stock-tracker
This commit is contained in:
C菌
@@ -0,0 +1,6 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2019/12/27 18:02
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Desc:
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"""
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@@ -0,0 +1,19 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2019/12/30 21:02
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Desc:
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"""
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stock_em_sy_js = """
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function getCode(num) {
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var str = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz";
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var codes = str.split('');
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num = num || 6;
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var code = "";
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for (var i = 0; i < num; i++) {
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code += codes[Math.floor(Math.random() * 52)]
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}
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return code
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}
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"""
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+79
@@ -0,0 +1,79 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/4/30 10:30
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Desc: 破净股统计历史走势
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https://www.legulegu.com/stockdata/below-net-asset-statistics
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"""
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import pandas as pd
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import requests
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from akshare.utils.cons import headers
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def stock_a_below_net_asset_statistics(symbol: str = "全部A股") -> pd.DataFrame:
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"""
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破净股统计历史走势
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https://www.legulegu.com/stockdata/below-net-asset-statistics
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:param symbol: choice of {"全部A股", "沪深300", "上证50", "中证500"}
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:type symbol: str
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:return: 破净股统计历史走势
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:rtype: pandas.DataFrame
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"""
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symbol_map = {
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"全部A股": "1",
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"沪深300": "000300.XSHG",
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"上证50": "000016.SH",
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"中证500": "000905.SH",
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}
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url = "https://legulegu.com/stockdata/below-net-asset-statistics-data"
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params = {
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"marketId": symbol_map[symbol],
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"token": "325843825a2745a2a8f9b9e3355cb864",
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}
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r = requests.get(url, params=params, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json)
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temp_df["date"] = pd.to_datetime(temp_df["date"], unit="ms").dt.date
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del temp_df["marketId"]
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big_df = temp_df.iloc[:, :3]
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big_df.columns = ["below_net_asset", "total_company", "date"]
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big_df["below_net_asset_ratio"] = round(
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big_df["below_net_asset"] / big_df["total_company"], 4
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)
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big_df = big_df[
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["date", "below_net_asset", "total_company", "below_net_asset_ratio"]
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]
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big_df["date"] = pd.to_datetime(big_df["date"], errors="coerce").dt.date
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big_df["below_net_asset"] = pd.to_numeric(
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big_df["below_net_asset"], errors="coerce"
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)
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big_df["total_company"] = pd.to_numeric(big_df["total_company"], errors="coerce")
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big_df["below_net_asset_ratio"] = pd.to_numeric(
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big_df["below_net_asset_ratio"], errors="coerce"
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)
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big_df.sort_values(["date"], inplace=True, ignore_index=True)
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return big_df
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if __name__ == "__main__":
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stock_a_below_net_asset_statistics_df = stock_a_below_net_asset_statistics(
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symbol="全部A股"
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)
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print(stock_a_below_net_asset_statistics_df)
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stock_a_below_net_asset_statistics_df = stock_a_below_net_asset_statistics(
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symbol="沪深300"
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)
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print(stock_a_below_net_asset_statistics_df)
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stock_a_below_net_asset_statistics_df = stock_a_below_net_asset_statistics(
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symbol="上证50"
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)
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print(stock_a_below_net_asset_statistics_df)
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stock_a_below_net_asset_statistics_df = stock_a_below_net_asset_statistics(
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symbol="中证500"
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)
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print(stock_a_below_net_asset_statistics_df)
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@@ -0,0 +1,52 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2026/5/20 17:30
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Desc: 乐咕乐股-创新高、新低的股票数量
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https://www.legulegu.com/stockdata/high-low-statistics
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"""
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import pandas as pd
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import requests
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from akshare.utils.cons import headers
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def stock_a_high_low_statistics(symbol: str = "all") -> pd.DataFrame:
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"""
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乐咕乐股-创新高、新低的股票数量
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https://www.legulegu.com/stockdata/high-low-statistics
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:param symbol: choice of {"all", "sz50", "hs300", "zz500"}
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:type symbol: str
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:return: 创新高、新低的股票数量
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:rtype: pandas.DataFrame
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"""
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url = f"https://www.legulegu.com/stockdata/member-ship/get-high-low-statistics/{symbol}"
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r = requests.get(url, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json)
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del temp_df["indexCode"]
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temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
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temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
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temp_df["high20"] = pd.to_numeric(temp_df["high20"], errors="coerce")
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temp_df["low20"] = pd.to_numeric(temp_df["low20"], errors="coerce")
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temp_df["high60"] = pd.to_numeric(temp_df["high60"], errors="coerce")
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temp_df["low60"] = pd.to_numeric(temp_df["low60"], errors="coerce")
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temp_df["high120"] = pd.to_numeric(temp_df["high120"], errors="coerce")
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temp_df["low120"] = pd.to_numeric(temp_df["low120"], errors="coerce")
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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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if __name__ == "__main__":
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stock_a_high_low_statistics_df = stock_a_high_low_statistics(symbol="all")
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print(stock_a_high_low_statistics_df)
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stock_a_high_low_statistics_df = stock_a_high_low_statistics(symbol="sz50")
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print(stock_a_high_low_statistics_df)
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stock_a_high_low_statistics_df = stock_a_high_low_statistics(symbol="hs300")
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print(stock_a_high_low_statistics_df)
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stock_a_high_low_statistics_df = stock_a_high_low_statistics(symbol="zz500")
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print(stock_a_high_low_statistics_df)
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@@ -0,0 +1,95 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/10/30 20:24
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Desc: 市盈率, 市净率和股息率查询
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https://www.legulegu.com/stocklist
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https://www.legulegu.com/s/000001
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"""
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from datetime import datetime
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from hashlib import md5
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import pandas as pd
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import requests
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from bs4 import BeautifulSoup
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from akshare.utils.cons import headers
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def get_cookie_csrf(url: str = "") -> dict:
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"""
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乐咕乐股-主板市盈率
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https://legulegu.com/stockdata/shanghaiPE
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:return: 指定市场的市盈率数据
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:rtype: pandas.DataFrame
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"""
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# 创建独立的 session，避免污染全局状态
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session = requests.Session()
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session.headers.update(headers)
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r = session.get(url)
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soup = BeautifulSoup(r.text, features="lxml")
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csrf_tag = soup.find(name="meta", attrs={"name": "_csrf"})
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csrf_token = csrf_tag.attrs["content"]
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# 创建新的 headers
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local_headers = headers.copy()
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local_headers.update({"X-CSRF-Token": csrf_token})
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return {"cookies": r.cookies, "headers": local_headers}
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def get_token_lg() -> str:
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"""
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生成乐咕的 token
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https://legulegu.com/s/002488
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:return: token
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:rtype: str
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"""
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current_date_str = datetime.now().date().isoformat()
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obj = md5()
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obj.update(current_date_str.encode("utf-8"))
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token = obj.hexdigest()
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return token
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def stock_hk_indicator_eniu(
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symbol: str = "hk01093", indicator: str = "市盈率"
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) -> pd.DataFrame:
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"""
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亿牛网-港股指标
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https://eniu.com/gu/hk01093/roe
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:param symbol: 港股代码
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:type symbol: str
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:param indicator: 需要获取的指标, choice of {"港股", "市盈率", "市净率", "股息率", "ROE", "市值"}
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:type indicator: str
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:return: 指定 symbol 和 indicator 的数据
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:rtype: pandas.DataFrame
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"""
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if indicator == "港股":
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url = "https://eniu.com/static/data/stock_list.json"
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r = requests.get(url, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json)
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temp_df = temp_df[temp_df["stock_id"].str.contains("hk")]
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temp_df.reset_index(inplace=True, drop=True)
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return temp_df
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if indicator == "市盈率":
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url = f"https://eniu.com/chart/peh/{symbol}"
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elif indicator == "市净率":
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url = f"https://eniu.com/chart/pbh/{symbol}"
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elif indicator == "股息率":
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url = f"https://eniu.com/chart/dvh/{symbol}"
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elif indicator == "ROE":
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url = f"https://eniu.com/chart/roeh/{symbol}"
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else:
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url = f"https://eniu.com/chart/marketvalueh/{symbol}"
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r = requests.get(url, headers=headers)
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data_json = r.json()
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temp_df = pd.DataFrame(data_json)
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return temp_df
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if __name__ == "__main__":
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stock_hk_indicator_eniu_df = stock_hk_indicator_eniu(
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symbol="hk01093", indicator="市盈率"
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)
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print(stock_hk_indicator_eniu_df)
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@@ -0,0 +1,621 @@
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#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2025/3/20 20:00
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Desc: 乐咕乐股-A 股市盈率和市净率
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https://legulegu.com/stockdata/shanghaiPE
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"""
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from datetime import datetime
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import pandas as pd
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import py_mini_racer
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import requests
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from akshare.stock_feature.stock_a_indicator import get_cookie_csrf
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hash_code = """
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function e(n) {
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var e, t, r = "", o = -1, f;
|
||||
if (n && n.length) {
|
||||
f = n.length;
|
||||
while ((o += 1) < f) {
|
||||
e = n.charCodeAt(o);
|
||||
t = o + 1 < f ? n.charCodeAt(o + 1) : 0;
|
||||
if (55296 <= e && e <= 56319 && 56320 <= t && t <= 57343) {
|
||||
e = 65536 + ((e & 1023) << 10) + (t & 1023);
|
||||
o += 1
|
||||
}
|
||||
if (e <= 127) {
|
||||
r += String.fromCharCode(e)
|
||||
} else if (e <= 2047) {
|
||||
r += String.fromCharCode(192 | e >>> 6 & 31, 128 | e & 63)
|
||||
} else if (e <= 65535) {
|
||||
r += String.fromCharCode(224 | e >>> 12 & 15, 128 | e >>> 6 & 63, 128 | e & 63)
|
||||
} else if (e <= 2097151) {
|
||||
r += String.fromCharCode(240 | e >>> 18 & 7, 128 | e >>> 12 & 63, 128 | e >>> 6 & 63, 128 | e & 63)
|
||||
}
|
||||
}
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function t(n) {
|
||||
var e, t, r, o, f, i = [], h;
|
||||
e = t = r = o = f = 0;
|
||||
if (n && n.length) {
|
||||
h = n.length;
|
||||
n += "";
|
||||
while (e < h) {
|
||||
r = n.charCodeAt(e);
|
||||
t += 1;
|
||||
if (r < 128) {
|
||||
i[t] = String.fromCharCode(r);
|
||||
e += 1
|
||||
} else if (r > 191 && r < 224) {
|
||||
o = n.charCodeAt(e + 1);
|
||||
i[t] = String.fromCharCode((r & 31) << 6 | o & 63);
|
||||
e += 2
|
||||
} else {
|
||||
o = n.charCodeAt(e + 1);
|
||||
f = n.charCodeAt(e + 2);
|
||||
i[t] = String.fromCharCode(
|
||||
(r & 15) << 12 | (o & 63) << 6 | f & 63);
|
||||
e += 3
|
||||
}
|
||||
}
|
||||
}
|
||||
return i.join("")
|
||||
}
|
||||
|
||||
function r(n, e) {
|
||||
var t = (n & 65535) + (e & 65535)
|
||||
, r = (n >> 16) + (e >> 16) + (t >> 16);
|
||||
return r << 16 | t & 65535
|
||||
}
|
||||
|
||||
function o(n, e) {
|
||||
return n << e | n >>> 32 - e
|
||||
}
|
||||
|
||||
function f(n, e) {
|
||||
var t = e ? "0123456789ABCDEF" : "0123456789abcdef", r = "", o, f = 0, i = n.length;
|
||||
for (; f < i; f += 1) {
|
||||
o = n.charCodeAt(f);
|
||||
r += t.charAt(o >>> 4 & 15) + t.charAt(o & 15)
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function i(n) {
|
||||
var e, t = n.length, r = "";
|
||||
for (e = 0; e < t; e += 1) {
|
||||
r += String.fromCharCode(n.charCodeAt(e) & 255, n.charCodeAt(e) >>> 8 & 255)
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function h(n) {
|
||||
var e, t = n.length, r = "";
|
||||
for (e = 0; e < t; e += 1) {
|
||||
r += String.fromCharCode(n.charCodeAt(e) >>> 8 & 255, n.charCodeAt(e) & 255)
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function u(n) {
|
||||
var e, t = n.length * 32, r = "";
|
||||
for (e = 0; e < t; e += 8) {
|
||||
r += String.fromCharCode(n[e >> 5] >>> 24 - e % 32 & 255)
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function a(n) {
|
||||
var e, t = n.length * 32, r = "";
|
||||
for (e = 0; e < t; e += 8) {
|
||||
r += String.fromCharCode(n[e >> 5] >>> e % 32 & 255)
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function c(n) {
|
||||
var e, t = n.length * 8, r = Array(n.length >> 2), o = r.length;
|
||||
for (e = 0; e < o; e += 1) {
|
||||
r[e] = 0
|
||||
}
|
||||
for (e = 0; e < t; e += 8) {
|
||||
r[e >> 5] |= (n.charCodeAt(e / 8) & 255) << e % 32
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function l(n) {
|
||||
var e, t = n.length * 8, r = Array(n.length >> 2), o = r.length;
|
||||
for (e = 0; e < o; e += 1) {
|
||||
r[e] = 0
|
||||
}
|
||||
for (e = 0; e < t; e += 8) {
|
||||
r[e >> 5] |= (n.charCodeAt(e / 8) & 255) << 24 - e % 32
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function D(n, e) {
|
||||
var t = e.length, r = Array(), o, f, i, h, u, a, c, l;
|
||||
a = Array(Math.ceil(n.length / 2));
|
||||
h = a.length;
|
||||
for (o = 0; o < h; o += 1) {
|
||||
a[o] = n.charCodeAt(o * 2) << 8 | n.charCodeAt(o * 2 + 1)
|
||||
}
|
||||
while (a.length > 0) {
|
||||
u = Array();
|
||||
i = 0;
|
||||
for (o = 0; o < a.length; o += 1) {
|
||||
i = (i << 16) + a[o];
|
||||
f = Math.floor(i / t);
|
||||
i -= f * t;
|
||||
if (u.length > 0 || f > 0) {
|
||||
u[u.length] = f
|
||||
}
|
||||
}
|
||||
r[r.length] = i;
|
||||
a = u
|
||||
}
|
||||
c = "";
|
||||
for (o = r.length - 1; o >= 0; o--) {
|
||||
c += e.charAt(r[o])
|
||||
}
|
||||
l = Math.ceil(n.length * 8 / (Math.log(e.length) / Math.log(2)));
|
||||
for (o = c.length; o < l; o += 1) {
|
||||
c = e[0] + c
|
||||
}
|
||||
return c
|
||||
}
|
||||
|
||||
function B(n, e) {
|
||||
var t = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/", r = "", o = n.length, f, i, h;
|
||||
e = e || "=";
|
||||
for (f = 0; f < o; f += 3) {
|
||||
h = n.charCodeAt(f) << 16 | (f + 1 < o ? n.charCodeAt(f + 1) << 8 : 0) | (f + 2 < o ? n.charCodeAt(f + 2) : 0);
|
||||
for (i = 0; i < 4; i += 1) {
|
||||
if (f * 8 + i * 6 > n.length * 8) {
|
||||
r += e
|
||||
} else {
|
||||
r += t.charAt(h >>> 6 * (3 - i) & 63)
|
||||
}
|
||||
}
|
||||
}
|
||||
return r
|
||||
}
|
||||
|
||||
function hex(n) {
|
||||
return f(u(n, h), t)
|
||||
}
|
||||
|
||||
function u(n) {
|
||||
n = h ? e(n) : n;
|
||||
return a(C(c(n), n.length * 8))
|
||||
}
|
||||
|
||||
function l(n, t) {
|
||||
var r, o, f, i, u;
|
||||
n = h ? e(n) : n;
|
||||
t = h ? e(t) : t;
|
||||
r = c(n);
|
||||
if (r.length > 16) {
|
||||
r = C(r, n.length * 8)
|
||||
}
|
||||
o = Array(16),
|
||||
f = Array(16);
|
||||
for (u = 0; u < 16; u += 1) {
|
||||
o[u] = r[u] ^ 909522486;
|
||||
f[u] = r[u] ^ 1549556828
|
||||
}
|
||||
i = C(o.concat(c(t)), 512 + t.length * 8);
|
||||
return a(C(f.concat(i), 512 + 128))
|
||||
}
|
||||
|
||||
function C(n, e) {
|
||||
var t, o, f, i, h, u = 1732584193, a = -271733879, c = -1732584194, l = 271733878;
|
||||
n[e >> 5] |= 128 << e % 32;
|
||||
n[(e + 64 >>> 9 << 4) + 14] = e;
|
||||
for (t = 0; t < n.length; t += 16) {
|
||||
o = u;
|
||||
f = a;
|
||||
i = c;
|
||||
h = l;
|
||||
u = s(u, a, c, l, n[t + 0], 7, -680876936);
|
||||
l = s(l, u, a, c, n[t + 1], 12, -389564586);
|
||||
c = s(c, l, u, a, n[t + 2], 17, 606105819);
|
||||
a = s(a, c, l, u, n[t + 3], 22, -1044525330);
|
||||
u = s(u, a, c, l, n[t + 4], 7, -176418897);
|
||||
l = s(l, u, a, c, n[t + 5], 12, 1200080426);
|
||||
c = s(c, l, u, a, n[t + 6], 17, -1473231341);
|
||||
a = s(a, c, l, u, n[t + 7], 22, -45705983);
|
||||
u = s(u, a, c, l, n[t + 8], 7, 1770035416);
|
||||
l = s(l, u, a, c, n[t + 9], 12, -1958414417);
|
||||
c = s(c, l, u, a, n[t + 10], 17, -42063);
|
||||
a = s(a, c, l, u, n[t + 11], 22, -1990404162);
|
||||
u = s(u, a, c, l, n[t + 12], 7, 1804603682);
|
||||
l = s(l, u, a, c, n[t + 13], 12, -40341101);
|
||||
c = s(c, l, u, a, n[t + 14], 17, -1502002290);
|
||||
a = s(a, c, l, u, n[t + 15], 22, 1236535329);
|
||||
u = w(u, a, c, l, n[t + 1], 5, -165796510);
|
||||
l = w(l, u, a, c, n[t + 6], 9, -1069501632);
|
||||
c = w(c, l, u, a, n[t + 11], 14, 643717713);
|
||||
a = w(a, c, l, u, n[t + 0], 20, -373897302);
|
||||
u = w(u, a, c, l, n[t + 5], 5, -701558691);
|
||||
l = w(l, u, a, c, n[t + 10], 9, 38016083);
|
||||
c = w(c, l, u, a, n[t + 15], 14, -660478335);
|
||||
a = w(a, c, l, u, n[t + 4], 20, -405537848);
|
||||
u = w(u, a, c, l, n[t + 9], 5, 568446438);
|
||||
l = w(l, u, a, c, n[t + 14], 9, -1019803690);
|
||||
c = w(c, l, u, a, n[t + 3], 14, -187363961);
|
||||
a = w(a, c, l, u, n[t + 8], 20, 1163531501);
|
||||
u = w(u, a, c, l, n[t + 13], 5, -1444681467);
|
||||
l = w(l, u, a, c, n[t + 2], 9, -51403784);
|
||||
c = w(c, l, u, a, n[t + 7], 14, 1735328473);
|
||||
a = w(a, c, l, u, n[t + 12], 20, -1926607734);
|
||||
u = F(u, a, c, l, n[t + 5], 4, -378558);
|
||||
l = F(l, u, a, c, n[t + 8], 11, -2022574463);
|
||||
c = F(c, l, u, a, n[t + 11], 16, 1839030562);
|
||||
a = F(a, c, l, u, n[t + 14], 23, -35309556);
|
||||
u = F(u, a, c, l, n[t + 1], 4, -1530992060);
|
||||
l = F(l, u, a, c, n[t + 4], 11, 1272893353);
|
||||
c = F(c, l, u, a, n[t + 7], 16, -155497632);
|
||||
a = F(a, c, l, u, n[t + 10], 23, -1094730640);
|
||||
u = F(u, a, c, l, n[t + 13], 4, 681279174);
|
||||
l = F(l, u, a, c, n[t + 0], 11, -358537222);
|
||||
c = F(c, l, u, a, n[t + 3], 16, -722521979);
|
||||
a = F(a, c, l, u, n[t + 6], 23, 76029189);
|
||||
u = F(u, a, c, l, n[t + 9], 4, -640364487);
|
||||
l = F(l, u, a, c, n[t + 12], 11, -421815835);
|
||||
c = F(c, l, u, a, n[t + 15], 16, 530742520);
|
||||
a = F(a, c, l, u, n[t + 2], 23, -995338651);
|
||||
u = E(u, a, c, l, n[t + 0], 6, -198630844);
|
||||
l = E(l, u, a, c, n[t + 7], 10, 1126891415);
|
||||
c = E(c, l, u, a, n[t + 14], 15, -1416354905);
|
||||
a = E(a, c, l, u, n[t + 5], 21, -57434055);
|
||||
u = E(u, a, c, l, n[t + 12], 6, 1700485571);
|
||||
l = E(l, u, a, c, n[t + 3], 10, -1894986606);
|
||||
c = E(c, l, u, a, n[t + 10], 15, -1051523);
|
||||
a = E(a, c, l, u, n[t + 1], 21, -2054922799);
|
||||
u = E(u, a, c, l, n[t + 8], 6, 1873313359);
|
||||
l = E(l, u, a, c, n[t + 15], 10, -30611744);
|
||||
c = E(c, l, u, a, n[t + 6], 15, -1560198380);
|
||||
a = E(a, c, l, u, n[t + 13], 21, 1309151649);
|
||||
u = E(u, a, c, l, n[t + 4], 6, -145523070);
|
||||
l = E(l, u, a, c, n[t + 11], 10, -1120210379);
|
||||
c = E(c, l, u, a, n[t + 2], 15, 718787259);
|
||||
a = E(a, c, l, u, n[t + 9], 21, -343485551);
|
||||
u = r(u, o);
|
||||
a = r(a, f);
|
||||
c = r(c, i);
|
||||
l = r(l, h)
|
||||
}
|
||||
return Array(u, a, c, l)
|
||||
}
|
||||
|
||||
function A(n, e, t, f, i, h) {
|
||||
return r(o(r(r(e, n), r(f, h)), i), t)
|
||||
}
|
||||
|
||||
function s(n, e, t, r, o, f, i) {
|
||||
return A(e & t | ~e & r, n, e, o, f, i)
|
||||
}
|
||||
|
||||
function w(n, e, t, r, o, f, i) {
|
||||
return A(e & r | t & ~r, n, e, o, f, i)
|
||||
}
|
||||
|
||||
function F(n, e, t, r, o, f, i) {
|
||||
return A(e ^ t ^ r, n, e, o, f, i)
|
||||
}
|
||||
|
||||
function E(n, e, t, r, o, f, i) {
|
||||
return A(t ^ (e | ~r), n, e, o, f, i)
|
||||
}
|
||||
"""
|
||||
|
||||
|
||||
def stock_market_pe_lg(symbol: str = "深证") -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-主板市盈率
|
||||
https://legulegu.com/stockdata/shanghaiPE
|
||||
:param symbol: choice of {"上证", "深证", "创业板", "科创版"}
|
||||
:type symbol: str
|
||||
:return: 指定市场的市盈率数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_functions = py_mini_racer.MiniRacer()
|
||||
js_functions.eval(hash_code)
|
||||
token = js_functions.call("hex", datetime.now().date().isoformat()).lower()
|
||||
if symbol in {"上证", "深证", "创业板"}:
|
||||
url = "https://legulegu.com/api/stock-data/market-pe"
|
||||
symbol_map = {
|
||||
"上证": "1",
|
||||
"深证": "2",
|
||||
"创业板": "4",
|
||||
}
|
||||
url_map = {
|
||||
"上证": "https://legulegu.com/stockdata/shanghaiPE",
|
||||
"深证": "https://legulegu.com/stockdata/shenzhenPE",
|
||||
"创业板": "https://legulegu.com/stockdata/cybPE",
|
||||
}
|
||||
params = {"token": token, "marketId": symbol_map[symbol]}
|
||||
r = requests.get(url, params=params, **get_cookie_csrf(url=url_map[symbol]))
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"pe",
|
||||
]
|
||||
]
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"指数",
|
||||
"平均市盈率",
|
||||
]
|
||||
return temp_df
|
||||
else:
|
||||
url = "https://legulegu.com/api/stockdata/get-ke-chuang-ban-pe"
|
||||
params = {"token": token}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/ke-chuang-ban-pe"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"pe",
|
||||
]
|
||||
]
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"总市值",
|
||||
"市盈率",
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_index_pe_lg(symbol: str = "沪深300") -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-指数市盈率
|
||||
https://legulegu.com/stockdata/sz50-ttm-lyr
|
||||
:param symbol: choice of {"上证50", "沪深300", "上证380", "创业板50", "中证500", "上证180", "深证红利", "深证100", "中证1000", "上证红利", "中证100", "中证800"}
|
||||
:type symbol: str
|
||||
:return: 指定指数的市盈率数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_functions = py_mini_racer.MiniRacer()
|
||||
js_functions.eval(hash_code)
|
||||
token = js_functions.call("hex", datetime.now().date().isoformat()).lower()
|
||||
symbol_map = {
|
||||
"上证50": "000016.SH",
|
||||
"沪深300": "000300.SH",
|
||||
"上证380": "000009.SH",
|
||||
"创业板50": "399673.SZ",
|
||||
"中证500": "000905.SH",
|
||||
"上证180": "000010.SH",
|
||||
"深证红利": "399324.SZ",
|
||||
"深证100": "399330.SZ",
|
||||
"中证1000": "000852.SH",
|
||||
"上证红利": "000015.SH",
|
||||
"中证100": "000903.SH",
|
||||
"中证800": "000906.SH",
|
||||
}
|
||||
url = "https://legulegu.com/api/stockdata/index-basic-pe"
|
||||
params = {"token": token, "indexCode": symbol_map[symbol]}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/sz50-ttm-lyr"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"lyrPe",
|
||||
"addLyrPe",
|
||||
"middleLyrPe",
|
||||
"ttmPe",
|
||||
"addTtmPe",
|
||||
"middleTtmPe",
|
||||
]
|
||||
]
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"指数",
|
||||
"等权静态市盈率",
|
||||
"静态市盈率",
|
||||
"静态市盈率中位数",
|
||||
"等权滚动市盈率",
|
||||
"滚动市盈率",
|
||||
"滚动市盈率中位数",
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_market_pb_lg(symbol: str = "上证") -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-主板市净率
|
||||
https://legulegu.com/stockdata/shanghaiPB
|
||||
:param symbol: choice of {"上证", "深证", "创业板", "科创版"}
|
||||
:type symbol: str
|
||||
:return: 指定市场的市净率数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_functions = py_mini_racer.MiniRacer()
|
||||
js_functions.eval(hash_code)
|
||||
token = js_functions.call("hex", datetime.now().date().isoformat()).lower()
|
||||
url = "https://legulegu.com/api/stockdata/index-basic-pb"
|
||||
symbol_map = {"上证": "1", "深证": "2", "创业板": "4", "科创版": "7"}
|
||||
url_map = {
|
||||
"上证": "https://legulegu.com/stockdata/shanghaiPB",
|
||||
"深证": "https://legulegu.com/stockdata/shenzhenPB",
|
||||
"创业板": "https://legulegu.com/stockdata/cybPB",
|
||||
"科创版": "https://legulegu.com/stockdata/ke-chuang-ban-pb",
|
||||
}
|
||||
params = {"token": token, "indexCode": symbol_map[symbol]}
|
||||
r = requests.get(url, params=params, **get_cookie_csrf(url=url_map[symbol]))
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"addPb",
|
||||
"pb",
|
||||
"middlePb",
|
||||
]
|
||||
]
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"指数",
|
||||
"市净率",
|
||||
"等权市净率",
|
||||
"市净率中位数",
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_index_pb_lg(symbol: str = "上证50") -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-指数市净率
|
||||
https://legulegu.com/stockdata/sz50-pb
|
||||
:param symbol: choice of {"上证50", "沪深300", "上证380", "创业板50", "中证500", "上证180", "深证红利", "深证100", "中证1000", "上证红利", "中证100", "中证800"}
|
||||
:type symbol: str
|
||||
:return: 指定指数的市净率数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_functions = py_mini_racer.MiniRacer()
|
||||
js_functions.eval(hash_code)
|
||||
token = js_functions.call("hex", datetime.now().date().isoformat()).lower()
|
||||
symbol_map = {
|
||||
"上证50": "000016.SH",
|
||||
"沪深300": "000300.SH",
|
||||
"上证380": "000009.SH",
|
||||
"创业板50": "399673.SZ",
|
||||
"中证500": "000905.SH",
|
||||
"上证180": "000010.SH",
|
||||
"深证红利": "399324.SZ",
|
||||
"深证100": "399330.SZ",
|
||||
"中证1000": "000852.SH",
|
||||
"上证红利": "000015.SH",
|
||||
"中证100": "000903.SH",
|
||||
"中证800": "000906.SH",
|
||||
}
|
||||
url = "https://legulegu.com/api/stockdata/index-basic-pb"
|
||||
params = {"token": token, "indexCode": symbol_map[symbol]}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/zz500-ttm-lyr"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"addPb",
|
||||
"pb",
|
||||
"middlePb",
|
||||
]
|
||||
]
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"指数",
|
||||
"市净率",
|
||||
"等权市净率",
|
||||
"市净率中位数",
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_market_pe_lg_df = stock_market_pe_lg(symbol="上证")
|
||||
print(stock_market_pe_lg_df)
|
||||
|
||||
stock_market_pe_lg_df = stock_market_pe_lg(symbol="科创版")
|
||||
print(stock_market_pe_lg_df)
|
||||
|
||||
for item in {"上证", "深证", "创业板", "科创版"}:
|
||||
print(item)
|
||||
stock_market_pe_lg_df = stock_market_pe_lg(symbol=item)
|
||||
print(stock_market_pe_lg_df)
|
||||
|
||||
stock_index_pe_lg_df = stock_index_pe_lg(symbol="沪深300")
|
||||
print(stock_index_pe_lg_df)
|
||||
|
||||
for item in [
|
||||
"上证50",
|
||||
"沪深300",
|
||||
"上证380",
|
||||
"创业板50",
|
||||
"中证500",
|
||||
"上证180",
|
||||
"深证红利",
|
||||
"深证100",
|
||||
"中证1000",
|
||||
"上证红利",
|
||||
"中证100",
|
||||
"中证800",
|
||||
]:
|
||||
stock_index_pe_lg_df = stock_index_pe_lg(symbol=item)
|
||||
print(stock_index_pe_lg_df)
|
||||
|
||||
for item in {"上证", "深证", "创业板", "科创版"}:
|
||||
stock_market_pb_lg_df = stock_market_pb_lg(symbol=item)
|
||||
print(stock_market_pb_lg_df)
|
||||
|
||||
for item in [
|
||||
"上证50",
|
||||
"沪深300",
|
||||
"上证380",
|
||||
"创业板50",
|
||||
"中证500",
|
||||
"上证180",
|
||||
"深证红利",
|
||||
"深证100",
|
||||
"中证1000",
|
||||
"上证红利",
|
||||
"中证100",
|
||||
"中证800",
|
||||
]:
|
||||
stock_index_pe_lg_df = stock_index_pb_lg(symbol=item)
|
||||
print(stock_index_pe_lg_df)
|
||||
@@ -0,0 +1,97 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/4/21 17:00
|
||||
Desc: 东方财富网-数据中心-特色数据-股票账户统计
|
||||
东方财富网-数据中心-特色数据-股票账户统计: 股票账户统计详细数据
|
||||
https://data.eastmoney.com/cjsj/gpkhsj.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_account_statistics_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股票账户统计
|
||||
https://data.eastmoney.com/cjsj/gpkhsj.html
|
||||
:return: 股票账户统计数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"reportName": "RPT_STOCK_OPEN_DATA",
|
||||
"columns": "ALL",
|
||||
"pageSize": "500",
|
||||
"sortColumns": "STATISTICS_DATE",
|
||||
"sortTypes": "-1",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"p": "1",
|
||||
"pageNo": "1",
|
||||
"pageNum": "1",
|
||||
"pageNumber": "1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.columns = [
|
||||
"数据日期",
|
||||
"新增投资者-数量",
|
||||
"新增投资者-环比",
|
||||
"新增投资者-同比",
|
||||
"期末投资者-总量",
|
||||
"期末投资者-A股账户",
|
||||
"期末投资者-B股账户",
|
||||
"上证指数-收盘",
|
||||
"上证指数-涨跌幅",
|
||||
"沪深总市值",
|
||||
"沪深户均市值",
|
||||
"-",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"数据日期",
|
||||
"新增投资者-数量",
|
||||
"新增投资者-环比",
|
||||
"新增投资者-同比",
|
||||
"期末投资者-总量",
|
||||
"期末投资者-A股账户",
|
||||
"期末投资者-B股账户",
|
||||
"沪深总市值",
|
||||
"沪深户均市值",
|
||||
"上证指数-收盘",
|
||||
"上证指数-涨跌幅",
|
||||
]
|
||||
]
|
||||
temp_df["新增投资者-数量"] = pd.to_numeric(
|
||||
temp_df["新增投资者-数量"], errors="coerce"
|
||||
)
|
||||
temp_df["新增投资者-环比"] = pd.to_numeric(
|
||||
temp_df["新增投资者-环比"], errors="coerce"
|
||||
)
|
||||
temp_df["新增投资者-同比"] = pd.to_numeric(
|
||||
temp_df["新增投资者-同比"], errors="coerce"
|
||||
)
|
||||
temp_df["期末投资者-总量"] = pd.to_numeric(
|
||||
temp_df["期末投资者-总量"], errors="coerce"
|
||||
)
|
||||
temp_df["期末投资者-A股账户"] = pd.to_numeric(
|
||||
temp_df["期末投资者-A股账户"], errors="coerce"
|
||||
)
|
||||
temp_df["期末投资者-B股账户"] = pd.to_numeric(
|
||||
temp_df["期末投资者-B股账户"], errors="coerce"
|
||||
)
|
||||
temp_df["沪深总市值"] = pd.to_numeric(temp_df["沪深总市值"], errors="coerce")
|
||||
temp_df["沪深户均市值"] = pd.to_numeric(temp_df["沪深户均市值"], errors="coerce")
|
||||
temp_df["上证指数-收盘"] = pd.to_numeric(temp_df["上证指数-收盘"], errors="coerce")
|
||||
temp_df["上证指数-涨跌幅"] = pd.to_numeric(
|
||||
temp_df["上证指数-涨跌幅"], errors="coerce"
|
||||
)
|
||||
temp_df.sort_values(["数据日期"], ignore_index=True, inplace=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_account_statistics_em_df = stock_account_statistics_em()
|
||||
print(stock_account_statistics_em_df)
|
||||
@@ -0,0 +1,45 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2023/4/11 20:40
|
||||
Desc: 全部A股-等权重市净率、中位数市净率
|
||||
https://www.legulegu.com/stockdata/all-pb
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_a_all_pb() -> pd.DataFrame:
|
||||
"""
|
||||
全部A股-等权重市净率、中位数市净率
|
||||
https://legulegu.com/stockdata/all-pb
|
||||
:return: 全部A股-等权重市盈率、中位数市盈率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://legulegu.com/api/stock-data/market-index-pb"
|
||||
params = {
|
||||
"marketId": "ALL",
|
||||
"token": get_token_lg(),
|
||||
}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/all-pb"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
del temp_df["weightingAveragePB"]
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_a_all_pb_df = stock_a_all_pb()
|
||||
print(stock_a_all_pb_df)
|
||||
@@ -0,0 +1,271 @@
|
||||
#!/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)
|
||||
+328
@@ -0,0 +1,328 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/3/27 14:20
|
||||
Desc: 同花顺-板块-概念板块
|
||||
https://q.10jqka.com.cn/thshy/
|
||||
"""
|
||||
|
||||
from typing import Dict
|
||||
from datetime import datetime
|
||||
from functools import lru_cache
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import py_mini_racer
|
||||
|
||||
from akshare.datasets import get_ths_js
|
||||
from akshare.utils import demjson
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def _get_file_content_ths(file: str = "ths.js") -> str:
|
||||
"""
|
||||
获取 JS 文件的内容
|
||||
:param file: JS 文件名
|
||||
:type file: str
|
||||
:return: 文件内容
|
||||
:rtype: str
|
||||
"""
|
||||
setting_file_path = get_ths_js(file)
|
||||
with open(setting_file_path, encoding="utf-8") as f:
|
||||
file_data = f.read()
|
||||
return file_data
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def _get_stock_board_concept_name_ths() -> dict:
|
||||
"""
|
||||
获取同花顺概念板块代码和名称字典
|
||||
:return: 获取同花顺概念板块代码和名称字典
|
||||
:rtype: dict
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "https://q.10jqka.com.cn/gn/detail/code/307822/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
code_list = [
|
||||
item["href"].split("/")[-2]
|
||||
for item in soup.find(name="div", attrs={"class": "cate_inner"}).find_all("a")
|
||||
]
|
||||
name_list = [
|
||||
item.text
|
||||
for item in soup.find(name="div", attrs={"class": "cate_inner"}).find_all("a")
|
||||
]
|
||||
name_code_map = dict(zip(name_list, code_list))
|
||||
temp_dict = __stock_board_concept_summary_ths()
|
||||
name_code_map.update(temp_dict)
|
||||
return name_code_map
|
||||
|
||||
|
||||
def stock_board_concept_name_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-概念板块-概念
|
||||
http://q.10jqka.com.cn/thshy/
|
||||
:return: 所有概念板块的名称和链接
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
code_name_ths_map = _get_stock_board_concept_name_ths()
|
||||
temp_df = pd.DataFrame.from_dict(code_name_ths_map, orient="index")
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df.columns = ["name", "code"]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"name",
|
||||
"code",
|
||||
]
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_board_concept_info_ths(symbol: str = "阿里巴巴概念") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-概念板块-板块简介
|
||||
http://q.10jqka.com.cn/gn/detail/code/301558/
|
||||
:param symbol: 板块简介
|
||||
:type symbol: str
|
||||
:return: 板块简介
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
stock_board_ths_map_df = stock_board_concept_name_ths()
|
||||
symbol_code = stock_board_ths_map_df[stock_board_ths_map_df["name"] == symbol][
|
||||
"code"
|
||||
].values[0]
|
||||
url = f"http://q.10jqka.com.cn/gn/detail/code/{symbol_code}/"
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
name_list = [
|
||||
item.text.strip()
|
||||
for item in soup.find(name="div", attrs={"class": "board-infos"}).find_all("dt")
|
||||
]
|
||||
value_list = [
|
||||
item.text.strip().replace("\n", "/")
|
||||
for item in soup.find(name="div", attrs={"class": "board-infos"}).find_all("dd")
|
||||
]
|
||||
temp_df = pd.DataFrame([name_list, value_list]).T
|
||||
temp_df.columns = ["项目", "值"]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_board_concept_index_ths(
|
||||
symbol: str = "阿里巴巴概念",
|
||||
start_date: str = "20200101",
|
||||
end_date: str = "20250228",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-概念板块-指数数据
|
||||
https://q.10jqka.com.cn/gn/detail/code/301558/
|
||||
:param start_date: 开始时间
|
||||
:type start_date: str
|
||||
:param end_date: 结束时间
|
||||
:type end_date: str
|
||||
:param symbol: 指数数据
|
||||
:type symbol: str
|
||||
:return: 指数数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
|
||||
code_map = _get_stock_board_concept_name_ths()
|
||||
symbol_code = code_map[symbol]
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
}
|
||||
url = f"https://q.10jqka.com.cn/gn/detail/code/{symbol_code}"
|
||||
r = requests.get(url=url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
inner_code = soup.find(name="input", attrs={"id": "clid"})["value"]
|
||||
big_df = pd.DataFrame()
|
||||
current_year = datetime.now().year
|
||||
begin_year = int(start_date[:4])
|
||||
tqdm = get_tqdm()
|
||||
for year in tqdm(range(begin_year, current_year + 1), leave=False):
|
||||
url = f"https://d.10jqka.com.cn/v4/line/bk_{inner_code}/01/{year}.js"
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Referer": "http://q.10jqka.com.cn",
|
||||
"Host": "d.10jqka.com.cn",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
data_text = r.text
|
||||
|
||||
try:
|
||||
demjson.decode(data_text[data_text.find("{") : -1])
|
||||
except: # noqa: E722
|
||||
continue
|
||||
temp_df = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
temp_df = pd.DataFrame(temp_df["data"].split(";"))
|
||||
temp_df = temp_df.iloc[:, 0].str.split(",", expand=True)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
if len(big_df.columns) == 11:
|
||||
big_df.columns = [
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
else:
|
||||
big_df.columns = [
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
]
|
||||
]
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
big_df.index = pd.to_datetime(big_df["日期"], errors="coerce")
|
||||
big_df = big_df[start_date:end_date]
|
||||
big_df.reset_index(drop=True, inplace=True)
|
||||
big_df["开盘价"] = pd.to_numeric(big_df["开盘价"], errors="coerce")
|
||||
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
|
||||
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
|
||||
big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def __stock_board_concept_summary_ths() -> Dict:
|
||||
"""
|
||||
同花顺-数据中心-概念板块-概念时间表-辅助函数
|
||||
https://q.10jqka.com.cn/gn/
|
||||
:return: 概念时间表
|
||||
:rtype: dict
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://q.10jqka.com.cn/gn/index/field/addtime/order/desc/page/1/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_num = soup.find(name="span", attrs={"class": "page_info"}).text.split("/")[1]
|
||||
big_dict = dict()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
url = f"http://q.10jqka.com.cn/gn/index/field/addtime/order/desc/page/{page}/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
try:
