",
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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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"序号",
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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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"最高",
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"最低",
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"今开",
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"昨收",
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"量比",
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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["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["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
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return temp_df |