知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
333 lines
11 KiB
Python
333 lines
11 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/10/27 22:30
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Desc: 雪球基金-基金详情
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https://danjuanfunds.com/funding/003545
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"""
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import pandas as pd
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import requests
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def fund_individual_basic_info_xq(
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symbol: str = "000001", timeout: float = None
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) -> pd.DataFrame:
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"""
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雪球基金-基金详情
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https://danjuanfunds.com/djapi/fund/675091
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:param symbol: 基金代码
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:type symbol: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 基金信息
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:rtype: pandas.DataFrame
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"""
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url = f"https://danjuanfunds.com/djapi/fund/{symbol}"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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r = requests.get(url, headers=headers, timeout=timeout)
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json_data = r.json()["data"]
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temp_df = pd.json_normalize(json_data)
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temp_df.rename(
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columns={
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"fd_code": "基金代码",
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"fd_name": "基金名称",
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"fd_full_name": "基金全称",
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"found_date": "成立时间",
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"totshare": "最新规模",
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"keeper_name": "基金公司",
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"manager_name": "基金经理",
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"trup_name": "托管银行",
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"type_desc": "基金类型",
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"rating_source": "评级机构",
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"rating_desc": "基金评级",
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"invest_orientation": "投资策略",
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"invest_target": "投资目标",
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"performance_bench_mark": "业绩比较基准",
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},
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inplace=True,
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)
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if "评级机构" not in temp_df.columns:
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temp_df["评级机构"] = pd.NA
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temp_df = temp_df[
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[
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"基金代码",
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"基金名称",
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"基金全称",
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"成立时间",
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"最新规模",
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"基金公司",
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"基金经理",
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"托管银行",
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"基金类型",
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"评级机构",
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"基金评级",
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"投资策略",
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"投资目标",
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"业绩比较基准",
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]
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]
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temp_df = temp_df.T.reset_index()
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temp_df.columns = ["item", "value"]
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return temp_df
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def fund_individual_achievement_xq(
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symbol: str = "000001", timeout: float = None
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) -> pd.DataFrame:
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"""
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雪球基金-基金业绩
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https://danjuanfunds.com/djapi/fundx/base/fund/achievement/675091
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:param symbol: 基金代码
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:type symbol: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 基金业绩
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:rtype: pandas.DataFrame
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"""
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url = f"https://danjuanfunds.com/djapi/fundx/base/fund/achievement/{symbol}"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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r = requests.get(url, headers=headers, timeout=timeout)
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json_data = r.json()["data"]
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combined_df = None
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type_dict = {
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"annual_performance_list": "年度业绩",
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"stage_performance_list": "阶段业绩",
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}
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for k, v in type_dict.items():
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temp_df = pd.DataFrame.from_dict(json_data[k], orient="columns")
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temp_df["type"] = v
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temp_df = temp_df[
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[
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"type",
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"period_time",
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"self_nav",
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"self_max_draw_down",
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"self_nav_rank",
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]
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]
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temp_df.columns = [
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"业绩类型",
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"周期",
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"本产品区间收益",
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"本产品最大回撒",
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"周期收益同类排名",
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]
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combined_df = pd.concat([combined_df, temp_df], ignore_index=True)
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combined_df = combined_df.map(
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lambda x: x if "%" not in str(x) else x.replace("%", "")
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)
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combined_df[["本产品区间收益", "本产品最大回撒"]] = combined_df[
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["本产品区间收益", "本产品最大回撒"]
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].astype(float)
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return combined_df
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def fund_individual_analysis_xq(
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symbol: str = "000001", timeout: float = None
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) -> pd.DataFrame:
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"""
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雪球基金-基金数据分析
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https://danjuanfunds.com/djapi/fund/base/quote/data/index/analysis/675091
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:param symbol: 基金代码
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:type symbol: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 基金数据分析
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:rtype: pandas.DataFrame
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"""
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url = f"https://danjuanfunds.com/djapi/fund/base/quote/data/index/analysis/{symbol}"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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r = requests.get(url, headers=headers, timeout=timeout)
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json_data = r.json()["data"]["index_data_list"]
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temp_df = pd.json_normalize(json_data)
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temp_df = temp_df[
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[
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"index_time_period",
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"investment_cost_performance",
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"risk_control",
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"self_index.volatility_rank",
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"self_index.sharpe_rank",
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"self_index.max_draw_down",
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]
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]
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temp_df.columns = [
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"周期",
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"较同类风险收益比",
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"较同类抗风险波动",
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"年化波动率",
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"年化夏普比率",
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"最大回撤",
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]
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temp_df = temp_df.map(lambda x: x if "%" not in str(x) else x.replace("%", ""))
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temp_df[["年化波动率", "最大回撤"]] *= 100
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temp_df["较同类风险收益比"] = pd.to_numeric(
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temp_df["较同类风险收益比"], errors="coerce"