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
temp_dict = {
|
||||
item.get_text(): item["href"].rsplit("/")[-2]
|
||||
for item in soup.find_all(name="a")
|
||||
if "detail" in item["href"]
|
||||
}
|
||||
big_dict.update(temp_dict)
|
||||
except ValueError:
|
||||
break
|
||||
return big_dict
|
||||
|
||||
|
||||
def stock_board_concept_summary_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-概念板块-概念时间表
|
||||
https://q.10jqka.com.cn/gn/
|
||||
:return: 概念时间表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://q.10jqka.com.cn/gn/index/field/addtime/order/desc/page/1/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_num = soup.find(name="span", attrs={"class": "page_info"}).text.split("/")[1]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
url = f"http://q.10jqka.com.cn/gn/index/field/addtime/order/desc/page/{page}/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
try:
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
except ValueError:
|
||||
break
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
big_df["成分股数量"] = pd.to_numeric(big_df["成分股数量"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_board_concept_name_ths_df = stock_board_concept_name_ths()
|
||||
print(stock_board_concept_name_ths_df)
|
||||
|
||||
stock_board_concept_info_ths_df = stock_board_concept_info_ths(
|
||||
symbol="阿里巴巴概念"
|
||||
)
|
||||
print(stock_board_concept_info_ths_df)
|
||||
|
||||
stock_board_concept_index_ths_df = stock_board_concept_index_ths(
|
||||
symbol="DeepSeek概念", start_date="20200101", end_date="20250321"
|
||||
)
|
||||
print(stock_board_concept_index_ths_df)
|
||||
|
||||
stock_board_concept_summary_ths_df = stock_board_concept_summary_ths()
|
||||
print(stock_board_concept_summary_ths_df)
|
||||
|
||||
for stock in stock_board_concept_name_ths_df["name"]:
|
||||
print(stock)
|
||||
stock_board_industry_index_ths_df = stock_board_concept_index_ths(symbol=stock)
|
||||
print(stock_board_industry_index_ths_df)
|
||||
+409
@@ -0,0 +1,409 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/2/28 13:20
|
||||
Desc: 同花顺-板块-同花顺行业
|
||||
https://q.10jqka.com.cn/thshy/
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
from functools import lru_cache
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import py_mini_racer
|
||||
|
||||
from akshare.datasets import get_ths_js
|
||||
from akshare.utils import demjson
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def _get_file_content_ths(file: str = "ths.js") -> str:
|
||||
"""
|
||||
获取 JS 文件的内容
|
||||
:param file: JS 文件名
|
||||
:type file: str
|
||||
:return: 文件内容
|
||||
:rtype: str
|
||||
"""
|
||||
setting_file_path = get_ths_js(file)
|
||||
with open(setting_file_path, encoding="utf-8") as f:
|
||||
file_data = f.read()
|
||||
return file_data
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def _get_stock_board_industry_name_ths() -> dict:
|
||||
"""
|
||||
获取同花顺行业代码和名称字典
|
||||
:return: 获取同花顺行业代码和名称字典
|
||||
:rtype: dict
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "https://q.10jqka.com.cn/thshy/detail/code/881272/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
code_list = [
|
||||
item["href"].split("/")[-2]
|
||||
for item in soup.find(name="div", attrs={"class": "cate_inner"}).find_all("a")
|
||||
]
|
||||
name_list = [
|
||||
item.text
|
||||
for item in soup.find(name="div", attrs={"class": "cate_inner"}).find_all("a")
|
||||
]
|
||||
name_code_map = dict(zip(name_list, code_list))
|
||||
return name_code_map
|
||||
|
||||
|
||||
def stock_board_industry_name_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-行业板块-行业
|
||||
http://q.10jqka.com.cn/thshy/
|
||||
:return: 所有行业板块的名称和链接
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
code_name_ths_map = _get_stock_board_industry_name_ths()
|
||||
temp_df = pd.DataFrame.from_dict(code_name_ths_map, orient="index")
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df.columns = ["name", "code"]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"name",
|
||||
"code",
|
||||
]
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_board_industry_info_ths(symbol: str = "半导体") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-行业板块-板块简介
|
||||
http://q.10jqka.com.cn/gn/detail/code/301558/
|
||||
:param symbol: 板块简介
|
||||
:type symbol: str
|
||||
:return: 板块简介
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
stock_board_ths_map_df = stock_board_industry_name_ths()
|
||||
symbol_code = stock_board_ths_map_df[stock_board_ths_map_df["name"] == symbol][
|
||||
"code"
|
||||
].values[0]
|
||||
url = f"http://q.10jqka.com.cn/thshy/detail/code/{symbol_code}/"
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
name_list = [
|
||||
item.text.strip()
|
||||
for item in soup.find(name="div", attrs={"class": "board-infos"}).find_all("dt")
|
||||
]
|
||||
value_list = [
|
||||
item.text.strip().replace("\n", "/")
|
||||
for item in soup.find(name="div", attrs={"class": "board-infos"}).find_all("dd")
|
||||
]
|
||||
temp_df = pd.DataFrame([name_list, value_list]).T
|
||||
temp_df.columns = ["项目", "值"]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_board_industry_index_ths(
|
||||
symbol: str = "元件",
|
||||
start_date: str = "20200101",
|
||||
end_date: str = "20240108",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-板块-行业板块-指数数据
|
||||
https://q.10jqka.com.cn/thshy/detail/code/881270/
|
||||
:param start_date: 开始时间
|
||||
:type start_date: str
|
||||
:param end_date: 结束时间
|
||||
:type end_date: str
|
||||
:param symbol: 指数数据
|
||||
:type symbol: str
|
||||
:return: 指数数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
code_map = _get_stock_board_industry_name_ths()
|
||||
symbol_code = code_map[symbol]
|
||||
big_df = pd.DataFrame()
|
||||
current_year = datetime.now().year
|
||||
begin_year = int(start_date[:4])
|
||||
tqdm = get_tqdm()
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
for year in tqdm(range(begin_year, current_year + 1), leave=False):
|
||||
url = f"https://d.10jqka.com.cn/v4/line/bk_{symbol_code}/01/{year}.js"
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Referer": "http://q.10jqka.com.cn",
|
||||
"Host": "d.10jqka.com.cn",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
data_text = r.text
|
||||
|
||||
try:
|
||||
demjson.decode(data_text[data_text.find("{") : -1])
|
||||
except: # noqa: E722
|
||||
continue
|
||||
temp_df = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
temp_df = pd.DataFrame(temp_df["data"].split(";"))
|
||||
temp_df = temp_df.iloc[:, 0].str.split(",", expand=True)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
if len(big_df.columns) == 11:
|
||||
big_df.columns = [
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
else:
|
||||
big_df.columns = [
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"日期",
|
||||
"开盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"收盘价",
|
||||
"成交量",
|
||||
"成交额",
|
||||
]
|
||||
]
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
big_df.index = pd.to_datetime(big_df["日期"], errors="coerce")
|
||||
big_df = big_df[start_date:end_date]
|
||||
big_df.reset_index(drop=True, inplace=True)
|
||||
big_df["开盘价"] = pd.to_numeric(big_df["开盘价"], errors="coerce")
|
||||
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
|
||||
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
|
||||
big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_xgsr_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-新股数据-新股上市首日
|
||||
https://data.10jqka.com.cn/ipo/xgsr/
|
||||
:return: 新股上市首日
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
"hexin-v": v_code,
|
||||
}
|
||||
url = "https://data.10jqka.com.cn/ipo/xgsr/field/SSRQ/order/desc/page/1/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_num = soup.find(name="span", attrs={"class": "page_info"}).text.split("/")[1]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
url = f"https://data.10jqka.com.cn/ipo/xgsr/field/SSRQ/order/desc/page/{page}/ajax/1/free/1/"
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
"hexin-v": v_code,
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(columns={"发行价(元)": "发行价"}, inplace=True)
|
||||
big_df["序号"] = pd.to_numeric(big_df["序号"], errors="coerce")
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["发行价"] = pd.to_numeric(big_df["发行价"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["首日开盘价"] = pd.to_numeric(big_df["首日开盘价"], errors="coerce")
|
||||
big_df["首日收盘价"] = pd.to_numeric(big_df["首日收盘价"], errors="coerce")
|
||||
big_df["首日最高价"] = pd.to_numeric(big_df["首日最高价"], errors="coerce")
|
||||
big_df["首日最低价"] = pd.to_numeric(big_df["首日最低价"], errors="coerce")
|
||||
big_df["首日涨跌幅"] = (
|
||||
pd.to_numeric(big_df["首日涨跌幅"].str.strip("%"), errors="coerce") / 100
|
||||
)
|
||||
big_df["上市日期"] = pd.to_datetime(big_df["上市日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_ipo_benefit_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-新股数据-IPO受益股
|
||||
https://data.10jqka.com.cn/ipo/syg/
|
||||
:return: IPO受益股
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
"hexin-v": v_code,
|
||||
}
|
||||
url = "https://data.10jqka.com.cn/ipo/syg/field/invest/order/desc/page/1/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_num = soup.find(name="span", attrs={"class": "page_info"}).text.split("/")[1]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
url = f"https://data.10jqka.com.cn/ipo/syg/field/invest/order/desc/page/{page}/ajax/1/free/1/"
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
"hexin-v": v_code,
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"收盘价",
|
||||
"涨跌幅",
|
||||
"市值",
|
||||
"参股家数",
|
||||
"投资总额",
|
||||
"投资占市值比",
|
||||
"参股对象",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["序号"] = pd.to_numeric(big_df["序号"], errors="coerce")
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["参股家数"] = pd.to_numeric(big_df["参股家数"], errors="coerce")
|
||||
big_df["投资占市值比"] = pd.to_numeric(big_df["投资占市值比"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_board_industry_summary_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-行业板块-同花顺行业一览表
|
||||
https://q.10jqka.com.cn/thshy/
|
||||
:return: 同花顺行业一览表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://q.10jqka.com.cn/thshy/index/field/199112/order/desc/page/1/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_num = soup.find(name="span", attrs={"class": "page_info"}).text.split("/")[1]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
url = f"http://q.10jqka.com.cn/thshy/index/field/199112/order/desc/page/{page}/ajax/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"板块",
|
||||
"涨跌幅",
|
||||
"总成交量",
|
||||
"总成交额",
|
||||
"净流入",
|
||||
"上涨家数",
|
||||
"下跌家数",
|
||||
"均价",
|
||||
"领涨股",
|
||||
"领涨股-最新价",
|
||||
"领涨股-涨跌幅",
|
||||
]
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["总成交量"] = pd.to_numeric(big_df["总成交量"], errors="coerce")
|
||||
big_df["总成交额"] = pd.to_numeric(big_df["总成交额"], errors="coerce")
|
||||
big_df["净流入"] = pd.to_numeric(big_df["净流入"], errors="coerce")
|
||||
big_df["上涨家数"] = pd.to_numeric(big_df["上涨家数"], errors="coerce")
|
||||
big_df["下跌家数"] = pd.to_numeric(big_df["下跌家数"], errors="coerce")
|
||||
big_df["均价"] = pd.to_numeric(big_df["均价"], errors="coerce")
|
||||
big_df["领涨股-最新价"] = pd.to_numeric(big_df["领涨股-最新价"], errors="coerce")
|
||||
big_df["领涨股-涨跌幅"] = pd.to_numeric(big_df["领涨股-涨跌幅"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_board_industry_name_ths_df = stock_board_industry_name_ths()
|
||||
print(stock_board_industry_name_ths_df)
|
||||
|
||||
stock_board_industry_info_ths_df = stock_board_industry_info_ths(symbol="橡胶制品")
|
||||
print(stock_board_industry_info_ths_df)
|
||||
|
||||
stock_board_industry_index_ths_df = stock_board_industry_index_ths(
|
||||
symbol="消费电子", start_date="20240101", end_date="20240724"
|
||||
)
|
||||
print(stock_board_industry_index_ths_df)
|
||||
|
||||
stock_board_industry_summary_ths_df = stock_board_industry_summary_ths()
|
||||
print(stock_board_industry_summary_ths_df)
|
||||
|
||||
for stock in stock_board_industry_name_ths_df["name"]:
|
||||
print(stock)
|
||||
stock_board_industry_index_ths_df = stock_board_industry_index_ths(symbol=stock)
|
||||
print(stock_board_industry_index_ths_df)
|
||||
|
||||
stock_xgsr_ths_df = stock_xgsr_ths()
|
||||
print(stock_xgsr_ths_df)
|
||||
|
||||
stock_ipo_benefit_ths_df = stock_ipo_benefit_ths()
|
||||
print(stock_ipo_benefit_ths_df)
|
||||
+62
@@ -0,0 +1,62 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2026/5/27 17:05
|
||||
Desc: 乐估乐股-底部研究-巴菲特指标
|
||||
https://legulegu.com/stockdata/marketcap-gdp
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_buffett_index_lg() -> pd.DataFrame:
|
||||
"""
|
||||
乐估乐股-底部研究-巴菲特指标
|
||||
https://legulegu.com/stockdata/marketcap-gdp
|
||||
:return: 巴菲特指标
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
token = get_token_lg()
|
||||
url = "https://legulegu.com/api/stockdata/marketcap-gdp/get-marketcap-gdp"
|
||||
params = {"token": token}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/marketcap-gdp"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
rename_map = {
|
||||
"marketCap": "总市值",
|
||||
"gdp": "GDP",
|
||||
"close": "收盘价",
|
||||
"date": "日期",
|
||||
"quantileInAllHistory": "总历史分位数",
|
||||
"quantileInRecent10Years": "近十年分位数",
|
||||
}
|
||||
existing_rename = {k: v for k, v in rename_map.items() if k in temp_df.columns}
|
||||
temp_df.rename(columns=existing_rename, inplace=True)
|
||||
|
||||
base_cols = ["日期", "收盘价", "总市值", "GDP"]
|
||||
optional_cols = ["近十年分位数", "总历史分位数"]
|
||||
available_cols = base_cols + [c for c in optional_cols if c in temp_df.columns]
|
||||
temp_df = temp_df[available_cols]
|
||||
|
||||
temp_df["日期"] = pd.to_datetime(temp_df["日期"], utc=True).dt.date
|
||||
temp_df["收盘价"] = pd.to_numeric(temp_df["收盘价"], errors="coerce")
|
||||
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
|
||||
temp_df["GDP"] = pd.to_numeric(temp_df["GDP"], errors="coerce")
|
||||
|
||||
for col in optional_cols:
|
||||
if col in temp_df.columns:
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_buffett_index_lg_df = stock_buffett_index_lg()
|
||||
print(stock_buffett_index_lg_df)
|
||||
+87
@@ -0,0 +1,87 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2022/7/13 16:16
|
||||
Desc: 新浪财经-股票-行业分类
|
||||
http://vip.stock.finance.sina.com.cn/mkt/
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def stock_classify_board() -> dict:
|
||||
"""
|
||||
http://vip.stock.finance.sina.com.cn/mkt/
|
||||
:return: 股票分类字典
|
||||
:rtype: dict
|
||||
"""
|
||||
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodes"
|
||||
r = requests.get(url)
|
||||
data_json = r.json()
|
||||
big_dict = {}
|
||||
class_name_list = [
|
||||
BeautifulSoup(item[0], "lxml").find("font").text
|
||||
if "font" in item[0]
|
||||
else item[0]
|
||||
for item in data_json[1][0][1]
|
||||
] # 沪深股市
|
||||
for num, class_name in enumerate(class_name_list):
|
||||
temp_df = pd.DataFrame([item for item in data_json[1][0][1][num][1:][0]])
|
||||
if temp_df.shape[1] == 5:
|
||||
temp_df.columns = ["name", "_", "code", "_", "_"]
|
||||
temp_df = temp_df[["name", "code"]]
|
||||
if temp_df.shape[1] == 4:
|
||||
temp_df.columns = ["name", "_", "code", "_"]
|
||||
temp_df = temp_df[["name", "code"]]
|
||||
if temp_df.shape[1] == 3:
|
||||
temp_df.columns = ["name", "_", "code"]
|
||||
temp_df = temp_df[["name", "code"]]
|
||||
big_dict.update({class_name: temp_df})
|
||||
return big_dict
|
||||
|
||||
|
||||
def stock_classify_sina(symbol: str = "热门概念") -> pd.DataFrame:
|
||||
"""
|
||||
按 symbol 分类后的股票
|
||||
http://vip.stock.finance.sina.com.cn/mkt/
|
||||
:param symbol: choice of {'申万行业', '申万二级', '热门概念', '地域板块'}
|
||||
:type symbol: str
|
||||
:return: 分类后的股票
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
stock_classify_board_dict = stock_classify_board()
|
||||
data_df = pd.DataFrame()
|
||||
for num in tqdm(range(len(stock_classify_board_dict[symbol]["code"])), leave=False):
|
||||
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeStockCount"
|
||||
params = {"node": stock_classify_board_dict[symbol]["code"][num]}
|
||||
r = requests.get(url, params=params)
|
||||
page_num = math.ceil(int(r.json()) / 80)
|
||||
url = "http://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQNodeData"
|
||||
big_df = pd.DataFrame()
|
||||
for page in range(1, page_num + 1):
|
||||
params = {
|
||||
"page": page,
|
||||
"num": "80",
|
||||
"sort": "symbol",
|
||||
"asc": "1",
|
||||
"node": stock_classify_board_dict[symbol]["code"][num],
|
||||
"symbol": "",
|
||||
"_s_r_a": "init",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json)
|
||||
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
||||
big_df["class"] = stock_classify_board_dict[symbol]["name"][num]
|
||||
data_df = pd.concat([data_df, big_df], ignore_index=True)
|
||||
return data_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_classify_sina_df = stock_classify_sina(symbol="热门概念")
|
||||
print(stock_classify_sina_df)
|
||||
@@ -0,0 +1,313 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/4/4 18:20
|
||||
Desc: 东方财富网-数据中心-特色数据-千股千评
|
||||
https://data.eastmoney.com/stockcomment/
|
||||
"""
|
||||
|
||||
import json
|
||||
import re
|
||||
import time
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_comment_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-千股千评
|
||||
https://data.eastmoney.com/stockcomment/
|
||||
:return: 千股千评数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "SECURITY_CODE",
|
||||
"sortTypes": "1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_DMSK_TS_STOCKNEW",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~CLOSE_PRICE,f8~01~SECURITY_CODE~TURNOVERRATE,"
|
||||
"f3~01~SECURITY_CODE~CHANGE_RATE,f9~01~SECURITY_CODE~PE_DYNAMIC",
|
||||
"columns": "ALL",
|
||||
"filter": "",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"-",
|
||||
"代码",
|
||||
"-",
|
||||
"交易日",
|
||||
"名称",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"-",
|
||||
"换手率",
|
||||
"主力成本",
|
||||
"市盈率",
|
||||
"-",
|
||||
"-",
|
||||
"机构参与度",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"综合得分",
|
||||
"上升",
|
||||
"目前排名",
|
||||
"关注指数",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
"市盈率",
|
||||
"主力成本",
|
||||
"机构参与度",
|
||||
"综合得分",
|
||||
"上升",
|
||||
"目前排名",
|
||||
"关注指数",
|
||||
"交易日",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], 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["主力成本"] = pd.to_numeric(big_df["主力成本"], errors="coerce")
|
||||
big_df["机构参与度"] = pd.to_numeric(big_df["机构参与度"], errors="coerce")
|
||||
big_df["综合得分"] = pd.to_numeric(big_df["综合得分"], errors="coerce")
|
||||
big_df["上升"] = pd.to_numeric(big_df["上升"], errors="coerce")
|
||||
big_df["目前排名"] = pd.to_numeric(big_df["目前排名"], errors="coerce")
|
||||
big_df["关注指数"] = pd.to_numeric(big_df["关注指数"], errors="coerce")
|
||||
big_df["交易日"] = pd.to_datetime(big_df["交易日"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_comment_detail_zlkp_jgcyd_em(symbol: str = "600000") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-千股千评-主力控盘-机构参与度
|
||||
https://data.eastmoney.com/stockcomment/stock/600000.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 主力控盘-机构参与度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"reportName": "RPT_DMSK_TS_STOCKEVALUATE",
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"sortColumns": "TRADE_DATE",
|
||||
"sortTypes": "-1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df = temp_df[["TRADE_DATE", "ORG_PARTICIPATE"]]
|
||||
temp_df.columns = ["交易日", "机构参与度"]
|
||||
temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date
|
||||
temp_df.sort_values(["交易日"], inplace=True)
|
||||
temp_df.reset_index(inplace=True, drop=True)
|
||||
temp_df["机构参与度"] = pd.to_numeric(temp_df["机构参与度"], errors="coerce") * 100
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_comment_detail_zhpj_lspf_em(symbol: str = "600000") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-千股千评-综合评价-历史评分
|
||||
https://data.eastmoney.com/stockcomment/stock/600000.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 综合评价-历史评分
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"reportName": "RPT_STOCK_HISTORYMARK",
|
||||
"sortColumns": "DIAGNOSE_DATE",
|
||||
"sortTypes": "1",
|
||||
}
|
||||
r = requests.get(url=url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"TOTAL_SCORE": "评分",
|
||||
"DIAGNOSE_DATE": "交易日",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[["交易日", "评分"]]
|
||||
temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date
|
||||
temp_df.sort_values(by=["交易日"], inplace=True)
|
||||
temp_df.reset_index(inplace=True, drop=True)
|
||||
temp_df["评分"] = pd.to_numeric(temp_df["评分"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_comment_detail_scrd_focus_em(symbol: str = "600000") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-千股千评-市场热度-用户关注指数
|
||||
https://data.eastmoney.com/stockcomment/stock/600000.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 市场热度-用户关注指数
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"reportName": "RPT_STOCK_MARKETFOCUS",
|
||||
"sortColumns": "TRADE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "30",
|
||||
}
|
||||
r = requests.get(url=url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"MARKET_FOCUS": "用户关注指数",
|
||||
"TRADE_DATE": "交易日",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[["交易日", "用户关注指数"]]
|
||||
temp_df["交易日"] = pd.to_datetime(temp_df["交易日"], errors="coerce").dt.date
|
||||
temp_df.sort_values(by=["交易日"], inplace=True)
|
||||
temp_df.reset_index(inplace=True, drop=True)
|
||||
temp_df["用户关注指数"] = pd.to_numeric(temp_df["用户关注指数"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_comment_detail_scrd_desire_em(
|
||||
symbol: str = "600000",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-千股千评-市场热度-市场参与意愿
|
||||
https://data.eastmoney.com/stockcomment/stock/600000.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 市场热度-市场参与意愿
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"callback": f"jQuery11230899775623921407_{int(time.time() * 1000)}",
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"reportName": "RPT_STOCK_PARTICIPATION",
|
||||
"sortColumns": "TRADE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "30",
|
||||
"_": int(time.time() * 1000),
|
||||
}
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/91.0.4472.124 Safari/537.36",
|
||||
"Referer": "https://data.eastmoney.com/",
|
||||
"Accept": "*/*",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
jsonp_data = r.text
|
||||
json_str = re.search(r"\((.*)\)", jsonp_data).group(1)
|
||||
data_json = json.loads(json_str)
|
||||
data_list = data_json["result"]["data"]
|
||||
temp_df = pd.DataFrame(data_list)
|
||||
temp_df["TRADE_DATE"] = pd.to_datetime(
|
||||
temp_df["TRADE_DATE"], errors="coerce"
|
||||
).dt.date
|
||||
column_mapping = {
|
||||
"SECURITY_INNER_CODE": "内部代码",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"TRADE_DATE": "交易日期",
|
||||
"PARTICIPATION_WISH": "参与意愿",
|
||||
"PARTICIPATION_WISH_5DAYS": "5日平均参与意愿",
|
||||
"PARTICIPATION_WISH_CHANGE": "参与意愿变化",
|
||||
"PARTICIPATION_WISH_5DAYSCHANGE": "5日平均变化",
|
||||
}
|
||||
temp_df = temp_df.rename(columns=column_mapping)
|
||||
column_order = [
|
||||
"交易日期",
|
||||
"股票代码",
|
||||
"内部代码",
|
||||
"参与意愿",
|
||||
"5日平均参与意愿",
|
||||
"参与意愿变化",
|
||||
"5日平均变化",
|
||||
]
|
||||
temp_df = temp_df[column_order]
|
||||
del temp_df["内部代码"]
|
||||
temp_df.sort_values(by=["交易日期"], ignore_index=True, inplace=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_comment_em_df = stock_comment_em()
|
||||
print(stock_comment_em_df)
|
||||
|
||||
stock_comment_detail_zlkp_jgcyd_em_df = stock_comment_detail_zlkp_jgcyd_em(
|
||||
symbol="600000"
|
||||
)
|
||||
print(stock_comment_detail_zlkp_jgcyd_em_df)
|
||||
|
||||
stock_comment_detail_zhpj_lspf_em_df = stock_comment_detail_zhpj_lspf_em(
|
||||
symbol="600000"
|
||||
)
|
||||
print(stock_comment_detail_zhpj_lspf_em_df)
|
||||
|
||||
stock_comment_detail_scrd_focus_em_df = stock_comment_detail_scrd_focus_em(
|
||||
symbol="600000"
|
||||
)
|
||||
print(stock_comment_detail_scrd_focus_em_df)
|
||||
|
||||
stock_comment_detail_scrd_desire_em_df = stock_comment_detail_scrd_desire_em(
|
||||
symbol="600000"
|
||||
)
|
||||
print(stock_comment_detail_scrd_desire_em_df)
|
||||
+183
@@ -0,0 +1,183 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/7/16 22:00
|
||||
Desc: 富途牛牛-主题投资-概念板块-成分股
|
||||
https://www.futunn.com/quote/sparks-us
|
||||
"""
|
||||
|
||||
import json
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
|
||||
def _stock_concept_cons_futu(symbol: str = "巴菲特持仓") -> pd.DataFrame:
|
||||
"""
|
||||
富途牛牛-主题投资-概念板块-成分股
|
||||
https://www.futunn.com/quote/sparks-us
|
||||
:param symbol: 板块名称; choice of {"巴菲特持仓", "佩洛西持仓"}
|
||||
:type symbol: str
|
||||
:return: 概念板块
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"巴菲特持仓": "BK2999",
|
||||
"佩洛西持仓": "BK20883",
|
||||
}
|
||||
url = f"https://www.futunn.com/stock/{symbol_map[symbol]}"
|
||||
# 定义查询参数
|
||||
params = {"global_content": json.dumps({"promote_id": 13766, "sub_promote_id": 24})}
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
temp_code_name = [
|
||||
item.find_all("div", attrs={"class": "fix-left"})
|
||||
for item in soup.find(name="div", attrs={"class": "content-main"}).find_all("a")
|
||||
]
|
||||
temp_value_list = [
|
||||
item.find_all("div", attrs={"class": "middle"})
|
||||
for item in soup.find(name="div", attrs={"class": "content-main"}).find_all("a")
|
||||
]
|
||||
code_name_list = [item[0].find_all("span") for item in temp_code_name]
|
||||
|
||||
quant_list = [item[0].find_all("span") for item in temp_value_list]
|
||||
temp_df = pd.DataFrame(
|
||||
[
|
||||
[item[0]["title"] for item in code_name_list],
|
||||
[item[1]["title"] for item in code_name_list],
|
||||
[item[0]["title"] for item in quant_list],
|
||||
[item[1]["title"] for item in quant_list],
|
||||
[item[2]["title"] for item in quant_list],
|
||||
[item[3]["title"] for item in quant_list],
|
||||
[item[4]["title"] for item in quant_list],
|
||||
[item[5]["title"] for item in quant_list],
|
||||
[item[6]["title"] for item in quant_list],
|
||||
[item[7]["title"] for item in quant_list],
|
||||
[item[8]["title"] for item in quant_list],
|
||||
[item[9]["title"] for item in quant_list],
|
||||
[item[10]["title"] for item in quant_list],
|
||||
[item[11]["title"] for item in quant_list],
|
||||
[item[12]["title"] for item in quant_list],
|
||||
[item[13]["title"] for item in quant_list],
|
||||
[item[14]["title"] for item in quant_list],
|
||||
]
|
||||
).T
|
||||
temp_df.columns = [
|
||||
"代码",
|
||||
"股票名称",
|
||||
"最新价",
|
||||
"涨跌额",
|
||||
"涨跌幅",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"代码",
|
||||
"股票名称",
|
||||
"最新价",
|
||||
"涨跌额",
|
||||
"涨跌幅",
|
||||
"成交量",
|
||||
"成交额",
|
||||
]
|
||||
]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_concept_cons_futu(symbol: str = "特朗普概念股") -> pd.DataFrame:
|
||||
"""
|
||||
富途牛牛-主题投资-概念板块-成分股
|
||||
https://www.futunn.com/quote/sparks-us
|
||||
:param symbol: 板块名称; choice of {"巴菲特持仓", "佩洛西持仓", "特朗普概念股"}
|
||||
:type symbol: str
|
||||
:return: 概念板块
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
if symbol == "特朗普概念股":
|
||||
url = "https://www.futunn.com/quote-api/quote-v2/get-plate-stock"
|
||||
params = {
|
||||
"marketType": "2",
|
||||
"plateId": "10102960",
|
||||
"page": "0",
|
||||
"pageSize": "30",
|
||||
}
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/126.0.0.0 Safari/537.36",
|
||||
"Quote-Token": "7f74cd2a5e",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
total_page = data_json["data"]["pagination"]["pageCount"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in range(0, total_page):
|
||||
params.update(
|
||||
{
|
||||
"page": page,
|
||||
}
|
||||
)
|
||||
if page == 1:
|
||||
headers.update({"Quote-Token": "a3043d6fed"})
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"stockCode": "代码",
|
||||
"name": "股票名称",
|
||||
"price": "最新价",
|
||||
"change": "涨跌额",
|
||||
"changeRatio": "涨跌幅",
|
||||
"tradeVolumn": "成交量",
|
||||
"tradeTrunover": "成交额",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"股票名称",
|
||||
"最新价",
|
||||
"涨跌额",
|
||||
"涨跌幅",
|
||||
"成交量",
|
||||
"成交额",
|
||||
]
|
||||
]
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["涨跌额"] = pd.to_numeric(big_df["涨跌额"], errors="coerce")
|
||||
return big_df
|
||||
else:
|
||||
temp_df = _stock_concept_cons_futu(symbol)
|
||||
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
|
||||
temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_concept_cons_futu_df = stock_concept_cons_futu(symbol="特朗普概念股")
|
||||
print(stock_concept_cons_futu_df)
|
||||
|
||||
stock_concept_cons_futu_df = stock_concept_cons_futu(symbol="巴菲特持仓")
|
||||
print(stock_concept_cons_futu_df)
|
||||
|
||||
stock_concept_cons_futu_df = stock_concept_cons_futu(symbol="佩洛西持仓")
|
||||
print(stock_concept_cons_futu_df)
|
||||
+47
@@ -0,0 +1,47 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2023/4/7 15:05
|
||||
Desc: 乐咕乐股-大盘拥挤度
|
||||
https://legulegu.com/stockdata/ashares-congestion
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_a_congestion_lg() -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-大盘拥挤度
|
||||
https://legulegu.com/stockdata/ashares-congestion
|
||||
:return: 大盘拥挤度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://legulegu.com/api/stockdata/ashares-congestion"
|
||||
token = get_token_lg()
|
||||
params = {"token": token}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/ashares-congestion"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["items"])
|
||||
temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"date",
|
||||
"close",
|
||||
"congestion",
|
||||
]
|
||||
]
|
||||
temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce")
|
||||
temp_df["congestion"] = pd.to_numeric(temp_df["congestion"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_a_congestion_lg_df = stock_a_congestion_lg()
|
||||
print(stock_a_congestion_lg_df)
|
||||
@@ -0,0 +1,313 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/11/4 18:00
|
||||
Desc: 东方财富网-概念板-行情中心-日K-筹码分布
|
||||
https://quote.eastmoney.com/concept/sz000001.html
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
import pandas as pd
|
||||
import py_mini_racer
|
||||
import requests
|
||||
|
||||
|
||||
def stock_cyq_em(symbol: str = "000001", adjust: str = "") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-概念板-行情中心-日K-筹码分布
|
||||
https://quote.eastmoney.com/concept/sz000001.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"}
|
||||
:type adjust: str
|
||||
:return: 筹码分布
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
html_str = """
|
||||
// @ts-nocheck
|
||||
|
||||
/**
|
||||
* 计算分布及相关指标
|
||||
* @param {number} index 当前选中的K线的索引
|
||||
* @return {{x: Array.<number>, y: Array.<number>}}
|
||||
*/
|
||||
/**
|
||||
this.range = 120;
|
||||
*/
|
||||
function CYQCalculator(index, klinedata) {
|
||||
var maxprice = 0;
|
||||
var minprice = 0;
|
||||
var factor = 150;
|
||||
var start = this.range ? Math.max(0, index - this.range + 1) : 0;
|
||||
/**
|
||||
* K图数据[time,open,close,high,low,volume,amount,amplitude,turnoverRate]
|
||||
*/
|
||||
var kdata = klinedata.slice(start, Math.max(1, index + 1));
|
||||
if (kdata.length === 0) throw 'invaild index';
|
||||
for (var i = 0; i < kdata.length; i++) {
|
||||
var elements = kdata[i];
|
||||
maxprice = !maxprice ? elements.high : Math.max(maxprice, elements.high);
|
||||
minprice = !minprice ? elements.low : Math.min(minprice, elements.low);
|
||||
}
|
||||
|
||||
// 精度不小于0.01 产品逻辑
|
||||
var accuracy = Math.max(0.01, (maxprice - minprice) / (factor - 1));
|
||||
/**
|
||||
* 值域
|
||||
* @type {Array.<number>}
|
||||
*/
|
||||
var yrange = [];
|
||||
for (var i = 0; i < factor; i++) {
|
||||
yrange.push((minprice + accuracy * i).toFixed(2) / 1);
|
||||
}
|
||||
/**
|
||||
* 横轴数据
|
||||
*/
|
||||
var xdata = createNumberArray(factor);
|
||||
|
||||
for (var i = 0; i < kdata.length; i++) {
|
||||
var eles = kdata[i];
|
||||
|
||||
var open = eles.open,
|
||||
close = eles.close,
|
||||
high = eles.high,
|
||||
low = eles.low,
|
||||
avg = (open + close + high + low) / 4,
|
||||
turnoverRate = Math.min(1, eles.hsl / 100 || 0);
|
||||
|
||||
var H = Math.floor((high - minprice) / accuracy),
|
||||
L = Math.ceil((low - minprice) / accuracy),
|
||||
// G点坐标, 一字板时, X为进度因子
|
||||
GPoint = [high == low ? factor - 1 : 2 / (high - low), Math.floor((avg - minprice) / accuracy)];
|
||||
// 衰减
|
||||
for (var n = 0; n < xdata.length; n++) {
|
||||
xdata[n] *= (1 - turnoverRate);
|
||||
}
|
||||
|
||||
if (high == low) {
|
||||
// 一字板时，画矩形面积是三角形的2倍
|
||||
xdata[GPoint[1]] += GPoint[0] * turnoverRate / 2;
|
||||
} else {
|
||||
for (var j = L; j <= H; j++) {
|
||||
var curprice = minprice + accuracy * j;
|
||||
if (curprice <= avg) {
|
||||
// 上半三角叠加分布分布
|
||||
if (Math.abs(avg - low) < 1e-8) {
|
||||
xdata[j] += GPoint[0] * turnoverRate;
|
||||
} else {
|
||||
xdata[j] += (curprice - low) / (avg - low) * GPoint[0] * turnoverRate;
|
||||
}
|
||||
} else {
|
||||
// 下半三角叠加分布分布
|
||||
if (Math.abs(high - avg) < 1e-8) {
|
||||
xdata[j] += GPoint[0] * turnoverRate;
|
||||
} else {
|
||||
xdata[j] += (high - curprice) / (high - avg) * GPoint[0] * turnoverRate;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
}
|
||||
|
||||
|
||||
var currentprice = klinedata[index].close;
|
||||
var totalChips = 0;
|
||||
for (var i = 0; i < factor; i++) {
|
||||
var x = xdata[i].toPrecision(12) / 1;
|
||||
//if (x < 0) xdata[i] = 0;
|
||||
totalChips += x;
|
||||
}
|
||||
var result = new CYQData();
|
||||
result.x = xdata;
|
||||
result.y = yrange;
|
||||
result.benefitPart = result.getBenefitPart(currentprice);
|
||||
result.avgCost = getCostByChip(totalChips * 0.5).toFixed(2);
|
||||
result.percentChips = {
|
||||
'90': result.computePercentChips(0.9),
|
||||
'70': result.computePercentChips(0.7)
|
||||
};
|
||||
return result;
|
||||
|
||||
/**
|
||||
* 获取指定筹码处的成本
|
||||
* @param {number} chip 堆叠筹码
|
||||
*/
|
||||
function getCostByChip(chip) {
|
||||
var result = 0,
|
||||
sum = 0;
|
||||
for (var i = 0; i < factor; i++) {
|
||||
var x = xdata[i].toPrecision(12) / 1;
|
||||
if (sum + x > chip) {
|
||||
result = minprice + i * accuracy;
|
||||
break;
|
||||
}
|
||||
sum += x;
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/**
|
||||
* 筹码分布数据
|
||||
*/
|
||||
function CYQData() {
|
||||
/**
|
||||
* 筹码堆叠
|
||||
* @type {Array.<number>}
|
||||
*/
|
||||
this.x = arguments[0];
|
||||
/**
|
||||
* 价格分布
|
||||
* @type {Array.<number>}
|
||||
*/
|
||||
this.y = arguments[1];
|
||||
/**
|
||||
* 获利比例
|
||||
* @type {number}
|
||||
*/
|
||||
this.benefitPart = arguments[2];
|
||||
/**
|
||||
* 平均成本
|
||||
* @type {number}
|
||||
*/
|
||||
this.avgCost = arguments[3];
|
||||
/**
|
||||
* 百分比筹码
|
||||
* @type {{Object.<string, {{priceRange: number[], concentration: number}}>}}
|
||||
*/
|
||||
this.percentChips = arguments[4];
|
||||
/**
|
||||
* 计算指定百分比的筹码
|
||||
* @param {number} percent 百分比大于0，小于1
|
||||
*/
|
||||
this.computePercentChips = function (percent) {
|
||||
if (percent > 1 || percent < 0) throw 'argument "percent" out of range';
|
||||
var ps = [(1 - percent) / 2, (1 + percent) / 2];
|
||||
var pr = [getCostByChip(totalChips * ps[0]), getCostByChip(totalChips * ps[1])];
|
||||
return {
|
||||
priceRange: [pr[0].toFixed(2), pr[1].toFixed(2)],
|
||||
concentration: pr[0] + pr[1] === 0 ? 0 : (pr[1] - pr[0]) / (pr[0] + pr[1])
|
||||
};
|
||||
};
|
||||
/**
|
||||
* 获取指定价格的获利比例
|
||||
* @param {number} price 价格
|
||||
*/
|
||||
this.getBenefitPart = function (price) {
|
||||
var below = 0;
|
||||
for (var i = 0; i < factor; i++) {
|
||||
var x = xdata[i].toPrecision(12) / 1;
|
||||
if (price >= minprice + i * accuracy) {
|
||||
below += x;
|
||||
}
|
||||
}
|
||||
return totalChips == 0 ? 0 : below / totalChips;
|
||||
};
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
function createNumberArray(count) {
|
||||
var array = [];
|
||||
for (var i = 0; i < count; i++) {
|
||||
array.push(0);
|
||||
}
|
||||
return array;
|
||||
}
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_code.eval(html_str)
|
||||
adjust_dict = {"qfq": "1", "hfq": "2", "": "0"}
|
||||
market_code = 1 if symbol.startswith("6") else 0
|
||||
url = "https://push2his.eastmoney.com/api/qt/stock/kline/get"
|
||||
params = {
|
||||
"secid": f"{market_code}.{symbol}",
|
||||
"fields1": "f1,f2,f3,f4,f5,f6",
|
||||
"fields2": "f51,f52,f53,f54,f55,f56,f57,f58,f59,f60,f61",
|
||||
"klt": "101",
|
||||
"fqt": adjust_dict[adjust],
|
||||
"end": datetime.now().date().strftime("%Y%m%d"),
|
||||
"lmt": "210",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame([item.split(",") for item in data_json["data"]["klines"]])
|
||||
temp_df.columns = [
|
||||
"date",
|
||||
"open",
|
||||
"close",
|
||||
"high",
|
||||
"low",
|
||||
"volume",
|
||||
"volume_money",
|
||||
"zf",
|
||||
"zdf",
|
||||
"zde",
|
||||
"hsl",
|
||||
]
|
||||
for item in temp_df.columns[1:]:
|
||||
temp_df[item] = pd.to_numeric(temp_df[item])
|
||||
temp_df["index"] = range(0, len(temp_df))
|
||||
records = temp_df.to_dict(orient="records")
|
||||
date_list = []
|
||||
benefit_part = []
|
||||
avg_cost = []
|
||||
pct_70_low = []
|
||||
pct_70_high = []
|
||||
pct_90_low = []
|
||||
pct_90_high = []
|
||||
pct_70_con = []
|
||||
pct_90_con = []
|
||||
for i in range(0, len(records)):
|
||||
mcode = js_code.call("CYQCalculator", i, records)
|
||||
date_list.append(records[i]["date"])
|
||||
benefit_part.append(mcode["benefitPart"])
|
||||
avg_cost.append(mcode["avgCost"])
|
||||
pct_70_low.append(mcode["percentChips"]["70"]["priceRange"][0])
|
||||
pct_70_high.append(mcode["percentChips"]["70"]["priceRange"][1])
|
||||
pct_90_low.append(mcode["percentChips"]["90"]["priceRange"][0])
|
||||
pct_90_high.append(mcode["percentChips"]["90"]["priceRange"][1])
|
||||
pct_70_con.append(mcode["percentChips"]["70"]["concentration"])
|
||||
pct_90_con.append(mcode["percentChips"]["90"]["concentration"])
|
||||
temp_df = pd.DataFrame(
|
||||
[
|
||||
date_list,
|
||||
benefit_part,
|
||||
avg_cost,
|
||||
pct_90_low,
|
||||
pct_90_high,
|
||||
pct_90_con,
|
||||
pct_70_low,
|
||||
pct_70_high,
|
||||
pct_70_con,
|
||||
]
|
||||
).T
|
||||
temp_df.columns = [
|
||||
"日期",
|
||||
"获利比例",
|
||||
"平均成本",
|
||||
"90成本-低",
|
||||
"90成本-高",
|
||||
"90集中度",
|
||||
"70成本-低",
|
||||
"70成本-高",
|
||||
"70集中度",
|
||||
]
|
||||
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
|
||||
temp_df["获利比例"] = pd.to_numeric(temp_df["获利比例"], errors="coerce")
|
||||
temp_df["平均成本"] = pd.to_numeric(temp_df["平均成本"], errors="coerce")
|
||||
temp_df["90成本-低"] = pd.to_numeric(temp_df["90成本-低"], errors="coerce")
|
||||
temp_df["90成本-高"] = pd.to_numeric(temp_df["90成本-高"], errors="coerce")
|
||||
temp_df["90集中度"] = pd.to_numeric(temp_df["90集中度"], errors="coerce")
|
||||
temp_df["70成本-低"] = pd.to_numeric(temp_df["70成本-低"], errors="coerce")
|
||||
temp_df["70成本-高"] = pd.to_numeric(temp_df["70成本-高"], errors="coerce")
|
||||
temp_df["70集中度"] = pd.to_numeric(temp_df["70集中度"], errors="coerce")
|
||||
temp_df = temp_df.iloc[-90:, :].copy()
|
||||
temp_df.reset_index(inplace=True, drop=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_cyq_em_df = stock_cyq_em(symbol="000001", adjust="")
|
||||
print(stock_cyq_em_df)
|
||||
+298
@@ -0,0 +1,298 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/1 20:00
|
||||
Desc: 巨潮资讯-首页-公告查询-信息披露
|
||||
http://www.cninfo.com.cn/new/commonUrl/pageOfSearch?url=disclosure/list/search
|
||||
"""
|
||||
|
||||
import math
|
||||
from functools import lru_cache
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def __get_category_dict() -> dict:
|
||||
"""
|
||||
获取巨潮资讯-首页-公告查询-信息披露-类别字典
|
||||
http://www.cninfo.com.cn/new/js/app/disclosure/notice/history-notice.js?v=20231124083101
|
||||
:return: dict
|
||||
:rtype: dict
|
||||
"""
|
||||
big_dict = {
|
||||
"年报": "category_ndbg_szsh",
|
||||
"半年报": "category_bndbg_szsh",
|
||||
"一季报": "category_yjdbg_szsh",
|
||||
"三季报": "category_sjdbg_szsh",
|
||||
"业绩预告": "category_yjygjxz_szsh",
|
||||
"权益分派": "category_qyfpxzcs_szsh",
|
||||
"董事会": "category_dshgg_szsh",
|
||||
"监事会": "category_jshgg_szsh",
|
||||
"股东大会": "category_gddh_szsh",
|
||||
"日常经营": "category_rcjy_szsh",
|
||||
"公司治理": "category_gszl_szsh",
|
||||
"中介报告": "category_zj_szsh",
|
||||
"首发": "category_sf_szsh",
|
||||
"增发": "category_zf_szsh",
|
||||
"股权激励": "category_gqjl_szsh",
|
||||
"配股": "category_pg_szsh",
|
||||
"解禁": "category_jj_szsh",
|
||||
"公司债": "category_gszq_szsh",
|
||||
"可转债": "category_kzzq_szsh",
|
||||
"其他融资": "category_qtrz_szsh",
|
||||
"股权变动": "category_gqbd_szsh",
|
||||
"补充更正": "category_bcgz_szsh",
|
||||
"澄清致歉": "category_cqdq_szsh",
|
||||
"风险提示": "category_fxts_szsh",
|
||||
"特别处理和退市": "category_tbclts_szsh",
|
||||
"退市整理期": "category_tszlq_szsh",
|
||||
}
|
||||
return big_dict
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def __get_stock_json(symbol: str = "沪深京") -> dict:
|
||||
"""
|
||||
获取巨潮资讯-首页-公告查询-信息披露-股票代码字典
|
||||
:param symbol: choice of {"沪深京", "港股", "三板", "基金", "债券"}
|
||||
:type symbol: str
|
||||
:return: 股票代码字典
|
||||
:rtype: dict
|
||||
"""
|
||||
url = "http://www.cninfo.com.cn/new/data/szse_stock.json"
|
||||
if symbol == "沪深京":
|
||||
url = "http://www.cninfo.com.cn/new/data/szse_stock.json"
|
||||
elif symbol == "港股":
|
||||
url = "http://www.cninfo.com.cn/new/data/hke_stock.json"
|
||||
elif symbol == "三板":
|
||||
url = "http://www.cninfo.com.cn/new/data/gfzr_stock.json"
|
||||
elif symbol == "基金":
|
||||
url = "http://www.cninfo.com.cn/new/data/fund_stock.json"
|
||||
elif symbol == "债券":
|
||||
url = "http://www.cninfo.com.cn/new/data/bond_stock.json"
|
||||
r = requests.get(url)
|
||||
text_json = r.json()
|
||||
temp_df = pd.DataFrame([item for item in text_json["stockList"]])
|
||||
return dict(zip(temp_df["code"], temp_df["orgId"]))
|
||||
|
||||
|
||||
def stock_zh_a_disclosure_report_cninfo(
|
||||
symbol: str = "000001",
|
||||
market: str = "沪深京",
|
||||
keyword: str = "",
|
||||
category: str = "",
|
||||
start_date: str = "20230618",
|
||||
end_date: str = "20231219",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
巨潮资讯-首页-公告查询-信息披露公告
|
||||
http://www.cninfo.com.cn/new/commonUrl/pageOfSearch?url=disclosure/list/search
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param market: choice of {"沪深京", "港股", "三板", "基金", "债券", "监管", "预披露"}
|
||||
:type market: str
|
||||
:param keyword: 关键词
|
||||
:type keyword: str
|
||||
:param category: choice of {'年报', '半年报', '一季报', '三季报', '业绩预告', '权益分派',
|
||||
'董事会', '监事会', '股东大会', '日常经营', '公司治理', '中介报告',
|
||||
'首发', '增发', '股权激励', '配股', '解禁', '公司债', '可转债', '其他融资',
|
||||
'股权变动', '补充更正', '澄清致歉', '风险提示', '特别处理和退市', '退市整理期'}
|
||||
:type category: str
|
||||
:param start_date: 开始时间
|
||||
:type start_date: str
|
||||
:param end_date: 开始时间
|
||||
:type end_date: str
|
||||
:return: 指定 symbol 的数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
column_map = {
|
||||
"沪深京": "szse",
|
||||
"港股": "hke",
|
||||
"三板": "third",
|
||||
"基金": "fund",
|
||||
"债券": "bond",
|
||||
"监管": "regulator",
|
||||
"预披露": "pre_disclosure",
|
||||
}
|
||||
stock_id_map = ""
|
||||
if market == "沪深京" or "基金":
|
||||
stock_id_map = __get_stock_json(market)
|
||||
category_dict = __get_category_dict()
|
||||
url = "http://www.cninfo.com.cn/new/hisAnnouncement/query"
|
||||
stock_item = "" if symbol == "" else f"{symbol},{stock_id_map[symbol]}"
|
||||
category_item = "" if category == "" else f"{category_dict[category]}"
|
||||
payload = {
|
||||
"pageNum": "1",
|
||||
"pageSize": "30",
|
||||
"column": column_map[market],
|
||||
"tabName": "fulltext",
|
||||
"plate": "",
|
||||
"stock": stock_item,
|
||||
"searchkey": keyword,
|
||||
"secid": "",
|
||||
"category": category_item,
|
||||
"trade": "",
|
||||
"seDate": f"{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}~"
|
||||
f"{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}",
|
||||
"sortName": "",
|
||||
"sortType": "",
|
||||
"isHLtitle": "true",
|
||||
}
|
||||
r = requests.post(url, params=payload)
|
||||
text_json = r.json()
|
||||
page_num = math.ceil(int(text_json["totalAnnouncement"]) / 30)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
payload.update({"pageNum": page})
|
||||
r = requests.post(url, data=payload)
|
||||
text_json = r.json()
|
||||
temp_df = pd.DataFrame(text_json["announcements"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"secCode": "代码",
|
||||
"secName": "简称",
|
||||
"announcementTitle": "公告标题",
|
||||
"announcementTime": "公告时间",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[["代码", "简称", "公告标题", "公告时间", "announcementId", "orgId"]]
|
||||
big_df["公告时间"] = pd.to_datetime(
|
||||
big_df["公告时间"], unit="ms", utc=True, errors="coerce"
|
||||
)
|
||||
