#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/2/1 16:20 Desc: 东方财富网-数据中心-新股数据-打新收益率 东方财富网-数据中心-新股申购-打新收益率 https://data.eastmoney.com/xg/xg/dxsyl.html 东方财富网-数据中心-新股数据-新股申购与中签查询 https://data.eastmoney.com/xg/xg/default_2.html """ import pandas as pd import requests def stock_xgsglb_em(symbol: str = "全部股票") -> pd.DataFrame: """ 新股申购与中签查询 https://data.eastmoney.com/xg/xg/default_2.html :param symbol: choice of {"全部股票", "沪市主板", "科创板", "深市主板", "创业板", "北交所"} :type symbol: str :return: 新股申购与中签数据 :rtype: pandas.DataFrame """ market_map = { "全部股票": """(APPLY_DATE>'2010-01-01')""", "沪市主板": """(APPLY_DATE>'2010-01-01')(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE in ("069001001001","069001001003","069001001006"))""", "科创板": """(APPLY_DATE>'2010-01-01')(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE="069001001006")""", "深市主板": """(APPLY_DATE>'2010-01-01')(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE in ("069001002001","069001002002","069001002003","069001002005"))""", "创业板": """(APPLY_DATE>'2010-01-01')(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE="069001002002")""", } url = "http://datacenter-web.eastmoney.com/api/data/v1/get" params = { "sortColumns": "APPLY_DATE,SECURITY_CODE", "sortTypes": "-1,-1", "pageSize": "50", "pageNumber": "1", "reportName": "RPTA_APP_IPOAPPLY", "columns": "SECURITY_CODE,SECURITY_NAME,TRADE_MARKET_CODE,APPLY_CODE,TRADE_MARKET,MARKET_TYPE,ORG_TYPE,ISSUE_NUM,ONLINE_ISSUE_NUM,OFFLINE_PLACING_NUM,TOP_APPLY_MARKETCAP,PREDICT_ONFUND_UPPER,ONLINE_APPLY_UPPER,PREDICT_ONAPPLY_UPPER,ISSUE_PRICE,LATELY_PRICE,CLOSE_PRICE,APPLY_DATE,BALLOT_NUM_DATE,BALLOT_PAY_DATE,LISTING_DATE,AFTER_ISSUE_PE,ONLINE_ISSUE_LWR,INITIAL_MULTIPLE,INDUSTRY_PE_NEW,OFFLINE_EP_OBJECT,CONTINUOUS_1WORD_NUM,TOTAL_CHANGE,PROFIT,LIMIT_UP_PRICE,INFO_CODE,OPEN_PRICE,LD_OPEN_PREMIUM,LD_CLOSE_CHANGE,TURNOVERRATE,LD_HIGH_CHANG,LD_AVERAGE_PRICE,OPEN_DATE,OPEN_AVERAGE_PRICE,PREDICT_PE,PREDICT_ISSUE_PRICE2,PREDICT_ISSUE_PRICE,PREDICT_ISSUE_PRICE1,PREDICT_ISSUE_PE,PREDICT_PE_THREE,ONLINE_APPLY_PRICE,MAIN_BUSINESS", "filter": market_map[symbol], "source": "WEB", "client": "WEB", } r = requests.get(url, params=params) data_json = r.json() big_df = pd.DataFrame() temp_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat([big_df, temp_df], ignore_index=True) big_df.rename( columns={ "SECURITY_CODE": "股票代码", "SECURITY_NAME": "股票简称", "TRADE_MARKET_CODE": "-", "APPLY_CODE": "申购代码", "TRADE_MARKET": "-", "MARKET_TYPE": "-", "ORG_TYPE": "-", "ISSUE_NUM": "发行总数", "ONLINE_ISSUE_NUM": "网上发行", "OFFLINE_PLACING_NUM": "_", "TOP_APPLY_MARKETCAP": "顶格申购需配市值", "PREDICT_ONFUND_UPPER": "_", "ONLINE_APPLY_UPPER": "申购上限", "PREDICT_ONAPPLY_UPPER": "_", "ISSUE_PRICE": "发行价格", "LATELY_PRICE": "最新价", "CLOSE_PRICE": "首日收盘价", "APPLY_DATE": "申购日期", "BALLOT_NUM_DATE": "中签号公布日", "BALLOT_PAY_DATE": "中签缴款日期", "LISTING_DATE": "上市日期", "AFTER_ISSUE_PE": "发行市盈率", "ONLINE_ISSUE_LWR": "中签率", "INITIAL_MULTIPLE": "询价累计报价倍数", "INDUSTRY_PE_NEW": "行业市盈率", "OFFLINE_EP_OBJECT": "配售对象报价家数", "CONTINUOUS_1WORD_NUM": "连续一字板数量", "TOTAL_CHANGE": "涨幅", "PROFIT": "每中一签获利", "LIMIT_UP_PRICE": "_", "INFO_CODE": "_", "OPEN_PRICE": "_", "LD_OPEN_PREMIUM": "_", "LD_CLOSE_CHANGE": "_", "TURNOVERRATE": "_", "LD_HIGH_CHANG": "_", "LD_AVERAGE_PRICE": "_", "OPEN_DATE": "_", "OPEN_AVERAGE_PRICE": "_", "PREDICT_PE": "_", "PREDICT_ISSUE_PRICE2": "_", "PREDICT_ISSUE_PRICE": "_", "PREDICT_ISSUE_PRICE1": "_", "PREDICT_ISSUE_PE": "_", "PREDICT_PE_THREE": "_", "ONLINE_APPLY_PRICE": "_", "MAIN_BUSINESS": "_", "IS_REGISTRATION": "_", }, inplace=True, ) big_df = big_df[ [ "股票代码", "股票简称", "申购代码", "发行总数", "网上发行", "顶格申购需配市值", "申购上限", "发行价格", "最新价", "首日收盘价", "申购日期", "中签号公布日", "中签缴款日期", "上市日期", "发行市盈率", "行业市盈率", "中签率", "询价累计报价倍数", "配售对象报价家数", "连续一字板数量", "涨幅", "每中一签获利", ] ] big_df["申购日期"] = pd.to_datetime(big_df["申购日期"], errors="coerce").dt.date big_df["中签号公布日"] = pd.to_datetime(big_df["中签号公布日"]).dt.date big_df["中签缴款日期"] = pd.to_datetime(big_df["中签缴款日期"]).dt.date big_df["发行总数"] = pd.to_numeric(big_df["发行总数"], errors="coerce") big_df["网上发行"] = pd.to_numeric(big_df["网上发行"], errors="coerce") big_df["顶格申购需配市值"] = pd.to_numeric(big_df["顶格申购需配市值"], errors="coerce") big_df["申购上限"] = pd.to_numeric(big_df["申购上限"], errors="coerce") big_df["发行价格"] = pd.to_numeric(big_df["发行价格"], errors="coerce") big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce") big_df["首日收盘价"] = pd.to_numeric(big_df["首日收盘价"], errors="coerce") big_df["发行市盈率"] = pd.to_numeric(big_df["发行市盈率"], errors="coerce") big_df["行业市盈率"] = pd.to_numeric(big_df["行业市盈率"], errors="coerce") big_df["中签率"] = pd.to_numeric(big_df["中签率"], errors="coerce") big_df["询价累计报价倍数"] = pd.to_numeric(big_df["询价累计报价倍数"], errors="coerce") big_df["配售对象报价家数"] = pd.to_numeric(big_df["配售对象报价家数"], errors="coerce") big_df["涨幅"] = pd.to_numeric(big_df["涨幅"], errors="coerce") big_df["每中一签获利"] = pd.to_numeric(big_df["每中一签获利"], errors="coerce") return big_df if __name__ == "__main__": #显示所有列 pd.set_option('display.max_columns', None) #显示所有行 pd.set_option('display.max_rows', None) #设置value的显示长度为100，默认为50 pd.set_option('max_colwidth',100) stock_xgsglb_em_df = stock_xgsglb_em(symbol="全部股票") print(stock_xgsglb_em_df)