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
568 lines
21 KiB
Python
568 lines
21 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/10/23 13:00
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Desc: 申万宏源研究-指数系列
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex
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"""
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import math
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import pandas as pd
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import requests
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from akshare.utils.tqdm import get_tqdm
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def index_hist_sw(symbol: str = "801030", period: str = "day") -> pd.DataFrame:
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"""
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申万宏源研究-指数发布-指数详情-指数历史数据
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex/releasedetail?code=801001&name=%E7%94%B3%E4%B8%8750
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:param symbol: 指数代码
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:type symbol: str
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:param period: choice of {"day", "week", "month"}
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:type period: str
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:return: 指数历史数据
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:rtype: pandas.DataFrame
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"""
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period_map = {
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"day": "DAY",
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"week": "WEEK",
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"month": "MONTH",
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}
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url = "https://www.swsresearch.com/institute-sw/api/index_publish/trend/"
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params = {
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"swindexcode": symbol,
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"period": period_map[period],
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}
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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/114.0.0.0 Safari/537.36",
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.rename(
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columns={
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"swindexcode": "代码",
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"bargaindate": "日期",
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"openindex": "开盘",
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"maxindex": "最高",
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"minindex": "最低",
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"closeindex": "收盘",
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"hike": "",
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"markup": "",
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"bargainamount": "成交量",
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"bargainsum": "成交额",
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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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"代码",
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"日期",
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"收盘",
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"开盘",
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"最高",
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"最低",
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"成交量",
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"成交额",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["收盘"] = pd.to_numeric(temp_df["收盘"], 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["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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return temp_df
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def index_min_sw(symbol: str = "801001") -> pd.DataFrame:
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"""
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申万宏源研究-指数发布-指数详情-指数分时数据
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex/releasedetail?code=801001&name=%E7%94%B3%E4%B8%8750
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:param symbol: 指数代码
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:type symbol: str
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:return: 指数分时数据
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:rtype: pandas.DataFrame
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"""
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url = (
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"https://www.swsresearch.com/institute-sw/api/index_publish/details/timelines/"
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)
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params = {
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"swindexcode": symbol,
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}
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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/114.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df.rename(
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columns={
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"l1": "代码",
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"l2": "名称",
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"l8": "价格",
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"trading_date": "日期",
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"trading_time": "时间",
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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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"代码",
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"名称",
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"价格",
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"日期",
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"时间",
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]
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]