big_df["公告时间"] = (
|
||||
big_df["公告时间"]
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.tz_localize(None)
|
||||
.astype(str)
|
||||
)
|
||||
url_list = []
|
||||
for item in zip(
|
||||
big_df["代码"], big_df["announcementId"], big_df["orgId"], big_df["公告时间"]
|
||||
):
|
||||
url_format = (
|
||||
f"http://www.cninfo.com.cn/new/disclosure/detail?stockCode={item[0]}&"
|
||||
f"announcementId={item[1]}&orgId={item[2]}&announcementTime={item[3]}"
|
||||
)
|
||||
url_list.append(url_format)
|
||||
big_df["公告链接"] = url_list
|
||||
big_df = big_df[["代码", "简称", "公告标题", "公告时间", "公告链接"]]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_zh_a_disclosure_relation_cninfo(
|
||||
symbol: str = "000001",
|
||||
market: str = "沪深京",
|
||||
start_date: str = "20230618",
|
||||
end_date: str = "20231219",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
巨潮资讯-首页-数据-预约披露调研
|
||||
http://www.cninfo.com.cn/new/commonUrl?url=data/yypl
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param market: choice of {"沪深京", "港股", "三板", "基金", "债券", "监管", "预披露"}
|
||||
:type market: str
|
||||
:param start_date: 开始时间
|
||||
:type start_date: str
|
||||
:param end_date: 开始时间
|
||||
:type end_date: str
|
||||
:return: 指定 symbol 的数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
column_map = {
|
||||
"沪深京": "szse",
|
||||
"港股": "hke",
|
||||
"三板": "third",
|
||||
"基金": "fund",
|
||||
"债券": "bond",
|
||||
"监管": "regulator",
|
||||
"预披露": "pre_disclosure",
|
||||
}
|
||||
stock_id_map = ""
|
||||
if market == "沪深京":
|
||||
stock_id_map = __get_stock_json(symbol)
|
||||
stock_item = "" if symbol == "" else f"{symbol},{stock_id_map[symbol]}"
|
||||
url = "http://www.cninfo.com.cn/new/hisAnnouncement/query"
|
||||
payload = {
|
||||
"pageNum": "1",
|
||||
"pageSize": "30",
|
||||
"column": column_map[market],
|
||||
"tabName": "relation",
|
||||
"plate": "",
|
||||
"stock": stock_item,
|
||||
"searchkey": "",
|
||||
"secid": "",
|
||||
"category": "",
|
||||
"trade": "",
|
||||
"seDate": f"{'-'.join([start_date[:4], start_date[4:6], start_date[6:]])}~"
|
||||
f"{'-'.join([end_date[:4], end_date[4:6], end_date[6:]])}",
|
||||
"sortName": "",
|
||||
"sortType": "",
|
||||
"isHLtitle": "true",
|
||||
}
|
||||
r = requests.post(url, data=payload)
|
||||
text_json = r.json()
|
||||
page_num = math.ceil(int(text_json["totalAnnouncement"]) / 30)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
payload.update({"pageNum": page})
|
||||
r = requests.post(url, data=payload)
|
||||
text_json = r.json()
|
||||
temp_df = pd.DataFrame(text_json["announcements"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"secCode": "代码",
|
||||
"secName": "简称",
|
||||
"announcementTitle": "公告标题",
|
||||
"announcementTime": "公告时间",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[["代码", "简称", "公告标题", "公告时间", "announcementId", "orgId"]]
|
||||
big_df["公告时间"] = pd.to_datetime(
|
||||
big_df["公告时间"], unit="ms", utc=True, errors="coerce"
|
||||
)
|
||||
big_df["公告时间"] = (
|
||||
big_df["公告时间"]
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.tz_convert(None)
|
||||
.astype(str)
|
||||
)
|
||||
url_list = []
|
||||
for item in zip(
|
||||
big_df["代码"], big_df["announcementId"], big_df["orgId"], big_df["公告时间"]
|
||||
):
|
||||
url_format = (
|
||||
f"http://www.cninfo.com.cn/new/disclosure/detail?stockCode={item[0]}"
|
||||
f"&announcementId={item[1]}&orgId={item[2]}&announcementTime={item[3]}"
|
||||
)
|
||||
url_list.append(url_format)
|
||||
big_df["公告链接"] = url_list
|
||||
big_df = big_df[["代码", "简称", "公告标题", "公告时间", "公告链接"]]
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zh_a_disclosure_report_cninfo_df = stock_zh_a_disclosure_report_cninfo(
|
||||
symbol="164701",
|
||||
market="基金",
|
||||
keyword="大模型",
|
||||
category="",
|
||||
start_date="20240422",
|
||||
end_date="20250422",
|
||||
)
|
||||
print(stock_zh_a_disclosure_report_cninfo_df)
|
||||
|
||||
stock_zh_a_disclosure_relation_cninfo_df = stock_zh_a_disclosure_relation_cninfo(
|
||||
symbol="000001", market="沪深京", start_date="20230619", end_date="20231220"
|
||||
)
|
||||
print(stock_zh_a_disclosure_relation_cninfo_df)
|
||||
@@ -0,0 +1,432 @@
|
||||
#!/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
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_dxsyl_em() -> pd.DataFrame:
|
||||
"""
|
||||
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
|
||||
:return: 打新收益率数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "LISTING_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPTA_APP_IPOAPPLY",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE,f14~01~SECURITY_CODE",
|
||||
"quoteType": "0",
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": """((APPLY_DATE>'2010-01-01')(|@APPLY_DATE="NULL"))((LISTING_DATE>'2010-01-01')(|@LISTING_DATE="NULL"))(TRADE_MARKET_CODE!="069001017")""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"f14": "股票简称",
|
||||
"ISSUE_PRICE": "发行价",
|
||||
"LATELY_PRICE": "最新价",
|
||||
"ONLINE_ISSUE_LWR": "网上-发行中签率",
|
||||
"ONLINE_VA_SHARES": "网上-有效申购股数",
|
||||
"ONLINE_VA_NUM": "网上-有效申购户数",
|
||||
"ONLINE_ES_MULTIPLE": "网上-超额认购倍数",
|
||||
"OFFLINE_VAP_RATIO": "网下-配售中签率",
|
||||
"OFFLINE_VATS": "网下-有效申购股数",
|
||||
"OFFLINE_VAP_OBJECT": "网下-有效申购户数",
|
||||
"OFFLINE_VAS_MULTIPLE": "网下-配售认购倍数",
|
||||
"ISSUE_NUM": "总发行数量",
|
||||
"LD_OPEN_PREMIUM": "开盘溢价",
|
||||
"LD_CLOSE_CHANGE": "首日涨幅",
|
||||
"LISTING_DATE": "上市日期",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"发行价",
|
||||
"最新价",
|
||||
"网上-发行中签率",
|
||||
"网上-有效申购股数",
|
||||
"网上-有效申购户数",
|
||||
"网上-超额认购倍数",
|
||||
"网下-配售中签率",
|
||||
"网下-有效申购股数",
|
||||
"网下-有效申购户数",
|
||||
"网下-配售认购倍数",
|
||||
"总发行数量",
|
||||
"开盘溢价",
|
||||
"首日涨幅",
|
||||
"上市日期",
|
||||
]
|
||||
]
|
||||
big_df["发行价"] = pd.to_numeric(big_df["发行价"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["网上-发行中签率"] = pd.to_numeric(
|
||||
big_df["网上-发行中签率"], errors="coerce"
|
||||
)
|
||||
big_df["网上-有效申购股数"] = pd.to_numeric(
|
||||
big_df["网上-有效申购股数"], errors="coerce"
|
||||
)
|
||||
big_df["网上-有效申购户数"] = pd.to_numeric(
|
||||
big_df["网上-有效申购户数"], errors="coerce"
|
||||
)
|
||||
big_df["网上-超额认购倍数"] = pd.to_numeric(
|
||||
big_df["网上-超额认购倍数"], errors="coerce"
|
||||
)
|
||||
big_df["网下-配售中签率"] = pd.to_numeric(
|
||||
big_df["网下-配售中签率"], errors="coerce"
|
||||
)
|
||||
big_df["网下-有效申购股数"] = pd.to_numeric(
|
||||
big_df["网下-有效申购股数"], errors="coerce"
|
||||
)
|
||||
big_df["网下-有效申购户数"] = pd.to_numeric(
|
||||
big_df["网下-有效申购户数"], errors="coerce"
|
||||
)
|
||||
big_df["网下-配售认购倍数"] = pd.to_numeric(
|
||||
big_df["网下-配售认购倍数"], errors="coerce"
|
||||
)
|
||||
big_df["总发行数量"] = pd.to_numeric(big_df["总发行数量"], errors="coerce")
|
||||
big_df["开盘溢价"] = pd.to_numeric(big_df["开盘溢价"], errors="coerce")
|
||||
big_df["首日涨幅"] = pd.to_numeric(big_df["首日涨幅"], errors="coerce")
|
||||
big_df["上市日期"] = pd.to_datetime(big_df["上市日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
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"
|
||||
if symbol == "北交所":
|
||||
params = {
|
||||
"sortColumns": "APPLY_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"reportName": "RPT_NEEQ_ISSUEINFO_LIST",
|
||||
"quoteColumns": "f14~01~SECURITY_CODE~SECURITY_NAME_ABBR",
|
||||
"source": "NEEQSELECT",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, 1 + int(total_page)), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"ORG_CODE": "-",
|
||||
"SECURITY_CODE": "代码",
|
||||
"SECUCODE": "带市场标识股票代码",
|
||||
"SECURITY_NAME_ABBR": "简称",
|
||||
"APPLY_CODE": "申购代码",
|
||||
"EXPECT_ISSUE_NUM": "发行总数",
|
||||
"PRICE_WAY": "定价方式",
|
||||
"ISSUE_PRICE": "发行价格",
|
||||
"ISSUE_PE_RATIO": "发行市盈率",
|
||||
"APPLY_DATE": "申购日",
|
||||
"RESULT_NOTICE_DATE": "发行结果公告日期",
|
||||
"SELECT_LISTING_DATE": "上市首日-上市日",
|
||||
"ONLINE_ISSUE_NUM": "网上-发行数量",
|
||||
"APPLY_AMT_UPPER": "网上-顶格所需资金",
|
||||
"APPLY_NUM_UPPER": "网上-申购上限",
|
||||
"ONLINE_PAY_DATE": "网上申购缴款日期",
|
||||
"ONLINE_REFUND_DATE": "网上申购资金退款日",
|
||||
"INFO_CODE": "-",
|
||||
"ONLINE_ISSUE_LWR": "中签率",
|
||||
"NEWEST_PRICE": "最新价格-价格",
|
||||
"CLOSE_PRICE": "首日收盘价",
|
||||
"INITIAL_MULTIPLE": "-",
|
||||
"PER_SHARES_INCOME": "上市首日-每百股获利",
|
||||
"LD_CLOSE_CHANGE": "上市首日-涨幅",
|
||||
"TURNOVERRATE": "首日换手率",
|
||||
"AMPLITUDE": "首日振幅",
|
||||
"ONLINE_APPLY_LOWER": "-",
|
||||
"MAIN_BUSINESS": "主营业务",
|
||||
"INDUSTRY_PE_RATIO": "行业市盈率",
|
||||
"APPLY_AMT_100": "稳获百股需配资金",
|
||||
"TAKE_UP_TIME": "资金占用时间",
|
||||
"CAPTURE_PROFIT": "上市首日-约合年化收益",
|
||||
"APPLY_SHARE_100": "每获配百股需配股数",
|
||||
"AVERAGE_PRICE": "上市首日-均价",
|
||||
"ORG_VAN": "参与申购人数",
|
||||
"VA_AMT": "参与申购资金",
|
||||
"ISSUE_PRICE_ADJFACTOR": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df["最新价格-累计涨幅"] = big_df["首日收盘价"] / big_df["最新价格-价格"]
|
||||
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"简称",
|
||||
"申购代码",
|
||||
"发行总数",
|
||||
"网上-发行数量",
|
||||
"网上-申购上限",
|
||||
"网上-顶格所需资金",
|
||||
"发行价格",
|
||||
"申购日",
|
||||
"中签率",
|
||||
"稳获百股需配资金",
|
||||
"最新价格-价格",
|
||||
"最新价格-累计涨幅",
|
||||
"上市首日-上市日",
|
||||
"上市首日-均价",
|
||||
"上市首日-涨幅",
|
||||
"上市首日-每百股获利",
|
||||
"上市首日-约合年化收益",
|
||||
"发行市盈率",
|
||||
"行业市盈率",
|
||||
"参与申购资金",
|
||||
"参与申购人数",
|
||||
]
|
||||
]
|
||||
big_df["发行总数"] = pd.to_numeric(big_df["发行总数"], errors="coerce")
|
||||
big_df["网上-发行数量"] = pd.to_numeric(
|
||||
big_df["网上-发行数量"], errors="coerce"
|
||||
)
|
||||
big_df["网上-申购上限"] = pd.to_numeric(
|
||||
big_df["网上-申购上限"], errors="coerce"
|
||||
)
|
||||
big_df["网上-顶格所需资金"] = pd.to_numeric(
|
||||
big_df["网上-顶格所需资金"], errors="coerce"
|
||||
)
|
||||
big_df["发行价格"] = pd.to_numeric(big_df["发行价格"], errors="coerce")
|
||||
big_df["中签率"] = pd.to_numeric(big_df["中签率"], errors="coerce")
|
||||
big_df["稳获百股需配资金"] = pd.to_numeric(
|
||||
big_df["稳获百股需配资金"], errors="coerce"
|
||||
)
|
||||
big_df["最新价格-价格"] = pd.to_numeric(
|
||||
big_df["最新价格-价格"], errors="coerce"
|
||||
)
|
||||
big_df["最新价格-累计涨幅"] = pd.to_numeric(
|
||||
big_df["最新价格-累计涨幅"], errors="coerce"
|
||||
)
|
||||
big_df["上市首日-均价"] = pd.to_numeric(
|
||||
big_df["上市首日-均价"], errors="coerce"
|
||||
)
|
||||
big_df["上市首日-涨幅"] = pd.to_numeric(
|
||||
big_df["上市首日-涨幅"], errors="coerce"
|
||||
)
|
||||
big_df["上市首日-每百股获利"] = pd.to_numeric(
|
||||
big_df["上市首日-每百股获利"], errors="coerce"
|
||||
)
|
||||
big_df["上市首日-约合年化收益"] = pd.to_numeric(
|
||||
big_df["上市首日-约合年化收益"], 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["参与申购资金"] = pd.to_numeric(big_df["参与申购资金"], errors="coerce")
|
||||
big_df["参与申购人数"] = pd.to_numeric(big_df["参与申购人数"], errors="coerce")
|
||||
big_df["申购日"] = pd.to_datetime(big_df["申购日"], errors="coerce").dt.date
|
||||
big_df["上市首日-上市日"] = pd.to_datetime(
|
||||
big_df["上市首日-上市日"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
else:
|
||||
params = {
|
||||
"sortColumns": "APPLY_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "5000",
|
||||
"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()
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[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": "交易所",
|
||||
"MARKET_TYPE": "板块",
|
||||
"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",
|
||||
"交易所",
|
||||
"板块",
|
||||
"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__":
|
||||
stock_dxsyl_em_df = stock_dxsyl_em()
|
||||
print(stock_dxsyl_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="全部股票")
|
||||
print(stock_xgsglb_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="沪市主板")
|
||||
print(stock_xgsglb_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="科创板")
|
||||
print(stock_xgsglb_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="深市主板")
|
||||
print(stock_xgsglb_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="创业板")
|
||||
print(stock_xgsglb_em_df)
|
||||
|
||||
stock_xgsglb_em_df = stock_xgsglb_em(symbol="北交所")
|
||||
print(stock_xgsglb_em_df)
|
||||
@@ -0,0 +1,59 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/3/19 15:30
|
||||
Desc: 乐咕乐股-股债利差
|
||||
https://legulegu.com/stockdata/equity-bond-spread
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_ebs_lg() -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-股债利差
|
||||
https://legulegu.com/stockdata/equity-bond-spread
|
||||
:return: 股债利差
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://legulegu.com/api/stockdata/equity-bond-spread"
|
||||
token = get_token_lg()
|
||||
params = {"token": token, "code": "000300.SH"}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/equity-bond-spread"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"date": "日期",
|
||||
"close": "沪深300指数",
|
||||
"peSpread": "股债利差",
|
||||
"peSpreadAverage": "股债利差均线",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"日期",
|
||||
"沪深300指数",
|
||||
"股债利差",
|
||||
"股债利差均线",
|
||||
]
|
||||
]
|
||||
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
|
||||
temp_df["沪深300指数"] = pd.to_numeric(temp_df["沪深300指数"], errors="coerce")
|
||||
temp_df["股债利差"] = pd.to_numeric(temp_df["股债利差"], errors="coerce")
|
||||
temp_df["股债利差均线"] = pd.to_numeric(temp_df["股债利差均线"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_ebs_lg_df = stock_ebs_lg()
|
||||
print(stock_ebs_lg_df)
|
||||
@@ -0,0 +1,343 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/7/15 16:30
|
||||
Desc: 新浪财经-ESG评级中心
|
||||
https://finance.sina.com.cn/esg/
|
||||
"""
|
||||
|
||||
import math
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_esg_msci_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-ESG评级中心-ESG评级-MSCI
|
||||
https://finance.sina.com.cn/esg/grade.shtml
|
||||
:return: MSCI
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://global.finance.sina.com.cn/api/openapi.php/EsgService.getMsciEsgStocks?p=1&num=100"
|
||||
r = requests.get(url)
|
||||
data_json = r.json()
|
||||
page_num = math.ceil(int(data_json["result"]["data"]["total"]) / 100)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
headers = {
|
||||
"Referer": "https://finance.sina.com.cn/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/123.0.0.0 Safari/537.36",
|
||||
}
|
||||
url = f"https://global.finance.sina.com.cn/api/openapi.php/EsgService.getMsciEsgStocks?p={page}&num=100"
|
||||
r = requests.get(url, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"agency_id": "-",
|
||||
"agency_name": "评级机构",
|
||||
"symbol": "股票代码",
|
||||
"delist": "-",
|
||||
"comp_code": "-",
|
||||
"name": "股票名称",
|
||||
"market": "交易市场",
|
||||
"industry_code": "行业代码",
|
||||
"industry_name": "行业名称",
|
||||
"sw1_code": "-",
|
||||
"sw1_name": "-",
|
||||
"sw2_code": "-",
|
||||
"sw2_name": "-",
|
||||
"sw3_code": "-",
|
||||
"sw3_name": "-",
|
||||
"hs1_code": "-",
|
||||
"hs1_name": "-",
|
||||
"hs2_code": "-",
|
||||
"hs2_name": "-",
|
||||
"hs3_code": "-",
|
||||
"hs3_name": "-",
|
||||
"factset_sector_code": "-",
|
||||
"factset_sector_name": "-",
|
||||
"factset_industry_code": "-",
|
||||
"factset_industry_name": "-",
|
||||
"date": "-",
|
||||
"quarter_date": "评级日期",
|
||||
"grade": "ESG等级",
|
||||
"score": "-",
|
||||
"env_score": "环境总评",
|
||||
"env_grade": "-",
|
||||
"social_score": "社会责任总评",
|
||||
"social_grade": "-",
|
||||
"governance_score": "治理总评",
|
||||
"governance_grade": "-",
|
||||
"change_status": "-",
|
||||
"updated_time": "更新时间",
|
||||
"created_time": "创建时间",
|
||||
"esg_rating": "ESG评分",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"ESG评分",
|
||||
"环境总评",
|
||||
"社会责任总评",
|
||||
"治理总评",
|
||||
"评级日期",
|
||||
"交易市场",
|
||||
]
|
||||
]
|
||||
big_df["评级日期"] = pd.to_datetime(big_df["评级日期"], errors="coerce").dt.date
|
||||
big_df["环境总评"] = pd.to_numeric(big_df["环境总评"], errors="coerce")
|
||||
big_df["社会责任总评"] = pd.to_numeric(big_df["社会责任总评"], errors="coerce")
|
||||
big_df["治理总评"] = pd.to_numeric(big_df["治理总评"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_esg_rft_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-ESG评级中心-ESG评级-路孚特
|
||||
https://finance.sina.com.cn/esg/grade.shtml
|
||||
:return: 路孚特
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://global.finance.sina.com.cn/api/openapi.php/EsgService.getRftEsgStocks?p=1&num=20000"
|
||||
r = requests.get(url)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame(data_json["result"]["data"]["data"])
|
||||
big_df.rename(
|
||||
columns={
|
||||
"symbol": "股票代码",
|
||||
"esg_score": "ESG评分",
|
||||
"esg_score_date": "ESG评分日期",
|
||||
"env_score": "环境总评",
|
||||
"env_score_date": "环境总评日期",
|
||||
"social_score": "社会责任总评",
|
||||
"social_score_date": "社会责任总评日期",
|
||||
"governance_score": "治理总评",
|
||||
"governance_score_date": "治理总评日期",
|
||||
"zy_score": "争议总评",
|
||||
"zy_score_date": "争议总评日期",
|
||||
"industry": "行业",
|
||||
"exchange": "交易所",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"ESG评分",
|
||||
"ESG评分日期",
|
||||
"环境总评",
|
||||
"环境总评日期",
|
||||
"社会责任总评",
|
||||
"社会责任总评日期",
|
||||
"治理总评",
|
||||
"治理总评日期",
|
||||
"争议总评",
|
||||
"争议总评日期",
|
||||
"行业",
|
||||
"交易所",
|
||||
]
|
||||
]
|
||||
big_df["ESG评分日期"] = pd.to_datetime(
|
||||
big_df["ESG评分日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["环境总评日期"] = pd.to_datetime(
|
||||
big_df["环境总评日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["社会责任总评日期"] = pd.to_datetime(
|
||||
big_df["社会责任总评日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["治理总评日期"] = pd.to_datetime(
|
||||
big_df["治理总评日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["争议总评日期"] = pd.to_datetime(
|
||||
big_df["争议总评日期"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_esg_rate_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-ESG评级中心-ESG评级-ESG评级数据
|
||||
https://finance.sina.com.cn/esg/grade.shtml
|
||||
:return: ESG评级数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://global.finance.sina.com.cn/api/openapi.php/EsgService.getEsgStocks?page=1&num=200"
|
||||
r = requests.get(url)
|
||||
data_json = r.json()
|
||||
page_num = math.ceil(int(data_json["result"]["data"]["info"]["total"]) / 200)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
url = f"https://global.finance.sina.com.cn/api/openapi.php/EsgService.getEsgStocks?page={page}&num=200"
|
||||
r = requests.get(url)
|
||||
data_json = r.json()
|
||||
stock_num = len(data_json["result"]["data"]["info"]["stocks"])
|
||||
for num in range(stock_num):
|
||||
temp_df = pd.DataFrame(
|
||||
data_json["result"]["data"]["info"]["stocks"][num]["esg_info"]
|
||||
)
|
||||
temp_df["symbol"] = data_json["result"]["data"]["info"]["stocks"][num][
|
||||
"symbol"
|
||||
]
|
||||
temp_df["market"] = data_json["result"]["data"]["info"]["stocks"][num][
|
||||
"market"
|
||||
]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"symbol": "成分股代码",
|
||||
"agency_name": "评级机构",
|
||||
"esg_score": "评级",
|
||||
"esg_dt": "评级季度",
|
||||
"remark": "标识",
|
||||
"market": "交易市场",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"成分股代码",
|
||||
"评级机构",
|
||||
"评级",
|
||||
"评级季度",
|
||||
"标识",
|
||||
"交易市场",
|
||||
]
|
||||
]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_esg_zd_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-ESG评级中心-ESG评级-秩鼎
|
||||
https://finance.sina.com.cn/esg/grade.shtml
|
||||
:return: 秩鼎
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://global.finance.sina.com.cn/api/openapi.php/EsgService.getZdEsgStocks"
|
||||
params = {"p": "1", "num": "100"}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
tqdm = get_tqdm()
|
||||
total_page = math.ceil(int(data_json["result"]["data"]["total"]) / 100)
|
||||
temp_list = []
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params = {"p": str(page), "num": "100"}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"]["data"])
|
||||
temp_list.append(temp_df)
|
||||
big_df = pd.concat(temp_list, ignore_index=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"ticker": "股票代码",
|
||||
"esg_score": "ESG评分",
|
||||
"report_date": "评分日期",
|
||||
"environmental_score": "环境总评",
|
||||
"social_score": "社会责任总评",
|
||||
"governance_score": "治理总评",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"ESG评分",
|
||||
"环境总评",
|
||||
"社会责任总评",
|
||||
"治理总评",
|
||||
"评分日期",
|
||||
]
|
||||
]
|
||||
big_df["评分日期"] = pd.to_datetime(big_df["评分日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_esg_hz_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-ESG评级中心-ESG评级-华证指数
|
||||
https://finance.sina.com.cn/esg/grade.shtml
|
||||
:return: 华证指数
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://global.finance.sina.com.cn/api/openapi.php/EsgService.getHzEsgStocks"
|
||||
params = {"p": 1, "num": "100"}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = math.ceil(int(data_json["result"]["data"]["total"]) / 100)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params = {"p": str(page), "num": "100"}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"date": "日期",
|
||||
"symbol": "股票代码",
|
||||
"market": "交易市场",
|
||||
"name": "股票名称",
|
||||
"esg_score": "ESG评分",
|
||||
"esg_score_grade": "ESG等级",
|
||||
"e_score": "环境",
|
||||
"e_score_grade": "环境等级",
|
||||
"s_score": "社会",
|
||||
"s_score_grade": "社会等级",
|
||||
"g_score": "公司治理",
|
||||
"g_score_grade": "公司治理等级",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"日期",
|
||||
"股票代码",
|
||||
"交易市场",
|
||||
"股票名称",
|
||||
"ESG评分",
|
||||
"ESG等级",
|
||||
"环境",
|
||||
"环境等级",
|
||||
"社会",
|
||||
"社会等级",
|
||||
"公司治理",
|
||||
"公司治理等级",
|
||||
]
|
||||
]
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
big_df["ESG评分"] = pd.to_numeric(big_df["ESG评分"], errors="coerce")
|
||||
big_df["环境"] = pd.to_numeric(big_df["环境"], errors="coerce")
|
||||
big_df["社会"] = pd.to_numeric(big_df["社会"], errors="coerce")
|
||||
big_df["公司治理"] = pd.to_numeric(big_df["公司治理"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_esg_msci_sina_df = stock_esg_msci_sina()
|
||||
print(stock_esg_msci_sina_df)
|
||||
|
||||
stock_esg_rft_sina_df = stock_esg_rft_sina()
|
||||
print(stock_esg_rft_sina_df)
|
||||
|
||||
stock_esg_rate_sina_df = stock_esg_rate_sina()
|
||||
print(stock_esg_rate_sina_df)
|
||||
|
||||
stock_esg_zd_sina_df = stock_esg_zd_sina()
|
||||
print(stock_esg_zd_sina_df)
|
||||
|
||||
stock_esg_hz_sina_df = stock_esg_hz_sina()
|
||||
print(stock_esg_hz_sina_df)
|
||||
@@ -0,0 +1,273 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/3/18 15:00
|
||||
Desc: 东方财富网-数据中心-年报季报-分红送配
|
||||
https://data.eastmoney.com/yjfp/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_fhps_em(date: str = "20231231") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-年报季报-分红送配
|
||||
https://data.eastmoney.com/yjfp/
|
||||
:param date: 分红送配报告期
|
||||
:type date: str
|
||||
:return: 分红送配
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter(action="ignore", category=FutureWarning)
|
||||
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "PLAN_NOTICE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_SHAREBONUS_DET",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"js": '{"data":(x),"pages":(tp)}',
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_pages = int(data_json["result"]["pages"])
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_pages + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"_",
|
||||
"名称",
|
||||
"_",
|
||||
"_",
|
||||
"代码",
|
||||
"送转股份-送转总比例",
|
||||
"送转股份-送转比例",
|
||||
"送转股份-转股比例",
|
||||
"现金分红-现金分红比例",
|
||||
"预案公告日",
|
||||
"股权登记日",
|
||||
"除权除息日",
|
||||
"_",
|
||||
"方案进度",
|
||||
"_",
|
||||
"最新公告日期",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"每股收益",
|
||||
"每股净资产",
|
||||
"每股公积金",
|
||||
"每股未分配利润",
|
||||
"净利润同比增长",
|
||||
"总股本",
|
||||
"_",
|
||||
"现金分红-股息率",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"名称",
|
||||
"送转股份-送转总比例",
|
||||
"送转股份-送转比例",
|
||||
"送转股份-转股比例",
|
||||
"现金分红-现金分红比例",
|
||||
"现金分红-股息率",
|
||||
"每股收益",
|
||||
"每股净资产",
|
||||
"每股公积金",
|
||||
"每股未分配利润",
|
||||
"净利润同比增长",
|
||||
"总股本",
|
||||
"预案公告日",
|
||||
"股权登记日",
|
||||
"除权除息日",
|
||||
"方案进度",
|
||||
"最新公告日期",
|
||||
]
|
||||
]
|
||||
big_df["送转股份-送转总比例"] = pd.to_numeric(
|
||||
big_df["送转股份-送转总比例"], errors="coerce"
|
||||
)
|
||||
big_df["送转股份-送转比例"] = pd.to_numeric(
|
||||
big_df["送转股份-送转比例"], errors="coerce"
|
||||
)
|
||||
big_df["送转股份-转股比例"] = pd.to_numeric(
|
||||
big_df["送转股份-转股比例"], errors="coerce"
|
||||
)
|
||||
big_df["现金分红-现金分红比例"] = pd.to_numeric(
|
||||
big_df["现金分红-现金分红比例"], errors="coerce"
|
||||
)
|
||||
big_df["现金分红-股息率"] = pd.to_numeric(
|
||||
big_df["现金分红-股息率"], errors="coerce"
|
||||
)
|
||||
big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce")
|
||||
big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce")
|
||||
big_df["每股公积金"] = pd.to_numeric(big_df["每股公积金"], errors="coerce")
|
||||
big_df["每股未分配利润"] = pd.to_numeric(big_df["每股未分配利润"], errors="coerce")
|
||||
big_df["净利润同比增长"] = pd.to_numeric(big_df["净利润同比增长"], errors="coerce")
|
||||
big_df["总股本"] = pd.to_numeric(big_df["总股本"], errors="coerce")
|
||||
|
||||
big_df["预案公告日"] = pd.to_datetime(big_df["预案公告日"], errors="coerce").dt.date
|
||||
big_df["股权登记日"] = pd.to_datetime(big_df["股权登记日"], errors="coerce").dt.date
|
||||
big_df["除权除息日"] = pd.to_datetime(big_df["除权除息日"], errors="coerce").dt.date
|
||||
big_df["最新公告日期"] = pd.to_datetime(
|
||||
big_df["最新公告日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df.sort_values(["最新公告日期"], inplace=True, ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_fhps_detail_em(symbol: str = "300073") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-分红送配-分红送配详情
|
||||
https://data.eastmoney.com/yjfp/detail/300073.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 分红送配详情
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "REPORT_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_SHAREBONUS_DET",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"js": '{"data":(x),"pages":(tp)}',
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"""(SECURITY_CODE="{symbol}")""",
|
||||
}
|
||||
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_pages = int(data_json["result"]["pages"])
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_pages + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"_",
|
||||
"-",
|
||||
"_",
|
||||
"_",
|
||||
"-",
|
||||
"送转股份-送转总比例",
|
||||
"送转股份-送股比例",
|
||||
"送转股份-转股比例",
|
||||
"现金分红-现金分红比例",
|
||||
"业绩披露日期",
|
||||
"股权登记日",
|
||||
"除权除息日",
|
||||
"报告期",
|
||||
"方案进度",
|
||||
"现金分红-现金分红比例描述",
|
||||
"最新公告日期",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
"每股收益",
|
||||
"每股净资产",
|
||||
"每股公积金",
|
||||
"每股未分配利润",
|
||||
"净利润同比增长",
|
||||
"总股本",
|
||||
"预案公告日",
|
||||
"现金分红-股息率",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"报告期",
|
||||
"业绩披露日期",
|
||||
"送转股份-送转总比例",
|
||||
"送转股份-送股比例",
|
||||
"送转股份-转股比例",
|
||||
"现金分红-现金分红比例",
|
||||
"现金分红-现金分红比例描述",
|
||||
"现金分红-股息率",
|
||||
"每股收益",
|
||||
"每股净资产",
|
||||
"每股公积金",
|
||||
"每股未分配利润",
|
||||
"净利润同比增长",
|
||||
"总股本",
|
||||
"预案公告日",
|
||||
"股权登记日",
|
||||
"除权除息日",
|
||||
"方案进度",
|
||||
"最新公告日期",
|
||||
]
|
||||
]
|
||||
big_df["报告期"] = pd.to_datetime(big_df["报告期"], errors="coerce").dt.date
|
||||
big_df["业绩披露日期"] = pd.to_datetime(
|
||||
big_df["业绩披露日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["预案公告日"] = pd.to_datetime(big_df["预案公告日"], errors="coerce").dt.date
|
||||
big_df["股权登记日"] = pd.to_datetime(big_df["股权登记日"], errors="coerce").dt.date
|
||||
big_df["除权除息日"] = pd.to_datetime(big_df["除权除息日"], errors="coerce").dt.date
|
||||
big_df["最新公告日期"] = pd.to_datetime(
|
||||
big_df["最新公告日期"], errors="coerce"
|
||||
).dt.date
|
||||
|
||||
big_df["送转股份-送转总比例"] = pd.to_numeric(
|
||||
big_df["送转股份-送转总比例"], errors="coerce"
|
||||
)
|
||||
big_df["送转股份-送股比例"] = pd.to_numeric(
|
||||
big_df["送转股份-送股比例"], errors="coerce"
|
||||
)
|
||||
big_df["送转股份-转股比例"] = pd.to_numeric(
|
||||
big_df["送转股份-转股比例"], errors="coerce"
|
||||
)
|
||||
big_df["现金分红-现金分红比例"] = pd.to_numeric(
|
||||
big_df["现金分红-现金分红比例"], errors="coerce"
|
||||
)
|
||||
big_df["现金分红-股息率"] = pd.to_numeric(
|
||||
big_df["现金分红-股息率"], errors="coerce"
|
||||
)
|
||||
big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce")
|
||||
big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce")
|
||||
big_df["每股公积金"] = pd.to_numeric(big_df["每股公积金"], errors="coerce")
|
||||
big_df["每股未分配利润"] = pd.to_numeric(big_df["每股未分配利润"], errors="coerce")
|
||||
big_df["净利润同比增长"] = pd.to_numeric(big_df["净利润同比增长"], errors="coerce")
|
||||
big_df["总股本"] = pd.to_numeric(big_df["总股本"], errors="coerce")
|
||||
big_df.sort_values(["报告期"], inplace=True, ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_fhps_em_df = stock_fhps_em(date="20231231")
|
||||
print(stock_fhps_em_df)
|
||||
|
||||
stock_fhps_detail_em_df = stock_fhps_detail_em(symbol="000005")
|
||||
print(stock_fhps_detail_em_df)
|
||||
@@ -0,0 +1,62 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/13 10:30
|
||||
Desc: 同花顺-分红情况
|
||||
https://basic.10jqka.com.cn/new/603444/bonus.html
|
||||
"""
|
||||
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_fhps_detail_ths(symbol: str = "603444") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-分红情况
|
||||
https://basic.10jqka.com.cn/new/603444/bonus.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 分红融资
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = f"https://basic.10jqka.com.cn/new/{symbol}/bonus.html"
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
|
||||
"AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/89.0.4389.90 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, headers=headers)
|
||||
r.encoding = "gbk"
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
temp_df["董事会日期"] = pd.to_datetime(
|
||||
temp_df["董事会日期"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
temp_df["股东大会预案公告日期"] = pd.to_datetime(
|
||||
temp_df["股东大会预案公告日期"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
temp_df["实施公告日"] = pd.to_datetime(
|
||||
temp_df["实施公告日"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
if "A股股权登记日" in temp_df.columns:
|
||||
temp_df["A股股权登记日"] = pd.to_datetime(
|
||||
temp_df["A股股权登记日"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
temp_df["A股除权除息日"] = pd.to_datetime(
|
||||
temp_df["A股除权除息日"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
else:
|
||||
temp_df["B股股权登记日"] = pd.to_datetime(
|
||||
temp_df["B股股权登记日"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
temp_df["B股除权除息日"] = pd.to_datetime(
|
||||
temp_df["B股除权除息日"], format="%Y-%m-%d", errors="coerce"
|
||||
).dt.date
|
||||
temp_df.sort_values(by=["董事会日期"], ignore_index=True, inplace=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_fhps_detail_ths_df = stock_fhps_detail_ths(symbol="200596")
|
||||
print(stock_fhps_detail_ths_df)
|
||||
@@ -0,0 +1,472 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/8/15 18:00
|
||||
Desc: 同花顺-数据中心-资金流向
|
||||
同花顺-数据中心-资金流向-个股资金流
|
||||
https://data.10jqka.com.cn/funds/ggzjl/#refCountId=data_55f13c2c_254
|
||||
同花顺-数据中心-资金流向-概念资金流
|
||||
https://data.10jqka.com.cn/funds/gnzjl/#refCountId=data_55f13c2c_254
|
||||
同花顺-数据中心-资金流向-行业资金流
|
||||
https://data.10jqka.com.cn/funds/hyzjl/#refCountId=data_55f13c2c_254
|
||||
同花顺-数据中心-资金流向-打单追踪
|
||||
https://data.10jqka.com.cn/funds/ddzz/#refCountId=data_55f13c2c_254
|
||||
"""
|
||||
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
import py_mini_racer
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
from akshare.datasets import get_ths_js
|
||||
|
||||
|
||||
def _get_file_content_ths(file: str = "ths.js") -> str:
|
||||
"""
|
||||
获取 JS 文件的内容
|
||||
:param file: JS 文件名
|
||||
:type file: str
|
||||
:return: 文件内容
|
||||
:rtype: str
|
||||
"""
|
||||
setting_file_path = get_ths_js(file)
|
||||
with open(setting_file_path, encoding="utf-8") as f:
|
||||
file_data = f.read()
|
||||
return file_data
|
||||
|
||||
|
||||
def stock_fund_flow_individual(symbol: str = "即时") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-资金流向-个股资金流
|
||||
https://data.10jqka.com.cn/funds/ggzjl/#refCountId=data_55f13c2c_254
|
||||
:param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"}
|
||||
:type symbol: str
|
||||
:return: 个股资金流
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/field/code/order/desc/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
raw_page = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
page_num = raw_page.split("/")[1]
|
||||
if symbol == "3日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/board/3/field/zdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "5日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/board/5/field/zdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "10日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/board/10/field/zdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "20日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/board/20/field/zdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
else:
|
||||
url = "http://data.10jqka.com.cn/funds/ggzjl/field/zdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url.format(page), headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
del big_df["序号"]
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
if symbol == "即时":
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
"流入资金",
|
||||
"流出资金",
|
||||
"净额",
|
||||
"成交额",
|
||||
]
|
||||
else:
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"阶段涨跌幅",
|
||||
"连续换手率",
|
||||
"资金流入净额",
|
||||
]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_fund_flow_concept(symbol: str = "即时") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-资金流向-概念资金流
|
||||
https://data.10jqka.com.cn/funds/gnzjl/#refCountId=data_55f13c2c_254
|
||||
:param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"}
|
||||
:type symbol: str
|
||||
:return: 概念资金流
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/gnzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
url = (
|
||||
"http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
raw_page = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
page_num = raw_page.split("/")[1]
|
||||
if symbol == "3日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/gnzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "5日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/gnzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "10日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/gnzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "20日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/gnzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
else:
|
||||
url = "http://data.10jqka.com.cn/funds/gnzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/gnzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url.format(page), headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
del big_df["序号"]
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
if symbol == "即时":
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"行业",
|
||||
"行业指数",
|
||||
"行业-涨跌幅",
|
||||
"流入资金",
|
||||
"流出资金",
|
||||
"净额",
|
||||
"公司家数",
|
||||
"领涨股",
|
||||
"领涨股-涨跌幅",
|
||||
"当前价",
|
||||
]
|
||||
big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%")
|
||||
big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%")
|
||||
big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce")
|
||||
big_df["领涨股-涨跌幅"] = pd.to_numeric(
|
||||
big_df["领涨股-涨跌幅"], errors="coerce"
|
||||
)
|
||||
else:
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"行业",
|
||||
"公司家数",
|
||||
"行业指数",
|
||||
"阶段涨跌幅",
|
||||
"流入资金",
|
||||
"流出资金",
|
||||
"净额",
|
||||
]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_fund_flow_industry(symbol: str = "即时") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-资金流向-行业资金流
|
||||
https://data.10jqka.com.cn/funds/hyzjl/#refCountId=data_55f13c2c_254
|
||||
:param symbol: choice of {“即时”, "3日排行", "5日排行", "10日排行", "20日排行"}
|
||||
:type symbol: str
|
||||
:return: 行业资金流
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
url = (
|
||||
"http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
raw_page = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
page_num = raw_page.split("/")[1]
|
||||
if symbol == "3日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/hyzjl/board/3/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "5日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/hyzjl/board/5/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "10日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/hyzjl/board/10/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
elif symbol == "20日排行":
|
||||
url = "http://data.10jqka.com.cn/funds/hyzjl/board/20/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
else:
|
||||
url = "http://data.10jqka.com.cn/funds/hyzjl/field/tradezdf/order/desc/page/{}/ajax/1/free/1/"
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url.format(page), headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
del big_df["序号"]
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
if symbol == "即时":
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"行业",
|
||||
"行业指数",
|
||||
"行业-涨跌幅",
|
||||
"流入资金",
|
||||
"流出资金",
|
||||
"净额",
|
||||
"公司家数",
|
||||
"领涨股",
|
||||
"领涨股-涨跌幅",
|
||||
"当前价",
|
||||
]
|
||||
big_df["行业-涨跌幅"] = big_df["行业-涨跌幅"].str.strip("%")
|
||||
big_df["领涨股-涨跌幅"] = big_df["领涨股-涨跌幅"].str.strip("%")
|
||||
big_df["行业-涨跌幅"] = pd.to_numeric(big_df["行业-涨跌幅"], errors="coerce")
|
||||
big_df["领涨股-涨跌幅"] = pd.to_numeric(
|
||||
big_df["领涨股-涨跌幅"], errors="coerce"
|
||||
)
|
||||
else:
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"行业",
|
||||
"公司家数",
|
||||
"行业指数",
|
||||
"阶段涨跌幅",
|
||||
"流入资金",
|
||||
"流出资金",
|
||||
"净额",
|
||||
]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_fund_flow_big_deal() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-资金流向-大单追踪
|
||||
https://data.10jqka.com.cn/funds/ddzz
|
||||
:return: 大单追踪
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/funds/ddzz/order/desc/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
raw_page = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
page_num = raw_page.split("/")[1]
|
||||
url = "http://data.10jqka.com.cn/funds/ddzz/order/asc/page/{}/ajax/1/free/1/"
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(page_num) + 1), leave=False):
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"Accept": "text/html, */*; q=0.01",
|
||||
"Accept-Encoding": "gzip, deflate",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"hexin-v": v_code,
|
||||
"Host": "data.10jqka.com.cn",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "http://data.10jqka.com.cn/funds/hyzjl/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/90.0.4430.85 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url.format(page), headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"成交时间",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"成交价格",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"大单性质",
|
||||
"涨跌幅",
|
||||
"涨跌额",
|
||||
"详细",
|
||||
]
|
||||
del big_df["详细"]
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 同花顺-数据中心-资金流向-个股资金流
|
||||
stock_fund_flow_individual_df = stock_fund_flow_individual(symbol="即时")
|
||||
print(stock_fund_flow_individual_df)
|
||||
|
||||
stock_fund_flow_individual_df = stock_fund_flow_individual(symbol="3日排行")
|
||||
print(stock_fund_flow_individual_df)
|
||||
|
||||
stock_fund_flow_individual_df = stock_fund_flow_individual(symbol="5日排行")
|
||||
print(stock_fund_flow_individual_df)
|
||||
|
||||
stock_fund_flow_individual_df = stock_fund_flow_individual(symbol="10日排行")
|
||||
print(stock_fund_flow_individual_df)
|
||||
|
||||
stock_fund_flow_individual_df = stock_fund_flow_individual(symbol="20日排行")
|
||||
print(stock_fund_flow_individual_df)
|
||||
|
||||
# 同花顺-数据中心-资金流向-概念资金流
|
||||
stock_fund_flow_concept_df = stock_fund_flow_concept(symbol="即时")
|
||||
print(stock_fund_flow_concept_df)
|
||||
|
||||
stock_fund_flow_concept_df = stock_fund_flow_concept(symbol="3日排行")
|
||||
print(stock_fund_flow_concept_df)
|
||||
|
||||
stock_fund_flow_concept_df = stock_fund_flow_concept(symbol="5日排行")
|
||||
print(stock_fund_flow_concept_df)
|
||||
|
||||
stock_fund_flow_concept_df = stock_fund_flow_concept(symbol="10日排行")
|
||||
print(stock_fund_flow_concept_df)
|
||||
|
||||
stock_fund_flow_concept_df = stock_fund_flow_concept(symbol="20日排行")
|
||||
print(stock_fund_flow_concept_df)
|
||||
|
||||
# 同花顺-数据中心-资金流向-行业资金流
|
||||
stock_fund_flow_industry_df = stock_fund_flow_industry(symbol="即时")
|
||||
print(stock_fund_flow_industry_df)
|
||||
|
||||
stock_fund_flow_industry_df = stock_fund_flow_industry(symbol="3日排行")
|
||||
print(stock_fund_flow_industry_df)
|
||||
|
||||
stock_fund_flow_industry_df = stock_fund_flow_industry(symbol="5日排行")
|
||||
print(stock_fund_flow_industry_df)
|
||||
|
||||
stock_fund_flow_industry_df = stock_fund_flow_industry(symbol="10日排行")
|
||||
print(stock_fund_flow_industry_df)
|
||||
|
||||
stock_fund_flow_industry_df = stock_fund_flow_industry(symbol="20日排行")
|
||||
print(stock_fund_flow_industry_df)
|
||||
|
||||
# 同花顺-数据中心-资金流向-大单追踪
|
||||
stock_fund_flow_big_deal_df = stock_fund_flow_big_deal()
|
||||
print(stock_fund_flow_big_deal_df)
|
||||
@@ -0,0 +1,100 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/2/11 16:00
|
||||
Desc: 东方财富网-数据中心-股东大会
|
||||
https://data.eastmoney.com/gddh/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_gddh_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-股东大会
|
||||
https://data.eastmoney.com/gddh/
|
||||
:return: 股东大会
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_GENERALMEETING_DETAIL",
|
||||
"columns": "SECURITY_CODE,SECURITY_NAME_ABBR,MEETING_TITLE,START_ADJUST_DATE,EQUITY_RECORD_DATE,"
|
||||
"ONSITE_RECORD_DATE,DECISION_NOTICE_DATE,NOTICE_DATE,WEB_START_DATE,"
|
||||
"WEB_END_DATE,SERIAL_NUM,PROPOSAL",
|
||||
"filter": '(IS_LASTDATE="1")',
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], axis=0, ignore_index=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"SECURITY_CODE": "代码",
|
||||
"SECURITY_NAME_ABBR": "简称",
|
||||
"MEETING_TITLE": "股东大会名称",
|
||||
"START_ADJUST_DATE": "召开开始日",
|
||||
"EQUITY_RECORD_DATE": "股权登记日",
|
||||
"ONSITE_RECORD_DATE": "现场登记日",
|
||||
"DECISION_NOTICE_DATE": "决议公告日",
|
||||
"NOTICE_DATE": "公告日",
|
||||
"WEB_START_DATE": "网络投票时间-开始日",
|
||||
"WEB_END_DATE": "网络投票时间-结束日",
|
||||
"SERIAL_NUM": "序列号",
|
||||
"PROPOSAL": "提案",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"简称",
|
||||
"股东大会名称",
|
||||
"召开开始日",
|
||||
"股权登记日",
|
||||
"现场登记日",
|
||||
"网络投票时间-开始日",
|
||||
"网络投票时间-结束日",
|
||||
"决议公告日",
|
||||
"公告日",
|
||||
"序列号",
|
||||
"提案",
|
||||
]
|
||||
]
|
||||
big_df["召开开始日"] = pd.to_datetime(big_df["召开开始日"], errors="coerce").dt.date
|
||||
big_df["股权登记日"] = pd.to_datetime(big_df["股权登记日"], errors="coerce").dt.date
|
||||
big_df["现场登记日"] = pd.to_datetime(big_df["现场登记日"], errors="coerce").dt.date
|
||||
big_df["网络投票时间-开始日"] = pd.to_datetime(
|
||||
big_df["网络投票时间-开始日"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["网络投票时间-结束日"] = pd.to_datetime(
|
||||
big_df["网络投票时间-结束日"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["决议公告日"] = pd.to_datetime(big_df["决议公告日"], errors="coerce").dt.date
|
||||
big_df["公告日"] = pd.to_datetime(big_df["公告日"], errors="coerce").dt.date
|
||||
big_df["提案"] = big_df["提案"].str.replace("\r\n2", "").str.replace("\r\n3", "")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_gddh_em_df = stock_gddh_em()
|
||||
print(stock_gddh_em_df)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,235 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/10/1 22:00
|
||||
Desc: 东方财富网-数据中心-特色数据-股东户数
|
||||
https://data.eastmoney.com/gdhs/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_zh_a_gdhs(symbol: str = "20230930") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股东户数
|
||||
https://data.eastmoney.com/gdhs/
|
||||
:param symbol: choice of {"最新", "每个季度末"}, 其中 每个季度末需要写成 `20230930` 格式
|
||||
:type symbol: str
|
||||
:return: 股东户数
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
if symbol == "最新":
|
||||
params = {
|
||||
"sortColumns": "HOLD_NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_HOLDERNUMLATEST",
|
||||
"columns": "SECURITY_CODE,SECURITY_NAME_ABBR,END_DATE,INTERVAL_CHRATE,AVG_MARKET_CAP,AVG_HOLD_NUM,"
|
||||
"TOTAL_MARKET_CAP,TOTAL_A_SHARES,HOLD_NOTICE_DATE,HOLDER_NUM,PRE_HOLDER_NUM,"
|
||||
"HOLDER_NUM_CHANGE,HOLDER_NUM_RATIO,END_DATE,PRE_END_DATE",
|
||||
"quoteColumns": "f2,f3",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
else:
|
||||
params = {
|
||||
"sortColumns": "HOLD_NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_HOLDERNUM_DET",
|
||||
"columns": "SECURITY_CODE,SECURITY_NAME_ABBR,END_DATE,INTERVAL_CHRATE,AVG_MARKET_CAP,"
|
||||
"AVG_HOLD_NUM,TOTAL_MARKET_CAP,TOTAL_A_SHARES,HOLD_NOTICE_DATE,HOLDER_NUM,"
|
||||
"PRE_HOLDER_NUM,HOLDER_NUM_CHANGE,HOLDER_NUM_RATIO,END_DATE,PRE_END_DATE",
|
||||
"quoteColumns": "f2,f3",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"(END_DATE='{symbol[:4] + '-' + symbol[4:6] + '-' + symbol[6:]}')",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page_num in tqdm(range(1, total_page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page_num,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"代码",
|
||||
"名称",
|
||||
"股东户数统计截止日-本次",
|
||||
"区间涨跌幅",
|
||||
