#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2023/8/20 20:00 Desc: 东方财富网-数据中心-研究报告-东方财富分析师指数 https://data.eastmoney.com/invest/invest/list.html """ import pandas as pd import requests from akshare.utils.tqdm import get_tqdm from akshare.utils.cons import headers def stock_analyst_rank_em(year: str = "2024") -> pd.DataFrame: """ 东方财富网-数据中心-研究报告-东方财富分析师指数-东方财富分析师指数 https://data.eastmoney.com/invest/invest/list.html :param year: 从 2015 年至今 :type year: str :return: 东方财富分析师指数 :rtype: pandas.DataFrame """ url = "https://data.eastmoney.com/dataapi/invest/list" params = { "sortColumns": "YEAR_YIELD", "sortTypes": "-1", "pageSize": "500", "pageNumber": "1", "reportName": "RPT_ANALYST_INDEX_RANK", "columns": "ALL", "source": "WEB", "client": "WEB", "filter": f'(YEAR="{year}")', "distinct": "ANALYST_CODE", "limit": "top100", } r = requests.get(url, params=params, headers=headers) data_json = r.json() total_page = data_json["result"]["pages"] big_df = pd.DataFrame() tqdm = get_tqdm() for page in tqdm(range(1, total_page + 1), leave=False): params.update({"pageNumber": page}) r = requests.get(url, params=params, headers=headers) data_json = r.json() data_df = pd.DataFrame(data_json["result"]["data"]) big_df = pd.concat(objs=[big_df, data_df], ignore_index=True) big_df.reset_index(inplace=True) big_df["index"] = list(range(1, len(big_df) + 1)) big_df.columns = [ "序号", "分析师ID", "分析师名称", "更新日期", "年度", "分析师单位", "_", "年度指数", f"{year}年收益率", "3个月收益率", "6个月收益率", "12个月收益率", "成分股个数", f"{year}最新个股评级-股票名称", "_", f"{year}最新个股评级-股票代码", "_", "行业代码", "行业", ] big_df = big_df[ [ "序号", "分析师名称", "分析师单位", "年度指数", f"{year}年收益率", "3个月收益率", "6个月收益率", "12个月收益率", "成分股个数", f"{year}最新个股评级-股票名称", f"{year}最新个股评级-股票代码", "分析师ID", "行业代码", "行业", "更新日期", "年度", ] ] big_df["更新日期"] = pd.to_datetime(big_df["更新日期"], errors="coerce").dt.date big_df["年度指数"] = pd.to_numeric(big_df["年度指数"], errors="coerce") big_df[f"{year}年收益率"] = pd.to_numeric( big_df[f"{year}年收益率"], errors="coerce" ) big_df["3个月收益率"] = pd.to_numeric(big_df["3个月收益率"], errors="coerce") big_df["6个月收益率"] = pd.to_numeric(big_df["6个月收益率"], errors="coerce") big_df["12个月收益率"] = pd.to_numeric(big_df["12个月收益率"], errors="coerce") big_df["成分股个数"] = pd.to_numeric(big_df["成分股个数"], errors="coerce") return big_df def stock_analyst_detail_em( analyst_id: str = "11000200926", indicator: str = "最新跟踪成分股" ) -> pd.DataFrame: """ 东方财富网-数据中心-研究报告-东方财富分析师指数-东方财富分析师指数2020最新排行-分析师详情 https://data.eastmoney.com/invest/invest/11000257131.html :param analyst_id: 分析师 ID, 从 ak.stock_analyst_rank_em() 获取 :type analyst_id: str :param indicator: choice of {"最新跟踪成分股", "历史跟踪成分股", "历史指数"} :type indicator: str :return: 具体指标的数据 :rtype: pandas.DataFrame """ url = "https://datacenter.eastmoney.com/special/api/data/v1/get" if indicator == "最新跟踪成分股": params = { "reportName": "RPT_RESEARCHER_NTCSTOCK", "columns": "ALL", "source": "WEB", "client": "WEB", "sortColumns": "CHANGE_DATE", "sortTypes": "-1", "pageNumber": "1", "pageSize": "1000", "filter": f'(ANALYST_CODE="{analyst_id}")', } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = list(range(1, len(temp_df) + 1)) temp_df.columns = [ "序号", "最新评级日期", "-", "-", "-", "-", "股票代码", "-", "股票名称", "调入日期", "当前评级名称", "成交价格(前复权)", "最新价格", "阶段涨跌幅", ] temp_df = temp_df[ [ "序号", "股票代码", "股票名称", "调入日期", "最新评级日期", "当前评级名称", "成交价格(前复权)", "最新价格", "阶段涨跌幅", ] ] temp_df["调入日期"] = pd.to_datetime( temp_df["调入日期"], errors="coerce" ).dt.date temp_df["最新评级日期"] = pd.to_datetime( temp_df["最新评级日期"], errors="coerce" ).dt.date temp_df["成交价格(前复权)"] = pd.to_numeric( temp_df["成交价格(前复权)"], errors="coerce" ) temp_df["最新价格"] = pd.to_numeric(temp_df["最新价格"], errors="coerce") temp_df["阶段涨跌幅"] = pd.to_numeric(temp_df["阶段涨跌幅"], errors="coerce") return temp_df elif indicator == "历史跟踪成分股": params = { "reportName": "RPT_RESEARCHER_HISTORYSTOCK", "columns": "ALL", "source": "WEB", "client": "WEB", "sortColumns": "CHANGE_DATE", "sortTypes": "-1", "pageNumber": "1", "pageSize": "1000", "filter": f'(ANALYST_CODE="{analyst_id}")', } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df.reset_index(inplace=True) temp_df["index"] = list(range(1, len(temp_df) + 1)) temp_df.columns = [ "序号", "-", "-", "-", "股票代码", "-", "股票名称", "调入日期", "调出日期", "调入时评级名称", "调出原因", "累计涨跌幅", ] temp_df = temp_df[ [ "序号", "股票代码", "股票名称", "调入日期", "调出日期", "调入时评级名称", "调出原因", "累计涨跌幅", ] ] temp_df["调入日期"] = pd.to_datetime( temp_df["调入日期"], errors="coerce" ).dt.date temp_df["调出日期"] = pd.to_datetime( temp_df["调出日期"], errors="coerce" ).dt.date temp_df["累计涨跌幅"] = pd.to_numeric(temp_df["累计涨跌幅"], errors="coerce") return temp_df elif indicator == "历史指数": params = { "reportName": "RPT_RESEARCHER_DETAILS", "columns": "ALL", "sortColumns": "TRADE_DATE", "sortTypes": "-1", "filter": f'(ANALYST_CODE="{analyst_id}")', "source": "WEB", "client": "WEB", } r = requests.get(url, params=params, headers=headers) data_json = r.json() temp_df = pd.DataFrame(data_json["result"]["data"]) temp_df = temp_df[ [ "TRADE_DATE", "INDEX_HVALUE", ] ] temp_df.columns = ["date", "value"] temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date temp_df["value"] = pd.to_numeric(temp_df["value"], errors="coerce") temp_df.sort_values(["date"], inplace=True, ignore_index=True) return temp_df if __name__ == "__main__": stock_analyst_rank_em_df = stock_analyst_rank_em(year="2024") print(stock_analyst_rank_em_df) stock_analyst_detail_em_df = stock_analyst_detail_em( analyst_id="11000200926", indicator="最新跟踪成分股" ) print(stock_analyst_detail_em_df) stock_analyst_detail_em_df = stock_analyst_detail_em( analyst_id="11000200926", indicator="历史跟踪成分股" ) print(stock_analyst_detail_em_df) stock_analyst_detail_em_df = stock_analyst_detail_em( analyst_id="11000200926", indicator="历史指数" ) print(stock_analyst_detail_em_df)