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temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
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temp_df["价格"] = pd.to_numeric(temp_df["价格"], errors="coerce")
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return temp_df
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def index_component_sw(symbol: str = "801001") -> pd.DataFrame:
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"""
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申万宏源研究-指数发布-指数详情-成分股
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex/releasedetail?code=801001&name=%E7%94%B3%E4%B8%8750
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:param symbol: 指数代码
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:type symbol: str
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:return: 成分股
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:rtype: pandas.DataFrame
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"""
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url = "https://www.swsresearch.com/institute-sw/api/index_publish/details/component_stocks/"
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params = {"swindexcode": symbol, "page": "1", "page_size": "10000"}
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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/114.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["results"])
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temp_df.reset_index(inplace=True)
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temp_df["index"] = temp_df["index"] + 1
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temp_df.rename(
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columns={
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"index": "序号",
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"stockcode": "证券代码",
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"stockname": "证券名称",
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"newweight": "最新权重",
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"beginningdate": "计入日期",
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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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"证券代码",
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"证券名称",
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"最新权重",
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"计入日期",
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]
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]
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temp_df["计入日期"] = pd.to_datetime(temp_df["计入日期"], errors="coerce").dt.date
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temp_df["最新权重"] = pd.to_numeric(temp_df["最新权重"], errors="coerce")
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return temp_df
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def __index_realtime_sw(symbol: str = "大类风格指数") -> pd.DataFrame:
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"""
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申万宏源研究-申万指数-股票指数
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex
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:param symbol: choice of {"大类风格指数", "金创指数"}
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:type symbol: str
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:return: 指数系列实时行情数据
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:rtype: pandas.DataFrame
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"""
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url = "https://www.swsresearch.com/insWechatSw/dflgOrJcIndex/pageList"
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payload = {
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"pageNo": 1,
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"pageSize": 10,
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"indexTypeName": symbol,
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"sortField": "",
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"rule": "",
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"indexType": 1,
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}
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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/114.0.0.0 Safari/537.36"
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}
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r = requests.post(url, json=payload, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["list"])
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temp_df.rename(
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columns={
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"swIndexCode": "指数代码",
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"swIndexName": "指数名称",
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"lastCloseIndex": "昨收盘",
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"lastMarkup": "日涨跌幅",
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"yearMarkup": "年涨跌幅",
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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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"指数代码",
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"指数名称",
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"昨收盘",
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"日涨跌幅",
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"年涨跌幅",
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]
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]
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temp_df["昨收盘"] = pd.to_numeric(temp_df["昨收盘"], errors="coerce")
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temp_df["日涨跌幅"] = pd.to_numeric(temp_df["日涨跌幅"], errors="coerce")
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temp_df["年涨跌幅"] = pd.to_numeric(temp_df["年涨跌幅"], errors="coerce")
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return temp_df
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def index_realtime_sw(symbol: str = "二级行业") -> pd.DataFrame:
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"""
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申万宏源研究-指数系列
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https://www.swsresearch.com/institute_sw/allIndex/releasedIndex