"户均持股市值",
|
||||
"户均持股数量",
|
||||
"总市值",
|
||||
"总股本",
|
||||
"公告日期",
|
||||
"股东户数-本次",
|
||||
"股东户数-上次",
|
||||
"股东户数-增减",
|
||||
"股东户数-增减比例",
|
||||
"股东户数统计截止日-上次",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"股东户数-本次",
|
||||
"股东户数-上次",
|
||||
"股东户数-增减",
|
||||
"股东户数-增减比例",
|
||||
"区间涨跌幅",
|
||||
"股东户数统计截止日-本次",
|
||||
"股东户数统计截止日-上次",
|
||||
"户均持股市值",
|
||||
"户均持股数量",
|
||||
"总市值",
|
||||
"总股本",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["股东户数-本次"] = pd.to_numeric(big_df["股东户数-本次"], errors="coerce")
|
||||
big_df["股东户数-上次"] = pd.to_numeric(big_df["股东户数-上次"], errors="coerce")
|
||||
big_df["股东户数-增减"] = pd.to_numeric(big_df["股东户数-增减"], errors="coerce")
|
||||
big_df["股东户数-增减比例"] = pd.to_numeric(
|
||||
big_df["股东户数-增减比例"], errors="coerce"
|
||||
)
|
||||
big_df["区间涨跌幅"] = pd.to_numeric(big_df["区间涨跌幅"], errors="coerce")
|
||||
big_df["股东户数统计截止日-本次"] = pd.to_datetime(
|
||||
big_df["股东户数统计截止日-本次"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["股东户数统计截止日-上次"] = pd.to_datetime(
|
||||
big_df["股东户数统计截止日-上次"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["户均持股市值"] = pd.to_numeric(big_df["户均持股市值"], errors="coerce")
|
||||
big_df["户均持股数量"] = pd.to_numeric(big_df["户均持股数量"], errors="coerce")
|
||||
big_df["总市值"] = pd.to_numeric(big_df["总市值"], errors="coerce")
|
||||
big_df["总股本"] = pd.to_numeric(big_df["总股本"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_zh_a_gdhs_detail_em(symbol: str = "000001") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股东户数详情
|
||||
https://data.eastmoney.com/gdhs/detail/000002.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 股东户数
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "END_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_HOLDERNUM_DET",
|
||||
"columns": "SECURITY_CODE,SECURITY_NAME_ABBR,CHANGE_SHARES,CHANGE_REASON,END_DATE,INTERVAL_CHRATE,"
|
||||
"AVG_MARKET_CAP,AVG_HOLD_NUM,TOTAL_MARKET_CAP,TOTAL_A_SHARES,HOLD_NOTICE_DATE,HOLDER_NUM,"
|
||||
"PRE_HOLDER_NUM,HOLDER_NUM_CHANGE,HOLDER_NUM_RATIO,END_DATE,PRE_END_DATE",
|
||||
"quoteColumns": "f2,f3",
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page_num in tqdm(range(1, total_page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page_num,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"代码",
|
||||
"名称",
|
||||
"股本变动",
|
||||
"股本变动原因",
|
||||
"股东户数统计截止日",
|
||||
"区间涨跌幅",
|
||||
"户均持股市值",
|
||||
"户均持股数量",
|
||||
"总市值",
|
||||
"总股本",
|
||||
"股东户数公告日期",
|
||||
"股东户数-本次",
|
||||
"股东户数-上次",
|
||||
"股东户数-增减",
|
||||
"股东户数-增减比例",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"股东户数统计截止日",
|
||||
"区间涨跌幅",
|
||||
"股东户数-本次",
|
||||
"股东户数-上次",
|
||||
"股东户数-增减",
|
||||
"股东户数-增减比例",
|
||||
"户均持股市值",
|
||||
"户均持股数量",
|
||||
"总市值",
|
||||
"总股本",
|
||||
"股本变动",
|
||||
"股本变动原因",
|
||||
"股东户数公告日期",
|
||||
"代码",
|
||||
"名称",
|
||||
]
|
||||
]
|
||||
big_df["区间涨跌幅"] = pd.to_numeric(big_df["区间涨跌幅"], errors="coerce")
|
||||
big_df["股东户数-本次"] = pd.to_numeric(big_df["股东户数-本次"], errors="coerce")
|
||||
big_df["股东户数-上次"] = pd.to_numeric(big_df["股东户数-上次"], errors="coerce")
|
||||
big_df["股东户数-增减"] = pd.to_numeric(big_df["股东户数-增减"], errors="coerce")
|
||||
big_df["股东户数-增减比例"] = pd.to_numeric(
|
||||
big_df["股东户数-增减比例"], errors="coerce"
|
||||
)
|
||||
big_df["户均持股市值"] = pd.to_numeric(big_df["户均持股市值"], errors="coerce")
|
||||
big_df["户均持股数量"] = pd.to_numeric(big_df["户均持股数量"], errors="coerce")
|
||||
big_df["总市值"] = pd.to_numeric(big_df["总市值"], errors="coerce")
|
||||
big_df["总股本"] = pd.to_numeric(big_df["总股本"], errors="coerce")
|
||||
big_df["股本变动"] = pd.to_numeric(big_df["股本变动"], errors="coerce")
|
||||
big_df["股东户数统计截止日"] = pd.to_datetime(
|
||||
big_df["股东户数统计截止日"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["股东户数公告日期"] = pd.to_datetime(
|
||||
big_df["股东户数公告日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df.sort_values(by=["股东户数统计截止日"], ignore_index=True, inplace=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zh_a_gdhs_df = stock_zh_a_gdhs(symbol="20230930")
|
||||
print(stock_zh_a_gdhs_df)
|
||||
|
||||
stock_zh_a_gdhs_detail_em_df = stock_zh_a_gdhs_detail_em(symbol="002631")
|
||||
print(stock_zh_a_gdhs_detail_em_df)
|
||||
@@ -0,0 +1,133 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/6/16 18:03
|
||||
Desc: 东方财富网-数据中心-特色数据-高管持股
|
||||
https://data.eastmoney.com/executive/gdzjc.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_ggcg_em(symbol: str = "全部") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-高管持股
|
||||
https://data.eastmoney.com/executive/gdzjc.html
|
||||
:param symbol: choice of {"全部", "股东增持", "股东减持"}
|
||||
:type symbol: str
|
||||
:return: 高管持股
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"全部": "",
|
||||
"股东增持": '(DIRECTION="增持")',
|
||||
"股东减持": '(DIRECTION="减持")',
|
||||
}
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "END_DATE,SECURITY_CODE,EITIME",
|
||||
"sortTypes": "-1,-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_SHARE_HOLDER_INCREASE",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~NEWEST_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE_QUOTES",
|
||||
"quoteType": "0",
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": symbol_map[symbol],
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"持股变动信息-变动数量",
|
||||
"公告日",
|
||||
"代码",
|
||||
"股东名称",
|
||||
"持股变动信息-占总股本比例",
|
||||
"_",
|
||||
"-",
|
||||
"变动截止日",
|
||||
"-",
|
||||
"变动后持股情况-持股总数",
|
||||
"变动后持股情况-占总股本比例",
|
||||
"_",
|
||||
"变动后持股情况-占流通股比例",
|
||||
"变动后持股情况-持流通股数",
|
||||
"_",
|
||||
"名称",
|
||||
"持股变动信息-增减",
|
||||
"_",
|
||||
"持股变动信息-占流通股比例",
|
||||
"变动开始日",
|
||||
"_",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"代码",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"股东名称",
|
||||
"持股变动信息-增减",
|
||||
"持股变动信息-变动数量",
|
||||
"持股变动信息-占总股本比例",
|
||||
"持股变动信息-占流通股比例",
|
||||
"变动后持股情况-持股总数",
|
||||
"变动后持股情况-占总股本比例",
|
||||
"变动后持股情况-持流通股数",
|
||||
"变动后持股情况-占流通股比例",
|
||||
"变动开始日",
|
||||
"变动截止日",
|
||||
"公告日",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["持股变动信息-变动数量"] = pd.to_numeric(big_df["持股变动信息-变动数量"])
|
||||
big_df["持股变动信息-占总股本比例"] = pd.to_numeric(
|
||||
big_df["持股变动信息-占总股本比例"]
|
||||
)
|
||||
big_df["持股变动信息-占流通股比例"] = pd.to_numeric(
|
||||
big_df["持股变动信息-占流通股比例"]
|
||||
)
|
||||
big_df["变动后持股情况-持股总数"] = pd.to_numeric(big_df["变动后持股情况-持股总数"])
|
||||
big_df["变动后持股情况-占总股本比例"] = pd.to_numeric(
|
||||
big_df["变动后持股情况-占总股本比例"]
|
||||
)
|
||||
big_df["变动后持股情况-持流通股数"] = pd.to_numeric(
|
||||
big_df["变动后持股情况-持流通股数"]
|
||||
)
|
||||
big_df["变动后持股情况-占流通股比例"] = pd.to_numeric(
|
||||
big_df["变动后持股情况-占流通股比例"]
|
||||
)
|
||||
big_df["变动开始日"] = pd.to_datetime(big_df["变动开始日"]).dt.date
|
||||
big_df["变动截止日"] = pd.to_datetime(big_df["变动截止日"]).dt.date
|
||||
big_df["公告日"] = pd.to_datetime(big_df["公告日"]).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_ggcg_em_df = stock_ggcg_em(symbol="股东增持")
|
||||
print(stock_ggcg_em_df)
|
||||
@@ -0,0 +1,532 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/12/18 17:30
|
||||
Desc: 东方财富网-数据中心-特色数据-股权质押
|
||||
东方财富网-数据中心-特色数据-股权质押-股权质押市场概况: https://data.eastmoney.com/gpzy/marketProfile.aspx
|
||||
东方财富网-数据中心-特色数据-股权质押-上市公司质押比例: https://data.eastmoney.com/gpzy/pledgeRatio.aspx
|
||||
东方财富网-数据中心-特色数据-股权质押-重要股东股权质押明细: https://data.eastmoney.com/gpzy/pledgeDetail.aspx
|
||||
东方财富网-数据中心-特色数据-股权质押-质押机构分布统计-证券公司: https://data.eastmoney.com/gpzy/distributeStatistics.aspx
|
||||
东方财富网-数据中心-特色数据-股权质押-质押机构分布统计-银行: https://data.eastmoney.com/gpzy/distributeStatistics.aspx
|
||||
东方财富网-数据中心-特色数据-股权质押-行业数据: https://data.eastmoney.com/gpzy/industryData.aspx
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_gpzy_profile_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-股权质押市场概况
|
||||
https://data.eastmoney.com/gpzy/marketProfile.aspx
|
||||
:return: 股权质押市场概况
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "TRADE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_CSDC_STATISTICS",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in range(1, total_page + 1):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.columns = [
|
||||
"交易日期",
|
||||
"质押总股数",
|
||||
"质押总市值",
|
||||
"沪深300指数",
|
||||
"涨跌幅",
|
||||
"A股质押总比例",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"交易日期",
|
||||
"A股质押总比例",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
"质押总股数",
|
||||
"质押总市值",
|
||||
"沪深300指数",
|
||||
"涨跌幅",
|
||||
]
|
||||
]
|
||||
big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
|
||||
big_df["A股质押总比例"] = pd.to_numeric(big_df["A股质押总比例"], errors="coerce")
|
||||
big_df["质押公司数量"] = pd.to_numeric(big_df["质押公司数量"], errors="coerce")
|
||||
big_df["质押笔数"] = pd.to_numeric(big_df["质押笔数"], errors="coerce")
|
||||
big_df["质押总股数"] = pd.to_numeric(big_df["质押总股数"], errors="coerce")
|
||||
big_df["质押总市值"] = pd.to_numeric(big_df["质押总市值"], errors="coerce")
|
||||
big_df["沪深300指数"] = pd.to_numeric(big_df["沪深300指数"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["A股质押总比例"] = big_df["A股质押总比例"] / 100
|
||||
big_df["A股质押总比例"] = pd.to_numeric(big_df["A股质押总比例"], errors="coerce")
|
||||
big_df.sort_values(["交易日期"], inplace=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_gpzy_pledge_ratio_em(date: str = "20240906") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-上市公司质押比例
|
||||
https://data.eastmoney.com/gpzy/pledgeRatio.aspx
|
||||
:param date: 指定交易日, 访问 https://data.eastmoney.com/gpzy/pledgeRatio.aspx 查询
|
||||
:type date: str
|
||||
:return: 上市公司质押比例
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
trade_date = "-".join([date[:4], date[4:6], date[6:]])
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "PLEDGE_RATIO",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_CSDC_LIST",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"(TRADE_DATE='{trade_date}')",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"-",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"交易日期",
|
||||
"质押比例",
|
||||
"质押股数",
|
||||
"质押笔数",
|
||||
"无限售股质押数",
|
||||
"限售股质押数",
|
||||
"质押市值",
|
||||
"所属行业",
|
||||
"近一年涨跌幅",
|
||||
"所属行业代码",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"交易日期",
|
||||
"所属行业",
|
||||
"质押比例",
|
||||
"质押股数",
|
||||
"质押市值",
|
||||
"质押笔数",
|
||||
"无限售股质押数",
|
||||
"限售股质押数",
|
||||
"近一年涨跌幅",
|
||||
"所属行业代码",
|
||||
]
|
||||
]
|
||||
big_df["质押比例"] = pd.to_numeric(big_df["质押比例"], errors="coerce")
|
||||
big_df["质押股数"] = pd.to_numeric(big_df["质押股数"], errors="coerce")
|
||||
big_df["质押市值"] = pd.to_numeric(big_df["质押市值"], errors="coerce")
|
||||
big_df["质押笔数"] = pd.to_numeric(big_df["质押笔数"], errors="coerce")
|
||||
big_df["无限售股质押数"] = pd.to_numeric(big_df["无限售股质押数"], errors="coerce")
|
||||
big_df["限售股质押数"] = pd.to_numeric(big_df["限售股质押数"], errors="coerce")
|
||||
big_df["近一年涨跌幅"] = pd.to_numeric(big_df["近一年涨跌幅"], errors="coerce")
|
||||
big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def _get_page_num_gpzy_market_pledge_ratio_detail(filter: str = None) -> int:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-重要股东股权质押明细
|
||||
https://data.eastmoney.com/gpzy/pledgeDetail.aspx
|
||||
:return: int 获取 重要股东股权质押明细 的总页数
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPTA_APP_ACCUMDETAILS",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": filter,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = math.ceil(int(data_json["result"]["count"]) / 500)
|
||||
return total_page
|
||||
|
||||
|
||||
def _stock_gpzy_pledge_ratio_detail_em(filter: str = None) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-重要股东股权质押明细
|
||||
https://data.eastmoney.com/gpzy/pledgeDetail.aspx
|
||||
:return: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
total_page = _get_page_num_gpzy_market_pledge_ratio_detail(filter)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": page,
|
||||
"reportName": "RPTA_APP_ACCUMDETAILS",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": filter,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票简称",
|
||||
"_",
|
||||
"股票代码",
|
||||
"股东名称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"公告日期",
|
||||
"质押机构",
|
||||
"质押股份数量",
|
||||
"占所持股份比例",
|
||||
"占总股本比例",
|
||||
"质押日收盘价",
|
||||
"质押开始日期",
|
||||
"质押结束日期",
|
||||
"状态",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"预估平仓线",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"最新价",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"股东名称",
|
||||
"质押股份数量",
|
||||
"占所持股份比例",
|
||||
"占总股本比例",
|
||||
"质押机构",
|
||||
"最新价",
|
||||
"质押日收盘价",
|
||||
"预估平仓线",
|
||||
"质押开始日期",
|
||||
"质押结束日期",
|
||||
"状态",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["质押股份数量"] = pd.to_numeric(big_df["质押股份数量"], errors="coerce")
|
||||
big_df["占所持股份比例"] = pd.to_numeric(big_df["占所持股份比例"], errors="coerce")
|
||||
big_df["占总股本比例"] = pd.to_numeric(big_df["占总股本比例"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["质押日收盘价"] = pd.to_numeric(big_df["质押日收盘价"], errors="coerce")
|
||||
big_df["预估平仓线"] = pd.to_numeric(big_df["预估平仓线"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["质押开始日期"] = pd.to_datetime(
|
||||
big_df["质押开始日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["质押结束日期"] = pd.to_datetime(
|
||||
big_df["质押结束日期"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_gpzy_pledge_ratio_detail_em() -> pd.DataFrame:
|
||||
return _stock_gpzy_pledge_ratio_detail_em()
|
||||
|
||||
|
||||
def stock_gpzy_individual_pledge_ratio_detail_em(symbol: str) -> pd.DataFrame:
|
||||
return _stock_gpzy_pledge_ratio_detail_em(filter=f'(SECURITY_CODE="{symbol}")')
|
||||
|
||||
|
||||
def stock_gpzy_distribute_statistics_company_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-质押机构分布统计-证券公司
|
||||
https://data.eastmoney.com/gpzy/distributeStatistics.aspx
|
||||
:return: 质押机构分布统计-证券公司
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "ORG_NUM",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_GDZY_ZYJG_SUM",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": '(PFORG_TYPE="证券")',
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = temp_df.index + 1
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"质押机构",
|
||||
"_",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
"质押数量",
|
||||
"未达预警线比例",
|
||||
"达到预警线未达平仓线比例",
|
||||
"达到平仓线比例",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"质押机构",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
"质押数量",
|
||||
"未达预警线比例",
|
||||
"达到预警线未达平仓线比例",
|
||||
"达到平仓线比例",
|
||||
]
|
||||
]
|
||||
temp_df["质押公司数量"] = pd.to_numeric(temp_df["质押公司数量"], errors="coerce")
|
||||
temp_df["质押笔数"] = pd.to_numeric(temp_df["质押笔数"], errors="coerce")
|
||||
temp_df["质押数量"] = pd.to_numeric(temp_df["质押数量"], errors="coerce")
|
||||
temp_df["未达预警线比例"] = pd.to_numeric(
|
||||
temp_df["未达预警线比例"], errors="coerce"
|
||||
)
|
||||
temp_df["达到预警线未达平仓线比例"] = pd.to_numeric(
|
||||
temp_df["达到预警线未达平仓线比例"], errors="coerce"
|
||||
)
|
||||
temp_df["达到平仓线比例"] = pd.to_numeric(
|
||||
temp_df["达到平仓线比例"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_gpzy_distribute_statistics_bank_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-质押机构分布统计-银行
|
||||
https://data.eastmoney.com/gpzy/distributeStatistics.aspx
|
||||
:return: 质押机构分布统计-银行
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "ORG_NUM",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_GDZY_ZYJG_SUM",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": '(PFORG_TYPE="银行")',
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = temp_df.index + 1
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"质押机构",
|
||||
"_",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
"质押数量",
|
||||
"未达预警线比例",
|
||||
"达到预警线未达平仓线比例",
|
||||
"达到平仓线比例",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"质押机构",
|
||||
"质押公司数量",
|
||||
"质押笔数",
|
||||
"质押数量",
|
||||
"未达预警线比例",
|
||||
"达到预警线未达平仓线比例",
|
||||
"达到平仓线比例",
|
||||
]
|
||||
]
|
||||
temp_df["质押公司数量"] = pd.to_numeric(temp_df["质押公司数量"], errors="coerce")
|
||||
temp_df["质押笔数"] = pd.to_numeric(temp_df["质押笔数"], errors="coerce")
|
||||
temp_df["质押数量"] = pd.to_numeric(temp_df["质押数量"], errors="coerce")
|
||||
temp_df["未达预警线比例"] = pd.to_numeric(
|
||||
temp_df["未达预警线比例"], errors="coerce"
|
||||
)
|
||||
temp_df["达到预警线未达平仓线比例"] = pd.to_numeric(
|
||||
temp_df["达到预警线未达平仓线比例"], errors="coerce"
|
||||
)
|
||||
temp_df["达到平仓线比例"] = pd.to_numeric(
|
||||
temp_df["达到平仓线比例"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_gpzy_industry_data_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-股权质押-上市公司质押比例-行业数据
|
||||
https://data.eastmoney.com/gpzy/industryData.aspx
|
||||
:return: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "AVERAGE_PLEDGE_RATIO",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_CSDC_INDUSTRY_STATISTICS",
|
||||
"columns": "INDUSTRY_CODE,INDUSTRY,TRADE_DATE,AVERAGE_PLEDGE_RATIO,ORG_NUM,PLEDGE_TOTAL_NUM,"
|
||||
"TOTAL_PLEDGE_SHARES,PLEDGE_TOTAL_MARKETCAP",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = temp_df.index + 1
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"-",
|
||||
"行业",
|
||||
"统计时间",
|
||||
"平均质押比例",
|
||||
"公司家数",
|
||||
"质押总笔数",
|
||||
"质押总股本",
|
||||
"最新质押市值",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"行业",
|
||||
"平均质押比例",
|
||||
"公司家数",
|
||||
"质押总笔数",
|
||||
"质押总股本",
|
||||
"最新质押市值",
|
||||
"统计时间",
|
||||
]
|
||||
]
|
||||
temp_df["统计时间"] = pd.to_datetime(temp_df["统计时间"], errors="coerce").dt.date
|
||||
temp_df["平均质押比例"] = pd.to_numeric(temp_df["平均质押比例"], errors="coerce")
|
||||
temp_df["公司家数"] = pd.to_numeric(temp_df["公司家数"], errors="coerce")
|
||||
temp_df["质押总笔数"] = pd.to_numeric(temp_df["质押总笔数"], errors="coerce")
|
||||
temp_df["质押总股本"] = pd.to_numeric(temp_df["质押总股本"], errors="coerce")
|
||||
temp_df["最新质押市值"] = pd.to_numeric(temp_df["最新质押市值"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_gpzy_profile_em_df = stock_gpzy_profile_em()
|
||||
print(stock_gpzy_profile_em_df)
|
||||
|
||||
stock_em_gpzy_pledge_ratio_df = stock_gpzy_pledge_ratio_em(date="20241220")
|
||||
print(stock_em_gpzy_pledge_ratio_df)
|
||||
|
||||
stock_gpzy_pledge_ratio_detail_em_df = stock_gpzy_pledge_ratio_detail_em()
|
||||
print(stock_gpzy_pledge_ratio_detail_em_df)
|
||||
|
||||
stock_gpzy_individual_pledge_ratio_detail_em_df = (
|
||||
stock_gpzy_individual_pledge_ratio_detail_em(symbol="603132")
|
||||
)
|
||||
print(stock_gpzy_individual_pledge_ratio_detail_em_df)
|
||||
|
||||
stock_em_gpzy_distribute_statistics_company_df = (
|
||||
stock_gpzy_distribute_statistics_company_em()
|
||||
)
|
||||
print(stock_em_gpzy_distribute_statistics_company_df)
|
||||
|
||||
stock_em_gpzy_distribute_statistics_bank_df = (
|
||||
stock_gpzy_distribute_statistics_bank_em()
|
||||
)
|
||||
print(stock_em_gpzy_distribute_statistics_bank_df)
|
||||
|
||||
stock_gpzy_industry_data_em_df = stock_gpzy_industry_data_em()
|
||||
print(stock_gpzy_industry_data_em_df)
|
||||
@@ -0,0 +1,94 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2026/4/3 16:05
|
||||
Desc: 乐咕乐股-股息率-A 股股息率
|
||||
https://legulegu.com/stockdata/guxilv
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_a_gxl_lg(symbol: str = "上证A股") -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-股息率-A 股股息率
|
||||
https://legulegu.com/stockdata/guxilv
|
||||
:param symbol: choice of {"上证A股", "深证A股", "创业板", "科创板"}
|
||||
:type symbol: str
|
||||
:return: A 股股息率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"上证A股": "shangzheng",
|
||||
"深证A股": "shenzheng",
|
||||
"创业板": "chuangyeban",
|
||||
"科创板": "kechuangban",
|
||||
}
|
||||
url = "https://legulegu.com/api/stockdata/guxilv"
|
||||
token = get_token_lg()
|
||||
params = {"token": token}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/guxilv"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json[symbol_map[symbol]])
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df.rename(columns={"addDvTtm": "股息率", "date": "日期"}, inplace=True)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"日期",
|
||||
"股息率",
|
||||
]
|
||||
]
|
||||
temp_df["股息率"] = pd.to_numeric(temp_df["股息率"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_hk_gxl_lg() -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股-股息率-恒生指数股息率
|
||||
https://legulegu.com/stockdata/market/hk/dv/hsi
|
||||
:return: 恒生指数股息率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://legulegu.com/api/stockdata/hs"
|
||||
token = get_token_lg()
|
||||
params = {"token": token, "indexCode": "HSI"}
|
||||
r = requests.get(
|
||||
url,
|
||||
params=params,
|
||||
**get_cookie_csrf(url="https://legulegu.com/stockdata/market/hk/dv/hsi"),
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json)
|
||||
temp_df["date"] = (
|
||||
pd.to_datetime(temp_df["date"], utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df.rename(columns={"dvRatio": "股息率", "date": "日期"}, inplace=True)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"日期",
|
||||
"股息率",
|
||||
]
|
||||
]
|
||||
temp_df["股息率"] = pd.to_numeric(temp_df["股息率"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_a_gxl_lg_df = stock_a_gxl_lg(symbol="上证A股")
|
||||
print(stock_a_gxl_lg_df)
|
||||
|
||||
stock_hk_gxl_lg_df = stock_hk_gxl_lg()
|
||||
print(stock_hk_gxl_lg_df)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,91 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/10/15 22:30
|
||||
Desc: 腾讯证券-行情首页-沪深京A股
|
||||
https://quote.eastmoney.com/
|
||||
"""
|
||||
|
||||
import datetime
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.index.index_stock_zh import get_tx_start_year
|
||||
from akshare.utils import demjson
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_zh_a_hist_tx(
|
||||
symbol: str = "sz000001",
|
||||
start_date: str = "19000101",
|
||||
end_date: str = "20500101",
|
||||
adjust: str = "",
|
||||
timeout: float = None,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
腾讯证券-日频-股票历史数据
|
||||
https://gu.qq.com/sh000919/zs
|
||||
:param symbol: 带市场标识的股票或者指数代码
|
||||
:type symbol: str
|
||||
:param start_date: 开始日期
|
||||
:type start_date: str
|
||||
:param end_date: 结束日期
|
||||
:type end_date: str
|
||||
:param adjust: choice of {"qfq": "前复权", "hfq": "后复权", "": "不复权"}
|
||||
:type adjust: str
|
||||
:param timeout: choice of None or a positive float number
|
||||
:type timeout: float
|
||||
:return: 前复权的股票和指数数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
init_start_date = get_tx_start_year(symbol=symbol)
|
||||
if int(start_date.replace("-", "")) < int(init_start_date.replace("-", "")):
|
||||
start_date = init_start_date
|
||||
url = "https://proxy.finance.qq.com/ifzqgtimg/appstock/app/newfqkline/get"
|
||||
range_start = int(start_date[:4])
|
||||
if int(end_date.split("-")[0]) > datetime.date.today().year:
|
||||
range_end = datetime.date.today().year + 1
|
||||
else:
|
||||
range_end = int(end_date.split("-")[0]) + 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for year in tqdm(range(range_start, range_end), leave=False):
|
||||
params = {
|
||||
"_var": f"kline_day{adjust}{year}",
|
||||
"param": f"{symbol},day,{year}-01-01,{year + 1}-12-31,640,{adjust}",
|
||||
"r": "0.8205512681390605",
|
||||
}
|
||||
r = requests.get(url, params=params, timeout=timeout)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("={") + 1 :])["data"][
|
||||
symbol
|
||||
]
|
||||
if "day" in data_json.keys():
|
||||
temp_df = pd.DataFrame(data_json["day"])
|
||||
elif "hfqday" in data_json.keys():
|
||||
temp_df = pd.DataFrame(data_json["hfqday"])
|
||||
else:
|
||||
temp_df = pd.DataFrame(data_json["qfqday"])
|
||||
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
||||
big_df = big_df.iloc[:, :6]
|
||||
big_df.columns = ["date", "open", "close", "high", "low", "amount"]
|
||||
big_df["date"] = pd.to_datetime(big_df["date"], errors="coerce").dt.date
|
||||
big_df["open"] = pd.to_numeric(big_df["open"], errors="coerce")
|
||||
big_df["close"] = pd.to_numeric(big_df["close"], errors="coerce")
|
||||
big_df["high"] = pd.to_numeric(big_df["high"], errors="coerce")
|
||||
big_df["low"] = pd.to_numeric(big_df["low"], errors="coerce")
|
||||
big_df["amount"] = pd.to_numeric(big_df["amount"], errors="coerce")
|
||||
big_df.drop_duplicates(inplace=True, ignore_index=True)
|
||||
big_df.index = pd.to_datetime(big_df["date"], errors="coerce")
|
||||
big_df.sort_index(inplace=True)
|
||||
big_df = big_df[start_date:end_date]
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zh_a_hist_tx_df = stock_zh_a_hist_tx(
|
||||
symbol="sz000001", start_date="20200101", end_date="20231027", adjust="hfq"
|
||||
)
|
||||
print(stock_zh_a_hist_tx_df)
|
||||
+65
@@ -0,0 +1,65 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/11/21 18:26
|
||||
Desc: 百度股市通-港股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/hk-06969
|
||||
"""
|
||||
|
||||
from curl_cffi import requests
|
||||
import json
|
||||
import urllib
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def stock_hk_valuation_baidu(
|
||||
symbol: str = "06969", indicator: str = "总市值", period: str = "近一年"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
百度股市通-港股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/hk-06969
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param indicator: choice of {"总市值", "市盈率(TTM)", "市盈率(静)", "市净率", "市现率"}
|
||||
:type indicator: str
|
||||
:param period: choice of {"近一年", "近三年", "全部"}
|
||||
:type period: str
|
||||
:return: 估值数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://finance.baidu.com/opendata"
|
||||
params = {
|
||||
"openapi": "1",
|
||||
"dspName": "iphone",
|
||||
"tn": "tangram",
|
||||
"client": "app",
|
||||
"query": indicator,
|
||||
"code": symbol,
|
||||
"word": "",
|
||||
"resource_id": "51171",
|
||||
"market": "hk",
|
||||
"tag": indicator,
|
||||
"chart_select": period,
|
||||
"industry_select": "",
|
||||
"skip_industry": "1",
|
||||
"finClientType": "pc",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(
|
||||
data_json["Result"][0]["DisplayData"]["resultData"]["tplData"]["result"][
|
||||
"chartInfo"
|
||||
][0]["body"]
|
||||
)
|
||||
temp_df.columns = ["date", "value"]
|
||||
temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
|
||||
temp_df["value"] = pd.to_numeric(temp_df["value"])
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_hk_valuation_baidu_df = stock_hk_valuation_baidu(
|
||||
symbol="06969", indicator="总市值", period="近三年"
|
||||
)
|
||||
print(stock_hk_valuation_baidu_df)
|
||||
@@ -0,0 +1,286 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2024/1/7 17:00
|
||||
Desc: 雪球-沪深股市-热度排行榜
|
||||
https://xueqiu.com/hq
|
||||
"""
|
||||
|
||||
import math
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_hot_follow_xq(symbol: str = "最热门") -> pd.DataFrame:
|
||||
"""
|
||||
雪球-沪深股市-热度排行榜-关注排行榜
|
||||
https://xueqiu.com/hq
|
||||
:param symbol: choice of {"本周新增", "最热门"}
|
||||
:type symbol: str
|
||||
:return: 关注排行榜
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"本周新增": "follow7d",
|
||||
"最热门": "follow",
|
||||
}
|
||||
url = "https://xueqiu.com/service/v5/stock/screener/screen"
|
||||
params = {
|
||||
"category": "CN",
|
||||
"size": "200",
|
||||
"order": "desc",
|
||||
"order_by": symbol_map[symbol],
|
||||
"only_count": "0",
|
||||
"page": "1",
|
||||
}
|
||||
headers = {
|
||||
"Accept": "*/*",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Host": "xueqiu.com",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "https://xueqiu.com/hq",
|
||||
"sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"Sec-Fetch-Dest": "empty",
|
||||
"Sec-Fetch-Mode": "cors",
|
||||
"Sec-Fetch-Site": "same-origin",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
total_num = data_json["data"]["count"]
|
||||
total_page = math.ceil(total_num / 200)
|
||||
tqdm = get_tqdm()
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"page": page})
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
try:
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
except TypeError:
|
||||
temp_df = pd.DataFrame()
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
if symbol == "本周新增":
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"follow7d",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
else:
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"follow",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
big_df.columns = [
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"关注",
|
||||
"最新价",
|
||||
]
|
||||
big_df["关注"] = pd.to_numeric(big_df["关注"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_hot_tweet_xq(symbol: str = "最热门") -> pd.DataFrame:
|
||||
"""
|
||||
雪球-沪深股市-热度排行榜-讨论排行榜
|
||||
https://xueqiu.com/hq
|
||||
:param symbol: choice of {"本周新增", "最热门"}
|
||||
:type symbol: str
|
||||
:return: 讨论排行榜
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"本周新增": "tweet7d",
|
||||
"最热门": "tweet",
|
||||
}
|
||||
url = "https://xueqiu.com/service/v5/stock/screener/screen"
|
||||
params = {
|
||||
"category": "CN",
|
||||
"size": "200",
|
||||
"order": "desc",
|
||||
"order_by": symbol_map[symbol],
|
||||
"only_count": "0",
|
||||
"page": "1",
|
||||
}
|
||||
headers = {
|
||||
"Accept": "*/*",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Host": "xueqiu.com",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "https://xueqiu.com/hq",
|
||||
"sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"Sec-Fetch-Dest": "empty",
|
||||
"Sec-Fetch-Mode": "cors",
|
||||
"Sec-Fetch-Site": "same-origin",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
total_num = data_json["data"]["count"]
|
||||
total_page = math.ceil(total_num / 200)
|
||||
tqdm = get_tqdm()
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"page": page})
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
try:
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
except TypeError:
|
||||
temp_df = pd.DataFrame()
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
if symbol == "本周新增":
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"tweet7d",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
else:
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"tweet",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
big_df.columns = [
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"关注",
|
||||
"最新价",
|
||||
]
|
||||
big_df["关注"] = pd.to_numeric(big_df["关注"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_hot_deal_xq(symbol: str = "最热门") -> pd.DataFrame:
|
||||
"""
|
||||
雪球-沪深股市-热度排行榜-分享交易排行榜
|
||||
https://xueqiu.com/hq
|
||||
:param symbol: choice of {"本周新增", "最热门"}
|
||||
:type symbol: str
|
||||
:return: 分享交易排行榜
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"本周新增": "deal7d",
|
||||
"最热门": "deal",
|
||||
}
|
||||
url = "https://xueqiu.com/service/v5/stock/screener/screen"
|
||||
params = {
|
||||
"category": "CN",
|
||||
"size": "10000",
|
||||
"order": "desc",
|
||||
"order_by": symbol_map[symbol],
|
||||
"only_count": "0",
|
||||
"page": "1",
|
||||
}
|
||||
headers = {
|
||||
"Accept": "*/*",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Host": "xueqiu.com",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "https://xueqiu.com/hq",
|
||||
"sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"Sec-Fetch-Dest": "empty",
|
||||
"Sec-Fetch-Mode": "cors",
|
||||
"Sec-Fetch-Site": "same-origin",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
total_num = data_json["data"]["count"]
|
||||
total_page = math.ceil(total_num / 200)
|
||||
tqdm = get_tqdm()
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"page": page})
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
try:
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
except TypeError:
|
||||
temp_df = pd.DataFrame()
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
if symbol == "本周新增":
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"deal7d",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
else:
|
||||
big_df = big_df[
|
||||
[
|
||||
"symbol",
|
||||
"name",
|
||||
"deal",
|
||||
"current",
|
||||
]
|
||||
]
|
||||
big_df.columns = [
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"关注",
|
||||
"最新价",
|
||||
]
|
||||
big_df["关注"] = pd.to_numeric(big_df["关注"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_hot_follow_xq_df = stock_hot_follow_xq(symbol="本周新增")
|
||||
print(stock_hot_follow_xq_df)
|
||||
|
||||
stock_hot_follow_xq_df = stock_hot_follow_xq(symbol="最热门")
|
||||
print(stock_hot_follow_xq_df)
|
||||
|
||||
stock_hot_tweet_xq_df = stock_hot_tweet_xq(symbol="本周新增")
|
||||
print(stock_hot_tweet_xq_df)
|
||||
|
||||
stock_hot_tweet_xq_df = stock_hot_tweet_xq(symbol="最热门")
|
||||
print(stock_hot_tweet_xq_df)
|
||||
|
||||
stock_hot_deal_xq_df = stock_hot_deal_xq(symbol="本周新增")
|
||||
print(stock_hot_deal_xq_df)
|
||||
|
||||
stock_hot_deal_xq_df = stock_hot_deal_xq(symbol="最热门")
|
||||
print(stock_hot_deal_xq_df)
|
||||
File diff suppressed because it is too large
Load Diff
+205
@@ -0,0 +1,205 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2025/2/19 22:00
|
||||
Desc: 参考汇率和结算汇率
|
||||
深港通-港股通业务信息
|
||||
深港通-港股通业务信息: https://www.szse.cn/szhk/hkbussiness/exchangerate/index.html
|
||||
沪港通-港股通信息披露: https://www.sse.com.cn/services/hkexsc/disclo/ratios/
|
||||
"""
|
||||
|
||||
import warnings
|
||||
from datetime import datetime
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_sgt_settlement_exchange_rate_szse() -> pd.DataFrame:
|
||||
"""
|
||||
深港通-港股通业务信息-结算汇率
|
||||
https://www.szse.cn/szhk/hkbussiness/exchangerate/index.html
|
||||
:return: 结算汇率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.szse.cn/api/report/ShowReport"
|
||||
params = {
|
||||
"SHOWTYPE": "xlsx",
|
||||
"CATALOGID": "SGT_LSHL",
|
||||
"TABKEY": "tab2",
|
||||
"random": "0.9184251620553985",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
with warnings.catch_warnings(record=True):
|
||||
warnings.simplefilter("always")
|
||||
temp_df = pd.read_excel(r.content, engine="openpyxl")
|
||||
temp_df.sort_values(by="适用日期", inplace=True, ignore_index=True)
|
||||
temp_df["适用日期"] = pd.to_datetime(temp_df["适用日期"], errors="coerce").dt.date
|
||||
temp_df["买入结算汇兑比率"] = pd.to_numeric(
|
||||
temp_df["买入结算汇兑比率"], errors="coerce"
|
||||
)
|
||||
temp_df["卖出结算汇兑比率"] = pd.to_numeric(
|
||||
temp_df["卖出结算汇兑比率"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_sgt_reference_exchange_rate_szse() -> pd.DataFrame:
|
||||
"""
|
||||
深港通-港股通业务信息-参考汇率
|
||||
https://www.szse.cn/szhk/hkbussiness/exchangerate/index.html
|
||||
:return: 参考汇率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.szse.cn/api/report/ShowReport"
|
||||
params = {
|
||||
"SHOWTYPE": "xlsx",
|
||||
"CATALOGID": "SGT_LSHL",
|
||||
"TABKEY": "tab1",
|
||||
"random": "0.9184251620553985",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
with warnings.catch_warnings(record=True):
|
||||
warnings.simplefilter("always")
|
||||
temp_df = pd.read_excel(r.content, engine="openpyxl")
|
||||
temp_df.sort_values(by="适用日期", inplace=True, ignore_index=True)
|
||||
temp_df["适用日期"] = pd.to_datetime(temp_df["适用日期"], errors="coerce").dt.date
|
||||
temp_df["参考汇率买入价"] = pd.to_numeric(
|
||||
temp_df["参考汇率买入价"], errors="coerce"
|
||||
)
|
||||
temp_df["参考汇率卖出价"] = pd.to_numeric(
|
||||
temp_df["参考汇率卖出价"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_sgt_reference_exchange_rate_sse() -> pd.DataFrame:
|
||||
"""
|
||||
沪港通-港股通信息披露-参考汇率
|
||||
https://www.sse.com.cn/services/hkexsc/disclo/ratios/
|
||||
:return: 参考汇率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
current_date = datetime.now().date().isoformat().replace("-", "")
|
||||
url = "https://query.sse.com.cn/commonSoaQuery.do"
|
||||
params = {
|
||||
"isPagination": "true",
|
||||
"updateDate": "20120601",
|
||||
"updateDateEnd": current_date,
|
||||
"sqlId": "FW_HGT_GGTHL",
|
||||
"pageHelp.cacheSize": "1",
|
||||
"pageHelp.pageSize": "10000",
|
||||
"pageHelp.pageNo": "1",
|
||||
"pageHelp.beginPage": "1",
|
||||
"pageHelp.endPage": "1",
|
||||
}
|
||||
headers = {
|
||||
"Host": "query.sse.com.cn",
|
||||
"Referer": "https://www.sse.com.cn/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/105.0.0.0 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"])
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"currencyType": "货币种类",
|
||||
"buyPrice": "参考汇率买入价",
|
||||
"updateDate": "-",
|
||||
"validDate": "适用日期",
|
||||
"sellPrice": "参考汇率卖出价",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"适用日期",
|
||||
"参考汇率买入价",
|
||||
"参考汇率卖出价",
|
||||
"货币种类",
|
||||
]
|
||||
]
|
||||
temp_df.sort_values("适用日期", inplace=True, ignore_index=True)
|
||||
temp_df["适用日期"] = pd.to_datetime(temp_df["适用日期"], errors="coerce").dt.date
|
||||
temp_df["参考汇率买入价"] = pd.to_numeric(
|
||||
temp_df["参考汇率买入价"], errors="coerce"
|
||||
)
|
||||
temp_df["参考汇率卖出价"] = pd.to_numeric(
|
||||
temp_df["参考汇率卖出价"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_sgt_settlement_exchange_rate_sse() -> pd.DataFrame:
|
||||
"""
|
||||
沪港通-港股通信息披露-结算汇兑
|
||||
https://www.sse.com.cn/services/hkexsc/disclo/ratios/
|
||||
:return: 结算汇兑比率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
current_date = datetime.now().date().isoformat().replace("-", "")
|
||||
url = "https://query.sse.com.cn/commonSoaQuery.do"
|
||||
params = {
|
||||
"isPagination": "true",
|
||||
"updateDate": "20120601",
|
||||
"updateDateEnd": current_date,
|
||||
"sqlId": "FW_HGT_JSHDBL",
|
||||
"pageHelp.cacheSize": "1",
|
||||
"pageHelp.pageSize": "10000",
|
||||
"pageHelp.pageNo": "1",
|
||||
"pageHelp.beginPage": "1",
|
||||
"pageHelp.endPage": "1",
|
||||
}
|
||||
headers = {
|
||||
"Host": "query.sse.com.cn",
|
||||
"Referer": "https://www.sse.com.cn/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/105.0.0.0 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"])
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"currencyType": "货币种类",
|
||||
"buyPrice": "买入结算汇兑比率",
|
||||
"updateDate": "-",
|
||||
"validDate": "适用日期",
|
||||
"sellPrice": "卖出结算汇兑比率",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"适用日期",
|
||||
"买入结算汇兑比率",
|
||||
"卖出结算汇兑比率",
|
||||
"货币种类",
|
||||
]
|
||||
]
|
||||
temp_df.sort_values("适用日期", inplace=True, ignore_index=True)
|
||||
temp_df["适用日期"] = pd.to_datetime(temp_df["适用日期"], errors="coerce").dt.date
|
||||
temp_df["买入结算汇兑比率"] = pd.to_numeric(
|
||||
temp_df["买入结算汇兑比率"], errors="coerce"
|
||||
)
|
||||
temp_df["卖出结算汇兑比率"] = pd.to_numeric(
|
||||
temp_df["卖出结算汇兑比率"], errors="coerce"
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_sgt_settlement_exchange_rate_szse_df = (
|
||||
stock_sgt_settlement_exchange_rate_szse()
|
||||
)
|
||||
print(stock_sgt_settlement_exchange_rate_szse_df)
|
||||
|
||||
stock_sgt_reference_exchange_rate_szse_df = stock_sgt_reference_exchange_rate_szse()
|
||||
print(stock_sgt_reference_exchange_rate_szse_df)
|
||||
|
||||
stock_sgt_reference_exchange_rate_sse_df = stock_sgt_reference_exchange_rate_sse()
|
||||
print(stock_sgt_reference_exchange_rate_sse_df)
|
||||
|
||||
stock_sgt_settlement_exchange_rate_sse_df = stock_sgt_settlement_exchange_rate_sse()
|
||||
print(stock_sgt_settlement_exchange_rate_sse_df)
|
||||
@@ -0,0 +1,63 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2024/2/5 18:00
|
||||
Desc: 东方财富网-数据中心-沪深港通-市场概括
|
||||
https://data.eastmoney.com/hsgt/hsgtDetail/scgk.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_hsgt_fund_min_em(symbol: str = "北向资金") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-沪深港通-市场概括-分时数据
|
||||
https://data.eastmoney.com/hsgt/hsgtDetail/scgk.html
|
||||
:param symbol: 北向资金; choice of {"北向资金", "南向资金"}
|
||||
:type symbol: str
|
||||
:return: 沪深港通持股-分时数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2.eastmoney.com/api/qt/kamtbs.rtmin/get"
|
||||
params = {
|
||||
"fields1": "f1,f2,f3,f4",
|
||||
"fields2": "f51,f54,f52,f58,f53,f62,f56,f57,f60,f61",
|
||||
"ut": "b2884a393a59ad64002292a3e90d46a5",
|
||||
"_": "1707125786160",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
|
||||
if symbol == "南向资金":
|
||||
n2s_str_list = data_json["data"]["n2s"]
|
||||
temp_df = pd.DataFrame([item.split(",") for item in n2s_str_list])
|
||||
temp_df["date"] = data_json["data"]["n2sDate"]
|
||||
temp_df = temp_df.iloc[:, [0, 1, 3, 5, -1]]
|
||||
temp_df.columns = ["时间", "港股通(沪)", "港股通(深)", "南向资金", "日期"]
|
||||
temp_df = temp_df[["日期", "时间", "港股通(沪)", "港股通(深)", "南向资金"]]
|
||||
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
|
||||
temp_df["港股通(沪)"] = pd.to_numeric(temp_df["港股通(沪)"], errors="coerce")
|
||||
temp_df["港股通(深)"] = pd.to_numeric(temp_df["港股通(深)"], errors="coerce")
|
||||
temp_df["南向资金"] = pd.to_numeric(temp_df["南向资金"], errors="coerce")
|
||||
return temp_df
|
||||
else:
|
||||
s2n_str_list = data_json["data"]["s2n"]
|
||||
temp_df = pd.DataFrame([item.split(",") for item in s2n_str_list])
|
||||
temp_df["date"] = data_json["data"]["s2nDate"]
|
||||
temp_df = temp_df.iloc[:, [0, 1, 3, 5, -1]]
|
||||
temp_df.columns = ["时间", "沪股通", "深股通", "北向资金", "日期"]
|
||||
temp_df = temp_df[["日期", "时间", "沪股通", "深股通", "北向资金"]]
|
||||
temp_df["日期"] = pd.to_datetime(temp_df["日期"], errors="coerce").dt.date
|
||||
temp_df["沪股通"] = pd.to_numeric(temp_df["沪股通"], errors="coerce")
|
||||
temp_df["深股通"] = pd.to_numeric(temp_df["深股通"], errors="coerce")
|
||||
temp_df["北向资金"] = pd.to_numeric(temp_df["北向资金"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_hsgt_fund_min_em_df = stock_hsgt_fund_min_em(symbol="北向资金")
|
||||
print(stock_hsgt_fund_min_em_df)
|
||||
|
||||
stock_hsgt_fund_min_em_df = stock_hsgt_fund_min_em(symbol="南向资金")
|
||||
print(stock_hsgt_fund_min_em_df)
|
||||
@@ -0,0 +1,241 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/8/25 15:00
|
||||
Desc: 东方财富-财经早餐
|
||||
https://stock.eastmoney.com/a/czpnc.html
|
||||
"""
|
||||
|
||||
from datetime import datetime
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.request import make_request_with_retry_json
|
||||
from akshare.utils.cons import headers
|
||||
|
||||
|
||||
def stock_info_cjzc_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-财经早餐
|
||||
https://stock.eastmoney.com/a/czpnc.html
|
||||
:return: 财经早餐
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://np-listapi.eastmoney.com/comm/web/getNewsByColumns"
|
||||
params = {
|
||||
"client": "web",
|
||||
"biz": "web_news_col",
|
||||
"column": "1207",
|
||||
"order": "1",
|
||||
"needInteractData": "0",
|
||||
"page_index": "1",
|
||||
"page_size": "200",
|
||||
"req_trace": "1710314682980",
|
||||
"fields": "code,showTime,title,mediaName,summary,image,url,uniqueUrl,Np_dst",
|
||||
}
|
||||
big_df = pd.DataFrame()
|
||||
for page in range(1, 3):
|
||||
params.update({"page_index": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df = big_df[["title", "summary", "showTime", "uniqueUrl"]]
|
||||
big_df.rename(
|
||||
columns={
|
||||
"title": "标题",
|
||||
"summary": "摘要",
|
||||
"showTime": "发布时间",
|
||||
"uniqueUrl": "链接",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_info_global_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-全球财经快讯
|
||||
https://kuaixun.eastmoney.com/7_24.html
|
||||
:return: 全球财经快讯
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://np-weblist.eastmoney.com/comm/web/getFastNewsList"
|
||||
params = {
|
||||
"client": "web",
|
||||
"biz": "web_724",
|
||||
"fastColumn": "102",
|
||||
"sortEnd": "",
|
||||
"pageSize": "200",
|
||||
"req_trace": "1710315450384",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["fastNewsList"])
|
||||
temp_df = temp_df[["title", "summary", "showTime", "code"]]
|
||||
temp_df["code"] = [
|
||||
f"https://finance.eastmoney.com/a/{item}.html" for item in temp_df["code"]
|
||||
]
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"title": "标题",
|
||||
"summary": "摘要",
|
||||
"showTime": "发布时间",
|
||||
"code": "链接",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_info_global_sina() -> pd.DataFrame:
|
||||
"""
|
||||
新浪财经-全球财经快讯
|
||||
https://finance.sina.com.cn/7x24
|
||||
:return: 全球财经快讯
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://zhibo.sina.com.cn/api/zhibo/feed"
|
||||
params = {
|
||||
"page": "1",
|
||||
"page_size": "20",
|
||||
"zhibo_id": "152",
|
||||
"tag_id": "0",
|
||||
"dire": "f",
|
||||
"dpc": "1",
|
||||
"pagesize": "20",
|
||||
"type": "1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
time_list = [
|
||||
item["create_time"] for item in data_json["result"]["data"]["feed"]["list"]
|
||||
]
|
||||
text_list = [
|
||||
item["rich_text"] for item in data_json["result"]["data"]["feed"]["list"]
|
||||
]
|
||||
temp_df = pd.DataFrame([time_list, text_list]).T
|
||||
temp_df.columns = ["时间", "内容"]
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_info_global_futu() -> pd.DataFrame:
|
||||
"""
|
||||
富途牛牛-快讯
|
||||
https://news.futunn.com/main/live
|
||||
:return: 快讯
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://news.futunn.com/news-site-api/main/get-flash-list"
|
||||
params = {
|
||||
"pageSize": "50",
|
||||
}
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)"
|
||||
" Chrome/111.0.0.0 Safari/537.36"
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["data"]["news"])
|
||||
temp_df = temp_df[["title", "content", "time", "detailUrl"]]
|
||||
temp_df["time"] = [
|
||||
