#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/10/27 22:30 Desc: 雪球基金-基金详情 https://danjuanfunds.com/funding/003545 """ import pandas as pd import requests def fund_individual_basic_info_xq( symbol: str = "000001", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-基金详情 https://danjuanfunds.com/djapi/fund/675091 :param symbol: 基金代码 :type symbol: str :param timeout: choice of None or a positive float number :type timeout: float :return: 基金信息 :rtype: pandas.DataFrame """ url = f"https://danjuanfunds.com/djapi/fund/{symbol}" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } r = requests.get(url, headers=headers, timeout=timeout) json_data = r.json()["data"] temp_df = pd.json_normalize(json_data) temp_df.rename( columns={ "fd_code": "基金代码", "fd_name": "基金名称", "fd_full_name": "基金全称", "found_date": "成立时间", "totshare": "最新规模", "keeper_name": "基金公司", "manager_name": "基金经理", "trup_name": "托管银行", "type_desc": "基金类型", "rating_source": "评级机构", "rating_desc": "基金评级", "invest_orientation": "投资策略", "invest_target": "投资目标", "performance_bench_mark": "业绩比较基准", }, inplace=True, ) if "评级机构" not in temp_df.columns: temp_df["评级机构"] = pd.NA temp_df = temp_df[ [ "基金代码", "基金名称", "基金全称", "成立时间", "最新规模", "基金公司", "基金经理", "托管银行", "基金类型", "评级机构", "基金评级", "投资策略", "投资目标", "业绩比较基准", ] ] temp_df = temp_df.T.reset_index() temp_df.columns = ["item", "value"] return temp_df def fund_individual_achievement_xq( symbol: str = "000001", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-基金业绩 https://danjuanfunds.com/djapi/fundx/base/fund/achievement/675091 :param symbol: 基金代码 :type symbol: str :param timeout: choice of None or a positive float number :type timeout: float :return: 基金业绩 :rtype: pandas.DataFrame """ url = f"https://danjuanfunds.com/djapi/fundx/base/fund/achievement/{symbol}" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } r = requests.get(url, headers=headers, timeout=timeout) json_data = r.json()["data"] combined_df = None type_dict = { "annual_performance_list": "年度业绩", "stage_performance_list": "阶段业绩", } for k, v in type_dict.items(): temp_df = pd.DataFrame.from_dict(json_data[k], orient="columns") temp_df["type"] = v temp_df = temp_df[ [ "type", "period_time", "self_nav", "self_max_draw_down", "self_nav_rank", ] ] temp_df.columns = [ "业绩类型", "周期", "本产品区间收益", "本产品最大回撒", "周期收益同类排名", ] combined_df = pd.concat([combined_df, temp_df], ignore_index=True) combined_df = combined_df.map( lambda x: x if "%" not in str(x) else x.replace("%", "") ) combined_df[["本产品区间收益", "本产品最大回撒"]] = combined_df[ ["本产品区间收益", "本产品最大回撒"] ].astype(float) return combined_df def fund_individual_analysis_xq( symbol: str = "000001", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-基金数据分析 https://danjuanfunds.com/djapi/fund/base/quote/data/index/analysis/675091 :param symbol: 基金代码 :type symbol: str :param timeout: choice of None or a positive float number :type timeout: float :return: 基金数据分析 :rtype: pandas.DataFrame """ url = f"https://danjuanfunds.com/djapi/fund/base/quote/data/index/analysis/{symbol}" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } r = requests.get(url, headers=headers, timeout=timeout) json_data = r.json()["data"]["index_data_list"] temp_df = pd.json_normalize(json_data) temp_df = temp_df[ [ "index_time_period", "investment_cost_performance", "risk_control", "self_index.volatility_rank", "self_index.sharpe_rank", "self_index.max_draw_down", ] ] temp_df.columns = [ "周期", "较同类风险收益比", "较同类抗风险波动", "年化波动率", "年化夏普比率", "最大回撤", ] temp_df = temp_df.map(lambda x: x if "%" not in str(x) else x.replace("%", "")) temp_df[["年化波动率", "最大回撤"]] *= 100 temp_df["较同类风险收益比"] = pd.to_numeric( temp_df["较同类风险收益比"], errors="coerce" ) temp_df["较同类抗风险波动"] = pd.to_numeric( temp_df["较同类抗风险波动"], errors="coerce" ) temp_df["年化波动率"] = pd.to_numeric(temp_df["年化波动率"], errors="coerce") temp_df["年化夏普比率"] = pd.to_numeric(temp_df["年化夏普比率"], errors="coerce") temp_df["最大回撤"] = pd.to_numeric(temp_df["最大回撤"], errors="coerce") return