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:param symbol: choice of {"市场表征", "一级行业", "二级行业", "风格指数", "大类风格指数", "金创指数"}
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:type symbol: str
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:return: 指数系列实时行情数据
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:rtype: pandas.DataFrame
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"""
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if symbol in {"大类风格指数", "金创指数"}:
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temp_df = __index_realtime_sw(symbol)
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return temp_df
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url = "https://www.swsresearch.com/institute-sw/api/index_publish/current/"
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params = {"page": "1", "page_size": "50", "indextype": 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/114.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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total_num = data_json["data"]["count"]
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total_page = math.ceil(total_num / 50)
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big_df = pd.DataFrame()
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tqdm = get_tqdm()
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for page in tqdm(range(1, total_page + 1), leave=False):
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params.update({"page": page})
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["results"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.columns = [
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"指数代码",
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"指数名称",
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"昨收盘",
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"今开盘",
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"成交额",
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"最高价",
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"最低价",
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"最新价",
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"成交量",
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]
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big_df = big_df[
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[
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"指数代码",
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"指数名称",
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"昨收盘",
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"今开盘",
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"最新价",
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"成交额",
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"成交量",
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"最高价",
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"最低价",
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]
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]
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big_df["昨收盘"] = pd.to_numeric(big_df["昨收盘"], errors="coerce")
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big_df["今开盘"] = pd.to_numeric(big_df["今开盘"], errors="coerce")
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big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
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big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
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big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
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big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
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big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
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return big_df
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def index_analysis_daily_sw(
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symbol: str = "市场表征",
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start_date: str = "20221103",
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end_date: str = "20221103",
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) -> pd.DataFrame:
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"""
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申万宏源研究-指数分析
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https://www.swsresearch.com/institute_sw/allIndex/analysisIndex
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:param symbol: choice of {"市场表征", "一级行业", "二级行业", "风格指数"}
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:type symbol: str
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:param start_date: 开始日期
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:type start_date: str
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:param end_date: 结束日期
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:type end_date: str
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:return: 指数分析
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:rtype: pandas.DataFrame
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"""
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url = "https://www.swsresearch.com/institute-sw/api/index_analysis/index_analysis_report/"
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params = {
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"page": "1",
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"page_size": "50",
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"index_type": symbol,
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"start_date": "-".join([start_date[:4], start_date[4:6], start_date[6:]]),
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"end_date": "-".join([end_date[:4], end_date[4:6], end_date[6:]]),
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"type": "DAY",
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"swindexcode": "all",
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}
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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/114.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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total_num = data_json["data"]["count"]
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total_page = math.ceil(total_num / 50)