datetime.fromtimestamp(int(item)).strftime("%Y-%m-%d %H:%M:%S")
|
||||
for item in temp_df["time"]
|
||||
]
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"title": "标题",
|
||||
"content": "内容",
|
||||
"time": "发布时间",
|
||||
"detailUrl": "链接",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_info_global_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺财经-全球财经直播
|
||||
https://news.10jqka.com.cn/realtimenews.html
|
||||
:return: 全球财经直播
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://news.10jqka.com.cn/tapp/news/push/stock"
|
||||
params = {
|
||||
"page": "1",
|
||||
"tag": "",
|
||||
"track": "website",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
temp_df = temp_df[["title", "digest", "rtime", "url"]]
|
||||
temp_df["rtime"] = [
|
||||
datetime.fromtimestamp(int(item)).strftime("%Y-%m-%d %H:%M:%S")
|
||||
for item in temp_df["rtime"]
|
||||
]
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"title": "标题",
|
||||
"digest": "内容",
|
||||
"rtime": "发布时间",
|
||||
"url": "链接",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_info_global_cls(symbol: str = "全部") -> pd.DataFrame:
|
||||
"""
|
||||
财联社-电报
|
||||
https://www.cls.cn/telegraph
|
||||
:param symbol: choice of {"全部", "重点"}
|
||||
:type symbol: str
|
||||
:return: 财联社-电报
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.cls.cn/nodeapi/telegraphList"
|
||||
data_json = make_request_with_retry_json(url, max_retries=10, headers=headers)
|
||||
temp_df = pd.DataFrame(data_json["data"]["roll_data"])
|
||||
big_df = temp_df.copy()
|
||||
big_df = big_df[["title", "content", "ctime", "level"]]
|
||||
big_df["ctime"] = pd.to_datetime(big_df["ctime"], unit="s", utc=True).dt.tz_convert(
|
||||
"Asia/Shanghai"
|
||||
)
|
||||
big_df.columns = ["标题", "内容", "发布时间", "等级"]
|
||||
big_df.sort_values(["发布时间"], inplace=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
big_df["发布日期"] = big_df["发布时间"].dt.date
|
||||
big_df["发布时间"] = big_df["发布时间"].dt.time
|
||||
if symbol == "重点":
|
||||
big_df = big_df[(big_df["等级"] == "B") | (big_df["等级"] == "A")]
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
big_df = big_df[["标题", "内容", "发布日期", "发布时间"]]
|
||||
return big_df
|
||||
else:
|
||||
big_df = big_df[["标题", "内容", "发布日期", "发布时间"]]
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_info_cjzc_em_df = stock_info_cjzc_em()
|
||||
print(stock_info_cjzc_em_df)
|
||||
|
||||
stock_info_global_em_df = stock_info_global_em()
|
||||
print(stock_info_global_em_df)
|
||||
|
||||
stock_info_global_sina_df = stock_info_global_sina()
|
||||
print(stock_info_global_sina_df)
|
||||
|
||||
stock_info_global_futu_df = stock_info_global_futu()
|
||||
print(stock_info_global_futu_df)
|
||||
|
||||
stock_info_global_ths_df = stock_info_global_ths()
|
||||
print(stock_info_global_ths_df)
|
||||
|
||||
stock_info_global_cls_df = stock_info_global_cls(symbol="全部")
|
||||
print(stock_info_global_cls_df)
|
||||
+86
@@ -0,0 +1,86 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2024/11/5 16:00
|
||||
Desc: 雪球-行情中心-沪深股市-内部交易
|
||||
https://xueqiu.com/hq/insider
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_inner_trade_xq() -> pd.DataFrame:
|
||||
"""
|
||||
雪球-行情中心-沪深股市-内部交易
|
||||
https://xueqiu.com/hq/insider
|
||||
:return: 内部交易
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://xueqiu.com/service/v5/stock/f10/cn/skholderchg"
|
||||
params = {
|
||||
"size": "100000",
|
||||
"page": "1",
|
||||
"extend": "true",
|
||||
}
|
||||
headers = {
|
||||
"Accept": "*/*",
|
||||
"Accept-Encoding": "gzip, deflate, br",
|
||||
"Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8",
|
||||
"Cache-Control": "no-cache",
|
||||
"Connection": "keep-alive",
|
||||
"Host": "xueqiu.com",
|
||||
"Pragma": "no-cache",
|
||||
"Referer": "https://xueqiu.com/hq",
|
||||
"sec-ch-ua": '" Not A;Brand";v="99", "Chromium";v="100", "Google Chrome";v="100"',
|
||||
"sec-ch-ua-mobile": "?0",
|
||||
"sec-ch-ua-platform": '"Windows"',
|
||||
"Sec-Fetch-Dest": "empty",
|
||||
"Sec-Fetch-Mode": "cors",
|
||||
"Sec-Fetch-Site": "same-origin",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/100.0.4896.127 Safari/537.36",
|
||||
"X-Requested-With": "XMLHttpRequest",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["items"])
|
||||
temp_df.columns = [
|
||||
"股票代码",
|
||||
"股票名称",
|
||||
"变动人",
|
||||
"-",
|
||||
"变动日期",
|
||||
"变动股数",
|
||||
"成交均价",
|
||||
"变动后持股数",
|
||||
"与董监高关系",
|
||||
"董监高职务",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"股票代码",
|
||||
"股票名称",
|
||||
"变动日期",
|
||||
"变动人",
|
||||
"变动股数",
|
||||
"成交均价",
|
||||
"变动后持股数",
|
||||
"与董监高关系",
|
||||
"董监高职务",
|
||||
]
|
||||
]
|
||||
temp_df["变动日期"] = (
|
||||
pd.to_datetime(temp_df["变动日期"], unit="ms", utc=True)
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.date
|
||||
)
|
||||
temp_df["变动股数"] = pd.to_numeric(temp_df["变动股数"], errors="coerce")
|
||||
temp_df["成交均价"] = pd.to_numeric(temp_df["成交均价"], errors="coerce")
|
||||
temp_df["变动后持股数"] = pd.to_numeric(temp_df["变动后持股数"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_inner_trade_xq_df = stock_inner_trade_xq()
|
||||
print(stock_inner_trade_xq_df)
|
||||
@@ -0,0 +1,199 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/20 16:00
|
||||
Desc: 互动易-提问与回答
|
||||
https://irm.cninfo.com.cn/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def _fetch_org_id(symbol: str = "000001") -> str:
|
||||
"""
|
||||
股票-互动易-组织代码
|
||||
https://irm.cninfo.com.cn/
|
||||
:return: 组织代码
|
||||
:rtype: str
|
||||
"""
|
||||
url = "https://irm.cninfo.com.cn/newircs/index/queryKeyboardInfo"
|
||||
params = {"_t": "1691144074"}
|
||||
data = {"keyWord": symbol}
|
||||
r = requests.post(url, params=params, data=data)
|
||||
data_json = r.json()
|
||||
org_id = data_json["data"][0]["secid"]
|
||||
return org_id
|
||||
|
||||
|
||||
def stock_irm_cninfo(symbol: str = "002594") -> pd.DataFrame:
|
||||
"""
|
||||
互动易-提问
|
||||
https://irm.cninfo.com.cn/ircs/question/questionDetail?questionId=1515236357817618432
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 提问
|
||||
:rtype: str
|
||||
"""
|
||||
url = "https://irm.cninfo.com.cn/newircs/company/question"
|
||||
params = {
|
||||
"_t": "1691142650",
|
||||
"stockcode": symbol,
|
||||
"orgId": _fetch_org_id(symbol),
|
||||
"pageSize": "1000",
|
||||
"pageNum": "1",
|
||||
"keyWord": "",
|
||||
"startDay": "",
|
||||
"endDay": "",
|
||||
}
|
||||
r = requests.post(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = int(data_json["totalPage"])
|
||||
total_page = 10 if total_page > 10 else total_page
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, 1 + total_page), leave=False):
|
||||
params.update({"pageNum": page})
|
||||
r = requests.post(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["rows"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"indexId": "问题编号",
|
||||
"contentType": "-",
|
||||
"trade": "行业",
|
||||
"mainContent": "问题",
|
||||
"attachmentUrl": "-",
|
||||
"boardType": "行业代码",
|
||||
"filetype": "-",
|
||||
"pubDate": "提问时间",
|
||||
"stockCode": "股票代码",
|
||||
"companyShortName": "公司简称",
|
||||
"author": "提问者编号",
|
||||
"authorName": "提问者",
|
||||
"authorLogo": "-",
|
||||
"pubClient": "来源",
|
||||
"attachedId": "回答ID",
|
||||
"attachedContent": "回答内容",
|
||||
"attachedAuthor": "回答者",
|
||||
"attachedPubDate": "-",
|
||||
"updateDate": "更新时间",
|
||||
"isPraise": "-",
|
||||
"isFavorite": "-",
|
||||
"isForward": "-",
|
||||
"praiseCount": "-",
|
||||
"qaStatus": "-",
|
||||
"rights": "-",
|
||||
"topStatus": "-",
|
||||
"companyLogo": "-",
|
||||
"favoriteCount": "-",
|
||||
"forwardCount": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"公司简称",
|
||||
"行业",
|
||||
"行业代码",
|
||||
"问题",
|
||||
"提问者",
|
||||
"来源",
|
||||
"提问时间",
|
||||
"更新时间",
|
||||
"提问者编号",
|
||||
"问题编号",
|
||||
"回答ID",
|
||||
"回答内容",
|
||||
"回答者",
|
||||
]
|
||||
]
|
||||
big_df["行业"] = [item[0] for item in big_df["行业"]]
|
||||
big_df["行业代码"] = [item[0] for item in big_df["行业代码"]]
|
||||
big_df["提问时间"] = (
|
||||
pd.to_datetime(big_df["提问时间"], unit="ms", errors="coerce")
|
||||
.dt.tz_localize("UTC")
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
)
|
||||
big_df["更新时间"] = (
|
||||
pd.to_datetime(big_df["更新时间"], unit="ms", errors="coerce")
|
||||
.dt.tz_localize("UTC")
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
)
|
||||
big_df["来源"] = big_df["来源"].map(
|
||||
{
|
||||
"2": "APP",
|
||||
"5": "公众号",
|
||||
"4": "网站",
|
||||
}
|
||||
)
|
||||
big_df["来源"] = big_df["来源"].fillna("网站")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_irm_ans_cninfo(symbol: str = "1513586704097333248") -> pd.DataFrame:
|
||||
"""
|
||||
互动易-回答
|
||||
https://irm.cninfo.com.cn/ircs/question/questionDetail?questionId=1515236357817618432
|
||||
:param symbol: 提问者编号; 通过 ak.stock_irm_cninfo() 来获取具体的提问者编号
|
||||
:type symbol: str
|
||||
:return: 回答
|
||||
:rtype: str
|
||||
"""
|
||||
url = "https://irm.cninfo.com.cn/newircs/question/getQuestionDetail"
|
||||
params = {"questionId": symbol, "_t": "1691146921"}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame.from_dict(data_json["data"], orient="index").T
|
||||
if "replyDate" not in temp_df.columns:
|
||||
return pd.DataFrame()
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"questionContent": "问题",
|
||||
"questioner": "提问者",
|
||||
"questionDate": "提问时间",
|
||||
"replyDate": "回答时间",
|
||||
"replyContent": "回答内容",
|
||||
"stockCode": "股票代码",
|
||||
"shortName": "公司简称",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"股票代码",
|
||||
"公司简称",
|
||||
"问题",
|
||||
"回答内容",
|
||||
"提问者",
|
||||
"提问时间",
|
||||
"回答时间",
|
||||
]
|
||||
]
|
||||
temp_df["提问时间"] = (
|
||||
pd.to_datetime(temp_df["提问时间"], unit="ms", errors="coerce")
|
||||
.dt.tz_localize("UTC")
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
)
|
||||
temp_df["回答时间"] = (
|
||||
pd.to_datetime(temp_df["回答时间"], unit="ms", errors="coerce")
|
||||
.dt.tz_localize("UTC")
|
||||
.dt.tz_convert("Asia/Shanghai")
|
||||
.dt.strftime("%Y-%m-%d %H:%M:%S")
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_irm_cninfo_df = stock_irm_cninfo(symbol="002594")
|
||||
print(stock_irm_cninfo_df)
|
||||
|
||||
stock_irm_ans_cninfo_df = stock_irm_ans_cninfo(symbol="1495108801386602496")
|
||||
print(stock_irm_ans_cninfo_df)
|
||||
@@ -0,0 +1,189 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
date: 2022/2/14 20:02
|
||||
desc: 东方财富网-数据中心-特色数据-机构调研
|
||||
http://data.eastmoney.com/jgdy/
|
||||
东方财富网-数据中心-特色数据-机构调研-机构调研统计: http://data.eastmoney.com/jgdy/tj.html
|
||||
东方财富网-数据中心-特色数据-机构调研-机构调研详细: http://data.eastmoney.com/jgdy/xx.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def stock_jgdy_tj_em(date: str = "20220101") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-机构调研-机构调研统计
|
||||
https://data.eastmoney.com/jgdy/tj.html
|
||||
:param date: 开始时间
|
||||
:type date: str
|
||||
:return: 机构调研统计
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SUM,RECEIVE_START_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1,-1,1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_ORG_SURVEYNEW",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"""(NUMBERNEW="1")(IS_SOURCE="1")(NOTICE_DATE>'{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat([big_df, temp_df])
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = list(range(1, len(big_df) + 1))
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"代码",
|
||||
"名称",
|
||||
"_",
|
||||
"公告日期",
|
||||
"接待日期",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"接待地点",
|
||||
"_",
|
||||
"接待方式",
|
||||
"_",
|
||||
"接待人员",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"接待机构数量",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"接待机构数量",
|
||||
"接待方式",
|
||||
"接待人员",
|
||||
"接待地点",
|
||||
"接待日期",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["接待机构数量"] = pd.to_numeric(big_df["接待机构数量"], errors="coerce")
|
||||
big_df["接待日期"] = pd.to_datetime(big_df["接待日期"], errors="coerce").dt.date
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_jgdy_detail_em(date: str = "20241211") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-机构调研-机构调研详细
|
||||
https://data.eastmoney.com/jgdy/xx.html
|
||||
:param date: 开始时间
|
||||
:type date: str
|
||||
:return: 机构调研详细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,RECEIVE_START_DATE,SECURITY_CODE,NUMBERNEW",
|
||||
"sortTypes": "-1,-1,1,-1",
|
||||
"pageSize": "50",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_ORG_SURVEY",
|
||||
"columns": "SECUCODE,SECURITY_CODE,SECURITY_NAME_ABBR,NOTICE_DATE,RECEIVE_START_DATE,"
|
||||
"RECEIVE_OBJECT,RECEIVE_PLACE,RECEIVE_WAY_EXPLAIN,INVESTIGATORS,RECEPTIONIST,ORG_TYPE",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~CLOSE_PRICE,f3~01~SECURITY_CODE~CHANGE_RATE",
|
||||
"quoteType": "0",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"""(IS_SOURCE="1")(RECEIVE_START_DATE>'{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat([big_df, temp_df])
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = list(range(1, len(big_df) + 1))
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"代码",
|
||||
"名称",
|
||||
"公告日期",
|
||||
"调研日期",
|
||||
"调研机构",
|
||||
"接待地点",
|
||||
"接待方式",
|
||||
"调研人员",
|
||||
"接待人员",
|
||||
"机构类型",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"调研机构",
|
||||
"机构类型",
|
||||
"调研人员",
|
||||
"接待方式",
|
||||
"接待人员",
|
||||
"接待地点",
|
||||
"调研日期",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["调研日期"] = pd.to_datetime(big_df["调研日期"], errors="coerce").dt.date
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_jgdy_tj_em_df = stock_jgdy_tj_em(date="20180928")
|
||||
print(stock_jgdy_tj_em_df)
|
||||
|
||||
stock_jgdy_detail_em_df = stock_jgdy_detail_em(date="20210915")
|
||||
print(stock_jgdy_detail_em_df)
|
||||
@@ -0,0 +1,96 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/6 20:30
|
||||
Desc: 同花顺-数据中心-营业部排名
|
||||
https://data.10jqka.com.cn/market/longhu/
|
||||
"""
|
||||
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
from akshare.utils.cons import headers
|
||||
|
||||
|
||||
def stock_lh_yyb_most() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-营业部排名-上榜次数最多
|
||||
https://data.10jqka.com.cn/market/longhu/
|
||||
:return: 上榜次数最多
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/sbcs/field/sbcs/sort/desc/page/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_str = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
total_page = int(page_str.split("/")[1]) + 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page), leave=False):
|
||||
url = f"https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/sbcs/field/sbcs/sort/desc/page/{page}/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lh_yyb_capital() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-营业部排名-资金实力最强
|
||||
https://data.10jqka.com.cn/market/longhu/
|
||||
:return: 资金实力最强
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/zjsl/field/zgczje/sort/desc/page/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_str = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
total_page = int(page_str.split("/")[1]) + 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page), leave=False):
|
||||
url = f"https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/zjsl/field/zgczje/sort/desc/page/{page}/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lh_yyb_control() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-营业部排名-抱团操作实力
|
||||
https://data.10jqka.com.cn/market/longhu/
|
||||
:return: 抱团操作实力
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/btcz/field/xsjs/sort/desc/page/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
page_str = soup.find(name="span", attrs={"class": "page_info"}).text
|
||||
total_page = int(page_str.split("/")[1]) + 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page), leave=False):
|
||||
url = f"https://data.10jqka.com.cn/ifmarket/lhbyyb/type/1/tab/btcz/field/xsjs/sort/desc/page/{page}/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_lh_yyb_most_df = stock_lh_yyb_most()
|
||||
print(stock_lh_yyb_most_df)
|
||||
|
||||
stock_lh_yyb_capital_df = stock_lh_yyb_capital()
|
||||
print(stock_lh_yyb_capital_df)
|
||||
|
||||
stock_lh_yyb_control_df = stock_lh_yyb_control()
|
||||
print(stock_lh_yyb_control_df)
|
||||
File diff suppressed because it is too large
Load Diff
@@ -0,0 +1,264 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/10 00:00
|
||||
Desc: 新浪财经-龙虎榜
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml
|
||||
"""
|
||||
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_lhb_detail_daily_sina(date: str = "20240222") -> pd.DataFrame:
|
||||
"""
|
||||
龙虎榜-每日详情
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 龙虎榜-每日详情
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
date = "-".join([date[:4], date[4:6], date[6:]])
|
||||
url = "https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml"
|
||||
params = {"tradedate": date}
|
||||
r = requests.get(url, params=params)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
selected_html = soup.find(name="div", attrs={"class": "list"}).find_all(
|
||||
name="table", attrs={"class": "list_table"}
|
||||
)
|
||||
big_df = pd.DataFrame()
|
||||
for table in selected_html:
|
||||
temp_df = pd.read_html(StringIO(table.prettify()), header=0, skiprows=1)[0]
|
||||
temp_symbol = pd.read_html(StringIO(table.prettify()))[0].iat[0, 0]
|
||||
temp_df["指标"] = temp_symbol
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
del big_df["查看详情"]
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票名称",
|
||||
"收盘价",
|
||||
"对应值",
|
||||
"成交量",
|
||||
"成交额",
|
||||
"指标",
|
||||
]
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["对应值"] = pd.to_numeric(big_df["对应值"], errors="coerce")
|
||||
big_df["成交量"] = pd.to_numeric(big_df["成交量"], errors="coerce")
|
||||
big_df["成交额"] = pd.to_numeric(big_df["成交额"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def _find_last_page(
|
||||
url: str = "https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml",
|
||||
recent_day: str = "60",
|
||||
):
|
||||
params = {
|
||||
"last": recent_day,
|
||||
"p": "1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
soup = BeautifulSoup(r.text, "lxml")
|
||||
try:
|
||||
previous_page = int(soup.find_all(attrs={"class": "page"})[-2].text)
|
||||
except: # noqa: E722
|
||||
previous_page = 1
|
||||
if previous_page != 1:
|
||||
while True:
|
||||
params = {
|
||||
"last": recent_day,
|
||||
"p": previous_page,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
last_page = int(soup.find_all(attrs={"class": "page"})[-2].text)
|
||||
if last_page != previous_page:
|
||||
previous_page = last_page
|
||||
continue
|
||||
else:
|
||||
break
|
||||
return previous_page
|
||||
|
||||
|
||||
def stock_lhb_ggtj_sina(symbol: str = "5") -> pd.DataFrame:
|
||||
"""
|
||||
龙虎榜-个股上榜统计
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml
|
||||
:param symbol: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
|
||||
:type symbol: str
|
||||
:return: 龙虎榜-个股上榜统计
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = (
|
||||
"https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml"
|
||||
)
|
||||
last_page_num = _find_last_page(url, symbol)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, last_page_num + 1), leave=False):
|
||||
params = {
|
||||
"last": symbol,
|
||||
"p": page,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0].iloc[0:, :]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df.columns = [
|
||||
"股票代码",
|
||||
"股票名称",
|
||||
"上榜次数",
|
||||
"累积购买额",
|
||||
"累积卖出额",
|
||||
"净额",
|
||||
"买入席位数",
|
||||
"卖出席位数",
|
||||
]
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lhb_yytj_sina(symbol: str = "5") -> pd.DataFrame:
|
||||
"""
|
||||
龙虎榜-营业部上榜统计
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/yytj/index.phtml
|
||||
:param symbol: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
|
||||
:type symbol: str
|
||||
:return: 龙虎榜-营业部上榜统计
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = (
|
||||
"https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/yytj/index.phtml"
|
||||
)
|
||||
last_page_num = _find_last_page(url, symbol)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, last_page_num + 1), leave=False):
|
||||
params = {
|
||||
"last": "5",
|
||||
"p": page,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0].iloc[0:, :]
|
||||
big_df = pd.concat([big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"营业部名称",
|
||||
"上榜次数",
|
||||
"累积购买额",
|
||||
"买入席位数",
|
||||
"累积卖出额",
|
||||
"卖出席位数",
|
||||
"买入前三股票",
|
||||
]
|
||||
big_df["上榜次数"] = pd.to_numeric(big_df["上榜次数"], errors="coerce")
|
||||
big_df["买入席位数"] = pd.to_numeric(big_df["买入席位数"], errors="coerce")
|
||||
big_df["卖出席位数"] = pd.to_numeric(big_df["卖出席位数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lhb_jgzz_sina(symbol: str = "5") -> pd.DataFrame:
|
||||
"""
|
||||
龙虎榜-机构席位追踪
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgzz/index.phtml
|
||||
:param symbol: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
|
||||
:type symbol: str
|
||||
:return: 龙虎榜-机构席位追踪
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = (
|
||||
"https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgzz/index.phtml"
|
||||
)
|
||||
last_page_num = _find_last_page(url, symbol)
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, last_page_num + 1), leave=False):
|
||||
params = {
|
||||
"last": symbol,
|
||||
"p": page,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0].iloc[0:, :]
|
||||
if temp_df.empty:
|
||||
continue
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
del big_df["当前价"]
|
||||
del big_df["涨跌幅"]
|
||||
big_df.columns = [
|
||||
"股票代码",
|
||||
"股票名称",
|
||||
"累积买入额",
|
||||
"买入次数",
|
||||
"累积卖出额",
|
||||
"卖出次数",
|
||||
"净额",
|
||||
]
|
||||
big_df["买入次数"] = pd.to_numeric(big_df["买入次数"], errors="coerce")
|
||||
big_df["卖出次数"] = pd.to_numeric(big_df["卖出次数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lhb_jgmx_sina() -> pd.DataFrame:
|
||||
"""
|
||||
龙虎榜-机构席位成交明细
|
||||
https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgmx/index.phtml
|
||||
:return: 龙虎榜-机构席位成交明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = (
|
||||
"https://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgmx/index.phtml"
|
||||
)
|
||||
params = {
|
||||
"p": "1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
last_page_num = int(soup.find_all(attrs={"class": "page"})[-2].text)
|
||||
except: # noqa: E722
|
||||
last_page_num = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, last_page_num + 1), leave=False):
|
||||
params = {
|
||||
"p": page,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0].iloc[0:, :]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["交易日期"] = pd.to_datetime(big_df["交易日期"], errors="coerce").dt.date
|
||||
big_df.rename(
|
||||
columns={
|
||||
"机构席位买入额(万)": "机构席位买入额",
|
||||
"机构席位卖出额(万)": "机构席位卖出额",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df["机构席位买入额"] = pd.to_numeric(big_df["机构席位买入额"], errors="coerce")
|
||||
big_df["机构席位卖出额"] = pd.to_numeric(big_df["机构席位卖出额"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_lhb_detail_daily_sina_df = stock_lhb_detail_daily_sina(date="20240222")
|
||||
print(stock_lhb_detail_daily_sina_df)
|
||||
|
||||
stock_lhb_ggtj_sina_df = stock_lhb_ggtj_sina(symbol="5")
|
||||
print(stock_lhb_ggtj_sina_df)
|
||||
|
||||
stock_lhb_yytj_sina_df = stock_lhb_yytj_sina(symbol="5")
|
||||
print(stock_lhb_yytj_sina_df)
|
||||
|
||||
stock_lhb_jgzz_sina_df = stock_lhb_jgzz_sina(symbol="5")
|
||||
print(stock_lhb_jgzz_sina_df)
|
||||
|
||||
stock_lhb_jgmx_sina_df = stock_lhb_jgmx_sina()
|
||||
print(stock_lhb_jgmx_sina_df)
|
||||
@@ -0,0 +1,101 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/6/14 17:00
|
||||
Desc: 东方财富网-数据中心-融资融券-融资融券账户统计-两融账户信息
|
||||
https://www.szse.cn/disclosure/margin/object/index.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_margin_account_info() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-融资融券-融资融券账户统计-两融账户信息
|
||||
https://data.eastmoney.com/rzrq/zhtjday.html
|
||||
:return: 融资融券账户统计
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"reportName": "RPTA_WEB_MARGIN_DAILYTRADE",
|
||||
"columns": "ALL",
|
||||
"pageNumber": "1",
|
||||
"pageSize": "500",
|
||||
"sortColumns": "STATISTICS_DATE",
|
||||
"sortTypes": "-1",
|
||||
"p": "1",
|
||||
"pageNo": "1",
|
||||
"pageNum": "1",
|
||||
}
|
||||
r = requests.get(url=url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
tqdm = get_tqdm()
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
"p": page,
|
||||
"pageNo": page,
|
||||
"pageNum": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url=url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"STATISTICS_DATE": "日期",
|
||||
"FIN_BALANCE": "融资余额",
|
||||
"LOAN_BALANCE": "融券余额",
|
||||
"FIN_BUY_AMT": "融资买入额",
|
||||
"LOAN_SELL_AMT": "融券卖出额",
|
||||
"SECURITY_ORG_NUM": "证券公司数量",
|
||||
"OPERATEDEPT_NUM": "营业部数量",
|
||||
"PERSONAL_INVESTOR_NUM": "个人投资者数量",
|
||||
"ORG_INVESTOR_NUM": "机构投资者数量",
|
||||
"INVESTOR_NUM": "参与交易的投资者数量",
|
||||
"MARGINLIAB_INVESTOR_NUM": "有融资融券负债的投资者数量",
|
||||
"TOTAL_GUARANTEE": "担保物总价值",
|
||||
"AVG_GUARANTEE_RATIO": "平均维持担保比例",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"日期",
|
||||
"融资余额",
|
||||
"融券余额",
|
||||
"融资买入额",
|
||||
"融券卖出额",
|
||||
"证券公司数量",
|
||||
"营业部数量",
|
||||
"个人投资者数量",
|
||||
"机构投资者数量",
|
||||
"参与交易的投资者数量",
|
||||
"有融资融券负债的投资者数量",
|
||||
"担保物总价值",
|
||||
"平均维持担保比例",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
for item in big_df.columns[1:]:
|
||||
big_df[item] = pd.to_numeric(big_df[item], errors="coerce")
|
||||
big_df.sort_values(["日期"], ignore_index=True, inplace=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_margin_account_info_df = stock_margin_account_info()
|
||||
print(stock_margin_account_info_df)
|
||||
@@ -0,0 +1,214 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2026/1/13 15:20
|
||||
Desc: 上海证券交易所-融资融券数据
|
||||
https://www.sse.com.cn/market/othersdata/margin/sum/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_margin_ratio_pa(symbol: str = "深市", date: str = "20260113") -> pd.DataFrame:
|
||||
"""
|
||||
融资融券-标的证券名单及保证金比例查询
|
||||
https://stock.pingan.com/static/webinfo/margin/business.html?businessType=0
|
||||
:param symbol: choice of {"深市", "沪市", "北交所"}
|
||||
:type symbol: str
|
||||
:param date: 交易日期
|
||||
:type date: str
|
||||
:return: 标的证券名单及保证金比例查询
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
market_code = {
|
||||
"深市": "00",
|
||||
"沪市": "10",
|
||||
"北交所": "30",
|
||||
}
|
||||
url = "https://stock.pingan.com/fss/servlet/fsscoreapp/stockSource/mrgRatio"
|
||||
payload = {
|
||||
"currentPage": 1,
|
||||
"pageSize": 50000,
|
||||
"type": "bdzq",
|
||||
"setdate": "-".join([date[:4], date[4:6], date[6:]]),
|
||||
"stockMes": "",
|
||||
"market": market_code[symbol],
|
||||
"appName": "AYLCH5",
|
||||
"tokenId": "",
|
||||
"appChannel": "LRSP",
|
||||
"requestId": "194055910e2075c03e25fabf6ffc5a7f",
|
||||
"channel": "pa18",
|
||||
}
|
||||
r = requests.post(url, json=payload)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["list"])
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"fiMarginRatio": "融资比例",
|
||||
"secuCode": "证券代码",
|
||||
"secuName": "证券简称",
|
||||
"slMarginRatio": "融券比例",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"证券代码",
|
||||
"证券简称",
|
||||
"融资比例",
|
||||
"融券比例",
|
||||
]
|
||||
]
|
||||
temp_df["融资比例"] = pd.to_numeric(temp_df["融资比例"], errors="coerce")
|
||||
temp_df["融券比例"] = pd.to_numeric(temp_df["融券比例"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_margin_sse(
|
||||
start_date: str = "20010106", end_date: str = "20230922"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
上海证券交易所-融资融券数据-融资融券汇总
|
||||
https://www.sse.com.cn/market/othersdata/margin/sum/
|
||||
:param start_date: 交易开始日期
|
||||
:type start_date: str
|
||||
:param end_date: 交易结束日期
|
||||
:type end_date: str
|
||||
:return: 融资融券汇总
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://query.sse.com.cn/marketdata/tradedata/queryMargin.do"
|
||||
params = {
|
||||
"isPagination": "true",
|
||||
"beginDate": start_date,
|
||||
"endDate": end_date,
|
||||
"tabType": "",
|
||||
"stockCode": "",
|
||||
"pageHelp.pageSize": "5000",
|
||||
"pageHelp.pageNo": "1",
|
||||
"pageHelp.beginPage": "1",
|
||||
"pageHelp.cacheSize": "1",
|
||||
"pageHelp.endPage": "5",
|
||||
}
|
||||
headers = {
|
||||
"Referer": "https://www.sse.com.cn/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/88.0.4324.150 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"])
|
||||
temp_df.columns = [
|
||||
"_",
|
||||
"信用交易日期",
|
||||
"_",
|
||||
"融券卖出量",
|
||||
"融券余量",
|
||||
"融券余量金额",
|
||||
"_",
|
||||
"_",
|
||||
"融资买入额",
|
||||
"融资融券余额",
|
||||
"融资余额",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"信用交易日期",
|
||||
"融资余额",
|
||||
"融资买入额",
|
||||
"融券余量",
|
||||
"融券余量金额",
|
||||
"融券卖出量",
|
||||
"融资融券余额",
|
||||
]
|
||||
]
|
||||
temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce")
|
||||
temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce")
|
||||
temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce")
|
||||
temp_df["融券余量金额"] = pd.to_numeric(temp_df["融券余量金额"], errors="coerce")
|
||||
temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce")
|
||||
temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_margin_detail_sse(date: str = "20230922") -> pd.DataFrame:
|
||||
"""
|
||||
上海证券交易所-融资融券数据-融资融券明细
|
||||
https://www.sse.com.cn/market/othersdata/margin/detail/
|
||||
:param date: 交易日期
|
||||
:type date: str
|
||||
:return: 融资融券明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://query.sse.com.cn/marketdata/tradedata/queryMargin.do"
|
||||
params = {
|
||||
"isPagination": "true",
|
||||
"tabType": "mxtype",
|
||||
"detailsDate": date,
|
||||
"stockCode": "",
|
||||
"beginDate": "",
|
||||
"endDate": "",
|
||||
"pageHelp.pageSize": "5000",
|
||||
"pageHelp.pageCount": "50",
|
||||
"pageHelp.pageNo": "1",
|
||||
"pageHelp.beginPage": "1",
|
||||
"pageHelp.cacheSize": "1",
|
||||
"pageHelp.endPage": "21",
|
||||
}
|
||||
headers = {
|
||||
"Referer": "https://www.sse.com.cn/",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/88.0.4324.150 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"])
|
||||
temp_df.columns = [
|
||||
"_",
|
||||
"信用交易日期",
|
||||
"融券偿还量",
|
||||
"融券卖出量",
|
||||
"融券余量",
|
||||
"_",
|
||||
"_",
|
||||
"融资偿还额",
|
||||
"融资买入额",
|
||||
"_",
|
||||
"融资余额",
|
||||
"标的证券简称",
|
||||
"标的证券代码",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"信用交易日期",
|
||||
"标的证券代码",
|
||||
"标的证券简称",
|
||||
"融资余额",
|
||||
"融资买入额",
|
||||
"融资偿还额",
|
||||
"融券余量",
|
||||
"融券卖出量",
|
||||
"融券偿还量",
|
||||
]
|
||||
]
|
||||
temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce")
|
||||
temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce")
|
||||
temp_df["融资偿还额"] = pd.to_numeric(temp_df["融资偿还额"], errors="coerce")
|
||||
temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce")
|
||||
temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce")
|
||||
temp_df["融券偿还量"] = pd.to_numeric(temp_df["融券偿还量"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_margin_ratio_pa_df = stock_margin_ratio_pa(symbol="沪市", date="20260113")
|
||||
print(stock_margin_ratio_pa_df)
|
||||
|
||||
stock_margin_sse_df = stock_margin_sse(start_date="20010106", end_date="20210401")
|
||||
print(stock_margin_sse_df)
|
||||
|
||||
stock_margin_detail_sse_df = stock_margin_detail_sse(date="20230922")
|
||||
print(stock_margin_detail_sse_df)
|
||||
@@ -0,0 +1,156 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/6/14 17:00
|
||||
Desc: 深圳证券交易所-融资融券数据
|
||||
https://www.szse.cn/disclosure/margin/object/index.html
|
||||
"""
|
||||
|
||||
import warnings
|
||||
from io import BytesIO
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_margin_underlying_info_szse(date: str = "20221129") -> pd.DataFrame:
|
||||
"""
|
||||
深圳证券交易所-融资融券数据-标的证券信息
|
||||
https://www.szse.cn/disclosure/margin/object/index.html
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 标的证券信息
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.szse.cn/api/report/ShowReport"
|
||||
params = {
|
||||
"SHOWTYPE": "xlsx",
|
||||
"CATALOGID": "1834_xxpl",
|
||||
"txtDate": "-".join([date[:4], date[4:6], date[6:]]),
|
||||
"tab1PAGENO": "1",
|
||||
"random": "0.7425245522795993",
|
||||
"TABKEY": "tab1",
|
||||
}
|
||||
headers = {
|
||||
"Referer": "https://www.szse.cn/disclosure/margin/object/index.html",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/88.0.4324.150 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
with warnings.catch_warnings(record=True):
|
||||
warnings.simplefilter("always")
|
||||
temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl", dtype={"证券代码": str})
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_margin_szse(date: str = "20240411") -> pd.DataFrame:
|
||||
"""
|
||||
深圳证券交易所-融资融券数据-融资融券汇总
|
||||
https://www.szse.cn/disclosure/margin/margin/index.html
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 融资融券汇总
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.szse.cn/api/report/ShowReport/data"
|
||||
params = {
|
||||
"SHOWTYPE": "JSON",
|
||||
"CATALOGID": "1837_xxpl",
|
||||
"txtDate": "-".join([date[:4], date[4:6], date[6:]]),
|
||||
"tab1PAGENO": "1",
|
||||
"random": "0.7425245522795993",
|
||||
}
|
||||
headers = {
|
||||
"Referer": "https://www.szse.cn/disclosure/margin/object/index.html",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/88.0.4324.150 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json[0]["data"])
|
||||
temp_df.columns = [
|
||||
"融资买入额",
|
||||
"融资余额",
|
||||
"融券卖出量",
|
||||
"融券余量",
|
||||
"融券余额",
|
||||
"融资融券余额",
|
||||
]
|
||||
temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "")
|
||||
temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce")
|
||||
temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "")
|
||||
temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce")
|
||||
temp_df["融券卖出量"] = temp_df["融券卖出量"].str.replace(",", "")
|
||||
temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce")
|
||||
temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "")
|
||||
temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce")
|
||||
temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "")
|
||||
temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"], errors="coerce")
|
||||
temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "")
|
||||
temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_margin_detail_szse(date: str = "20230925") -> pd.DataFrame:
|
||||
"""
|
||||
深证证券交易所-融资融券数据-融资融券交易明细
|
||||
https://www.szse.cn/disclosure/margin/margin/index.html
|
||||
:param date: 交易日期
|
||||
:type date: str
|
||||
:return: 融资融券明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://www.szse.cn/api/report/ShowReport"
|
||||
params = {
|
||||
"SHOWTYPE": "xlsx",
|
||||
"CATALOGID": "1837_xxpl",
|
||||
"txtDate": "-".join([date[:4], date[4:6], date[6:]]),
|
||||
"tab2PAGENO": "1",
|
||||
"random": "0.24279342734085696",
|
||||
"TABKEY": "tab2",
|
||||
}
|
||||
headers = {
|
||||
"Referer": "https://www.szse.cn/disclosure/margin/margin/index.html",
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) "
|
||||
"Chrome/88.0.4324.150 Safari/537.36",
|
||||
}
|
||||
r = requests.get(url, params=params, headers=headers)
|
||||
with warnings.catch_warnings(record=True):
|
||||
warnings.simplefilter("always")
|
||||
temp_df = pd.read_excel(BytesIO(r.content), engine="openpyxl", dtype={"证券代码": str})
|
||||
temp_df.columns = [
|
||||
"证券代码",
|
||||
"证券简称",
|
||||
"融资买入额",
|
||||
"融资余额",
|
||||
"融券卖出量",
|
||||
"融券余量",
|
||||
"融券余额",
|
||||
"融资融券余额",
|
||||
]
|
||||
temp_df["证券简称"] = temp_df["证券简称"].str.replace(" ", "")
|
||||
temp_df["融资买入额"] = temp_df["融资买入额"].str.replace(",", "")
|
||||
temp_df["融资买入额"] = pd.to_numeric(temp_df["融资买入额"], errors="coerce")
|
||||
temp_df["融资余额"] = temp_df["融资余额"].str.replace(",", "")
|
||||
temp_df["融资余额"] = pd.to_numeric(temp_df["融资余额"], errors="coerce")
|
||||
temp_df["融券卖出量"] = temp_df["融券卖出量"].astype(str).str.replace(",", "")
|
||||
temp_df["融券卖出量"] = pd.to_numeric(temp_df["融券卖出量"], errors="coerce")
|
||||
temp_df["融券余量"] = temp_df["融券余量"].str.replace(",", "")
|
||||
temp_df["融券余量"] = pd.to_numeric(temp_df["融券余量"], errors="coerce")
|
||||
temp_df["融券余额"] = temp_df["融券余额"].str.replace(",", "")
|
||||
temp_df["融券余额"] = pd.to_numeric(temp_df["融券余额"], errors="coerce")
|
||||
temp_df["融资融券余额"] = temp_df["融资融券余额"].str.replace(",", "")
|
||||
temp_df["融资融券余额"] = pd.to_numeric(temp_df["融资融券余额"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_margin_underlying_info_szse_df = stock_margin_underlying_info_szse(
|
||||
date="20221129"
|
||||
)
|
||||
print(stock_margin_underlying_info_szse_df)
|
||||
|
||||
stock_margin_szse_df = stock_margin_szse(date="20240411")
|
||||
print(stock_margin_szse_df)
|
||||
|
||||
stock_margin_detail_szse_df = stock_margin_detail_szse(date="20240411")
|
||||
print(stock_margin_detail_szse_df)
|
||||
@@ -0,0 +1,53 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2026/5/18 18:22
|
||||
Desc: 乐咕乐股网-赚钱效应分析
|
||||
https://www.legulegu.com/stockdata/market-activity
|
||||
"""
|
||||
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.utils.cons import headers
|
||||
|
||||
|
||||
def stock_market_activity_legu() -> pd.DataFrame:
|
||||
"""
|
||||
乐咕乐股网-赚钱效应分析
|
||||
https://www.legulegu.com/stockdata/market-activity
|
||||
:return: 乐咕乐股网-赚钱效应分析
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://legulegu.com/stockdata/market-activity"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0]
|
||||
temp_df_one = temp_df.iloc[:, :2]
|
||||
temp_df_one.columns = ["item", "value"]
|
||||
temp_df_two = temp_df.iloc[:, 2:4]
|
||||
temp_df_two.columns = ["item", "value"]
|
||||
temp_df_three = temp_df.iloc[:, 4:6]
|
||||
temp_df_three.columns = ["item", "value"]
|
||||
temp_df = pd.concat(
|
||||
objs=[temp_df_one, temp_df_two, temp_df_three], ignore_index=True
|
||||
)
|
||||
temp_df.dropna(how="all", axis=0, inplace=True)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
item_str = soup.find(name="div", attrs={"class": "metric-activity"}).text.strip()
|
||||
inner_temp_df = pd.DataFrame([item.strip() for item in item_str.split("\n")]).T
|
||||
inner_temp_df.columns = ["item", "value"]
|
||||
temp_df = pd.concat(objs=[temp_df, inner_temp_df], ignore_index=True)
|
||||
item_str = soup.find(name="div", attrs={"class": "market-activity-meta"}).text.strip()
|
||||
inner_temp_df = pd.DataFrame(["统计日期", item_str]).T
|
||||
inner_temp_df.columns = ["item", "value"]
|
||||
temp_df = pd.concat(objs=[temp_df, inner_temp_df], ignore_index=True)
|
||||
temp_df.reset_index(inplace=True, drop=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_market_activity_legu_df = stock_market_activity_legu()
|
||||
print(stock_market_activity_legu_df)
|
||||
@@ -0,0 +1,174 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2022/12/27 21:11
|
||||
Desc: 东方财富-行情中心-盘口异动
|
||||
https://quote.eastmoney.com/changes/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_changes_em(symbol: str = "大笔买入") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-行情中心-盘口异动
|
||||
https://quote.eastmoney.com/changes/
|
||||
:param symbol: choice of {'火箭发射', '快速反弹', '大笔买入', '封涨停板', '打开跌停板', '有大买盘',
|
||||
'竞价上涨', '高开5日线', '向上缺口', '60日新高', '60日大幅上涨', '加速下跌', '高台跳水',
|
||||
'大笔卖出', '封跌停板', '打开涨停板', '有大卖盘', '竞价下跌', '低开5日线', '向下缺口', '60日新低', '60日大幅下跌'}
|
||||
:type symbol: str
|
||||
:return: 盘口异动
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getAllStockChanges"
|
||||
symbol_map = {
|
||||
"火箭发射": "8201",
|
||||
"快速反弹": "8202",
|
||||
"大笔买入": "8193",
|
||||
"封涨停板": "4",
|
||||
"打开跌停板": "32",
|
||||
"有大买盘": "64",
|
||||
"竞价上涨": "8207",
|
||||
"高开5日线": "8209",
|
||||
"向上缺口": "8211",
|
||||
"60日新高": "8213",
|
||||
"60日大幅上涨": "8215",
|
||||
"加速下跌": "8204",
|
||||
"高台跳水": "8203",
|
||||
"大笔卖出": "8194",
|
||||
"封跌停板": "8",
|
||||
"打开涨停板": "16",
|
||||
"有大卖盘": "128",
|
||||
"竞价下跌": "8208",
|
||||
"低开5日线": "8210",
|
||||
"向下缺口": "8212",
|
||||
"60日新低": "8214",
|
||||
"60日大幅下跌": "8216",
|
||||
}
|
||||
reversed_symbol_map = {v: k for k, v in symbol_map.items()}
|
||||
params = {
|
||||
"type": symbol_map[symbol],
|
||||
"pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wzchanges",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"]["allstock"])
|
||||
temp_df["tm"] = pd.to_datetime(temp_df["tm"], format="%H%M%S").dt.time
|
||||
temp_df.columns = [
|
||||
"时间",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"板块",
|
||||
"相关信息",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"时间",
|
||||
"代码",
|
||||
"名称",
|
||||
"板块",
|
||||
"相关信息",
|
||||
]
|
||||
]
|
||||
temp_df["板块"] = temp_df["板块"].astype(str)
|
||||
temp_df["板块"] = temp_df["板块"].map(reversed_symbol_map)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_board_change_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-行情中心-当日板块异动详情
|
||||
https://quote.eastmoney.com/changes/
|
||||
:return: 当日板块异动详情页
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getAllBKChanges"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wzchanges",
|
||||
"pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
data_df = pd.DataFrame(data_json["data"]["allbk"])
|
||||
data_df.columns = [
|
||||
"-",
|
||||
"-",
|
||||
"板块名称",
|
||||
"涨跌幅",
|
||||
"主力净流入",
|
||||
"板块异动总次数",
|
||||
"ms",
|
||||
"板块具体异动类型列表及出现次数",
|
||||
]
|
||||
data_df["板块异动最频繁个股及所属类型-买卖方向"] = [
|
||||
item["m"] for item in data_df["ms"]
|
||||
]
|
||||
data_df["板块异动最频繁个股及所属类型-股票代码"] = [
|
||||
item["c"] for item in data_df["ms"]
|
||||
]
|
||||
data_df["板块异动最频繁个股及所属类型-股票名称"] = [
|
||||
item["n"] for item in data_df["ms"]
|
||||
]
|
||||
data_df["板块异动最频繁个股及所属类型-买卖方向"] = data_df[
|
||||
"板块异动最频繁个股及所属类型-买卖方向"
|
||||
].map({0: "大笔买入", 1: "大笔卖出"})
|
||||
data_df = data_df[
|
||||
[
|
||||
"板块名称",
|
||||
"涨跌幅",
|
||||
"主力净流入",
|
||||
"板块异动总次数",
|
||||
"板块异动最频繁个股及所属类型-股票代码",
|
||||
"板块异动最频繁个股及所属类型-股票名称",
|
||||
"板块异动最频繁个股及所属类型-买卖方向",
|
||||
"板块具体异动类型列表及出现次数",
|
||||
]
|
||||
]
|
||||
data_df["涨跌幅"] = pd.to_numeric(data_df["涨跌幅"], errors="coerce")
|
||||
data_df["主力净流入"] = pd.to_numeric(data_df["主力净流入"], errors="coerce")
|
||||
data_df["板块异动总次数"] = pd.to_numeric(
|
||||
data_df["板块异动总次数"], errors="coerce"
|
||||
)
|
||||
return data_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_changes_em_df = stock_changes_em(symbol="大笔买入")
|
||||
print(stock_changes_em_df)
|
||||
|
||||
stock_board_change_em_df = stock_board_change_em()
|
||||
print(stock_board_change_em_df)
|
||||
|
||||
for item in {
|
||||
"火箭发射",
|
||||
"快速反弹",
|
||||
"大笔买入",
|
||||
"封涨停板",
|
||||
"打开跌停板",
|
||||
"有大买盘",
|
||||
"竞价上涨",
|
||||
"高开5日线",
|
||||
"向上缺口",
|
||||
"60日新高",
|
||||
"60日大幅上涨",
|
||||
"加速下跌",
|
||||
"高台跳水",
|
||||
"大笔卖出",
|
||||
"封跌停板",
|
||||
"打开涨停板",
|
||||
"有大卖盘",
|
||||
"竞价下跌",
|
||||
"低开5日线",
|
||||
"向下缺口",
|
||||
"60日新低",
|
||||
"60日大幅下跌",
|
||||
}:
|
||||
stock_changes_em_df = stock_changes_em(symbol=item)
|
||||
print(stock_changes_em_df)
|
||||
@@ -0,0 +1,97 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2022/9/21 16:49
|
||||
Desc: 东方财富网-数据中心-特色数据-券商业绩月报
|
||||
http://data.eastmoney.com/other/qsjy.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_qsjy_em(date: str = "20200731") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-券商业绩月报
|
||||
http://data.eastmoney.com/other/qsjy.html
|
||||
:param date: 数据月份，从 2010-06-01 开始, e.g., 需要 2011 年 7 月, 则输入 2011-07-01
|
||||
:type date: str
|
||||
:return: 券商业绩月报
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "END_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_PERFORMANCE",
|
||||
"columns": "SECURITY_CODE,SECURITY_NAME_ABBR,END_DATE,NETPROFIT,NP_YOY,NP_QOQ,ACCUMPROFIT,ACCUMPROFIT_YOY,OPERATE_INCOME,OI_YOY,OI_QOQ,ACCUMOI,ACCUMOI_YOY,NET_ASSETS,NA_YOY",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"(END_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df.columns = [
|
||||
"代码",
|
||||
"简称",
|
||||
"-",
|
||||
"当月净利润-净利润",
|
||||
"当月净利润-同比增长",
|
||||
"当月净利润-环比增长",
|
||||
"当年累计净利润-累计净利润",
|
||||
"当年累计净利润-同比增长",
|
||||
"当月营业收入-营业收入",
|
||||
"当月营业收入-环比增长",
|
||||
"当月营业收入-同比增长",
|
||||
"当年累计营业收入-累计营业收入",
|
||||
"当年累计营业收入-同比增长",
|
||||
"净资产-净资产",
|
||||
"净资产-同比增长",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"简称",
|
||||
"代码",
|
||||
"当月净利润-净利润",
|
||||
"当月净利润-同比增长",
|
||||
"当月净利润-环比增长",
|
||||
"当年累计净利润-累计净利润",
|
||||
"当年累计净利润-同比增长",
|
||||
"当月营业收入-营业收入",
|
||||
"当月营业收入-环比增长",
|
||||
"当月营业收入-同比增长",
|
||||
"当年累计营业收入-累计营业收入",
|
||||
"当年累计营业收入-同比增长",
|
||||
"净资产-净资产",
|
||||
"净资产-同比增长",
|
||||
]
|
||||
]
|
||||
temp_df["当月净利润-净利润"] = pd.to_numeric(temp_df["当月净利润-净利润"])
|
||||
temp_df["当月净利润-同比增长"] = pd.to_numeric(temp_df["当月净利润-同比增长"])
|
||||
temp_df["当月净利润-环比增长"] = pd.to_numeric(temp_df["当月净利润-环比增长"])
|
||||
temp_df["当年累计净利润-累计净利润"] = pd.to_numeric(
|
||||
temp_df["当年累计净利润-累计净利润"]
|
||||
)
|
||||