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)
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temp_df["较同类抗风险波动"] = pd.to_numeric(
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temp_df["较同类抗风险波动"], errors="coerce"
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)
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temp_df["年化波动率"] = pd.to_numeric(temp_df["年化波动率"], errors="coerce")
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temp_df["年化夏普比率"] = pd.to_numeric(temp_df["年化夏普比率"], errors="coerce")
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temp_df["最大回撤"] = pd.to_numeric(temp_df["最大回撤"], errors="coerce")
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return temp_df
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def fund_individual_profit_probability_xq(
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symbol: str = "000001", timeout: float = None
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) -> pd.DataFrame:
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"""
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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%
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https://danjuanfunds.com/djapi/fundx/base/fund/profit/ratio/675091
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:param symbol: 基金代码
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:type symbol: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 盈利概率
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:rtype: pandas.DataFrame
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"""
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url = f"https://danjuanfunds.com/djapi/fundx/base/fund/profit/ratio/{symbol}"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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r = requests.get(url, headers=headers, timeout=timeout)
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json_data = r.json()["data"]["data_list"]
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temp_df = pd.DataFrame.from_dict(json_data, orient="columns")
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temp_df = temp_df[
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[
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"holding_time",
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"profit_ratio",
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"average_income",
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]
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]
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temp_df.columns = [
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"持有时长",
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"盈利概率",
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"平均收益",
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]
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temp_df = temp_df.map(lambda x: x if "%" not in str(x) else x.replace("%", ""))
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temp_df["盈利概率"] = pd.to_numeric(temp_df["盈利概率"], errors="coerce")
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temp_df["平均收益"] = pd.to_numeric(temp_df["平均收益"], errors="coerce")
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return temp_df
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def fund_individual_detail_info_xq(
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symbol: str = "000001", timeout: float = None
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) -> pd.DataFrame:
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"""
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雪球基金-交易规则
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https://danjuanfunds.com/djapi/fund/detail/675091
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:param symbol: 基金代码
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:type symbol: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 交易规则
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:rtype: pandas.DataFrame
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"""
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url = f"https://danjuanfunds.com/djapi/fund/detail/{symbol}"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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r = requests.get(url, headers=headers, timeout=timeout)
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json_data = r.json()["data"]
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combined_df = None
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rate_type_dict = {
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"declare_rate_table": "买入规则",
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"withdraw_rate_table": "卖出规则",
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"other_rate_table": "其他费用",
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}
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for k, v in rate_type_dict.items():
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temp_df = pd.DataFrame.from_dict(json_data["fund_rates"][k], orient="columns")
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temp_df["rate_type"] = v
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temp_df = temp_df[
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[
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"rate_type",
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"name",
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"value",
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]
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]
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temp_df.columns = [
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"费用类型",
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"条件或名称",
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"费用",
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]
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combined_df = pd.concat(objs=[combined_df, temp_df], ignore_index=True)
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combined_df["费用"] = pd.to_numeric(combined_df["费用"], errors="coerce")
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return combined_df
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def fund_individual_detail_hold_xq(
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symbol: str = "002804", date: str = "20231231", timeout: float = None
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) -> pd.DataFrame:
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"""
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雪球基金-持仓
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https://danjuanfunds.com/rn/fund-detail/archive?id=103&code=002804
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:param symbol: 基金代码
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:type symbol: str
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:param date: 财报日期
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:type date: str
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:param timeout: choice of None or a positive float number
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:type timeout: float
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:return: 雪球基金-持仓
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:rtype: pandas.DataFrame
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"""
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url = "https://danjuanfunds.com/djapi/fundx/base/fund/record/asset/percent"
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headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
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"Chrome/80.0.3987.149 Safari/537.36"
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}
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params = {
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"fund_code": f"{symbol}",
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"report_date": f"{'-'.join([date[:4], date[4:6], date[6:]])}",
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}
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r = requests.get(url, headers=headers, params=params, timeout=timeout)
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data_json = r.json()
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temp_df = pd.DataFrame.from_dict(data_json["data"]["chart_list"], orient="columns")
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temp_df = temp_df[
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[
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"type_desc",
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"percent",
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]
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]
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temp_df.columns = [
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"资产类型",
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"仓位占比",
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]
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temp_df["仓位占比"] = pd.to_numeric(temp_df["仓位占比"], errors="coerce")
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return temp_df
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if __name__ == "__main__":
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fund_individual_basic_info_xq_df = fund_individual_basic_info_xq(symbol="000005")
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print(fund_individual_basic_info_xq_df)
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fund_individual_achievement_xq_df = fund_individual_achievement_xq(symbol="000001")
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print(fund_individual_achievement_xq_df)
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fund_individual_analysis_xq_df = fund_individual_analysis_xq(symbol="000001")
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print(fund_individual_analysis_xq_df)
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fund_individual_profit_probability_xq_df = fund_individual_profit_probability_xq(
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symbol="000001"
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)
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print(fund_individual_profit_probability_xq_df)
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fund_individual_detail_info_xq_df = fund_individual_detail_info_xq(symbol="000001")
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print(fund_individual_detail_info_xq_df)
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fund_individual_detail_hold_xq_df = fund_individual_detail_hold_xq(
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symbol="002804", date="20231231"
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)
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print(fund_individual_detail_hold_xq_df)
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