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big_df = pd.DataFrame()
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tqdm = get_tqdm()
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for page in tqdm(range(1, total_page + 1), leave=False):
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params.update({"page": page})
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"]["results"])
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big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
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big_df.rename(
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columns={
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"swindexcode": "指数代码",
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"swindexname": "指数名称",
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"bargaindate": "发布日期",
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"closeindex": "收盘指数",
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"bargainamount": "成交量",
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"markup": "涨跌幅",
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"turnoverrate": "换手率",
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"pe": "市盈率",
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"pb": "市净率",
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"meanprice": "均价",
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"bargainsumrate": "成交额占比",
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"negotiablessharesum1": "流通市值",
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"negotiablessharesum2": "平均流通市值",
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"dp": "股息率",
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},
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inplace=True,
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)
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big_df["发布日期"] = pd.to_datetime(big_df["发布日期"], errors="coerce").dt.date
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big_df["收盘指数"] = pd.to_numeric(big_df["收盘指数"], errors="coerce")
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big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
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big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
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big_df["换手率"] = pd.to_numeric(big_df["换手率"], errors="coerce")
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big_df["市盈率"] = pd.to_numeric(big_df["市盈率"], errors="coerce")
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big_df["市净率"] = pd.to_numeric(big_df["市净率"], errors="coerce")
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big_df["均价"] = pd.to_numeric(big_df["均价"], errors="coerce")
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big_df["成交额占比"] = pd.to_numeric(big_df["成交额占比"], errors="coerce")
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big_df["流通市值"] = pd.to_numeric(big_df["流通市值"], errors="coerce")
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big_df["平均流通市值"] = pd.to_numeric(big_df["平均流通市值"], errors="coerce")
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big_df["股息率"] = pd.to_numeric(big_df["股息率"], errors="coerce")
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big_df.sort_values(by=["发布日期"], inplace=True, ignore_index=True)
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return big_df
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def index_analysis_week_month_sw(symbol: str = "month") -> pd.DataFrame:
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"""
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申万宏源研究-周/月报表-日期序列
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https://www.swsresearch.com/institute_sw/allIndex/analysisIndex
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:param symbol: choice of {"week", "month"}
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:type symbol: str
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:return: 日期序列
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:rtype: pandas.DataFrame
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"""
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url = "https://www.swsresearch.com/institute-sw/api/index_analysis/week_month_datetime/"
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params = {"type": symbol.upper()}
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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/114.0.0.0 Safari/537.36"
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}
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r = requests.get(url, params=params, headers=headers, verify=False)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json["data"])
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temp_df["bargaindate"] = pd.to_datetime(
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temp_df["bargaindate"], errors="coerce"
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).dt.date
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temp_df.columns = ["date"]
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temp_df.sort_values(by=["date"], inplace=True, ignore_index=True)
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return temp_df
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def index_analysis_weekly_sw(
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symbol: str = "市场表征",
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date: str = "20221104",
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) -> pd.DataFrame:
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"""
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申万宏源研究-指数分析-周报告
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https://www.swsresearch.com/institute_sw/allIndex/analysisIndex
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:param symbol: choice of {"市场表征", "一级行业", "二级行业", "风格指数"}
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:type symbol: str
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:param date: 查询日期; 通过调用 ak.index_analysis_week_month_sw(date="20221104") 接口获取
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:type date: str
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:return: 指数分析
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:rtype: pandas.DataFrame