temp_df["当年累计净利润-同比增长"] = pd.to_numeric(
|
||||
temp_df["当年累计净利润-同比增长"]
|
||||
)
|
||||
temp_df["当月营业收入-营业收入"] = pd.to_numeric(temp_df["当月营业收入-营业收入"])
|
||||
temp_df["当月营业收入-环比增长"] = pd.to_numeric(temp_df["当月营业收入-环比增长"])
|
||||
temp_df["当月营业收入-同比增长"] = pd.to_numeric(temp_df["当月营业收入-同比增长"])
|
||||
temp_df["当年累计营业收入-累计营业收入"] = pd.to_numeric(
|
||||
temp_df["当年累计营业收入-累计营业收入"]
|
||||
)
|
||||
temp_df["当年累计营业收入-同比增长"] = pd.to_numeric(
|
||||
temp_df["当年累计营业收入-同比增长"]
|
||||
)
|
||||
temp_df["净资产-净资产"] = pd.to_numeric(temp_df["净资产-净资产"])
|
||||
temp_df["净资产-同比增长"] = pd.to_numeric(temp_df["净资产-同比增长"])
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_qsjy_em_df = stock_qsjy_em(date="20200430")
|
||||
print(stock_qsjy_em_df)
|
||||
@@ -0,0 +1,586 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/9/20 15:30
|
||||
Desc: 东方财富-数据中心-年报季报-业绩快报-三大报表
|
||||
资产负债表
|
||||
https://data.eastmoney.com/bbsj/202003/zcfz.html
|
||||
利润表
|
||||
https://data.eastmoney.com/bbsj/202003/lrb.html
|
||||
现金流量表
|
||||
https://data.eastmoney.com/bbsj/202003/xjll.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_zcfz_em(date: str = "20240331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报-资产负债表
|
||||
https://data.eastmoney.com/bbsj/202003/zcfz.html
|
||||
:param date: choice of {"20200331", "20200630", "20200930", "20201231", "..."}; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 资产负债表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_DMSK_FN_BALANCE",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], join="outer", ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"股票代码",
|
||||
"_",
|
||||
"_",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"公告日期",
|
||||
"_",
|
||||
"资产-总资产",
|
||||
"_",
|
||||
"资产-货币资金",
|
||||
"_",
|
||||
"资产-应收账款",
|
||||
"_",
|
||||
"资产-存货",
|
||||
"_",
|
||||
"负债-总负债",
|
||||
"负债-应付账款",
|
||||
"_",
|
||||
"负债-预收账款",
|
||||
"_",
|
||||
"股东权益合计",
|
||||
"_",
|
||||
"资产-总资产同比",
|
||||
"负债-总负债同比",
|
||||
"_",
|
||||
"资产负债率",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"资产-货币资金",
|
||||
"资产-应收账款",
|
||||
"资产-存货",
|
||||
"资产-总资产",
|
||||
"资产-总资产同比",
|
||||
"负债-应付账款",
|
||||
"负债-预收账款",
|
||||
"负债-总负债",
|
||||
"负债-总负债同比",
|
||||
"资产负债率",
|
||||
"股东权益合计",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["资产-货币资金"] = pd.to_numeric(big_df["资产-货币资金"], errors="coerce")
|
||||
big_df["资产-应收账款"] = pd.to_numeric(big_df["资产-应收账款"], errors="coerce")
|
||||
big_df["资产-存货"] = pd.to_numeric(big_df["资产-存货"], errors="coerce")
|
||||
big_df["资产-总资产"] = pd.to_numeric(big_df["资产-总资产"], errors="coerce")
|
||||
big_df["资产-总资产同比"] = pd.to_numeric(
|
||||
big_df["资产-总资产同比"], errors="coerce"
|
||||
)
|
||||
big_df["负债-应付账款"] = pd.to_numeric(big_df["负债-应付账款"], errors="coerce")
|
||||
big_df["负债-预收账款"] = pd.to_numeric(big_df["负债-预收账款"], errors="coerce")
|
||||
big_df["负债-总负债"] = pd.to_numeric(big_df["负债-总负债"], errors="coerce")
|
||||
big_df["负债-总负债同比"] = pd.to_numeric(
|
||||
big_df["负债-总负债同比"], errors="coerce"
|
||||
)
|
||||
big_df["资产负债率"] = pd.to_numeric(big_df["资产负债率"], errors="coerce")
|
||||
big_df["股东权益合计"] = pd.to_numeric(big_df["股东权益合计"], errors="coerce")
|
||||
big_df["股东权益合计"] = pd.to_numeric(big_df["股东权益合计"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_zcfz_bj_em(date: str = "20240331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报-资产负债表
|
||||
https://data.eastmoney.com/bbsj/202003/zcfz.html
|
||||
:param date: choice of {"20200331", "20200630", "20200930", "20201231", "..."}; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 资产负债表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_DMSK_FN_BALANCE",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(TRADE_MARKET_CODE="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], join="outer", ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"股票代码",
|
||||
"_",
|
||||
"_",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"公告日期",
|
||||
"_",
|
||||
"资产-总资产",
|
||||
"_",
|
||||
"资产-货币资金",
|
||||
"_",
|
||||
"资产-应收账款",
|
||||
"_",
|
||||
"资产-存货",
|
||||
"_",
|
||||
"负债-总负债",
|
||||
"负债-应付账款",
|
||||
"_",
|
||||
"负债-预收账款",
|
||||
"_",
|
||||
"股东权益合计",
|
||||
"_",
|
||||
"资产-总资产同比",
|
||||
"负债-总负债同比",
|
||||
"_",
|
||||
"资产负债率",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"资产-货币资金",
|
||||
"资产-应收账款",
|
||||
"资产-存货",
|
||||
"资产-总资产",
|
||||
"资产-总资产同比",
|
||||
"负债-应付账款",
|
||||
"负债-预收账款",
|
||||
"负债-总负债",
|
||||
"负债-总负债同比",
|
||||
"资产负债率",
|
||||
"股东权益合计",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["资产-货币资金"] = pd.to_numeric(big_df["资产-货币资金"], errors="coerce")
|
||||
big_df["资产-应收账款"] = pd.to_numeric(big_df["资产-应收账款"], errors="coerce")
|
||||
big_df["资产-存货"] = pd.to_numeric(big_df["资产-存货"], errors="coerce")
|
||||
big_df["资产-总资产"] = pd.to_numeric(big_df["资产-总资产"], errors="coerce")
|
||||
big_df["资产-总资产同比"] = pd.to_numeric(
|
||||
big_df["资产-总资产同比"], errors="coerce"
|
||||
)
|
||||
big_df["负债-应付账款"] = pd.to_numeric(big_df["负债-应付账款"], errors="coerce")
|
||||
big_df["负债-预收账款"] = pd.to_numeric(big_df["负债-预收账款"], errors="coerce")
|
||||
big_df["负债-总负债"] = pd.to_numeric(big_df["负债-总负债"], errors="coerce")
|
||||
big_df["负债-总负债同比"] = pd.to_numeric(
|
||||
big_df["负债-总负债同比"], errors="coerce"
|
||||
)
|
||||
big_df["资产负债率"] = pd.to_numeric(big_df["资产负债率"], errors="coerce")
|
||||
big_df["股东权益合计"] = pd.to_numeric(big_df["股东权益合计"], errors="coerce")
|
||||
big_df["股东权益合计"] = pd.to_numeric(big_df["股东权益合计"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_lrb_em(date: str = "20240331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报-利润表
|
||||
https://data.eastmoney.com/bbsj/202003/lrb.html
|
||||
:param date: choice of {"20200331", "20200630", "20200930", "20201231", "..."}; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 利润表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_DMSK_FN_INCOME",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"股票代码",
|
||||
"_",
|
||||
"_",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"公告日期",
|
||||
"_",
|
||||
"净利润",
|
||||
"营业总收入",
|
||||
"营业总支出-营业总支出",
|
||||
"_",
|
||||
"营业总支出-营业支出",
|
||||
"_",
|
||||
"_",
|
||||
"营业总支出-销售费用",
|
||||
"营业总支出-管理费用",
|
||||
"营业总支出-财务费用",
|
||||
"营业利润",
|
||||
"利润总额",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"营业总收入同比",
|
||||
"_",
|
||||
"净利润同比",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"净利润",
|
||||
"净利润同比",
|
||||
"营业总收入",
|
||||
"营业总收入同比",
|
||||
"营业总支出-营业支出",
|
||||
"营业总支出-销售费用",
|
||||
"营业总支出-管理费用",
|
||||
"营业总支出-财务费用",
|
||||
"营业总支出-营业总支出",
|
||||
"营业利润",
|
||||
"利润总额",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["净利润"] = pd.to_numeric(big_df["净利润"], errors="coerce")
|
||||
big_df["净利润同比"] = pd.to_numeric(big_df["净利润同比"], errors="coerce")
|
||||
big_df["营业总收入"] = pd.to_numeric(big_df["营业总收入"], errors="coerce")
|
||||
big_df["营业总收入同比"] = pd.to_numeric(big_df["营业总收入同比"], errors="coerce")
|
||||
big_df["营业总支出-营业支出"] = pd.to_numeric(
|
||||
big_df["营业总支出-营业支出"], errors="coerce"
|
||||
)
|
||||
big_df["营业总支出-销售费用"] = pd.to_numeric(
|
||||
big_df["营业总支出-销售费用"], errors="coerce"
|
||||
)
|
||||
big_df["营业总支出-管理费用"] = pd.to_numeric(
|
||||
big_df["营业总支出-管理费用"], errors="coerce"
|
||||
)
|
||||
big_df["营业总支出-财务费用"] = pd.to_numeric(
|
||||
big_df["营业总支出-财务费用"], errors="coerce"
|
||||
)
|
||||
big_df["营业总支出-营业总支出"] = pd.to_numeric(
|
||||
big_df["营业总支出-营业总支出"], errors="coerce"
|
||||
)
|
||||
big_df["营业利润"] = pd.to_numeric(big_df["营业利润"], errors="coerce")
|
||||
big_df["利润总额"] = pd.to_numeric(big_df["利润总额"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_xjll_em(date: str = "20240331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报-现金流量表
|
||||
https://data.eastmoney.com/bbsj/202003/xjll.html
|
||||
:param date: choice of {"20200331", "20200630", "20200930", "20201231", "..."}; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 现金流量表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_DMSK_FN_CASHFLOW",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df.index + 1
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"股票代码",
|
||||
"_",
|
||||
"_",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"公告日期",
|
||||
"_",
|
||||
"经营性现金流-现金流量净额",
|
||||
"经营性现金流-净现金流占比",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"投资性现金流-现金流量净额",
|
||||
"投资性现金流-净现金流占比",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"融资性现金流-现金流量净额",
|
||||
"融资性现金流-净现金流占比",
|
||||
"净现金流-净现金流",
|
||||
"净现金流-同比增长",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"净现金流-净现金流",
|
||||
"净现金流-同比增长",
|
||||
"经营性现金流-现金流量净额",
|
||||
"经营性现金流-净现金流占比",
|
||||
"投资性现金流-现金流量净额",
|
||||
"投资性现金流-净现金流占比",
|
||||
"融资性现金流-现金流量净额",
|
||||
"融资性现金流-净现金流占比",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
|
||||
big_df["净现金流-净现金流"] = pd.to_numeric(
|
||||
big_df["净现金流-净现金流"], errors="coerce"
|
||||
)
|
||||
big_df["净现金流-同比增长"] = pd.to_numeric(
|
||||
big_df["净现金流-同比增长"], errors="coerce"
|
||||
)
|
||||
big_df["经营性现金流-现金流量净额"] = pd.to_numeric(
|
||||
big_df["经营性现金流-现金流量净额"], errors="coerce"
|
||||
)
|
||||
big_df["经营性现金流-净现金流占比"] = pd.to_numeric(
|
||||
big_df["经营性现金流-净现金流占比"], errors="coerce"
|
||||
)
|
||||
big_df["投资性现金流-现金流量净额"] = pd.to_numeric(
|
||||
big_df["投资性现金流-现金流量净额"], errors="coerce"
|
||||
)
|
||||
big_df["投资性现金流-净现金流占比"] = pd.to_numeric(
|
||||
big_df["投资性现金流-净现金流占比"], errors="coerce"
|
||||
)
|
||||
big_df["融资性现金流-现金流量净额"] = pd.to_numeric(
|
||||
big_df["融资性现金流-现金流量净额"], errors="coerce"
|
||||
)
|
||||
big_df["融资性现金流-净现金流占比"] = pd.to_numeric(
|
||||
big_df["融资性现金流-净现金流占比"], errors="coerce"
|
||||
)
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zcfz_em_df = stock_zcfz_em(date="20240331")
|
||||
print(stock_zcfz_em_df)
|
||||
|
||||
stock_zcfz_bj_em_df = stock_zcfz_bj_em(date="20240331")
|
||||
print(stock_zcfz_bj_em_df)
|
||||
|
||||
stock_lrb_em_df = stock_lrb_em(date="20240331")
|
||||
print(stock_lrb_em_df)
|
||||
|
||||
stock_xjll_em_df = stock_xjll_em(date="20240331")
|
||||
print(stock_xjll_em_df)
|
||||
+179
@@ -0,0 +1,179 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/2/28 13:00
|
||||
Desc: 东方财富网-数据中心-研究报告-个股研报
|
||||
https://data.eastmoney.com/report/stock.jshtml
|
||||
"""
|
||||
|
||||
import datetime
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_research_report_em(symbol: str = "000001") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-研究报告-个股研报
|
||||
https://data.eastmoney.com/report/stock.jshtml
|
||||
:param symbol: 个股代码
|
||||
:type symbol: str
|
||||
:return: 个股研报
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://reportapi.eastmoney.com/report/list"
|
||||
params = {
|
||||
"industryCode": "*",
|
||||
"pageSize": "5000",
|
||||
"industry": "*",
|
||||
"rating": "*",
|
||||
"ratingChange": "*",
|
||||
"beginTime": "2000-01-01",
|
||||
"endTime": f"{datetime.datetime.now().year + 1}-01-01",
|
||||
"pageNo": "1",
|
||||
"fields": "",
|
||||
"qType": "0",
|
||||
"orgCode": "",
|
||||
"code": symbol,
|
||||
"rcode": "",
|
||||
"p": "1",
|
||||
"pageNum": "1",
|
||||
"pageNumber": "1",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["TotalPage"]
|
||||
current_year = data_json["currentYear"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNo": page,
|
||||
"p": page,
|
||||
"pageNum": page,
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], axis=0, ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df["index"] + 1
|
||||
predict_this_year_eps_title = f"{current_year}-盈利预测-收益"
|
||||
predict_this_year_pe_title = f"{current_year}-盈利预测-市盈率"
|
||||
predict_next_year_eps_title = f"{current_year + 1}-盈利预测-收益"
|
||||
predict_next_year_pe_title = f"{current_year + 1}-盈利预测-市盈率"
|
||||
predict_next_two_year_eps_title = f"{current_year + 2}-盈利预测-收益"
|
||||
predict_next_two_year_pe_title = f"{current_year + 2}-盈利预测-市盈率"
|
||||
big_df["pdfUrl"] = big_df["infoCode"].apply(
|
||||
lambda x: f"https://pdf.dfcfw.com/pdf/H3_{x}_1.pdf"
|
||||
)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"title": "报告名称",
|
||||
"stockName": "股票简称",
|
||||
"stockCode": "股票代码",
|
||||
"orgCode": "-",
|
||||
"orgName": "-",
|
||||
"orgSName": "机构",
|
||||
"publishDate": "日期",
|
||||
"infoCode": "-",
|
||||
"column": "-",
|
||||
"predictNextTwoYearEps": predict_next_two_year_eps_title,
|
||||
"predictNextTwoYearPe": predict_next_two_year_pe_title,
|
||||
"predictNextYearEps": predict_next_year_eps_title,
|
||||
"predictNextYearPe": predict_next_year_pe_title,
|
||||
"predictThisYearEps": predict_this_year_eps_title,
|
||||
"predictThisYearPe": predict_this_year_pe_title,
|
||||
"predictLastYearEps": "-",
|
||||
"predictLastYearPe": "-",
|
||||
"actualLastTwoYearEps": "-",
|
||||
"actualLastYearEps": "-",
|
||||
"industryCode": "-",
|
||||
"industryName": "-",
|
||||
"emIndustryCode": "-",
|
||||
"indvInduCode": "-",
|
||||
"indvInduName": "行业",
|
||||
"emRatingCode": "-",
|
||||
"emRatingValue": "-",
|
||||
"emRatingName": "东财评级",
|
||||
"lastEmRatingCode": "-",
|
||||
"lastEmRatingValue": "-",
|
||||
"lastEmRatingName": "-",
|
||||
"ratingChange": "-",
|
||||
"reportType": "-",
|
||||
"author": "-",
|
||||
"indvIsNew": "-",
|
||||
"researcher": "-",
|
||||
"newListingDate": "-",
|
||||
"newPurchaseDate": "-",
|
||||
"newIssuePrice": "-",
|
||||
"newPeIssueA": "-",
|
||||
"indvAimPriceT": "-",
|
||||
"indvAimPriceL": "-",
|
||||
"attachType": "-",
|
||||
"attachSize": "-",
|
||||
"attachPages": "-",
|
||||
"encodeUrl": "-",
|
||||
"sRatingName": "-",
|
||||
"sRatingCode": "-",
|
||||
"market": "-",
|
||||
"authorID": "-",
|
||||
"count": "近一月个股研报数",
|
||||
"orgType": "-",
|
||||
"pdfUrl": "报告PDF链接",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"报告名称",
|
||||
"东财评级",
|
||||
"机构",
|
||||
"近一月个股研报数",
|
||||
predict_this_year_eps_title,
|
||||
predict_this_year_pe_title,
|
||||
predict_next_year_eps_title,
|
||||
predict_next_year_pe_title,
|
||||
predict_next_two_year_eps_title,
|
||||
predict_next_two_year_pe_title,
|
||||
"行业",
|
||||
"日期",
|
||||
"报告PDF链接",
|
||||
]
|
||||
]
|
||||
big_df["日期"] = pd.to_datetime(big_df["日期"], errors="coerce").dt.date
|
||||
big_df["近一月个股研报数"] = pd.to_numeric(
|
||||
big_df["近一月个股研报数"], errors="coerce"
|
||||
)
|
||||
big_df[predict_this_year_eps_title] = pd.to_numeric(
|
||||
big_df[predict_this_year_eps_title], errors="coerce"
|
||||
)
|
||||
big_df[predict_this_year_pe_title] = pd.to_numeric(
|
||||
big_df[predict_this_year_pe_title], errors="coerce"
|
||||
)
|
||||
big_df[predict_next_year_eps_title] = pd.to_numeric(
|
||||
big_df[predict_next_year_eps_title], errors="coerce"
|
||||
)
|
||||
big_df[predict_next_year_pe_title] = pd.to_numeric(
|
||||
big_df[predict_next_year_pe_title], errors="coerce"
|
||||
)
|
||||
big_df[predict_next_two_year_eps_title] = pd.to_numeric(
|
||||
big_df[predict_next_two_year_eps_title], errors="coerce"
|
||||
)
|
||||
big_df[predict_next_two_year_pe_title] = pd.to_numeric(
|
||||
big_df[predict_next_two_year_pe_title], errors="coerce"
|
||||
)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_research_report_em_df = stock_research_report_em(symbol="000001")
|
||||
print(stock_research_report_em_df)
|
||||
@@ -0,0 +1,151 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/21 19:20
|
||||
Desc: 上证e互动-提问与回答
|
||||
https://sns.sseinfo.com/
|
||||
"""
|
||||
|
||||
import warnings
|
||||
from functools import lru_cache
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def _fetch_stock_uid() -> dict:
|
||||
"""
|
||||
上证e互动-代码ID映射
|
||||
https://sns.sseinfo.com/list/company.do
|
||||
:return: 代码ID映射
|
||||
:rtype: str
|
||||
"""
|
||||
url = "https://sns.sseinfo.com/allcompany.do"
|
||||
data = {
|
||||
"code": "0",
|
||||
"order": "2",
|
||||
"areaId": "0",
|
||||
"page": "1",
|
||||
}
|
||||
uid_list = list()
|
||||
code_list = list()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, 73), leave=False):
|
||||
data.update({"page": page})
|
||||
r = requests.post(url, data=data)
|
||||
data_json = r.json()
|
||||
soup = BeautifulSoup(data_json["content"], features="lxml")
|
||||
soup.find_all(name="a", attrs={"rel": "tag"})
|
||||
uid_list.extend(
|
||||
[item["uid"] for item in soup.find_all(name="a", attrs={"rel": "tag"})]
|
||||
)
|
||||
code_list.extend(
|
||||
[
|
||||
item.find("img")["src"].split("?")[0].split("/")[-1].split(".")[0]
|
||||
for item in soup.find_all(name="a", attrs={"rel": "tag"})
|
||||
]
|
||||
)
|
||||
code_uid_map = dict(zip(code_list, uid_list))
|
||||
return code_uid_map
|
||||
|
||||
|
||||
def stock_sns_sseinfo(symbol: str = "603119") -> pd.DataFrame:
|
||||
"""
|
||||
上证e互动-提问与回答
|
||||
https://sns.sseinfo.com/company.do?uid=65
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 提问与回答
|
||||
:rtype: str
|
||||
"""
|
||||
code_uid_map = _fetch_stock_uid()
|
||||
url = "https://sns.sseinfo.com/ajax/userfeeds.do"
|
||||
params = {
|
||||
"typeCode": "company",
|
||||
"type": "11",
|
||||
"pageSize": "100",
|
||||
"uid": code_uid_map[symbol],
|
||||
"page": "1",
|
||||
}
|
||||
big_df = pd.DataFrame()
|
||||
page = 1
|
||||
warnings.warn("正在下载中")
|
||||
while True:
|
||||
params.update({"page": page})
|
||||
r = requests.post(url, params=params)
|
||||
if len(r.text) < 300:
|
||||
break
|
||||
else:
|
||||
page += 1
|
||||
r = requests.post(url, params=params)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
content_list = [
|
||||
item.get_text().strip()
|
||||
for item in soup.find_all(name="div", attrs={"class": "m_feed_txt"})
|
||||
]
|
||||
date_list = [
|
||||
item.get_text().strip().split("\n")[0]
|
||||
for item in soup.find_all(name="div", attrs={"class": "m_feed_from"})
|
||||
]
|
||||
source_list = [
|
||||
item.get_text().strip().split("\n")[2]
|
||||
for item in soup.find_all(name="div", attrs={"class": "m_feed_from"})
|
||||
]
|
||||
q_list = [
|
||||
item.split(")")[1]
|
||||
for index, item in enumerate(content_list)
|
||||
if index % 2 == 0
|
||||
]
|
||||
stock_name = [
|
||||
item.split("(")[0].strip(":")
|
||||
for index, item in enumerate(content_list)
|
||||
if index % 2 == 0
|
||||
]
|
||||
stock_code = [
|
||||
item.split("(")[1].split(")")[0]
|
||||
for index, item in enumerate(content_list)
|
||||
if index % 2 == 0
|
||||
]
|
||||
a_list = [item for index, item in enumerate(content_list) if index % 2 != 0]
|
||||
d_q_list = [item for index, item in enumerate(date_list) if index % 2 == 0]
|
||||
d_a_list = [item for index, item in enumerate(date_list) if index % 2 != 0]
|
||||
s_q_list = [item for index, item in enumerate(source_list) if index % 2 == 0]
|
||||
s_a_list = [item for index, item in enumerate(source_list) if index % 2 != 0]
|
||||
author_name = [
|
||||
item["title"] for item in soup.find_all(name="a", attrs={"rel": "face"})
|
||||
]
|
||||
temp_df = pd.DataFrame(
|
||||
[
|
||||
stock_code,
|
||||
stock_name,
|
||||
q_list,
|
||||
a_list,
|
||||
d_q_list,
|
||||
d_a_list,
|
||||
s_q_list,
|
||||
s_a_list,
|
||||
author_name,
|
||||
]
|
||||
).T
|
||||
temp_df.columns = [
|
||||
"股票代码",
|
||||
"公司简称",
|
||||
"问题",
|
||||
"回答",
|
||||
"问题时间",
|
||||
"回答时间",
|
||||
"问题来源",
|
||||
"回答来源",
|
||||
"用户名",
|
||||
]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_sns_sseinfo_df = stock_sns_sseinfo(symbol="603119")
|
||||
print(stock_sns_sseinfo_df)
|
||||
@@ -0,0 +1,468 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/9/15 15:30
|
||||
Desc: 东方财富网-数据中心-特色数据-商誉
|
||||
东方财富网-数据中心-特色数据-商誉-A股商誉市场概况: https://data.eastmoney.com/sy/scgk.html
|
||||
东方财富网-数据中心-特色数据-商誉-商誉减值预期明细: https://data.eastmoney.com/sy/yqlist.html
|
||||
东方财富网-数据中心-特色数据-商誉-个股商誉减值明细: https://data.eastmoney.com/sy/jzlist.html
|
||||
东方财富网-数据中心-特色数据-商誉-个股商誉明细: https://data.eastmoney.com/sy/list.html
|
||||
东方财富网-数据中心-特色数据-商誉-行业商誉: https://data.eastmoney.com/sy/hylist.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_sy_profile_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-商誉-A股商誉市场概况
|
||||
https://data.eastmoney.com/sy/scgk.html
|
||||
:return: A股商誉市场概况
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "REPORT_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_GOODWILL_MARKETSTATISTICS",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"columns": "ALL",
|
||||
"filter": """((GOODWILL_STATE="1")( | IMPAIRMENT_STATE="1"))(TRADE_BOARD="all")""",
|
||||
}
|
||||
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
data_df = pd.DataFrame(data_json["result"]["data"])
|
||||
data_df.columns = [
|
||||
"_",
|
||||
"报告期",
|
||||
"商誉",
|
||||
"商誉减值",
|
||||
"净资产",
|
||||
"商誉占净资产比例",
|
||||
"商誉减值占净资产比例",
|
||||
"净利润规模",
|
||||
"商誉减值占净利润比例",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
data_df = data_df[
|
||||
[
|
||||
"报告期",
|
||||
"商誉",
|
||||
"商誉减值",
|
||||
"净资产",
|
||||
"商誉占净资产比例",
|
||||
"商誉减值占净资产比例",
|
||||
"净利润规模",
|
||||
"商誉减值占净利润比例",
|
||||
]
|
||||
]
|
||||
data_df["报告期"] = pd.to_datetime(data_df["报告期"], errors="coerce").dt.date
|
||||
data_df["商誉"] = pd.to_numeric(data_df["商誉"], errors="coerce")
|
||||
data_df["商誉减值"] = pd.to_numeric(data_df["商誉减值"], errors="coerce")
|
||||
data_df["净资产"] = pd.to_numeric(data_df["净资产"], errors="coerce")
|
||||
data_df["商誉占净资产比例"] = pd.to_numeric(
|
||||
data_df["商誉占净资产比例"], errors="coerce"
|
||||
)
|
||||
data_df["商誉减值占净资产比例"] = pd.to_numeric(
|
||||
data_df["商誉减值占净资产比例"], errors="coerce"
|
||||
)
|
||||
data_df["净利润规模"] = pd.to_numeric(data_df["净利润规模"], errors="coerce")
|
||||
data_df["商誉减值占净利润比例"] = pd.to_numeric(
|
||||
data_df["商誉减值占净利润比例"], errors="coerce"
|
||||
)
|
||||
data_df.sort_values(["报告期"], inplace=True, ignore_index=True)
|
||||
return data_df
|
||||
|
||||
|
||||
def stock_sy_yq_em(date: str = "20240630") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-商誉-商誉减值预期明细
|
||||
https://data.eastmoney.com/sy/yqlist.html
|
||||
:param date: 参考网站指定的数据日期
|
||||
:type date: str
|
||||
:return: 商誉减值预期明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"reportName": "RPT_GOODWILL_STOCKPREDICT",
|
||||
"filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = int(data_json["result"]["pages"])
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df["index"] + 1
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"SECUCODE": "-",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"ORG_CODE": "-",
|
||||
"SECURITY_NAME_ABBR": "股票简称",
|
||||
"TRADE_MARKET": "交易市场",
|
||||
"INDUSTRY_NAME": "-",
|
||||
"INDUSTRY_CODE": "-",
|
||||
"NOTICE_DATE": "公告日期",
|
||||
"REPORT_DATE": "数据日期",
|
||||
"PE_REPORT_DATE": "-",
|
||||
"PREDICT_NETPROFIT_LOWER": "预计净利润-下限",
|
||||
"PREDICT_NETPROFIT_UPPER": "预计净利润-上限",
|
||||
"PERFORM_CHANGE_UPPER": "业绩变动幅度-上限",
|
||||
"PERFORM_CHANGE_LOWER": "业绩变动幅度-下限",
|
||||
"PERFORM_CHANGE": "-",
|
||||
"PERFORM_CHANGE_EXPLAIN": "业绩变动原因",
|
||||
"PREDICT_TYPE": "-",
|
||||
"PREDICT_INDICATOR_CODE": "-",
|
||||
"PREDICT_PERIOD": "-",
|
||||
"PE_SAMEREPORT_NETPROFIT": "上年度同期净利润",
|
||||
"NETPROFIT": "-",
|
||||
"PE_GOODWILL": "上年商誉",
|
||||
"NEWEST_REPORT_DATE": "最新商誉报告期",
|
||||
"NEWEST_GOODWILL": "最新一期商誉",
|
||||
"PE_SAMEREPORT_DATE": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"业绩变动原因",
|
||||
"最新商誉报告期",
|
||||
"最新一期商誉",
|
||||
"上年商誉",
|
||||
"预计净利润-下限",
|
||||
"预计净利润-上限",
|
||||
"业绩变动幅度-下限",
|
||||
"业绩变动幅度-上限",
|
||||
"上年度同期净利润",
|
||||
"公告日期",
|
||||
"交易市场",
|
||||
]
|
||||
]
|
||||
big_df["交易市场"] = big_df["交易市场"].map(
|
||||
{"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"}
|
||||
)
|
||||
big_df["最新商誉报告期"] = pd.to_datetime(
|
||||
big_df["最新商誉报告期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["最新一期商誉"] = pd.to_numeric(big_df["最新一期商誉"], errors="coerce")
|
||||
big_df["上年商誉"] = pd.to_numeric(big_df["上年商誉"], errors="coerce")
|
||||
big_df["预计净利润-下限"] = pd.to_numeric(
|
||||
big_df["预计净利润-下限"], errors="coerce"
|
||||
)
|
||||
big_df["预计净利润-上限"] = pd.to_numeric(
|
||||
big_df["预计净利润-上限"], errors="coerce"
|
||||
)
|
||||
big_df["业绩变动幅度-下限"] = pd.to_numeric(
|
||||
big_df["业绩变动幅度-下限"], errors="coerce"
|
||||
)
|
||||
big_df["业绩变动幅度-上限"] = pd.to_numeric(
|
||||
big_df["业绩变动幅度-上限"], errors="coerce"
|
||||
)
|
||||
big_df["上年度同期净利润"] = pd.to_numeric(
|
||||
big_df["上年度同期净利润"], errors="coerce"
|
||||
)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_sy_jz_em(date: str = "20240630") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-商誉-个股商誉减值明细
|
||||
https://data.eastmoney.com/sy/jzlist.html
|
||||
:param date: 参考网站指定的数据日期
|
||||
:type date: str
|
||||
:return: 个股商誉减值明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "GOODWILL_CHANGE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"reportName": "RPT_GOODWILL_STOCKDETAILS",
|
||||
"filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = int(data_json["result"]["pages"])
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df["index"] + 1
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"SECUCODE": "-",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"SECURITY_NAME_ABBR": "股票简称",
|
||||
"ORG_CODE": "-",
|
||||
"LISTING_DATE": "-",
|
||||
"LISTING_STATE": "-",
|
||||
"TRADE_BOARD": "交易市场",
|
||||
"GOODWILL": "商誉",
|
||||
"DATE_TYPE": "-",
|
||||
"REPORT_TYPE_CODE": "-",
|
||||
"DATA_ADJUST_TYPE": "-",
|
||||
"NOTICE_DATE": "公告日期",
|
||||
"REPORT_DATE": "数据日期",
|
||||
"GOODWILL_PRE": "-",
|
||||
"GOODWILL_CHANGE": "商誉减值",
|
||||
"SUMSHEQUITY": "-",
|
||||
"SUMSHEQUITY_RATIO": "商誉占净资产比例",
|
||||
"SE_CHANGE_RATIO": "商誉减值占净资产比例",
|
||||
"PARENTNETPROFIT": "净利润",
|
||||
"PNP_CHANGE_RATIO": "商誉减值占净利润比例",
|
||||
"PNP_YOY_RATIO": "-",
|
||||
"INDUSTRY_CFT": "-",
|
||||
"INDUSTRY_CFTCODE": "-",
|
||||
"IS_SHOW": "-",
|
||||
"MAXREPORTDATE": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"商誉",
|
||||
"商誉减值",
|
||||
"商誉占净资产比例",
|
||||
"商誉减值占净资产比例",
|
||||
"净利润",
|
||||
"商誉减值占净利润比例",
|
||||
"公告日期",
|
||||
"交易市场",
|
||||
]
|
||||
]
|
||||
big_df["交易市场"] = big_df["交易市场"].map(
|
||||
{"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"}
|
||||
)
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["商誉"] = pd.to_numeric(big_df["商誉"], errors="coerce")
|
||||
big_df["商誉减值"] = pd.to_numeric(big_df["商誉减值"], errors="coerce")
|
||||
big_df["商誉占净资产比例"] = pd.to_numeric(
|
||||
big_df["商誉占净资产比例"], errors="coerce"
|
||||
)
|
||||
big_df["商誉减值占净资产比例"] = pd.to_numeric(
|
||||
big_df["商誉减值占净资产比例"], errors="coerce"
|
||||
)
|
||||
big_df["净利润"] = pd.to_numeric(big_df["净利润"], errors="coerce")
|
||||
big_df["商誉减值占净利润比例"] = pd.to_numeric(
|
||||
big_df["商誉减值占净利润比例"], errors="coerce"
|
||||
)
|
||||
big_df["商誉减值占净利润比例"] = pd.to_numeric(
|
||||
big_df["商誉减值占净利润比例"], errors="coerce"
|
||||
)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_sy_em(date: str = "20231231") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-商誉-个股商誉明细
|
||||
https://data.eastmoney.com/sy/list.html
|
||||
:param date: 参考网站指定的数据日期
|
||||
:type date: str
|
||||
:return: 个股商誉明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"reportName": "RPT_GOODWILL_STOCKDETAILS",
|
||||
"filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = int(data_json["result"]["pages"])
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df["index"] + 1
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"SECUCODE": "-",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"SECURITY_NAME_ABBR": "股票简称",
|
||||
"ORG_CODE": "-",
|
||||
"LISTING_DATE": "-",
|
||||
"LISTING_STATE": "-",
|
||||
"TRADE_BOARD": "交易市场",
|
||||
"GOODWILL": "商誉",
|
||||
"DATE_TYPE": "-",
|
||||
"REPORT_TYPE_CODE": "-",
|
||||
"DATA_ADJUST_TYPE": "-",
|
||||
"NOTICE_DATE": "公告日期",
|
||||
"REPORT_DATE": "数据日期",
|
||||
"GOODWILL_PRE": "上年商誉",
|
||||
"GOODWILL_CHANGE": "商誉减值",
|
||||
"SUMSHEQUITY": "-",
|
||||
"SUMSHEQUITY_RATIO": "商誉占净资产比例",
|
||||
"SE_CHANGE_RATIO": "商誉减值占净资产比例",
|
||||
"PARENTNETPROFIT": "净利润",
|
||||
"PNP_CHANGE_RATIO": "商誉减值占净利润比例",
|
||||
"PNP_YOY_RATIO": "净利润同比",
|
||||
"INDUSTRY_CFT": "-",
|
||||
"INDUSTRY_CFTCODE": "-",
|
||||
"IS_SHOW": "-",
|
||||
"MAXREPORTDATE": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"商誉",
|
||||
"商誉占净资产比例",
|
||||
"净利润",
|
||||
"净利润同比",
|
||||
"上年商誉",
|
||||
"公告日期",
|
||||
"交易市场",
|
||||
]
|
||||
]
|
||||
big_df["交易市场"] = big_df["交易市场"].map(
|
||||
{"shzb": "沪市主板", "kcb": "科创板", "szzb": "深市主板", "cyb": "创业板"}
|
||||
)
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["商誉"] = pd.to_numeric(big_df["商誉"], errors="coerce")
|
||||
big_df["商誉占净资产比例"] = pd.to_numeric(
|
||||
big_df["商誉占净资产比例"], errors="coerce"
|
||||
)
|
||||
big_df["净利润"] = pd.to_numeric(big_df["净利润"], errors="coerce")
|
||||
big_df["净利润同比"] = pd.to_numeric(big_df["净利润同比"], errors="coerce")
|
||||
big_df["上年商誉"] = pd.to_numeric(big_df["上年商誉"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_sy_hy_em(date: str = "20240930") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-商誉-行业商誉
|
||||
https://data.eastmoney.com/sy/hylist.html
|
||||
:param date: 参考网站指定的数据日期
|
||||
:type date: str
|
||||
:return: 个股商誉明细
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "SUMSHEQUITY_RATIO",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"reportName": "RPT_GOODWILL_INDUSTATISTICS",
|
||||
"filter": f"""(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = int(data_json["result"]["pages"])
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.reset_index(inplace=True, drop=True)
|
||||
big_df.rename(
|
||||
columns={
|
||||
"REPORT_DATE": "数据日期",
|
||||
"INDUSTRY_NAME": "行业名称",
|
||||
"INDUSTRY_CODE": "-",
|
||||
"ORG_NUM": "公司家数",
|
||||
"GOODWILL": "商誉规模",
|
||||
"GOODWILL_CHANGE": "-",
|
||||
"SUMSHEQUITY": "净资产",
|
||||
"SUMSHEQUITY_RATIO": "商誉规模占净资产规模比例",
|
||||
"SE_CHANGE_RATIO": "-",
|
||||
"PARENTNETPROFIT": "净利润规模",
|
||||
"PNP_CHANGE_RATIO": "-",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"行业名称",
|
||||
"公司家数",
|
||||
"商誉规模",
|
||||
"净资产",
|
||||
"商誉规模占净资产规模比例",
|
||||
"净利润规模",
|
||||
]
|
||||
]
|
||||
big_df["公司家数"] = pd.to_numeric(big_df["公司家数"], errors="coerce")
|
||||
big_df["商誉规模"] = pd.to_numeric(big_df["商誉规模"], errors="coerce")
|
||||
big_df["净资产"] = pd.to_numeric(big_df["净资产"], errors="coerce")
|
||||
big_df["商誉规模占净资产规模比例"] = pd.to_numeric(
|
||||
big_df["商誉规模占净资产规模比例"], errors="coerce"
|
||||
)
|
||||
big_df["净利润规模"] = pd.to_numeric(big_df["净利润规模"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_sy_profile_em_df = stock_sy_profile_em()
|
||||
print(stock_sy_profile_em_df)
|
||||
|
||||
stock_sy_yq_em_df = stock_sy_yq_em(date="20240630")
|
||||
print(stock_sy_yq_em_df)
|
||||
|
||||
stock_sy_jz_em_df = stock_sy_jz_em(date="20240630")
|
||||
print(stock_sy_jz_em_df)
|
||||
|
||||
stock_sy_em_df = stock_sy_em(date="20240630")
|
||||
print(stock_sy_em_df)
|
||||
|
||||
stock_sy_hy_em_df = stock_sy_hy_em(date="20240930")
|
||||
print(stock_sy_hy_em_df)
|
||||
+970
@@ -0,0 +1,970 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2024/9/21 19:00
|
||||
Desc: 同花顺-数据中心-技术选股
|
||||
https://data.10jqka.com.cn/rank/cxg/
|
||||
"""
|
||||
|
||||
import re
|
||||
from io import StringIO
|
||||
|
||||
import pandas as pd
|
||||
import py_mini_racer
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.datasets import get_ths_js
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def _get_file_content_ths(file: str = "ths.js") -> str:
|
||||
"""
|
||||
获取 JS 文件的内容
|
||||
:param file: JS 文件名
|
||||
:type file: str
|
||||
:return: 文件内容
|
||||
:rtype: str
|
||||
"""
|
||||
setting_file_path = get_ths_js(file)
|
||||
with open(setting_file_path, encoding="utf-8") as f:
|
||||
file_data = f.read()
|
||||
return file_data
|
||||
|
||||
|
||||
def stock_rank_cxg_ths(symbol: str = "创月新高") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-创新高
|
||||
https://data.10jqka.com.cn/rank/cxg/
|
||||
:param symbol: choice of {"创月新高", "半年新高", "一年新高", "历史新高"}
|
||||
:type symbol: str
|
||||
:return: 创新高数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"创月新高": "4",
|
||||
"半年新高": "3",
|
||||
"一年新高": "2",
|
||||
"历史新高": "1",
|
||||
}
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/cxg/board/{symbol_map[symbol]}/field/"
|
||||
f"stockcode/order/asc/page/1/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/cxg/board/{symbol_map[symbol]}/field/stockcode/"
|
||||
f"order/asc/page/{page}/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
html_fixed = re.sub(r'\srowspan="\d+"', '', r.text)
|
||||
temp_df = pd.read_html(StringIO(html_fixed), header=0)[0] # 20260214 新增
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
"最新价",
|
||||
"前期高点",
|
||||
"前期高点日期",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].str.strip("%")
|
||||
big_df["换手率"] = big_df["换手率"].str.strip("%")
|
||||
big_df["前期高点日期"] = pd.to_datetime(
|
||||
big_df["前期高点日期"], errors="coerce"
|
||||
).dt.date
|
||||
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["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["前期高点"] = pd.to_numeric(big_df["前期高点"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_cxd_ths(symbol: str = "创月新低") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-创新低
|
||||
https://data.10jqka.com.cn/rank/cxd/
|
||||
:param symbol: choice of {"创月新低", "半年新低", "一年新低", "历史新低"}
|
||||
:type symbol: str
|
||||
:return: 创新低数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"创月新低": "4",
|
||||
"半年新低": "3",
|
||||
"一年新低": "2",
|
||||
"历史新低": "1",
|
||||
}
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/cxd/board/{symbol_map[symbol]}/field/"
|
||||
f"stockcode/order/asc/page/1/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/cxd/board/{symbol_map[symbol]}/field/"
|
||||
f"stockcode/order/asc/page/{page}/ajax/1/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text))[0].iloc[:, :-1] # 20260214 新增
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
"最新价",
|
||||
"前期低点",
|
||||
"前期低点日期",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].str.strip("%")
|
||||
big_df["换手率"] = big_df["换手率"].str.strip("%")
|
||||
big_df["前期低点日期"] = pd.to_datetime(
|
||||
big_df["前期低点日期"], errors="coerce"
|
||||
).dt.date
|
||||
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["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["前期低点"] = pd.to_numeric(big_df["前期低点"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_lxsz_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-连续上涨
|
||||
https://data.10jqka.com.cn/rank/lxsz/
|
||||
:return: 连续上涨
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/lxsz/field/lxts/order/desc/page/1/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/lxsz/field/lxts/order/desc/page/{page}/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text), converters={"股票代码": str})[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"收盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"连涨天数",
|
||||
"连续涨跌幅",
|
||||
"累计换手率",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["连续涨跌幅"] = big_df["连续涨跌幅"].str.strip("%")
|
||||
big_df["累计换手率"] = big_df["累计换手率"].str.strip("%")
|
||||
big_df["连续涨跌幅"] = pd.to_numeric(big_df["连续涨跌幅"], errors="coerce")
|
||||
big_df["累计换手率"] = pd.to_numeric(big_df["累计换手率"], errors="coerce")
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
|
||||
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
|
||||
big_df["连涨天数"] = pd.to_numeric(big_df["连涨天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_lxxd_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-连续下跌
|
||||
https://data.10jqka.com.cn/rank/lxxd/
|
||||
:return: 连续下跌
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/lxxd/field/lxts/order/desc/page/1/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/lxxd/field/lxts/order/desc/page/{page}/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
temp_df = pd.read_html(StringIO(r.text), converters={"股票代码": str})[0]
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"收盘价",
|
||||
"最高价",
|
||||
"最低价",
|
||||
"连涨天数",
|
||||
"连续涨跌幅",
|
||||
"累计换手率",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["连续涨跌幅"] = big_df["连续涨跌幅"].str.strip("%")
|
||||
big_df["累计换手率"] = big_df["累计换手率"].str.strip("%")
|
||||
big_df["连续涨跌幅"] = pd.to_numeric(big_df["连续涨跌幅"], errors="coerce")
|
||||
big_df["累计换手率"] = pd.to_numeric(big_df["累计换手率"], errors="coerce")
|
||||
big_df["收盘价"] = pd.to_numeric(big_df["收盘价"], errors="coerce")
|
||||
big_df["最高价"] = pd.to_numeric(big_df["最高价"], errors="coerce")
|
||||
big_df["最低价"] = pd.to_numeric(big_df["最低价"], errors="coerce")
|
||||
big_df["连涨天数"] = pd.to_numeric(big_df["连涨天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_cxfl_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-持续放量
|
||||
https://data.10jqka.com.cn/rank/cxfl/
|
||||
:return: 持续放量
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/cxfl/field/count/order/desc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/cxfl/field/count/order/desc/ajax/1/free/1/page/{page}/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 10: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'涨跌幅': cols[3].text.strip(),
|
||||
'最新价': cols[4].text.strip(),
|
||||
'成交量': cols[5].text.strip(),
|
||||
'基准日成交量': cols[6].text.strip(),
|
||||
'放量天数': cols[7].text.strip(),
|
||||
'阶段涨跌幅': cols[8].text.strip(),
|
||||
'所属行业': cols[9].find('a').text.strip() if cols[9].find('a') else cols[9].text.strip()
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"成交量",
|
||||
"基准日成交量",
|
||||
"放量天数",
|
||||
"阶段涨跌幅",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["阶段涨跌幅"] = big_df["阶段涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["阶段涨跌幅"] = pd.to_numeric(big_df["阶段涨跌幅"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["放量天数"] = pd.to_numeric(big_df["放量天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_cxsl_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-持续缩量
|
||||
https://data.10jqka.com.cn/rank/cxsl/
|
||||
:return: 持续缩量
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/cxsl/field/count/order/desc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/cxsl/field/count/order/desc/ajax/1/free/1/page/{page}/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 10: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'涨跌幅': cols[3].text.strip(),
|
||||
'最新价': cols[4].text.strip(),
|
||||
'成交量': cols[5].text.strip(),
|
||||
'基准日成交量': cols[6].text.strip(),
|
||||
'放量天数': cols[7].text.strip(),
|
||||
'阶段涨跌幅': cols[8].text.strip(),
|
||||
'所属行业': cols[9].find('a').text.strip() if cols[9].find('a') else cols[9].text.strip()
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"成交量",
|
||||
"基准日成交量",
|
||||
"缩量天数",
|
||||
"阶段涨跌幅",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["阶段涨跌幅"] = big_df["阶段涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["阶段涨跌幅"] = pd.to_numeric(big_df["阶段涨跌幅"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["缩量天数"] = pd.to_numeric(big_df["缩量天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_xstp_ths(symbol: str = "500日均线") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-向上突破
|
||||
https://data.10jqka.com.cn/rank/xstp/
|
||||
:param symbol: choice of {"5日均线", "10日均线", "20日均线", "30日均线", "60日均线", "90日均线", "250日均线", "500日均线"}
|
||||
:type symbol: str
|
||||
:return: 向上突破
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"5日均线": 5,
|
||||
"10日均线": 10,
|
||||
"20日均线": 20,
|
||||
"30日均线": 30,
|
||||
"60日均线": 60,
|
||||
"90日均线": 90,
|
||||
"250日均线": 250,
|
||||
"500日均线": 500,
|
||||
}
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/xstp/board/{symbol_map[symbol]}/order/asc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/xstp/board/{symbol_map[symbol]}/order/"
|
||||
f"asc/ajax/1/free/1/page/{page}/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
pd.read_html(StringIO(r.text))
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 8: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'最新价': cols[3].text.strip(),
|
||||
'成交额': cols[4].text.strip(),
|
||||
'成交量': cols[5].text.strip(),
|
||||
'涨跌幅': cols[6].text.strip(),
|
||||
'换手率': cols[7].text.strip(),
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"成交额",
|
||||
"成交量",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["换手率"] = big_df["换手率"].astype(str).str.strip("%")
|
||||
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["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_xxtp_ths(symbol: str = "500日均线") -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-向下突破
|
||||
https://data.10jqka.com.cn/rank/xxtp/
|
||||
:param symbol: choice of {"5日均线", "10日均线", "20日均线", "30日均线", "60日均线", "90日均线", "250日均线", "500日均线"}
|
||||
:type symbol: str
|
||||
:return: 向下突破
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
symbol_map = {
|
||||
"5日均线": 5,
|
||||
"10日均线": 10,
|
||||
"20日均线": 20,
|
||||
"30日均线": 30,
|
||||
"60日均线": 60,
|
||||
"90日均线": 90,
|
||||
"250日均线": 250,
|
||||
"500日均线": 500,
|
||||
}
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/xxtp/board/{symbol_map[symbol]}/order/asc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = (
|
||||
f"http://data.10jqka.com.cn/rank/xxtp/board/{symbol_map[symbol]}/order/"
|
||||
f"asc/ajax/1/free/1/page/{page}/free/1/"
|
||||
)
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 8: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'最新价': cols[3].text.strip(),
|
||||
'成交额': cols[4].text.strip(),
|
||||
'成交量': cols[5].text.strip(),
|
||||
'涨跌幅': cols[6].text.strip(),
|
||||
'换手率': cols[7].text.strip(),
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"成交额",
|
||||
"成交量",
|
||||
"涨跌幅",
|
||||
"换手率",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].astype(str).str.strip("%")
|
||||
big_df["换手率"] = big_df["换手率"].astype(str).str.strip("%")
|
||||
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["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_ljqs_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-量价齐升
|
||||
http://data.10jqka.com.cn/rank/ljqs/
|
||||
:return: 量价齐升
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/ljqs/field/count/order/desc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/ljqs/field/count/order/desc/ajax/1/free/1/page/{page}/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 8: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'最新价': cols[3].text.strip(),
|
||||
'量价齐升天数': cols[4].text.strip(),
|
||||
'阶段涨幅': cols[5].text.strip(),
|
||||
'累计换手率': cols[6].text.strip(),
|
||||
'所属行业': cols[7].find('a').text.strip() if cols[7].find('a') else cols[7].text.strip()
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"量价齐升天数",
|
||||
"阶段涨幅",
|
||||
"累计换手率",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["阶段涨幅"] = big_df["阶段涨幅"].astype(str).str.strip("%")
|
||||
big_df["累计换手率"] = big_df["累计换手率"].astype(str).str.strip("%")
|
||||
big_df["阶段涨幅"] = pd.to_numeric(big_df["阶段涨幅"], errors="coerce")
|
||||
big_df["累计换手率"] = pd.to_numeric(big_df["累计换手率"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["量价齐升天数"] = pd.to_numeric(big_df["量价齐升天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_ljqd_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-量价齐跌
|
||||
http://data.10jqka.com.cn/rank/ljqd/
|
||||
:return: 量价齐跌
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/rank/ljqd/field/count/order/desc/ajax/1/free/1/page/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
try:
|
||||
total_page = soup.find(name="span", attrs={"class": "page_info"}).text.split(
|
||||
"/"
|
||||
)[1]
|
||||
except AttributeError:
|
||||
total_page = 1
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, int(total_page) + 1), leave=False):
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = f"http://data.10jqka.com.cn/rank/ljqd/field/count/order/desc/ajax/1/free/1/page/{page}/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 8: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'股票代码': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票简称': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'最新价': cols[3].text.strip(),
|
||||
'量价齐跌天数': cols[4].text.strip(),
|
||||
'阶段涨幅': cols[5].text.strip(),
|
||||
'累计换手率': cols[6].text.strip(),
|
||||
'所属行业': cols[7].find('a').text.strip() if cols[7].find('a') else cols[7].text.strip()
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"最新价",
|
||||
"量价齐跌天数",
|
||||
"阶段涨幅",
|
||||
"累计换手率",
|
||||
"所属行业",
|
||||
]
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["阶段涨幅"] = big_df["阶段涨幅"].astype(str).str.strip("%")
|
||||
big_df["累计换手率"] = big_df["累计换手率"].astype(str).str.strip("%")
|
||||
big_df["阶段涨幅"] = pd.to_numeric(big_df["阶段涨幅"], errors="coerce")
|
||||
big_df["累计换手率"] = pd.to_numeric(big_df["累计换手率"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["量价齐跌天数"] = pd.to_numeric(big_df["量价齐跌天数"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_rank_xzjp_ths() -> pd.DataFrame:
|
||||
"""
|
||||
同花顺-数据中心-技术选股-险资举牌
|
||||
https://data.10jqka.com.cn/financial/xzjp/
|
||||
:return: 险资举牌
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
js_code = py_mini_racer.MiniRacer()
|
||||
js_content = _get_file_content_ths("ths.js")
|
||||
js_code.eval(js_content)
|
||||
big_df = pd.DataFrame()
|
||||
v_code = js_code.call("v")
|
||||
headers = {
|
||||
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 "
|
||||
"(KHTML, like Gecko) Chrome/89.0.4389.90 Safari/537.36",
|
||||
"Cookie": f"v={v_code}",
|
||||
}
|
||||
url = "http://data.10jqka.com.cn/ajax/xzjp/field/DECLAREDATE/order/desc/ajax/1/free/1/"