temp_df def fund_individual_profit_probability_xq( symbol: str = "000001", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-盈利概率-历史任意时点买入，持有满 X 年，盈利概率 Y% https://danjuanfunds.com/djapi/fundx/base/fund/profit/ratio/675091 :param symbol: 基金代码 :type symbol: str :param timeout: choice of None or a positive float number :type timeout: float :return: 盈利概率 :rtype: pandas.DataFrame """ url = f"https://danjuanfunds.com/djapi/fundx/base/fund/profit/ratio/{symbol}" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } r = requests.get(url, headers=headers, timeout=timeout) json_data = r.json()["data"]["data_list"] temp_df = pd.DataFrame.from_dict(json_data, orient="columns") temp_df = temp_df[ [ "holding_time", "profit_ratio", "average_income", ] ] temp_df.columns = [ "持有时长", "盈利概率", "平均收益", ] temp_df = temp_df.map(lambda x: x if "%" not in str(x) else x.replace("%", "")) temp_df["盈利概率"] = pd.to_numeric(temp_df["盈利概率"], errors="coerce") temp_df["平均收益"] = pd.to_numeric(temp_df["平均收益"], errors="coerce") return temp_df def fund_individual_detail_info_xq( symbol: str = "000001", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-交易规则 https://danjuanfunds.com/djapi/fund/detail/675091 :param symbol: 基金代码 :type symbol: str :param timeout: choice of None or a positive float number :type timeout: float :return: 交易规则 :rtype: pandas.DataFrame """ url = f"https://danjuanfunds.com/djapi/fund/detail/{symbol}" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } r = requests.get(url, headers=headers, timeout=timeout) json_data = r.json()["data"] combined_df = None rate_type_dict = { "declare_rate_table": "买入规则", "withdraw_rate_table": "卖出规则", "other_rate_table": "其他费用", } for k, v in rate_type_dict.items(): temp_df = pd.DataFrame.from_dict(json_data["fund_rates"][k], orient="columns") temp_df["rate_type"] = v temp_df = temp_df[ [ "rate_type", "name", "value", ] ] temp_df.columns = [ "费用类型", "条件或名称", "费用", ] combined_df = pd.concat(objs=[combined_df, temp_df], ignore_index=True) combined_df["费用"] = pd.to_numeric(combined_df["费用"], errors="coerce") return combined_df def fund_individual_detail_hold_xq( symbol: str = "002804", date: str = "20231231", timeout: float = None ) -> pd.DataFrame: """ 雪球基金-持仓 https://danjuanfunds.com/rn/fund-detail/archive?id=103&code=002804 :param symbol: 基金代码 :type symbol: str :param date: 财报日期 :type date: str :param timeout: choice of None or a positive float number :type timeout: float :return: 雪球基金-持仓 :rtype: pandas.DataFrame """ url = "https://danjuanfunds.com/djapi/fundx/base/fund/record/asset/percent" headers = { "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/80.0.3987.149 Safari/537.36" } params = { "fund_code": f"{symbol}", "report_date": f"{'-'.join([date[:4], date[4:6], date[6:]])}", } r = requests.get(url, headers=headers, params=params, timeout=timeout) data_json = r.json() temp_df = pd.DataFrame.from_dict(data_json["data"]["chart_list"], orient="columns") temp_df = temp_df[ [ "type_desc", "percent", ] ] temp_df.columns = [ "资产类型", "仓位占比", ] temp_df["仓位占比"] = pd.to_numeric(temp_df["仓位占比"], errors="coerce") return temp_df if __name__ == "__main__": fund_individual_basic_info_xq_df = fund_individual_basic_info_xq(symbol="000005") print(fund_individual_basic_info_xq_df) fund_individual_achievement_xq_df = fund_individual_achievement_xq(symbol="000001") print(fund_individual_achievement_xq_df) fund_individual_analysis_xq_df = fund_individual_analysis_xq(symbol="000001") print(fund_individual_analysis_xq_df) fund_individual_profit_probability_xq_df = fund_individual_profit_probability_xq( symbol="000001" ) print(fund_individual_profit_probability_xq_df) fund_individual_detail_info_xq_df = fund_individual_detail_info_xq(symbol="000001") print(fund_individual_detail_info_xq_df) fund_individual_detail_hold_xq_df = fund_individual_detail_hold_xq( symbol="002804", date="20231231" ) print(fund_individual_detail_hold_xq_df)