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"""
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url = "https://www.swsresearch.com/institute-sw/api/index_analysis/index_analysis_reports/"
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params = {
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"page": "1",
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"page_size": "50",
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"index_type": symbol,
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"bargaindate": "-".join([date[:4], date[4:6], date[6:]]),
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|
"type": "WEEK",
|
|
"swindexcode": "all",
|
|
}
|
|
headers = {
|
|
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
|
"Chrome/114.0.0.0 Safari/537.36"
|
|
}
|
|
r = requests.get(url, params=params, headers=headers, verify=False)
|
|
data_json = r.json()
|
|
total_num = data_json["data"]["count"]
|
|
total_page = math.ceil(total_num / 50)
|
|
big_df = pd.DataFrame()
|
|
tqdm = get_tqdm()
|
|
for page in tqdm(range(1, total_page + 1), leave=False):
|
|
params.update({"page": page})
|
|
r = requests.get(url, params=params, headers=headers, verify=False)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["data"]["results"])
|
|
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
|
big_df.rename(
|
|
columns={
|
|
"swindexcode": "指数代码",
|
|
"swindexname": "指数名称",
|
|
"bargaindate": "发布日期",
|
|
"closeindex": "收盘指数",
|
|
"bargainamount": "成交量",
|
|
"markup": "涨跌幅",
|
|
"turnoverrate": "换手率",
|
|
"pe": "市盈率",
|
|
"pb": "市净率",
|
|
"meanprice": "均价",
|
|
"bargainsumrate": "成交额占比",
|
|
"negotiablessharesum1": "流通市值",
|
|
"negotiablessharesum2": "平均流通市值",
|
|
"dp": "股息率",
|
|
},
|
|
inplace=True,
|
|
)
|
|
big_df["发布日期"] = pd.to_datetime(big_df["发布日期"], errors="coerce").dt.date
|
|
big_df["收盘指数"] = pd.to_numeric(big_df["收盘指数"], errors="coerce")
|
|
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
|
|
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
|
big_df["换手率"] = pd.to_numeric(big_df["换手率"], errors="coerce")
|
|
big_df["市盈率"] = pd.to_numeric(big_df["市盈率"], errors="coerce")
|
|
big_df["市净率"] = pd.to_numeric(big_df["市净率"], errors="coerce")
|
|
big_df["均价"] = pd.to_numeric(big_df["均价"], errors="coerce")
|
|
big_df["成交额占比"] = pd.to_numeric(big_df["成交额占比"], errors="coerce")
|
|
big_df["流通市值"] = pd.to_numeric(big_df["流通市值"], errors="coerce")
|
|
big_df["平均流通市值"] = pd.to_numeric(big_df["平均流通市值"], errors="coerce")
|
|
big_df["股息率"] = pd.to_numeric(big_df["股息率"], errors="coerce")
|
|
|
|
big_df.sort_values(by=["发布日期"], inplace=True, ignore_index=True)
|
|
return big_df
|
|
|
|
|
|
def index_analysis_monthly_sw(
|
|
symbol: str = "市场表征",
|
|
date: str = "20221031",
|
|
) -> pd.DataFrame:
|
|
"""
|
|
申万宏源研究-指数分析-月报告
|
|
https://www.swsresearch.com/institute_sw/allIndex/analysisIndex
|
|
:param symbol: choice of {"市场表征", "一级行业", "二级行业", "风格指数"}
|
|
:type symbol: str
|
|
:param date: 查询日期; 通过调用 ak.index_analysis_week_month_sw() 接口获取
|
|
:type date: str
|
|
:return: 指数分析
|
|
:rtype: pandas.DataFrame
|
|
"""
|
|
url = "https://www.swsresearch.com/institute-sw/api/index_analysis/index_analysis_reports/"
|
|
params = {
|
|
"page": "1",
|
|
"page_size": "50",
|
|
"index_type": symbol,
|
|
"bargaindate": "-".join([date[:4], date[4:6], date[6:]]),
|
|
"type": "MONTH",
|
|
"swindexcode": "all",
|
|
}
|
|
headers = {
|
|
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
|
"Chrome/114.0.0.0 Safari/537.36"
|
|
}
|
|
r = requests.get(url, params=params, headers=headers, verify=False)
|
|
data_json = r.json()
|
|
total_num = data_json["data"]["count"]
|
|
total_page = math.ceil(total_num / 50)
|
|
big_df = pd.DataFrame()
|
|
tqdm = get_tqdm()
|
|
for page in tqdm(range(1, total_page + 1), leave=False):
|
|
params.update({"page": page})
|
|
r = requests.get(url, params=params, headers=headers, verify=False)
|
|
data_json = r.json()
|
|
temp_df = pd.DataFrame(data_json["data"]["results"])
|
|
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
|
big_df.rename(
|
|
columns={
|
|
"swindexcode": "指数代码",
|
|
"swindexname": "指数名称",
|
|
"bargaindate": "发布日期",
|
|
"closeindex": "收盘指数",
|
|
"bargainamount": "成交量",
|
|
"markup": "涨跌幅",
|
|
"turnoverrate": "换手率",
|
|
"pe": "市盈率",
|
|
"pb": "市净率",
|
|
"meanprice": "均价",
|
|
"bargainsumrate": "成交额占比",
|
|
"negotiablessharesum1": "流通市值",
|
|
"negotiablessharesum2": "平均流通市值",
|
|
"dp": "股息率",
|
|
},
|
|
inplace=True,
|
|
)
|
|
big_df["发布日期"] = pd.to_datetime(big_df["发布日期"], errors="coerce").dt.date
|
|
big_df["收盘指数"] = pd.to_numeric(big_df["收盘指数"], errors="coerce")
|
|
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
|
|
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
|
big_df["换手率"] = pd.to_numeric(big_df["换手率"], errors="coerce")
|
|
big_df["市盈率"] = pd.to_numeric(big_df["市盈率"], errors="coerce")
|
|
big_df["市净率"] = pd.to_numeric(big_df["市净率"], errors="coerce")
|
|
big_df["均价"] = pd.to_numeric(big_df["均价"], errors="coerce")
|
|
big_df["成交额占比"] = pd.to_numeric(big_df["成交额占比"], errors="coerce")
|
|
big_df["流通市值"] = pd.to_numeric(big_df["流通市值"], errors="coerce")
|
|
big_df["平均流通市值"] = pd.to_numeric(big_df["平均流通市值"], errors="coerce")
|
|
big_df["股息率"] = pd.to_numeric(big_df["股息率"], errors="coerce")
|
|
big_df.sort_values(by=["发布日期"], inplace=True, ignore_index=True)
|
|
return big_df
|
|
|
|
|
|
if __name__ == "__main__":
|
|
index_hist_sw_df = index_hist_sw(symbol="801193", period="day")
|
|
print(index_hist_sw_df)
|
|
|
|
index_min_sw_df = index_min_sw(symbol="801001")
|
|
print(index_min_sw_df)
|
|
|
|
index_component_sw_df = index_component_sw(symbol="801001")
|
|
print(index_component_sw_df)
|
|
|
|
index_realtime_sw_df = index_realtime_sw(symbol="市场表征")
|
|
print(index_realtime_sw_df)
|
|
|
|
index_analysis_daily_sw_df = index_analysis_daily_sw(
|
|
symbol="市场表征", start_date="20241025", end_date="20241025"
|
|
)
|
|
print(index_analysis_daily_sw_df)
|
|
|
|
index_analysis_week_month_sw_df = index_analysis_week_month_sw(symbol="month")
|
|
print(index_analysis_week_month_sw_df)
|
|
|
|
index_analysis_weekly_sw_df = index_analysis_weekly_sw(
|
|
symbol="市场表征", date="20241025"
|
|
)
|
|
print(index_analysis_weekly_sw_df)
|
|
|
|
index_analysis_monthly_sw_df = index_analysis_monthly_sw(
|
|
symbol="市场表征", date="20240930"
|
|
)
|
|
print(index_analysis_monthly_sw_df)
|