|
||||
r = requests.get(url, headers=headers)
|
||||
soup = BeautifulSoup(r.text, 'html.parser')
|
||||
table = soup.find('table', class_='m-table J-ajax-table')
|
||||
if not table:
|
||||
print("未找到表格")
|
||||
exit()
|
||||
|
||||
rows = table.find('tbody').find_all('tr')
|
||||
data = []
|
||||
|
||||
for row in rows:
|
||||
cols = row.find_all('td')
|
||||
if len(cols) >= 8: # 确保是数据行
|
||||
item = {
|
||||
'序号': cols[0].text.strip(),
|
||||
'举牌公告日': cols[1].find('a').text.strip() if cols[1].find('a') else cols[1].text.strip(),
|
||||
'股票代码': cols[2].find('a').text.strip() if cols[2].find('a') else cols[2].text.strip(),
|
||||
'股票简称': cols[3].text.strip(),
|
||||
'现价': cols[4].text.strip(),
|
||||
'涨跌幅': cols[5].text.strip(),
|
||||
'举牌方': cols[6].text.strip(),
|
||||
'增持数量': cols[7].find('a').text.strip() if cols[7].find('a') else cols[7].text.strip(),
|
||||
'交易均价': cols[8].find('a').text.strip() if cols[8].find('a') else cols[8].text.strip(),
|
||||
'增持数量占总股本比例': cols[9].find('a').text.strip() if cols[9].find('a') else cols[9].text.strip(),
|
||||
'变动后持股总数': cols[10].find('a').text.strip() if cols[10].find('a') else cols[10].text.strip(),
|
||||
'变动后持股比例': cols[11].find('a').text.strip() if cols[11].find('a') else cols[11].text.strip(),
|
||||
'历史数据': cols[12].find('a').text.strip() if cols[12].find('a') else cols[12].text.strip(),
|
||||
}
|
||||
data.append(item)
|
||||
|
||||
temp_df = pd.DataFrame(data)
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"举牌公告日",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"现价",
|
||||
"涨跌幅",
|
||||
"举牌方",
|
||||
"增持数量",
|
||||
"交易均价",
|
||||
"增持数量占总股本比例",
|
||||
"变动后持股总数",
|
||||
"变动后持股比例",
|
||||
"历史数据",
|
||||
]
|
||||
big_df["涨跌幅"] = big_df["涨跌幅"].astype(str).str.zfill(6)
|
||||
big_df["增持数量占总股本比例"] = (
|
||||
big_df["增持数量占总股本比例"].astype(str).str.strip("%")
|
||||
)
|
||||
big_df["变动后持股比例"] = big_df["变动后持股比例"].astype(str).str.strip("%")
|
||||
big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce")
|
||||
big_df["增持数量占总股本比例"] = pd.to_numeric(
|
||||
big_df["增持数量占总股本比例"], errors="coerce"
|
||||
)
|
||||
big_df["变动后持股比例"] = pd.to_numeric(big_df["变动后持股比例"], errors="coerce")
|
||||
big_df["举牌公告日"] = pd.to_datetime(big_df["举牌公告日"], errors="coerce").dt.date
|
||||
big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
|
||||
big_df["现价"] = pd.to_numeric(big_df["现价"], errors="coerce")
|
||||
big_df["交易均价"] = pd.to_numeric(big_df["交易均价"], errors="coerce")
|
||||
del big_df["历史数据"]
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_rank_cxg_ths_df = stock_rank_cxg_ths(symbol="创月新高")
|
||||
print(stock_rank_cxg_ths_df)
|
||||
|
||||
stock_rank_cxg_ths_df = stock_rank_cxg_ths(symbol="半年新高")
|
||||
print(stock_rank_cxg_ths_df)
|
||||
|
||||
stock_rank_cxg_ths_df = stock_rank_cxg_ths(symbol="一年新高")
|
||||
print(stock_rank_cxg_ths_df)
|
||||
|
||||
stock_rank_cxg_ths_df = stock_rank_cxg_ths(symbol="历史新高")
|
||||
print(stock_rank_cxg_ths_df)
|
||||
|
||||
stock_rank_cxd_ths_df = stock_rank_cxd_ths(symbol="创月新低")
|
||||
print(stock_rank_cxd_ths_df)
|
||||
|
||||
stock_rank_cxd_ths_df = stock_rank_cxd_ths(symbol="半年新低")
|
||||
print(stock_rank_cxd_ths_df)
|
||||
|
||||
stock_rank_cxd_ths_df = stock_rank_cxd_ths(symbol="一年新低")
|
||||
print(stock_rank_cxd_ths_df)
|
||||
|
||||
stock_rank_cxd_ths_df = stock_rank_cxd_ths(symbol="历史新低")
|
||||
print(stock_rank_cxd_ths_df)
|
||||
|
||||
stock_rank_lxsz_ths_df = stock_rank_lxsz_ths()
|
||||
print(stock_rank_lxsz_ths_df)
|
||||
|
||||
stock_rank_lxxd_ths_df = stock_rank_lxxd_ths()
|
||||
print(stock_rank_lxxd_ths_df)
|
||||
|
||||
stock_rank_cxfl_ths_df = stock_rank_cxfl_ths()
|
||||
print(stock_rank_cxfl_ths_df)
|
||||
|
||||
stock_rank_cxsl_ths_df = stock_rank_cxsl_ths()
|
||||
print(stock_rank_cxsl_ths_df)
|
||||
|
||||
stock_rank_xstp_ths_df = stock_rank_xstp_ths(symbol="5日均线")
|
||||
print(stock_rank_xstp_ths_df)
|
||||
|
||||
stock_rank_xxtp_ths_df = stock_rank_xxtp_ths(symbol="5日均线")
|
||||
print(stock_rank_xxtp_ths_df)
|
||||
|
||||
stock_rank_ljqs_ths_df = stock_rank_ljqs_ths()
|
||||
print(stock_rank_ljqs_ths_df)
|
||||
|
||||
stock_rank_ljqd_ths_df = stock_rank_ljqd_ths()
|
||||
print(stock_rank_ljqd_ths_df)
|
||||
|
||||
stock_rank_xzjp_ths_df = stock_rank_xzjp_ths()
|
||||
print(stock_rank_xzjp_ths_df)
|
||||
@@ -0,0 +1,86 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/4/29 15:00
|
||||
Desc: 东方财富网-数据中心-特色数据-停复牌信息
|
||||
https://data.eastmoney.com/tfpxx/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_tfp_em(date: str = "20240426") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-停复牌信息
|
||||
https://data.eastmoney.com/tfpxx/
|
||||
:param date: 查询参数 "20240426"
|
||||
:type date: str
|
||||
:return: 停复牌信息表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "SUSPEND_START_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_CUSTOM_SUSPEND_DATA_INTERFACE",
|
||||
"columns": "ALL",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f"""(MARKET="全部")(DATETIME='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in range(1, total_page + 1):
|
||||
params.update({"pageNumber": page})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"停牌时间",
|
||||
"停牌截止时间",
|
||||
"停牌期限",
|
||||
"停牌原因",
|
||||
"所属市场",
|
||||
"停牌开始日期",
|
||||
"预计复牌时间",
|
||||
"-",
|
||||
"-",
|
||||
"-",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"停牌时间",
|
||||
"停牌截止时间",
|
||||
"停牌期限",
|
||||
"停牌原因",
|
||||
"所属市场",
|
||||
"预计复牌时间",
|
||||
]
|
||||
]
|
||||
big_df["停牌时间"] = pd.to_datetime(big_df["停牌时间"], errors="coerce").dt.date
|
||||
big_df["停牌截止时间"] = pd.to_datetime(
|
||||
big_df["停牌截止时间"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["预计复牌时间"] = pd.to_datetime(
|
||||
big_df["预计复牌时间"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_tfp_em_df = stock_tfp_em(date="20240426")
|
||||
print(stock_tfp_em_df)
|
||||
+629
@@ -0,0 +1,629 @@
|
||||
# -*- coding:utf-8 -*-
|
||||
# !/usr/bin/env python
|
||||
"""
|
||||
Date: 2025/5/8 20:00
|
||||
Desc: 东方财富-股票-财务分析
|
||||
"""
|
||||
|
||||
from functools import lru_cache
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from bs4 import BeautifulSoup
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
@lru_cache()
|
||||
def _stock_balance_sheet_by_report_ctype_em(symbol: str = "SH600519") -> str:
|
||||
"""
|
||||
东方财富-股票-财务分析-资产负债表-按报告期-公司类型判断
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh601878#zcfzb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 东方财富-股票-财务分析-资产负债表-按报告期-公司类型判断
|
||||
:rtype: str
|
||||
"""
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index"
|
||||
params = {"type": "web", "code": symbol.lower()}
|
||||
r = requests.get(url, params=params)
|
||||
soup = BeautifulSoup(r.text, features="lxml")
|
||||
company_type = soup.find(attrs={"id": "hidctype"})["value"]
|
||||
return company_type
|
||||
|
||||
|
||||
def stock_balance_sheet_by_report_em(symbol: str = "SH600519") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-资产负债表-按报告期
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 资产负债表-按报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/zcfzbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/zcfzbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"reportType": "1",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_balance_sheet_by_yearly_em(symbol: str = "SH600036") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-资产负债表-按年度
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 资产负债表-按年度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/zcfzbDateAjaxNew"
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol)
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
try:
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
except: # noqa: E722
|
||||
company_type = "3"
|
||||
params.update({"companyType": company_type})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(
|
||||
temp_df["REPORT_DATE"], errors="coerce"
|
||||
).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/zcfzbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"reportType": "1",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_profit_sheet_by_report_em(symbol: str = "SH600519") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-利润表-报告期
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 利润表-报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"reportType": "1",
|
||||
"code": symbol,
|
||||
"dates": item,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_profit_sheet_by_yearly_em(symbol: str = "SH600519") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-利润表-按年度
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 利润表-按年度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"reportType": "1",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_profit_sheet_by_quarterly_em(
|
||||
symbol: str = "SH600519",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-利润表-按单季度
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 利润表-按单季度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "2",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/lrbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"reportType": "2",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_cash_flow_sheet_by_report_em(
|
||||
symbol: str = "SH600519",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-现金流量表-按报告期
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 现金流量表-按报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params, timeout=10)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"reportType": "1",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_cash_flow_sheet_by_yearly_em(
|
||||
symbol: str = "SH600519",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-现金流量表-按年度
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 现金流量表-按年度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "1",
|
||||
"reportType": "1",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_cash_flow_sheet_by_quarterly_em(
|
||||
symbol: str = "SH600519",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-现金流量表-按单季度
|
||||
https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/Index?type=web&code=sh600519#lrb-0
|
||||
:param symbol: 股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 现金流量表-按单季度
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
company_type = _stock_balance_sheet_by_report_ctype_em(symbol=symbol)
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbDateAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "2",
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df["REPORT_DATE"] = temp_df["REPORT_DATE"].astype(str)
|
||||
need_date = temp_df["REPORT_DATE"].tolist()
|
||||
sep_list = [",".join(need_date[i : i + 5]) for i in range(0, len(need_date), 5)]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for item in tqdm(sep_list, leave=False):
|
||||
url = "https://emweb.securities.eastmoney.com/PC_HSF10/NewFinanceAnalysis/xjllbAjaxNew"
|
||||
params = {
|
||||
"companyType": company_type,
|
||||
"reportDateType": "0",
|
||||
"reportType": "2",
|
||||
"dates": item,
|
||||
"code": symbol,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if "data" not in data_json.keys():
|
||||
break
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
for col in temp_df.columns:
|
||||
if temp_df[col].isnull().all(): # 检查列是否包含 None 或 NaN
|
||||
temp_df[col] = pd.to_numeric(temp_df[col], errors="coerce")
|
||||
if big_df.empty:
|
||||
big_df = temp_df
|
||||
else:
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
return big_df
|
||||
|
||||
|
||||
def __get_report_date_list_delisted_em(symbol: str = "SZ000013") -> list:
|
||||
"""
|
||||
东方财富-股票-财务分析-资产负债表-已退市股票-所有报告期
|
||||
https://emweb.securities.eastmoney.com/pc_hsf10/pages/index.html?type=web&code=SZ000013
|
||||
:param symbol: 已退市股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 所有报告期
|
||||
:rtype: list
|
||||
"""
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/get"
|
||||
params = {
|
||||
"type": "RPT_F10_FINANCE_GINCOME",
|
||||
"sty": "SECUCODE,SECURITY_CODE,REPORT_DATE,REPORT_TYPE,REPORT_DATE_NAME",
|
||||
"filter": f'(SECUCODE="{symbol[2:]}.{symbol[:2]}")',
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"sr": "-1",
|
||||
"st": "REPORT_DATE",
|
||||
"source": "HSF10",
|
||||
"client": "PC",
|
||||
"v": "07306678536291241",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
report_date_list = [item[0] for item in temp_df["REPORT_DATE"].str.split(" ")]
|
||||
report_date_list = ["'" + item + "'" for item in report_date_list]
|
||||
return report_date_list
|
||||
|
||||
|
||||
def stock_balance_sheet_by_report_delisted_em(symbol: str = "SZ000013") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-资产负债表-已退市股票-按报告期
|
||||
https://emweb.securities.eastmoney.com/pc_hsf10/pages/index.html?type=web&code=SZ000013#/cwfx/zcfzb
|
||||
:param symbol: 已退市股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 资产负债表-按报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
report_date_list = __get_report_date_list_delisted_em(symbol)
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/get"
|
||||
params = {
|
||||
"type": "RPT_F10_FINANCE_GBALANCE",
|
||||
"sty": "F10_FINANCE_GBALANCE",
|
||||
"filter": f"""(SECUCODE="{symbol[2:]}.{symbol[:2]}")(REPORT_DATE in ({",".join(report_date_list)}))""",
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"sr": "-1",
|
||||
"st": "REPORT_DATE",
|
||||
"source": "HSF10",
|
||||
"client": "PC",
|
||||
"v": "05767841728614413",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df.sort_values(
|
||||
by=["REPORT_DATE"], ascending=False, inplace=True, ignore_index=True
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_profit_sheet_by_report_delisted_em(symbol: str = "SZ000013") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-利润表-已退市股票-按报告期
|
||||
https://emweb.securities.eastmoney.com/pc_hsf10/pages/index.html?type=web&code=SZ000013#/cwfx/lrb
|
||||
:param symbol: 已退市股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 利润表-按报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
report_date_list = __get_report_date_list_delisted_em(symbol)
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/get"
|
||||
params = {
|
||||
"type": "RPT_F10_FINANCE_GINCOME",
|
||||
"sty": "APP_F10_GINCOME",
|
||||
"filter": f"""(SECUCODE="{symbol[2:]}.{symbol[:2]}")(REPORT_DATE in ({",".join(report_date_list)}))""",
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"sr": "-1",
|
||||
"st": "REPORT_DATE",
|
||||
"source": "HSF10",
|
||||
"client": "PC",
|
||||
"v": "05767841728614413",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df.sort_values(
|
||||
by=["REPORT_DATE"], ascending=False, inplace=True, ignore_index=True
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_cash_flow_sheet_by_report_delisted_em(
|
||||
symbol: str = "SZ000013",
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-股票-财务分析-现金流量表-已退市股票-按报告期
|
||||
https://emweb.securities.eastmoney.com/pc_hsf10/pages/index.html?type=web&code=SZ000013#/cwfx/xjllb
|
||||
:param symbol: 已退市股票代码; 带市场标识
|
||||
:type symbol: str
|
||||
:return: 现金流量表-按报告期
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
report_date_list = __get_report_date_list_delisted_em(symbol)
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/get"
|
||||
params = {
|
||||
"type": "RPT_F10_FINANCE_GCASHFLOW",
|
||||
"sty": "APP_F10_GCASHFLOW",
|
||||
"filter": f"""(SECUCODE="{symbol[2:]}.{symbol[:2]}")(REPORT_DATE in ({",".join(report_date_list)}))""",
|
||||
"p": "1",
|
||||
"ps": "200",
|
||||
"sr": "-1",
|
||||
"st": "REPORT_DATE",
|
||||
"source": "HSF10",
|
||||
"client": "PC",
|
||||
"v": "05767841728614413",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
temp_df["REPORT_DATE"] = pd.to_datetime(temp_df["REPORT_DATE"]).dt.date
|
||||
temp_df.sort_values(
|
||||
by=["REPORT_DATE"], ascending=False, inplace=True, ignore_index=True
|
||||
)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_balance_sheet_by_report_em_df = stock_balance_sheet_by_report_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_balance_sheet_by_report_em_df)
|
||||
|
||||
stock_balance_sheet_by_yearly_em_df = stock_balance_sheet_by_yearly_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_balance_sheet_by_yearly_em_df)
|
||||
|
||||
stock_profit_sheet_by_report_em_df = stock_profit_sheet_by_report_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_profit_sheet_by_report_em_df)
|
||||
|
||||
stock_profit_sheet_by_yearly_em_df = stock_profit_sheet_by_yearly_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_profit_sheet_by_yearly_em_df)
|
||||
|
||||
stock_profit_sheet_by_quarterly_em_df = stock_profit_sheet_by_quarterly_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_profit_sheet_by_quarterly_em_df)
|
||||
|
||||
stock_cash_flow_sheet_by_report_em_df = stock_cash_flow_sheet_by_report_em(
|
||||
symbol="SH600519"
|
||||
)
|
||||
print(stock_cash_flow_sheet_by_report_em_df)
|
||||
|
||||
stock_cash_flow_sheet_by_yearly_em_df = stock_cash_flow_sheet_by_yearly_em(
|
||||
symbol="SH601398"
|
||||
)
|
||||
print(stock_cash_flow_sheet_by_yearly_em_df)
|
||||
|
||||
stock_cash_flow_sheet_by_quarterly_em_df = stock_cash_flow_sheet_by_quarterly_em(
|
||||
symbol="SH601398"
|
||||
)
|
||||
print(stock_cash_flow_sheet_by_quarterly_em_df)
|
||||
|
||||
stock_balance_sheet_by_report_delisted_em_df = (
|
||||
stock_balance_sheet_by_report_delisted_em(symbol="SZ000013")
|
||||
)
|
||||
print(stock_balance_sheet_by_report_delisted_em_df)
|
||||
|
||||
stock_profit_sheet_by_report_delisted_em_df = (
|
||||
stock_profit_sheet_by_report_delisted_em(symbol="SZ000013")
|
||||
)
|
||||
print(stock_profit_sheet_by_report_delisted_em_df)
|
||||
|
||||
stock_cash_flow_sheet_by_report_delisted_em_df = (
|
||||
stock_cash_flow_sheet_by_report_delisted_em(symbol="SZ000013")
|
||||
)
|
||||
print(stock_cash_flow_sheet_by_report_delisted_em_df)
|
||||
@@ -0,0 +1,55 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/10/30 15:00
|
||||
Desc: 全部A股-等权重市盈率、中位数市盈率
|
||||
https://www.legulegu.com/stockdata/a-ttm-lyr
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.stock_feature.stock_a_indicator import get_token_lg, get_cookie_csrf
|
||||
|
||||
|
||||
def stock_a_ttm_lyr() -> pd.DataFrame:
|
||||
"""
|
||||
全部 A 股-等权重市盈率、中位数市盈率
|
||||
:return: 全部A股-等权重市盈率、中位数市盈率
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
|
||||
url = "https://legulegu.com/api/stock-data/market-ttm-lyr"
|
||||
params = {
|
||||
"marketId": "5",
|
||||
"token": get_token_lg(),
|
||||
}
|
||||
# 获取 cookie 和 headers
|
||||
csrf_data = get_cookie_csrf(url="https://www.legulegu.com/stockdata/a-ttm-lyr")
|
||||
# 使用返回的 headers（已经是副本）
|
||||
request_headers = csrf_data["headers"].copy()
|
||||
request_headers.update(
|
||||
{
|
||||
"host": "www.legulegu.com",
|
||||
"referer": "https://www.legulegu.com/stockdata/a-ttm-lyr",
|
||||
}
|
||||
)
|
||||
# 使用独立的 session
|
||||
session = requests.Session()
|
||||
r = session.get(
|
||||
url,
|
||||
params=params,
|
||||
cookies=csrf_data["cookies"],
|
||||
headers=request_headers,
|
||||
)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["data"])
|
||||
temp_df["date"] = pd.to_datetime(temp_df["date"], errors="coerce").dt.date
|
||||
# 关闭 session
|
||||
session.close()
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_a_ttm_lyr_df = stock_a_ttm_lyr()
|
||||
print(stock_a_ttm_lyr_df)
|
||||
+66
@@ -0,0 +1,66 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/12/24 18:26
|
||||
Desc: 百度股市通-美股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/us-NVDA
|
||||
"""
|
||||
|
||||
import http.client
|
||||
import json
|
||||
import urllib
|
||||
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def stock_us_valuation_baidu(
|
||||
symbol: str = "NVDA", indicator: str = "总市值", period: str = "近一年"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
百度股市通-美股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/us-NVDA
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param indicator: choice of {"总市值", "市盈率(TTM)", "市盈率(静)", "市净率", "市现率"}
|
||||
:type indicator: str
|
||||
:param period: choice of {"近一年", "近三年", "全部"}
|
||||
:type period: str
|
||||
:return: 估值数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
params = {
|
||||
"openapi": "1",
|
||||
"dspName": "iphone",
|
||||
"tn": "tangram",
|
||||
"client": "app",
|
||||
"query": indicator,
|
||||
"code": symbol,
|
||||
"word": "",
|
||||
"resource_id": "51171",
|
||||
"market": "us",
|
||||
"tag": indicator,
|
||||
"chart_select": period,
|
||||
"industry_select": "",
|
||||
"skip_industry": "1",
|
||||
"finClientType": "pc",
|
||||
}
|
||||
conn = http.client.HTTPSConnection("gushitong.baidu.com")
|
||||
conn.request(method="GET", url=f"/opendata?{urllib.parse.urlencode(params)}")
|
||||
r = conn.getresponse()
|
||||
data_json = json.loads(r.read())
|
||||
temp_df = pd.DataFrame(
|
||||
data_json["Result"][0]["DisplayData"]["resultData"]["tplData"]["result"][
|
||||
"chartInfo"
|
||||
][0]["body"]
|
||||
)
|
||||
temp_df.columns = ["date", "value"]
|
||||
temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
|
||||
temp_df["value"] = pd.to_numeric(temp_df["value"])
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_us_valuation_baidu_df = stock_us_valuation_baidu(
|
||||
symbol="NVDA", indicator="总市值", period="近一年"
|
||||
)
|
||||
print(stock_us_valuation_baidu_df)
|
||||
@@ -0,0 +1,83 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/11/26 18:00
|
||||
Desc: 东方财富网-数据中心-估值分析-每日互动-每日互动-估值分析
|
||||
https://data.eastmoney.com/gzfx/detail/300766.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
from akshare.request import make_request_with_retry_json
|
||||
|
||||
|
||||
def stock_value_em(symbol: str = "300766") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-估值分析-每日互动-每日互动-估值分析
|
||||
https://data.eastmoney.com/gzfx/detail/300766.html
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:return: 估值分析
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "TRADE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "5000",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_VALUEANALYSIS_DET",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
"filter": f'(SECURITY_CODE="{symbol}")',
|
||||
}
|
||||
data_json = make_request_with_retry_json(url, params=params)
|
||||
temp_json = data_json["result"]["data"]
|
||||
temp_df = pd.DataFrame(temp_json)
|
||||
temp_df.rename(
|
||||
columns={
|
||||
"TRADE_DATE": "数据日期",
|
||||
"CLOSE_PRICE": "当日收盘价",
|
||||
"CHANGE_RATE": "当日涨跌幅",
|
||||
"TOTAL_MARKET_CAP": "总市值",
|
||||
"NOTLIMITED_MARKETCAP_A": "流通市值",
|
||||
"TOTAL_SHARES": "总股本",
|
||||
"FREE_SHARES_A": "流通股本",
|
||||
"PE_TTM": "PE(TTM)",
|
||||
"PE_LAR": "PE(静)",
|
||||
"PB_MRQ": "市净率",
|
||||
"PEG_CAR": "PEG值",
|
||||
"PCF_OCF_TTM": "市现率",
|
||||
"PS_TTM": "市销率",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"数据日期",
|
||||
"当日收盘价",
|
||||
"当日涨跌幅",
|
||||
"总市值",
|
||||
"流通市值",
|
||||
"总股本",
|
||||
"流通股本",
|
||||
"PE(TTM)",
|
||||
"PE(静)",
|
||||
"市净率",
|
||||
"PEG值",
|
||||
"市现率",
|
||||
"市销率",
|
||||
]
|
||||
]
|
||||
temp_df["数据日期"] = pd.to_datetime(temp_df["数据日期"], errors="coerce").dt.date
|
||||
for item in temp_df.columns[1:]:
|
||||
temp_df[item] = pd.to_numeric(temp_df[item], errors="coerce")
|
||||
temp_df.sort_values(by="数据日期", ignore_index=True, inplace=True)
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_value_em_df = stock_value_em(symbol="300766")
|
||||
print(stock_value_em_df)
|
||||
@@ -0,0 +1,148 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/3/20 18:00
|
||||
Desc: 东方财富-数据中心-年报季报
|
||||
东方财富-数据中心-年报季报-业绩快报-业绩报表
|
||||
https://data.eastmoney.com/bbsj/202003/yjbb.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_yjbb_em(date: str = "20200331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报-业绩报表
|
||||
https://data.eastmoney.com/bbsj/202003/yjbb.html
|
||||
:param date: "20200331", "20200630", "20200930", "20201231"; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 业绩报表
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter(action="ignore", category=FutureWarning) # 忽略所有
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "UPDATE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_LICO_FN_CPD",
|
||||
"columns": "ALL",
|
||||
"filter": f"(REPORTDATE='{'-'.join([date[:4], date[4:6], date[6:]])}')",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
page_num = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
big_list = []
|
||||
for page in tqdm(range(1, page_num + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_list.append(temp_df)
|
||||
big_df = pd.concat(big_list, ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"最新公告日期",
|
||||
"_",
|
||||
"每股收益",
|
||||
"_",
|
||||
"营业总收入-营业总收入",
|
||||
"净利润-净利润",
|
||||
"净资产收益率",
|
||||
"营业总收入-同比增长",
|
||||
"净利润-同比增长",
|
||||
"每股净资产",
|
||||
"每股经营现金流量",
|
||||
"销售毛利率",
|
||||
"营业总收入-季度环比增长",
|
||||
"净利润-季度环比增长",
|
||||
"_",
|
||||
"_",
|
||||
"所处行业",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"每股收益",
|
||||
"营业总收入-营业总收入",
|
||||
"营业总收入-同比增长",
|
||||
"营业总收入-季度环比增长",
|
||||
"净利润-净利润",
|
||||
"净利润-同比增长",
|
||||
"净利润-季度环比增长",
|
||||
"每股净资产",
|
||||
"净资产收益率",
|
||||
"每股经营现金流量",
|
||||
"销售毛利率",
|
||||
"所处行业",
|
||||
"最新公告日期",
|
||||
]
|
||||
]
|
||||
big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce")
|
||||
big_df["营业总收入-营业总收入"] = pd.to_numeric(
|
||||
big_df["营业总收入-营业总收入"], errors="coerce"
|
||||
)
|
||||
big_df["营业总收入-同比增长"] = pd.to_numeric(
|
||||
big_df["营业总收入-同比增长"], errors="coerce"
|
||||
)
|
||||
big_df["营业总收入-季度环比增长"] = pd.to_numeric(
|
||||
big_df["营业总收入-季度环比增长"], errors="coerce"
|
||||
)
|
||||
big_df["净利润-净利润"] = pd.to_numeric(big_df["净利润-净利润"], errors="coerce")
|
||||
big_df["净利润-同比增长"] = pd.to_numeric(
|
||||
big_df["净利润-同比增长"], errors="coerce"
|
||||
)
|
||||
big_df["净利润-季度环比增长"] = pd.to_numeric(
|
||||
big_df["净利润-季度环比增长"], errors="coerce"
|
||||
)
|
||||
big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce")
|
||||
big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce")
|
||||
big_df["每股经营现金流量"] = pd.to_numeric(
|
||||
big_df["每股经营现金流量"], errors="coerce"
|
||||
)
|
||||
big_df["销售毛利率"] = pd.to_numeric(big_df["销售毛利率"], errors="coerce")
|
||||
big_df["最新公告日期"] = pd.to_datetime(
|
||||
big_df["最新公告日期"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_yjbb_em_df = stock_yjbb_em(date="20220331")
|
||||
print(stock_yjbb_em_df)
|
||||
@@ -0,0 +1,94 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2023/6/8 16:34
|
||||
Desc: 巨潮资讯-首页-数据-预约披露
|
||||
http://www.cninfo.com.cn/new/commonUrl?url=data/yypl
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_report_disclosure(
|
||||
market: str = "沪深京", period: str = "2021年报"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
巨潮资讯-首页-数据-预约披露
|
||||
http://www.cninfo.com.cn/new/commonUrl?url=data/yypl
|
||||
:param market: choice of {"沪深京": "szsh", "深市": "sz", "深主板": "szmb", "中小板": "szsme",
|
||||
"创业板": "szcn", "沪市": "sh", "沪主板": "shmb", "科创板": "shkcp"}
|
||||
:type market: str
|
||||
:param period: 最近四期的财报
|
||||
:type period: str
|
||||
:return: 指定 market 和 period 的数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
market_map = {
|
||||
"沪深京": "szsh",
|
||||
"深市": "sz",
|
||||
"深主板": "szmb",
|
||||
"创业板": "szcn",
|
||||
"沪市": "sh",
|
||||
"沪主板": "shmb",
|
||||
"科创板": "shkcp",
|
||||
"北交所": "bj",
|
||||
}
|
||||
year = period[:4]
|
||||
period_map = {
|
||||
f"{year}一季": f"{year}-03-31",
|
||||
f"{year}半年报": f"{year}-06-30",
|
||||
f"{year}三季": f"{year}-09-30",
|
||||
f"{year}年报": f"{year}-12-31",
|
||||
}
|
||||
url = "http://www.cninfo.com.cn/new/information/getPrbookInfo"
|
||||
params = {
|
||||
"sectionTime": period_map[period],
|
||||
"firstTime": "",
|
||||
"lastTime": "",
|
||||
"market": market_map[market],
|
||||
"stockCode": "",
|
||||
"orderClos": "",
|
||||
"isDesc": "",
|
||||
"pagesize": "10000",
|
||||
"pagenum": "1",
|
||||
}
|
||||
r = requests.post(url, params=params)
|
||||
text_json = r.json()
|
||||
temp_df = pd.DataFrame(text_json["prbookinfos"])
|
||||
temp_df.columns = [
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"首次预约",
|
||||
"实际披露",
|
||||
"初次变更",
|
||||
"二次变更",
|
||||
"三次变更",
|
||||
"报告期",
|
||||
"-",
|
||||
"组织码",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"首次预约",
|
||||
"初次变更",
|
||||
"二次变更",
|
||||
"三次变更",
|
||||
"实际披露",
|
||||
]
|
||||
]
|
||||
temp_df["首次预约"] = pd.to_datetime(temp_df["首次预约"], errors="coerce").dt.date
|
||||
temp_df["初次变更"] = pd.to_datetime(temp_df["初次变更"], errors="coerce").dt.date
|
||||
temp_df["二次变更"] = pd.to_datetime(temp_df["二次变更"], errors="coerce").dt.date
|
||||
temp_df["三次变更"] = pd.to_datetime(temp_df["三次变更"], errors="coerce").dt.date
|
||||
temp_df["实际披露"] = pd.to_datetime(temp_df["实际披露"], errors="coerce").dt.date
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_report_disclosure_df = stock_report_disclosure(
|
||||
market="沪深京", period="2022年报"
|
||||
)
|
||||
print(stock_report_disclosure_df)
|
||||
@@ -0,0 +1,368 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/4/4 18:10
|
||||
Desc: 东方财富-数据中心-年报季报
|
||||
东方财富-数据中心-年报季报-业绩预告
|
||||
https://data.eastmoney.com/bbsj/202003/yjyg.html
|
||||
东方财富-数据中心-年报季报-预约披露时间
|
||||
https://data.eastmoney.com/bbsj/202003/yysj.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def stock_yjkb_em(date: str = "20211231") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩快报
|
||||
https://data.eastmoney.com/bbsj/202003/yjkb.html
|
||||
:param date: 财报发布日期; choice of {"20200331", "20200630", "20200930", "20201231", ...}; 从 20100331 开始
|
||||
:type date: str
|
||||
:return: 业绩快报
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "UPDATE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_FCI_PERFORMANCEE",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = data_json["result"]["pages"]
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"市场板块",
|
||||
"_",
|
||||
"证券类型",
|
||||
"_",
|
||||
"公告日期",
|
||||
"_",
|
||||
"每股收益",
|
||||
"营业收入-营业收入",
|
||||
"营业收入-去年同期",
|
||||
"净利润-净利润",
|
||||
"净利润-去年同期",
|
||||
"每股净资产",
|
||||
"净资产收益率",
|
||||
"营业收入-同比增长",
|
||||
"净利润-同比增长",
|
||||
"营业收入-季度环比增长",
|
||||
"净利润-季度环比增长",
|
||||
"所处行业",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"每股收益",
|
||||
"营业收入-营业收入",
|
||||
"营业收入-去年同期",
|
||||
"营业收入-同比增长",
|
||||
"营业收入-季度环比增长",
|
||||
"净利润-净利润",
|
||||
"净利润-去年同期",
|
||||
"净利润-同比增长",
|
||||
"净利润-季度环比增长",
|
||||
"每股净资产",
|
||||
"净资产收益率",
|
||||
"所处行业",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["每股收益"] = pd.to_numeric(big_df["每股收益"], errors="coerce")
|
||||
big_df["营业收入-营业收入"] = pd.to_numeric(
|
||||
big_df["营业收入-营业收入"], errors="coerce"
|
||||
)
|
||||
big_df["营业收入-去年同期"] = pd.to_numeric(
|
||||
big_df["营业收入-去年同期"], errors="coerce"
|
||||
)
|
||||
big_df["营业收入-同比增长"] = pd.to_numeric(
|
||||
big_df["营业收入-同比增长"], errors="coerce"
|
||||
)
|
||||
big_df["营业收入-季度环比增长"] = pd.to_numeric(
|
||||
big_df["营业收入-季度环比增长"], errors="coerce"
|
||||
)
|
||||
big_df["净利润-净利润"] = pd.to_numeric(big_df["净利润-净利润"], errors="coerce")
|
||||
big_df["净利润-去年同期"] = pd.to_numeric(
|
||||
big_df["净利润-去年同期"], errors="coerce"
|
||||
)
|
||||
big_df["净利润-同比增长"] = pd.to_numeric(
|
||||
big_df["净利润-同比增长"], errors="coerce"
|
||||
)
|
||||
big_df["净利润-季度环比增长"] = pd.to_numeric(
|
||||
big_df["净利润-季度环比增长"], errors="coerce"
|
||||
)
|
||||
big_df["每股净资产"] = pd.to_numeric(big_df["每股净资产"], errors="coerce")
|
||||
big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce")
|
||||
big_df["净资产收益率"] = pd.to_numeric(big_df["净资产收益率"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"]).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_yjyg_em(date: str = "20200331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-业绩预告
|
||||
https://data.eastmoney.com/bbsj/202003/yjyg.html
|
||||
:param date: 财报发布日期; choice of {"20200331", "20200630", "20200930", "20201231", ...}; 从 20081231 开始
|
||||
:type date: str
|
||||
:return: 业绩预告
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter.eastmoney.com/securities/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "NOTICE_DATE,SECURITY_CODE",
|
||||
"sortTypes": "-1,-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_PUBLIC_OP_NEWPREDICT",
|
||||
"columns": "ALL",
|
||||
"filter": f" (REPORT_DATE='{'-'.join([date[:4], date[4:6], date[6:]])}')",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
big_df = pd.DataFrame()
|
||||
total_page = data_json["result"]["pages"]
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"_",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"_",
|
||||
"公告日期",
|
||||
"报告日期",
|
||||
"_",
|
||||
"预测指标",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"业绩变动",
|
||||
"业绩变动原因",
|
||||
"预告类型",
|
||||
"上年同期值",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"业绩变动幅度",
|
||||
"预测数值",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"预测指标",
|
||||
"业绩变动",
|
||||
"预测数值",
|
||||
"业绩变动幅度",
|
||||
"业绩变动原因",
|
||||
"预告类型",
|
||||
"上年同期值",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["业绩变动幅度"] = pd.to_numeric(big_df["业绩变动幅度"], errors="coerce")
|
||||
big_df["预测数值"] = pd.to_numeric(big_df["预测数值"], errors="coerce")
|
||||
big_df["上年同期值"] = pd.to_numeric(big_df["上年同期值"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_yysj_em(symbol: str = "沪深A股", date: str = "20200331") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富-数据中心-年报季报-预约披露时间
|
||||
https://data.eastmoney.com/bbsj/202003/yysj.html
|
||||
:param symbol: choice of {'沪深A股', '沪市A股', '科创板', '深市A股', '创业板', '京市A股', 'ST板'}
|
||||
:type symbol: str
|
||||
:param date: "20190331", "20190630", "20190930", "20191231"; 从 20081231 开始
|
||||
:type date: str
|
||||
:return: 指定时间的上市公司预约披露时间数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "FIRST_APPOINT_DATE,SECURITY_CODE",
|
||||
"sortTypes": "1,1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_PUBLIC_BS_APPOIN",
|
||||
"columns": "ALL",
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE!="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')""",
|
||||
}
|
||||
if symbol == "沪市A股":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))
|
||||
(TRADE_MARKET_CODE in ("069001001001","069001001003","069001001006"))
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
elif symbol == "科创板":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(SECURITY_TYPE_CODE in ("058001001","058001008"))(TRADE_MARKET_CODE="069001001006")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
elif symbol == "深市A股":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE in
|
||||
("069001002001","069001002002","069001002003","069001002005"))
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
elif symbol == "创业板":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(SECURITY_TYPE_CODE="058001001")(TRADE_MARKET_CODE="069001002002")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
elif symbol == "京市A股":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(TRADE_MARKET_CODE="069001017")
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
elif symbol == "ST板":
|
||||
params.update(
|
||||
{
|
||||
"filter": f"""(TRADE_MARKET_CODE in("069001001003","069001002005"))
|
||||
(REPORT_DATE='{"-".join([date[:4], date[4:6], date[6:]])}')"""
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
total_page = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
for page in tqdm(range(1, total_page + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"pageNumber": page,
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"_",
|
||||
"_",
|
||||
"首次预约时间",
|
||||
"一次变更日期",
|
||||
"二次变更日期",
|
||||
"三次变更日期",
|
||||
"实际披露时间",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"首次预约时间",
|
||||
"一次变更日期",
|
||||
"二次变更日期",
|
||||
"三次变更日期",
|
||||
"实际披露时间",
|
||||
]
|
||||
]
|
||||
big_df["首次预约时间"] = pd.to_datetime(
|
||||
big_df["首次预约时间"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["一次变更日期"] = pd.to_datetime(
|
||||
big_df["一次变更日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["二次变更日期"] = pd.to_datetime(
|
||||
big_df["二次变更日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["三次变更日期"] = pd.to_datetime(
|
||||
big_df["三次变更日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["实际披露时间"] = pd.to_datetime(
|
||||
big_df["实际披露时间"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_yjkb_em_df = stock_yjkb_em(date="20200331")
|
||||
print(stock_yjkb_em_df)
|
||||
|
||||
stock_yjyg_em_df = stock_yjyg_em(date="20250331")
|
||||
print(stock_yjyg_em_df)
|
||||
|
||||
stock_yysj_em_df = stock_yysj_em(symbol="沪深A股", date="20211231")
|
||||
print(stock_yysj_em_df)
|
||||
@@ -0,0 +1,106 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2022/4/27 19:18
|
||||
Desc: 东方财富网-数据中心-特色数据-一致行动人
|
||||
https://data.eastmoney.com/yzxdr/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
from akshare.utils import demjson
|
||||
|
||||
|
||||
def stock_yzxdr_em(date: str = "20240930") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-特色数据-一致行动人
|
||||
https://data.eastmoney.com/yzxdr/
|
||||
:param date: 每年的季度末时间点
|
||||
:type date: str
|
||||
:return: 一致行动人
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.simplefilter(action="ignore", category=FutureWarning) # 忽略所有
|
||||
date = "-".join([date[:4], date[4:6], date[6:]])
|
||||
url = "https://datacenter.eastmoney.com/api/data/get"
|
||||
params = {
|
||||
"type": "RPTA_WEB_YZXDRINDEX",
|
||||
"sty": "ALL",
|
||||
"source": "WEB",
|
||||
"p": "1",
|
||||
"ps": "500",
|
||||
"st": "noticedate",
|
||||
"sr": "-1",
|
||||
"var": "mwUyirVm",
|
||||
"filter": f"(enddate='{date}')",
|
||||
"rt": "53575609",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
total_pages = data_json["result"]["pages"]
|
||||
big_df = pd.DataFrame()
|
||||
tqdm = get_tqdm()
|
||||
for page in tqdm(range(1, total_pages + 1), leave=False):
|
||||
params.update(
|
||||
{
|
||||
"p": str(page),
|
||||
"filter": f"(enddate='{date}')",
|
||||
}
|
||||
)
|
||||
r = requests.get(url, params=params)
|
||||
data_text = r.text
|
||||
data_json = demjson.decode(data_text[data_text.find("{") : -1])
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = range(1, len(big_df) + 1)
|
||||
big_df.columns = [
|
||||
"序号",
|
||||
"一致行动人",
|
||||
"股票代码",
|
||||
"股东排名",
|
||||
"公告日期",
|
||||
"股票简称",
|
||||
"持股数量",
|
||||
"持股比例",
|
||||
"持股数量变动",
|
||||
"_",
|
||||
"行业",
|
||||
"_",
|
||||
"_",
|
||||
"数据日期",
|
||||
"股票市场",
|
||||
]
|
||||
big_df["数据日期"] = pd.to_datetime(big_df["数据日期"], errors="coerce")
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce")
|
||||
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"一致行动人",
|
||||
"股东排名",
|
||||
"持股数量",
|
||||
"持股比例",
|
||||
"持股数量变动",
|
||||
"行业",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["持股数量"] = pd.to_numeric(big_df["持股数量"], errors="coerce")
|
||||
big_df["持股比例"] = pd.to_numeric(big_df["持股比例"], errors="coerce")
|
||||
big_df["持股数量变动"] = pd.to_numeric(big_df["持股数量变动"], errors="coerce")
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_yzxdr_em_df = stock_yzxdr_em(date="20240930")
|
||||
print(stock_yzxdr_em_df)
|
||||
@@ -0,0 +1,125 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2025/2/11 20:32
|
||||
Desc: 东方财富网-数据中心-重大合同-重大合同明细
|
||||
https://data.eastmoney.com/zdht/mx.html
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_zdhtmx_em(
|
||||
start_date: str = "20200819", end_date: str = "20230819"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-重大合同-重大合同明细
|
||||
https://data.eastmoney.com/zdht/mx.html
|
||||
:param start_date: 开始日期, eg 20200819
|
||||
:type start_date: str
|
||||
:param end_date: 结束日期, eg 20230819
|
||||
:type end_date: str
|
||||
:return: 股东大会
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "DIM_RDATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"columns": "ALL",
|
||||
"token": "894050c76af8597a853f5b408b759f5d",
|
||||
"reportName": "RPTA_WEB_ZDHT_LIST",
|
||||
"filter": f"""(DIM_RDATE>='{"-".join([start_date[:4], start_date[4:6], start_date[6:]])}')
|
||||
(DIM_RDATE<='{"-".join([end_date[:4], end_date[4:6], end_date[6:]])}')""",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], axis=0, ignore_index=True)
|
||||
big_df.reset_index(inplace=True)
|
||||
big_df["index"] = big_df["index"] + 1
|
||||
big_df.rename(
|
||||
columns={
|
||||
"index": "序号",
|
||||
"DIM_SCODE": "-",
|
||||
"CONTENTS": "-",
|
||||
"CONTRACTNAME": "合同名称",
|
||||
"CONTRACTTYPE": "-",
|
||||
"COUNTERPARTY": "其他签署方",
|
||||
"COUNTERPARTYREL": "-",
|
||||
"ISABOLISHED": "-",
|
||||
"DIM_RDATE": "公告日期",
|
||||
"REMARK": "-",
|
||||
"SIGNATORY": "签署主体",
|
||||
"SIGNATORYREL": "与上市公司关系",
|
||||
"SIGNDATE": "签署日期",
|
||||
"SIGNEFFECT": "-",
|
||||
"UPDATEDATE": "-",
|
||||
"YEAR": "-",
|
||||
"AMOUNTS": "合同金额",
|
||||
"SECURITYCODE": "股票代码",
|
||||
"SECURITYSHORTNAME": "股票简称",
|
||||
"CONTRACTTYPENAME": "合同类型",
|
||||
"SIGNATORYRELNAME": "签署主体-与上市公司关系",
|
||||
"COUNTERPARTYRELNAME": "其他签署方-与上市公司关系",
|
||||
"SNDYYSR": "上年度营业收入",
|
||||
"OPERATEREVE": "最新财务报表的营业收入",
|
||||
"RCHANGE1DC": "-",
|
||||
"RCHANGE20DC": "-",
|
||||
"ZSNDYYSRBL": "占上年度营业收入比例",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"序号",
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"签署主体",
|
||||
"签署主体-与上市公司关系",
|
||||
"其他签署方",
|
||||
"其他签署方-与上市公司关系",
|
||||
"合同类型",
|
||||
"合同名称",
|
||||
"合同金额",
|
||||
"上年度营业收入",
|
||||
"占上年度营业收入比例",
|
||||
"最新财务报表的营业收入",
|
||||
"签署日期",
|
||||
"公告日期",
|
||||
]
|
||||
]
|
||||
big_df["签署日期"] = pd.to_datetime(big_df["签署日期"], errors="coerce").dt.date
|
||||
big_df["公告日期"] = pd.to_datetime(big_df["公告日期"], errors="coerce").dt.date
|
||||
big_df["合同金额"] = pd.to_numeric(big_df["合同金额"], errors="coerce")
|
||||
big_df["上年度营业收入"] = pd.to_numeric(big_df["上年度营业收入"], errors="coerce")
|
||||
big_df["占上年度营业收入比例"] = pd.to_numeric(
|
||||
big_df["占上年度营业收入比例"], errors="coerce"
|
||||
)
|
||||
big_df["最新财务报表的营业收入"] = pd.to_numeric(
|
||||
big_df["最新财务报表的营业收入"], errors="coerce"
|
||||
)
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zdhtmx_em_df = stock_zdhtmx_em(start_date="20220819", end_date="20230819")
|
||||
print(stock_zdhtmx_em_df)
|
||||
@@ -0,0 +1,201 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/4/4 18:20
|
||||
Desc: 增发和配股
|
||||
东方财富网-数据中心-新股数据-增发-全部增发
|
||||
https://data.eastmoney.com/other/gkzf.html
|
||||
东方财富网-数据中心-新股数据-配股
|
||||
https://data.eastmoney.com/xg/pg/
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
from akshare.utils.tqdm import get_tqdm
|
||||
|
||||
|
||||
def stock_qbzf_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-新股数据-增发-全部增发
|
||||
https://data.eastmoney.com/other/gkzf.html
|
||||
:return: 全部增发
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "ISSUE_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "500",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_SEO_DETAIL",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~NEW_PRICE",
|
||||
"quoteType": "0",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
|
||||
big_df.rename(
|
||||
columns={
|
||||
"SECURITY_NAME_ABBR": "股票简称",
|
||||
"SECURITY_CODE": "股票代码",
|
||||
"CORRECODE": "增发代码",
|
||||
"SEO_TYPE": "发行方式",
|
||||
"ISSUE_NUM": "发行总数",
|
||||
"ONLINE_ISSUE_NUM": "网上发行",
|
||||
"ISSUE_PRICE": "发行价格",
|
||||
"NEW_PRICE": "最新价",
|
||||
"ISSUE_DATE": "发行日期",
|
||||
"ISSUE_LISTING_DATE": "增发上市日期",
|
||||
"LOCKIN_PERIOD": "锁定期",
|
||||
},
|
||||
inplace=True,
|
||||
)
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"增发代码",
|
||||
"发行方式",
|
||||
"发行总数",
|
||||
"网上发行",
|
||||
"发行价格",
|
||||
"最新价",
|
||||
"发行日期",
|
||||
"增发上市日期",
|
||||
"锁定期",
|
||||
]
|
||||
]
|
||||
big_df["发行总数"] = pd.to_numeric(big_df["发行总数"], 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["发行方式"] = big_df["发行方式"].map({"2": "公开增发", "1": "定向增发"})
|
||||
big_df["发行日期"] = pd.to_datetime(big_df["发行日期"], errors="coerce").dt.date
|
||||
big_df["增发上市日期"] = pd.to_datetime(
|
||||
big_df["增发上市日期"], errors="coerce"
|
||||
).dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
def stock_pg_em() -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-数据中心-新股数据-配股
|
||||
https://data.eastmoney.com/xg/pg/
|
||||
:return: 配股
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
import warnings
|
||||
|
||||
warnings.filterwarnings(action="ignore", category=FutureWarning)
|
||||
url = "https://datacenter-web.eastmoney.com/api/data/v1/get"
|
||||
params = {
|
||||
"sortColumns": "EQUITY_RECORD_DATE",
|
||||
"sortTypes": "-1",
|
||||
"pageSize": "50000",
|
||||
"pageNumber": "1",
|
||||
"reportName": "RPT_IPO_ALLOTMENT",
|
||||
"columns": "ALL",
|
||||
"quoteColumns": "f2~01~SECURITY_CODE~NEW_PRICE",
|
||||
"quoteType": "0",
|
||||
"source": "WEB",
|
||||
"client": "WEB",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
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)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(data_json["result"]["data"])
|
||||
big_df = pd.concat(objs=[big_df, temp_df], ignore_index=True)
|
||||
big_df.columns = [
|
||||
"_",
|
||||
"_",
|
||||
"股票代码",
|
||||
"-",
|
||||
"股票简称",
|
||||
"配售代码",
|
||||
"_",
|
||||
"配股比例",
|
||||
"配股价",
|
||||
"配股前总股本",
|
||||
"配股数量",
|
||||
"配股后总股本",
|
||||
"股权登记日",
|
||||
"缴款起始日期",
|
||||
"缴款截止日期",
|
||||
"上市日",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"_",
|
||||
"最新价",
|
||||
]
|
||||
big_df = big_df[
|
||||
[
|
||||
"股票代码",
|
||||
"股票简称",
|
||||
"配售代码",
|
||||
"配股数量",
|
||||
"配股比例",
|
||||
"配股价",
|
||||
"最新价",
|
||||
"配股前总股本",
|
||||
"配股后总股本",
|
||||
"股权登记日",
|
||||
"缴款起始日期",
|
||||
"缴款截止日期",
|
||||
"上市日",
|
||||
]
|
||||
]
|
||||
big_df["配股比例"] = "10配" + big_df["配股比例"].astype(str)
|
||||
big_df["配股数量"] = pd.to_numeric(big_df["配股数量"], errors="coerce")
|
||||
big_df["配股价"] = pd.to_numeric(big_df["配股价"], errors="coerce")
|
||||
big_df["最新价"] = pd.to_numeric(big_df["最新价"], errors="coerce")
|
||||
big_df["配股前总股本"] = pd.to_numeric(big_df["配股前总股本"], errors="coerce")
|
||||
big_df["配股后总股本"] = pd.to_numeric(big_df["配股后总股本"], errors="coerce")
|
||||
big_df["股权登记日"] = pd.to_datetime(big_df["股权登记日"], errors="coerce").dt.date
|
||||
big_df["缴款起始日期"] = pd.to_datetime(
|
||||
big_df["缴款起始日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["缴款截止日期"] = pd.to_datetime(
|
||||
big_df["缴款截止日期"], errors="coerce"
|
||||
).dt.date
|
||||
big_df["上市日"] = pd.to_datetime(big_df["上市日"], errors="coerce").dt.date
|
||||
return big_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_qbzf_em_df = stock_qbzf_em()
|
||||
print(stock_qbzf_em_df)
|
||||
|
||||
stock_pg_em_df = stock_pg_em()
|
||||
print(stock_pg_em_df)
|
||||
+62
@@ -0,0 +1,62 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/5/29 23:00
|
||||
Desc: 百度股市通-A股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/ab-002044
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_zh_valuation_baidu(
|
||||
symbol: str = "002044", indicator: str = "总市值", period: str = "近一年"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
百度股市通-A股-财务报表-估值数据
|
||||
https://gushitong.baidu.com/stock/ab-002044
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param indicator: choice of {"总市值", "市盈率(TTM)", "市盈率(静)", "市净率", "市现率"}
|
||||
:type indicator: str
|
||||
:param period: choice of {"近一年", "近三年", "近五年", "近十年", "全部"}
|
||||
:type period: str
|
||||
:return: 估值数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://gushitong.baidu.com/opendata"
|
||||
params = {
|
||||
"openapi": "1",
|
||||
"dspName": "iphone",
|
||||
"tn": "tangram",
|
||||
"client": "app",
|
||||
"query": indicator,
|
||||
"code": symbol,
|
||||
"word": "",
|
||||
"resource_id": "51171",
|
||||
"market": "ab",
|
||||
"tag": indicator,
|
||||
"chart_select": period,
|
||||
"industry_select": "",
|
||||
"skip_industry": "1",
|
||||
"finClientType": "pc",
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_df = pd.DataFrame(
|
||||
data_json["Result"][0]["DisplayData"]["resultData"]["tplData"]["result"][
|
||||
"chartInfo"
|
||||
][0]["body"]
|
||||
)
|
||||
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")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zh_valuation_baidu_df = stock_zh_valuation_baidu(
|
||||
symbol="002044", indicator="总市值", period="近一年"
|
||||
)
|
||||
print(stock_zh_valuation_baidu_df)
|
||||
+57
@@ -0,0 +1,57 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2022/10/10 17:26
|
||||
Desc: 百度股市通- A 股或指数-股评-投票
|
||||
https://gushitong.baidu.com/index/ab-000001
|
||||
"""
|
||||
|
||||
import requests
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def stock_zh_vote_baidu(
|
||||
symbol: str = "000001", indicator: str = "指数"
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
百度股市通- A 股或指数-股评-投票
|
||||
https://gushitong.baidu.com/index/ab-000001
|
||||
:param symbol: 股票代码
|
||||
:type symbol: str
|
||||
:param indicator: choice of {"指数", "股票"}
|
||||
:type indicator: str
|
||||
:return: 投票数据
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
indicator_map = {"股票": "stock", "指数": "index"}
|
||||
url = "https://finance.pae.baidu.com/vapi/v1/stockvoterecords"
|
||||
params = {
|
||||
"code": symbol,
|
||||
"market": "ab",
|
||||
"finance_type": indicator_map[indicator],
|
||||
"select_type": "week",
|
||||
"from_smart_app": "0",
|
||||
"method": "query",
|
||||
"finClientType": "pc",
|
||||
}
|
||||
temp_list = []
|
||||
for item_period in ["day", "week", "month", "year"]:
|
||||
params.update({"select_type": item_period})
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
temp_list.append(
|
||||
[
|
||||
item
|
||||
for item in data_json["Result"]["voteRecords"]["voteRes"]
|
||||
if item["type"] == item_period
|
||||
][0]
|
||||
)
|
||||
temp_df = pd.DataFrame(temp_list)
|
||||
temp_df.columns = ["周期", "-", "看涨", "看跌", "看涨比例", "看跌比例"]
|
||||
temp_df = temp_df[["周期", "看涨", "看跌", "看涨比例", "看跌比例"]]
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zh_vote_baidu_df = stock_zh_vote_baidu(symbol="000001", indicator="指数")
|
||||
print(stock_zh_vote_baidu_df)
|
||||
@@ -0,0 +1,542 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding:utf-8 -*-
|
||||
"""
|
||||
Date: 2024/11/22 21:30
|
||||
Desc: 首页-行情中心-涨停板行情-涨停股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=ztgc
|
||||
|
||||
涨停板行情专题为您展示了 6 个股票池，分别为：
|
||||
1. 涨停股池：包含当日当前涨停的所有A股股票(不含未中断连续一字涨停板的新股)；
|
||||
2. 昨日涨停股池：包含上一交易日收盘时涨停的所有A股股票(不含未中断连续一字涨停板的新股)；
|
||||
3. 强势股池：包含创下60日新高或近期多次涨停的A股股票；
|
||||
4. 次新股池：包含上市一年以内且中断了连续一字涨停板的A股股票；
|
||||
5. 炸板股池：包含当日触及过涨停板且当前未封板的A股股票；
|
||||
6. 跌停股池：包含当日当前跌停的所有A股股票。
|
||||
注：涨停板行情专题统计不包含ST股票及科创板股票。
|
||||
"""
|
||||
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
import pandas as pd
|
||||
import requests
|
||||
|
||||
|
||||
def stock_zt_pool_em(date: str = "20241008") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-涨停股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=ztgc
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 涨停股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getTopicZTPool"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "10000",
|
||||
"sort": "fbt:asc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if data_json["data"] is None:
|
||||
return pd.DataFrame()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"连板数",
|
||||
"首次封板时间",
|
||||
"最后封板时间",
|
||||
"封板资金",
|
||||
"炸板次数",
|
||||
"所属行业",
|
||||
"涨停统计",
|
||||
]
|
||||
temp_df["涨停统计"] = (
|
||||
temp_df["涨停统计"].apply(lambda x: dict(x)["days"]).astype(str)
|
||||
+ "/"
|
||||
+ temp_df["涨停统计"].apply(lambda x: dict(x)["ct"]).astype(str)
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"封板资金",
|
||||
"首次封板时间",
|
||||
"最后封板时间",
|
||||
"炸板次数",
|
||||
"涨停统计",
|
||||
"连板数",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["首次封板时间"] = temp_df["首次封板时间"].astype(str).str.zfill(6)
|
||||
temp_df["最后封板时间"] = temp_df["最后封板时间"].astype(str).str.zfill(6)
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
|
||||
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
|
||||
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
|
||||
temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
|
||||
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
|
||||
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
|
||||
temp_df["封板资金"] = pd.to_numeric(temp_df["封板资金"], errors="coerce")
|
||||
temp_df["炸板次数"] = pd.to_numeric(temp_df["炸板次数"], errors="coerce")
|
||||
temp_df["连板数"] = pd.to_numeric(temp_df["连板数"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_zt_pool_previous_em(date: str = "20240415") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-昨日涨停股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=zrzt
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 昨日涨停股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getYesterdayZTPool"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
"sort": "zs:desc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if data_json["data"] is None:
|
||||
return pd.DataFrame()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"振幅",
|
||||
"涨速",
|
||||
"昨日封板时间",
|
||||
"昨日连板数",
|
||||
"所属行业",
|
||||
"涨停统计",
|
||||
]
|
||||
temp_df["涨停统计"] = (
|
||||
temp_df["涨停统计"].apply(lambda x: dict(x)["days"]).astype(str)
|
||||
+ "/"
|
||||
+ temp_df["涨停统计"].apply(lambda x: dict(x)["ct"]).astype(str)
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"涨速",
|
||||
"振幅",
|
||||
"昨日封板时间",
|
||||
"昨日连板数",
|
||||
"涨停统计",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["涨停价"] = temp_df["涨停价"] / 1000
|
||||
temp_df["昨日封板时间"] = temp_df["昨日封板时间"].astype(str).str.zfill(6)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_zt_pool_strong_em(date: str = "20241231") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-强势股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=qsgc
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 强势股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getTopicQSPool"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
"sort": "zdp:desc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if data_json["data"] is None:
|
||||
return pd.DataFrame()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"_",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"是否新高",
|
||||
"入选理由",
|
||||
"量比",
|
||||
"涨速",
|
||||
"涨停统计",
|
||||
"所属行业",
|
||||
]
|
||||
temp_df["涨停统计"] = (
|
||||
temp_df["涨停统计"].apply(lambda x: dict(x)["days"]).astype(str)
|
||||
+ "/"
|
||||
+ temp_df["涨停统计"].apply(lambda x: dict(x)["ct"]).astype(str)
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"涨速",
|
||||
"是否新高",
|
||||
"量比",
|
||||
"涨停统计",
|
||||
"入选理由",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["涨停价"] = temp_df["涨停价"] / 1000
|
||||
explained_map = {1: "60日新高", 2: "近期多次涨停", 3: "60日新高且近期多次涨停"}
|
||||
temp_df["入选理由"] = temp_df["入选理由"].apply(lambda x: explained_map[x])
|
||||
temp_df["是否新高"] = temp_df["是否新高"].apply(lambda x: "是" if x == 1 else "否")
|
||||
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
|
||||
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
|
||||
temp_df["涨停价"] = pd.to_numeric(temp_df["涨停价"], errors="coerce")
|
||||
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
|
||||
temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
|
||||
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
|
||||
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
|
||||
temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce")
|
||||
temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_zt_pool_sub_new_em(date: str = "20241231") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-次新股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=cxgc
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 次新股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
url = "https://push2ex.eastmoney.com/getTopicCXPooll"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
"sort": "ods:asc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"_",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"转手率",
|
||||
"开板几日",
|
||||
"开板日期",
|
||||
"上市日期",
|
||||
"_",
|
||||
"是否新高",
|
||||
"涨停统计",
|
||||
"所属行业",
|
||||
]
|
||||
temp_df["涨停统计"] = (
|
||||
temp_df["涨停统计"].apply(lambda x: dict(x)["days"]).astype(str)
|
||||
+ "/"
|
||||
+ temp_df["涨停统计"].apply(lambda x: dict(x)["ct"]).astype(str)
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"转手率",
|
||||
"开板几日",
|
||||
"开板日期",
|
||||
"上市日期",
|
||||
"是否新高",
|
||||
"涨停统计",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["涨停价"] = temp_df["涨停价"] / 1000
|
||||
temp_df.loc[temp_df["涨停价"] > 100000, "涨停价"] = pd.NA
|
||||
temp_df["开板日期"] = pd.to_datetime(temp_df["开板日期"], format="%Y%m%d").dt.date
|
||||
temp_df["上市日期"] = pd.to_datetime(temp_df["上市日期"], format="%Y%m%d").dt.date
|
||||
temp_df.loc[temp_df["上市日期"] == 0, "上市日期"] = pd.NaT
|
||||
temp_df["是否新高"] = temp_df["是否新高"].apply(lambda x: "是" if x == 1 else "否")
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_zt_pool_zbgc_em(date: str = "20241011") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-炸板股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=zbgc
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 炸板股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
thirty_days_ago = datetime.now() - timedelta(days=30)
|
||||
thirty_days_ago_str = thirty_days_ago.strftime("%Y%m%d")
|
||||
if int(date) < int(thirty_days_ago_str):
|
||||
raise ValueError("炸板股池只能获取最近 30 个交易日的数据")
|
||||
|
||||
url = "https://push2ex.eastmoney.com/getTopicZBPool"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "5000",
|
||||
"sort": "fbt:asc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if data_json["data"] is None:
|
||||
return pd.DataFrame()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"首次封板时间",
|
||||
"炸板次数",
|
||||
"振幅",
|
||||
"涨速",
|
||||
"涨停统计",
|
||||
"所属行业",
|
||||
]
|
||||
temp_df["涨停统计"] = (
|
||||
temp_df["涨停统计"].apply(lambda x: dict(x)["days"]).astype(str)
|
||||
+ "/"
|
||||
+ temp_df["涨停统计"].apply(lambda x: dict(x)["ct"]).astype(str)
|
||||
)
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"涨停价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"换手率",
|
||||
"涨速",
|
||||
"首次封板时间",
|
||||
"炸板次数",
|
||||
"涨停统计",
|
||||
"振幅",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["涨停价"] = temp_df["涨停价"] / 1000
|
||||
temp_df["首次封板时间"] = temp_df["首次封板时间"].astype(str).str.zfill(6)
|
||||
return temp_df
|
||||
|
||||
|
||||
def stock_zt_pool_dtgc_em(date: str = "20241011") -> pd.DataFrame:
|
||||
"""
|
||||
东方财富网-行情中心-涨停板行情-跌停股池
|
||||
https://quote.eastmoney.com/ztb/detail#type=dtgc
|
||||
:param date: 交易日
|
||||
:type date: str
|
||||
:return: 跌停股池
|
||||
:rtype: pandas.DataFrame
|
||||
"""
|
||||
thirty_days_ago = datetime.now() - timedelta(days=30)
|
||||
thirty_days_ago_str = thirty_days_ago.strftime("%Y%m%d")
|
||||
if int(date) < int(thirty_days_ago_str):
|
||||
raise ValueError("跌停股池只能获取最近 30 个交易日的数据")
|
||||
|
||||
url = "https://push2ex.eastmoney.com/getTopicDTPool"
|
||||
params = {
|
||||
"ut": "7eea3edcaed734bea9cbfc24409ed989",
|
||||
"dpt": "wz.ztzt",
|
||||
"Pageindex": "0",
|
||||
"pagesize": "10000",
|
||||
"sort": "fund:asc",
|
||||
"date": date,
|
||||
}
|
||||
r = requests.get(url, params=params)
|
||||
data_json = r.json()
|
||||
if len(data_json["data"]["pool"]) == 0:
|
||||
return pd.DataFrame()
|
||||
temp_df = pd.DataFrame(data_json["data"]["pool"])
|
||||
temp_df.reset_index(inplace=True)
|
||||
temp_df["index"] = range(1, len(temp_df) + 1)
|
||||
temp_df.columns = [
|
||||
"序号",
|
||||
"代码",
|
||||
"_",
|
||||
"名称",
|
||||
"最新价",
|
||||
"涨跌幅",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"动态市盈率",
|
||||
"换手率",
|
||||
"封单资金",
|
||||
"最后封板时间",
|
||||
"板上成交额",
|
||||
"连续跌停",
|
||||
"开板次数",
|
||||
"所属行业",
|
||||
]
|
||||
temp_df = temp_df[
|
||||
[
|
||||
"序号",
|
||||
"代码",
|
||||
"名称",
|
||||
"涨跌幅",
|
||||
"最新价",
|
||||
"成交额",
|
||||
"流通市值",
|
||||
"总市值",
|
||||
"动态市盈率",
|
||||
"换手率",
|
||||
"封单资金",
|
||||
"最后封板时间",
|
||||
"板上成交额",
|
||||
"连续跌停",
|
||||
"开板次数",
|
||||
"所属行业",
|
||||
]
|
||||
]
|
||||
temp_df["最新价"] = temp_df["最新价"] / 1000
|
||||
temp_df["最后封板时间"] = temp_df["最后封板时间"].astype(str).str.zfill(6)
|
||||
temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce")
|
||||
temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce")
|
||||
temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce")
|
||||
temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce")
|
||||
temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce")
|
||||
temp_df["动态市盈率"] = pd.to_numeric(temp_df["动态市盈率"], errors="coerce")
|
||||
temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce")
|
||||
temp_df["封单资金"] = pd.to_numeric(temp_df["封单资金"], errors="coerce")
|
||||
temp_df["板上成交额"] = pd.to_numeric(temp_df["板上成交额"], errors="coerce")
|
||||
temp_df["连续跌停"] = pd.to_numeric(temp_df["连续跌停"], errors="coerce")
|
||||
temp_df["开板次数"] = pd.to_numeric(temp_df["开板次数"], errors="coerce")
|
||||
temp_df["开板次数"] = pd.to_numeric(temp_df["开板次数"], errors="coerce")
|
||||
return temp_df
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
stock_zt_pool_em_df = stock_zt_pool_em(date="20241008")
|
||||
print(stock_zt_pool_em_df)
|
||||
|
||||
stock_zt_pool_previous_em_df = stock_zt_pool_previous_em(date="20240415")
|
||||
print(stock_zt_pool_previous_em_df)
|
||||
|
||||
stock_zt_pool_strong_em_df = stock_zt_pool_strong_em(date="20241231")
|
||||
print(stock_zt_pool_strong_em_df)
|
||||
|
||||
stock_zt_pool_sub_new_em_df = stock_zt_pool_sub_new_em(date="20241231")
|
||||
print(stock_zt_pool_sub_new_em_df)
|
||||
|
||||
stock_zt_pool_zbgc_em_df = stock_zt_pool_zbgc_em(date="20241011")
|
||||
print(stock_zt_pool_zbgc_em_df)
|
||||
|
||||
stock_zt_pool_dtgc_em_df = stock_zt_pool_dtgc_em(date="20241011")
|
||||
print(stock_zt_pool_dtgc_em_df)
|
||||
@@ -0,0 +1,989 @@
|
||||
var TOKEN_SERVER_TIME = 1572845499.629;
|
||||
function v_cookie (r, n, t, e, a) {
|
||||
var u = n[0],
|
||||
c = n[1],
|
||||
v = a[0],
|
||||
s = t[0],
|
||||
f = t[1],
|
||||
l = r[0],
|
||||
d = hr(a[1], e[0], t[2]),
|
||||
p = t[3],
|
||||
h = e[1],
|
||||
g = yr(a[2], a[3], e[2]),
|
||||
m = yr(a[4], r[1], t[4]),
|
||||
w = r[2],
|
||||
I = a[5],
|
||||
_ = a[6],
|
||||
y = a[7],
|
||||
E = hr(n[2], r[3], r[4]),
|
||||
A = t[5],
|
||||
C = e[3],
|
||||
b = e[4],
|
||||
B = t[6],
|
||||
R = a[8],
|
||||
T = a[9],
|
||||
S = n[3],
|
||||
k = t[7],
|
||||
x = t[8],
|
||||
O = a[10],
|
||||
L = n[4],
|
||||
M = n[5],
|
||||
N = a[11],
|
||||
P = e[5],
|
||||
j = hr(n[6], e[6], t[9], r[5]),
|
||||
D = t[10],
|
||||
W = e[7],
|
||||
$ = r[6],
|
||||
F = yr(r[7], t[11], e[8], n[7]),
|
||||
X = r[8],
|
||||
H = t[12],
|
||||
K = r[9],
|
||||
U = n[8],
|
||||
V = e[9],
|
||||
Y = r[10],
|
||||
J = e[10],
|
||||
q = r[11],
|
||||
Q = a[12],
|
||||
Z = n[9],
|
||||
G = t[13],
|
||||
z = t[14],
|
||||
rr = t[15],
|
||||
nr = n[10],
|
||||
tr = a[13],
|
||||
er = a[14],
|
||||
ar = e[11],
|
||||
or = r[12],
|
||||
ir = yr(t[16], r[13], r[14], r[15]),
|
||||
ur = t[17],
|
||||
cr = t[18];
|
||||
function vr () {
|
||||
var r = arguments[n[11]];
|
||||
return r.split(n[12]).reverse().join(e[12])
|
||||
}
|
||||
var sr = [new e[13](hr(a[15], n[13], a[16])), new e[13](a[17])];
|
||||
function fr () {
|
||||
var n = arguments[a[18]];
|
||||
if (!n) return a[19];
|
||||
for (var o = t[19], i = e[14], u = e[15]; u < n.length; u++)
|
||||
{
|
||||
var c = n.charCodeAt(u),
|
||||
v = c ^ i;
|
||||
i = c,
|
||||
o += r[16].fromCharCode(v)
|
||||
}
|
||||
return o
|
||||
}
|
||||
var lr = '',
|
||||
dr; !
|
||||
function (o) {
|
||||
var i = e[18],
|
||||
c = e[19];
|
||||
o[e[20]] = a[21];
|
||||
function v (t, a, o, i, u) {
|
||||
var c, v, s;
|
||||
c = v = s = r;
|
||||
var f, l, d;
|
||||
f = l = d = n;
|
||||
var p, h, g;
|
||||
p = h = g = e;
|
||||
var m = t + g[21] + a;
|
||||
i && (m += l[15] + i),
|
||||
u && (m += h[22] + u),
|
||||
o && (m += v[17] + o),
|
||||
l[14][g[23]] = m
|
||||
}
|
||||
o[e[24]] = l;
|
||||
function s (t, e, a) {
|
||||
var o = n[16];
|
||||
this.setCookie(t, r[18], i + o + c, e, a)
|
||||
}
|
||||
o[t[22]] = f;
|
||||
function f (o) {
|
||||
var i = vr(e[25], a[22]),
|
||||
c = a[23][n[17]],
|
||||
v = u + i + o + t[23],
|
||||
s = '';
|
||||
if (s == -r[19])
|
||||
{
|
||||
if (v = o + t[23], c.substr(a[24], v.length) != v) return;
|
||||
s = a[24]
|
||||
}
|
||||
var f = s + v[r[20]],
|
||||
l = '';
|
||||
return l == -e[26] && (l = c[t[24]])
|
||||
|
||||
}
|
||||
o[e[27]] = v;
|
||||
function l () {
|
||||
var r, t, a;
|
||||
r = t = a = e;
|
||||
var i, u, c;
|
||||
i = u = c = n;
|
||||
var v = u[18];
|
||||
this.setCookie(v, a[28]),
|
||||
this.getCookie(v) || (o[i[19]] = u[20]),
|
||||
this.delCookie(v)
|
||||
}
|
||||
o[n[21]] = s
|
||||
}(dr || (dr = {}));
|
||||
var pr;
|
||||
function hr () {
|
||||
var r = arguments[a[25]];
|
||||
if (!r) return a[19];
|
||||
for (var e = a[19], o = t[25], i = n[22], u = t[18]; u < r.length; u++)
|
||||
{
|
||||
var c = r.charCodeAt(u);
|
||||
i = (i + t[26]) % o.length,
|
||||
c ^= o.charCodeAt(i),
|
||||
e += String.fromCharCode(c)
|
||||
}
|
||||
return e
|
||||
} !
|
||||
function (o) {
|
||||
var i, u, d;
|
||||
i = u = d = a;
|
||||
var p, h, g;
|
||||
p = h = g = t;
|
||||
var m, w, I;
|
||||
m = w = I = r;
|
||||
var _, y, E;
|
||||
_ = y = E = n;
|
||||
var b, B, R;
|
||||
b = B = R = e;
|
||||
var T = B[29],
|
||||
S = y[23],
|
||||
k = m[22],
|
||||
x = w[0],
|
||||
O = E[24],
|
||||
L = (C, Ar, R[30]),
|
||||
M = b[31],
|
||||
N = T + S,
|
||||
P = p[28],
|
||||
j,
|
||||
W = m[23][y[25]],
|
||||
$,
|
||||
F;
|
||||
function X (r) {
|
||||
return function () {
|
||||
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|
||||
j.addBehavior(u[26]),
|
||||
j.load(N);
|
||||
var n = r();
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
function H () {
|
||||
var r = A;
|
||||
r = D;
|
||||
try
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
function K (r) {
|
||||
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|
||||
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|
||||
function U () {
|
||||
if (P = H(), P) j = _[27][N];
|
||||
else if (W[k + c][I[24]]) try
|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
function q (r) {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
function Z (r) {
|
||||
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|
||||
if (n = t = e = g, P) q(r);
|
||||
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|
||||
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|
||||
if (!j) return void t[18];
|
||||
rr(r)
|
||||
}
|
||||
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|
||||
function G (r) {
|
||||
try
|
||||
{
|
||||
return j.getItem(r)
|
||||
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|
||||
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|
||||
return y[20]
|
||||
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|
||||
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|
||||
o[fr(w[33], p[30], R[35])] = Z;
|
||||
function z (r, n) {
|
||||
try
|
||||
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|
||||
j.setItem(r, n)
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}(pr || (pr = {}));
|
||||
var gr = function () {
|
||||
var o, i, u;
|
||||
o = i = u = e;
|
||||
var c, v, s;
|
||||
c = v = s = a;
|
||||
var f, l, g;
|
||||
f = l = g = n;
|
||||
var m, w, I;
|
||||
m = w = I = t;
|
||||
var _, E, A;
|
||||
_ = E = A = r;
|
||||
var C = yr(Cr, U, _[34]),
|
||||
b = vr(A[35], m[31]),
|
||||
R = hr(g[32], c[29], i[36]),
|
||||
T = hr(l[33], g[34], i[37], tr);
|
||||
function S (r) {
|
||||
this[m[32]] = r;
|
||||
for (var n = o[15], t = r[i[38]]; t > n; n++) this[n] = i[15]
|
||||
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|
||||
return S[d + p + C][b + h] = function () {
|
||||
for (var r = this[vr(h, E[36], E[37])], n = [], t = -I[26], e = o[15], a = r[A[20]]; a > e; e++) for (var u = this[e], f = r[e], d = t += f; n[d] = u & parseInt(v[30], l[35]), --f != s[24];)--d,
|
||||
u >>= parseInt(i[39], c[31]);
|
||||
return n
|
||||
},
|
||||
S[vr(w[33], v[32])][_[38]] = function (r) {
|
||||
var n = dr,
|
||||
t = this[vr(y, l[36], A[39])],
|
||||
e = f[26];
|
||||
n = B;
|
||||
for (var a = v[24], o = t[l[37]]; o > a; a++)
|
||||
{
|
||||
var i = t[a],
|
||||
u = l[26];
|
||||
do u = (u << parseInt(R + T, g[35])) + r[e++];
|
||||
while (--i > w[18]);
|
||||
this[a] = u >>> w[18]
|
||||
}
|
||||
},
|
||||
S
|
||||
}(),
|
||||
mr; !
|
||||
function (o) {
|
||||
var i, u, c;
|
||||
i = u = c = n;
|
||||
var v, s, f;
|
||||
v = s = f = e;
|
||||
var l, d, p;
|
||||
l = d = p = a;
|
||||
var h, w, I;
|
||||
h = w = I = r;
|
||||
var _, y, E;
|
||||
_ = y = E = t;
|
||||
var A = y[34],
|
||||
C = (nr, U, h[40]),
|
||||
b = p[25];
|
||||
function B (r) {
|
||||
for (var n = y[35], t = f[15], e = r[vr(c[38], I[41], H)], a = []; e > t;)
|
||||
{
|
||||
var o = k[r.charAt(t++)] << parseInt(g + A, d[31]) | k[r.charAt(t++)] << parseInt(n + m, h[42]) | k[r.charAt(t++)] << parseInt(I[43], i[35]) | k[r.charAt(t++)];
|
||||
a.push(o >> parseInt(_[36], h[42]), o >> l[31] & parseInt(u[39], i[40]), o & parseInt(d[30], c[35]))
|
||||
}
|
||||
return a
|
||||
}
|
||||
function T (r) {
|
||||
for (var n = (O, R, p[24]), t = I[27], e = r[E[24]]; e > t; t++) n = (n << E[37]) - n + r[t];
|
||||
return n & parseInt(E[38], p[33])
|
||||
}
|
||||
for (var S = s[40], k = {},
|
||||
x = s[15]; x < parseInt(I[44], l[34]); x++) k[S.charAt(x)] = x;
|
||||
function L (r) {
|
||||
var n = B(r),
|
||||
t = n[u[26]];
|
||||
if (t != b) return error = yr(V, u[41], s[41], v[42]),
|
||||
void 0;
|
||||
var e = n[s[26]],
|
||||
a = [];
|
||||
return P(n, +_[39], a, +_[18], e),
|
||||
T(a) == e ? a : void 0
|
||||
}
|
||||
function M (r) {
|
||||
var n = T(r),
|
||||
t = [b, n];
|
||||
return P(r, +l[24], t, +p[25], n),
|
||||
N(t)
|
||||
}
|
||||
function N (r) {
|
||||
var n, t, e;
|
||||
n = t = e = f;
|
||||
var a, o, u;
|
||||
a = o = u = y;
|
||||
var c, v, s;
|
||||
c = v = s = h;
|
||||
var d, p, g;
|
||||
d = p = g = l;
|
||||
var m, w, I;
|
||||
m = w = I = i;
|
||||
for (var _ = m[42], E = d[24], A = r[c[20]], b = []; A > E;)
|
||||
{
|
||||
var B = r[E++] << parseInt(fr(Z, d[35]), o[39]) | r[E++] << g[31] | r[E++];
|
||||
b.push(S.charAt(B >> parseInt(m[43], t[43])), S.charAt(B >> parseInt(p[36], o[40]) & parseInt(I[44], I[45])), S.charAt(B >> n[44] & parseInt(_ + C, n[42])), S.charAt(B & parseInt(fr(d[37], c[45], or), a[41])))
|
||||
}
|
||||
return b.join(o[19])
|
||||
}
|
||||
function P (r, n, t, e, a) {
|
||||
var o, i, u;
|
||||
o = i = u = w;
|
||||
var c, v, s;
|
||||
c = v = s = E;
|
||||
for (var f = r[v[24]]; f > n;) t[e++] = r[n++] ^ a & parseInt(u[46], s[42]),
|
||||
a = ~(a * parseInt(v[43], v[40]))
|
||||
}
|
||||
o[E[44]] = N,
|
||||
o[_[45]] = B,
|
||||
o[v[45]] = M,
|
||||
o[y[46]] = L
|
||||
}(mr || (mr = {}));
|
||||
var wr; !
|
||||
function (o) {
|
||||
var i = a[38],
|
||||
u = r[47],
|
||||
c = t[47],
|
||||
v = vr(n[46], a[39], a[40]),
|
||||
s = e[46],
|
||||
f = e[47],
|
||||
l = a[41],
|
||||
d = a[42];
|
||||
function p (o) {
|
||||
var i = a[43],
|
||||
u = vr(n[47], e[48], n[48]),
|
||||
c = {},
|
||||
v = function (o, c) {
|
||||
var s, f, l, d;
|
||||
for (c = c.replace(n[49], n[12]), c = c.substring(e[26], c[e[38]] - e[26]), s = c.split(e[49]), l = a[24]; l < s[yr(v, sr, t[48])]; l++) if (f = s[l].split(n[50]), f && !(f[a[44]] < t[39]))
|
||||
{
|
||||
for (d = n[35]; d < f[r[20]]; d++) f[n[11]] = f[n[11]] + r[48] + f[d];
|
||||
f[n[26]] = new a[45](r[49]).test(f[n[26]]) ? f[e[15]].substring(r[19], f[e[15]][a[44]] - n[11]) : f[n[26]],
|
||||
f[n[11]] = new r[50](i + u + w).test(f[n[11]]) ? f[e[26]].substring(t[26], f[r[19]][n[37]] - t[26]) : f[a[18]],
|
||||
o[f[r[27]]] = f[n[11]]
|
||||
}
|
||||
return o
|
||||
};
|
||||
return new a[45](I + _).test(o) && (c = v(c, o)),
|
||||
c
|
||||
}
|
||||
function h (n) {
|
||||
for (var t = [], e = a[24]; e < n[r[20]]; e++) t.push(n.charCodeAt(e));
|
||||
return t
|
||||
}
|
||||
function g (o) {
|
||||
var u = a[46];
|
||||
if (typeof o === vr(O, a[47], or) && o[a[48]]) try
|
||||
{
|
||||
var c = parseInt(o[a[48]]);
|
||||
switch (c)
|
||||
{
|
||||
case parseInt(i + u, t[42]): break;
|
||||
case parseInt(yr(t[49], r[51], e[50]), e[43]): top[t[50]][n[51]] = o[e[51]];
|
||||
break;
|
||||
case parseInt(yr(a[25], j, e[52]), n[52]): top[n[53]][t[51]] = o[t[52]]
|
||||
}
|
||||
} catch (v) { }
|
||||
}
|
||||
function m (r, n, t) {
|
||||
|
||||
}
|
||||
function L () {
|
||||
var e, a, o;
|
||||
e = a = o = r;
|
||||
var i, u, c;
|
||||
i = u = c = n;
|
||||
var v, s, f;
|
||||
v = s = f = t;
|
||||
var l = f[53],
|
||||
d = c[54],
|
||||
p = new e[52];
|
||||
return typeof TOKEN_SERVER_TIME == y + l + d ? s[18] : (time = parseInt(TOKEN_SERVER_TIME), time)
|
||||
}
|
||||
function M () {
|
||||
var o = new t[54];
|
||||
try
|
||||
{
|
||||
return time = n[2].now(),
|
||||
time / parseInt(fr(a[50], a[51], r[53]), t[40]) >>> e[15]
|
||||
} catch (i)
|
||||
{
|
||||
return time = o.getTime(),
|
||||
time / parseInt(e[53], a[25]) >>> r[27]
|
||||
}
|
||||
}
|
||||
function N (r) {
|
||||
for (var a = t[18], o = r[t[24]] - n[11]; o >= e[15]; o--) a = a << e[26] | +r[o];
|
||||
return a
|
||||
}
|
||||
function P (a) {
|
||||
var o = new r[50](n[55]);
|
||||
if (K(a)) return a;
|
||||
var i = o.test(a) ? -e[54] : -t[39],
|
||||
u = a.split(r[54]);
|
||||
return u.slice(i).join(fr(n[56], t[55], E))
|
||||
}
|
||||
function j (t) {
|
||||
for (var o = n[26], i = e[15], u = t[vr(r[55], a[52], D)]; u > i; i++) o = (o << r[56]) - o + t.charCodeAt(i),
|
||||
o >>>= n[26];
|
||||
return o
|
||||
}
|
||||
function W (n, o) {
|
||||
var i = new a[45](t[56], yr(r[57], $, t[57], r[58])),
|
||||
u = new a[45](t[58]);
|
||||
if (n)
|
||||
{
|
||||
var c = n.match(i);
|
||||
if (c)
|
||||
{
|
||||
var v = c[e[26]];
|
||||
return o && u.test(v) && (v = v.split(t[59]).pop().split(r[48])[e[15]]),
|
||||
v
|
||||
}
|
||||
}
|
||||
}
|
||||
function $ (o) {
|
||||
var i = n[57],
|
||||
u = vr(e[55], e[56]),
|
||||
f = e[4];
|
||||
if (!(o > t[60]))
|
||||
{
|
||||
o = o || a[24];
|
||||
var l = parseInt(E + c + A, r[42]),
|
||||
d = n[14].createElement(e[57]);
|
||||
d[r[59]] = n[58] + parseInt((new a[53]).getTime() / l) + r[60],
|
||||
d[r[61]] = function () {
|
||||
var n = a[46];
|
||||
cr = r[19],
|
||||
setTimeout(function () {
|
||||
$(++o)
|
||||
},
|
||||
o * parseInt(C + n, a[33]))
|
||||
},
|
||||
d[t[61]] = d[hr(a[54], a[55], t[62])] = function () {
|
||||
var a = n[59];
|
||||
this[i + v + u + b] && this[e[58]] !== n[60] && this[s + B + a + f] !== e[59] && this[t[63]] !== n[61] || (cr = e[15], d[hr(N, r[62], n[62], e[25])] = d[t[64]] = r[63])
|
||||
},
|
||||
e[60][e[61]].appendChild(d)
|
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}
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}
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function F () {
|
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var r = a[56];
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function X (r) {
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var e = new n[31](fr(t[65], t[66], a[57]), yr(c, n[63], t[57]));
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if (r)
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{
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var o = r.match(e);
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return o
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}
|
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}
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o[S + k] = p,
|
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o[r[64]] = $,
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o[t[67]] = g,
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o[t[68]] = h,
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o[t[69]] = j,
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o[t[70]] = F,
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o[r[65]] = K,
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o[x + l] = P,
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o[t[71]] = W,
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o[t[72]] = X,
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o[hr(r[66], t[73], r[67], C)] = N,
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o[t[74]] = M,
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o[d + O] = L;
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function K (n) {
|
||||
return new r[50](t[75]).test(n)
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}
|
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o[r[68]] = m
|
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}(wr || (wr = {}));
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var Ir; !
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function (o) {
|
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var i = t[76],
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u = t[77],
|
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c = n[65],
|
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v = t[78],
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s = a[24],
|
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f = n[26],
|
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l = t[18],
|
||||
d = t[18],
|
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p = e[15],
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h = a[24],
|
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g = r[69],
|
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wr.eventBind(e[60], n[67], E),
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|
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||||
function w () {
|
||||
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|
||||
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|
||||
function I (r) {
|
||||
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|
||||
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|
||||
function _ () {
|
||||
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|
||||
x: p,
|
||||
y: h,
|
||||
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|
||||
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|
||||
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|
||||
function y (r) {
|
||||
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|
||||
function E (r) {
|
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|
||||
function C () {
|
||||
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|
||||
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|
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function b (r) {
|
||||
var o, i, u;
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|
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||||
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||||
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|
||||
var I, _, y;
|
||||
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|
||||
var E = I[60],
|
||||
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|
||||
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|
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g = void 0 == r[E + A + v] || r[yr(f[80], s[81], i[68])],
|
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p = r[s[82]],
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function B () {
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|
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|
||||
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|
||||
o[r[73]] = R,
|
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|
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function w () {
|
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|
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|
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|
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function E (t) {
|
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|
||||
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|
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|
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|
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|
||||
|
||||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||||
function tr () {
|
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|
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|
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function yr () {
|
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|
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|
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var Er; !
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|
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|
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|
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|
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|
||||
p = n[87],
|
||||
h = parseInt(e[90], r[108]),
|
||||
g = a[82],
|
||||
m = parseInt(vr(s, t[104]), t[39]),
|
||||
w = r[109],
|
||||
I = t[40],
|
||||
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|
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y = parseInt(u + c, n[52]),
|
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E = parseInt(t[105], r[42]),
|
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|
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|
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b = parseInt(e[92], e[93]),
|
||||
B = t[106],
|
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|
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|
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|
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|
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|
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function O () {
|
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var r = t[26],
|
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a = n[35],
|
||||
o = e[54],
|
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i = n[88];
|
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k = new gr([i, i, i, i, r, r, r, o, a, a, a, a, a, a, a, i, a, r]),
|
||||
k[l] = wr.serverTimeNow(),
|
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|
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k[B] = cr,
|
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k[T] = ur,
|
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k[R] = e[15],
|
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k[C] = _r.getBrowserFeature(),
|
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k[g] = _r.getBrowserIndex(),
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k[m] = _r.getPluginNum()
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}
|
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function L () {
|
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var a = dr.getCookie(tr) || pr.get(ar);
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var o = mr.decode(a);
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|
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k[f] = wr.random()
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o[a[84]] = O;
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function M () {
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k[l] = wr.serverTimeNow(),
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k[d] = wr.timeNow(),
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k[B] = cr,
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k[w] = Ir.getMouseMove(),
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k[I] = Ir.getMouseClick(),
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k[_] = Ir.getMouseWhell(),
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k[y] = Ir.getKeyDown(),
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k[E] = Ir.getClickPos().x,
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k[A] = Ir.getClickPos().y;
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var r = k.toBuffer();
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o[yr(r[3], n[89], e[97])] = x
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var Ar; !
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|
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var i = n[90],
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|
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|
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|
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|
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|
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|
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function E (r) {
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|
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function A (o) {
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var i = wr.getOriginFromUrl(o);
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function C (e) {
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|
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|
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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function j () {
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var cookie_v;
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|
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o[n[111]] = j
|
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var Cr;
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var cookie = (function (a) {
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function _ () {
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var cookie_v;
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cookie_v = Ar.Init();
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|
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|
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|
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return r
|
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|
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|
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})()
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return cookie()
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}
|
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function v () {
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var v;
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v = v_cookie(
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|
||||
[";", "Element", Date, "par", "i", "DOMMous", 21, "xmT", "wserFe", "h", !0, 1, "", Boolean, '', "; domain=", "n 1970 00:", "cookie", "checkcookie", "allow", !1, "delCookie", 2333, "torage", ")*+,/\\\\:;", '', 0, '', "eliflmth", '', "ducument", RegExp, "W ", "qsU", 61, 2, "sdelif_esab", "length", "I ", "ff", 16, 45, "3", "10010", "77", 8, "6e%d", "DT{e", " $", / /g, ":", "href", 10, "location", "ned", "\\.com\\.cn$|\\.com\\.hk$", 63, "rea", "https://s.thsi.cn/js/chameleon/time.1", "tat", "loaded", "interactive", "WY:ZYS", "E?`a", "addEventListener", "eScroll", "onmousewheel", "mousemove", "\u255e\u096e\u096e\u09e3\u09a5\u092e\u099a\u09d4\u0990", "\u2550\u2535\u2541\u250c\u2563\u2516\u2565\u2500\u2543\u252f\u2546\u2525\u254e", "getKeyDown", "H69<J", "v~g -", "", "ature", "callPhantom", "ActiveXObject", "Uint8Array", "WeakMap", "JX%< ", "chrome", "@L:!", "20", "language", "localStorage", "^Win32", String, 3, 4, "=XAE", "hea", "&", "/", "\\R$", '^R"VP', "s", "include", "_raw", "x.", "isRst", "SCRIPT", "ta", "base", "$?", "^_self$", "#", "unload", "ro", "\u2550", "^(.*?):[ \\t]*([^\\r\\n]*)\\r?$", "g", "Init", "t6?x}", "\u2574\u0955\u097b\u09dc\u0995\u0911\u09ab\u09fe\u09ba\u09e2\u098e\u09fe\u09f9\u09f9\u09f3\u0a55", "=d' "],
|
||||
["<=>?@[\\]^", "e", "HE9", "tot", "\u2503", "0", "dyS", "se", "getRoot", "NR", "nd", 60, "ng", "s", "get", "mit", 13, 3, 0, "", '', "\u255f\u253a\u255b\u253f", "getCookie", "=", "length", "V587", 1, String, !0, "___", "\u2553\u2536\u255a", "uBot", "base_fileds", 32, "2", "1", "20", 5, "255", 2, 8, 16, 10, "203", "base64Encode", "base64Decode", "decode", "760", "\u255b\u0978\u0954\u09f6\u09a4\u0935", 70, "location", "href", "redirect_url", "efi", Date, "\u2519", "^\\s*(?:https?:)?\\/{2,}([^\\/\\?\\#\\\\]+)", "\u255e", "[@:]", "@", 7, "onload", 'WY$PYS/FLV"P[_7[_R', "readyState", "onreadystatechange", '" ^w', "\u2569\u2535\u2546\u256c\u2544\u257b\u2541\u2569\u2501\u2575\u2501\u2571\u2502\u253d\u2507\u252e\u2507\u2538\u2564\u254b\u2530\u2502\u252e\u2553\u257b\u2520\u257e\u2522\u250d\u2551\u256e\u2532\u2511\u254d\u2511\u254c\u2567\u254e", "analysisRst", "strToBytes", "strhash", "random", "getHostFromUrl", "getOriginFromUrl", 83, "timeNow", "^(\\d+\\.)+\\d+$", "d", "v", "ted", "touchmove", 85, "F(K9i", "clientX", "clientY", "\u257a\u2515\u256f\u253c", "postMessage", '', "ActiveXObject", "Apple Computer, Inc.", "Q", "chr", "\u2558\u2535\u2550", "BIDUBrowser", RegExp, "QQBrowser", "ro", "aef", "msDoNotTrack", "PDF|Acrobat", "canvas", "yE", "\u255b\u253a\u2554\u2533\u2546\u2527\u2540\u2525\u2556", "^Android", "^Linux [ix]\\d+", "011", "13", 15, "sub", "addEventListener", "jsonp_ignore", "\u2569", !1, 'L"', "Sj", "T{_,", "q*", "i", "tagName", "et", "{'K", "Pp<", "#x'", "open", "rS", " KN3", "#", "protocol", "\\.", "DEDAOL_NOELEMAHC"],
|
||||
[83, "ffer", "\u2505", "20", "e", "ngsE", Error, "est", "\u2552\u095b\u0956\u09f0\u09a3\u0935\u09c0\u09e2", "1", "sr", "hexin-v", "", RegExp, 9527, 0, "**l>", "head", "Thu, 01 Ja", "00:00 GMT", "allow", "=", "; path=", "cookie", "Init", 33, 1, "setCookie", !0, "localS", "`{|}~]", "g", '', "frames", "___$&", 56, " ", "\b", "length", "10", "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789-_", "\u2552\u096f\u0948\u09fe\u09a2", 16, 2, 6, "encode", "rea", "729", "*.", ",", "\u2506\u092c\u090b\u09a0\u09e1\u096d\u09df\u0981\u09c4\u098c", "redirect_url", "\u2506\u092d\u090a\u09a3", "1111101000", 3, 47, "tat", "script", "readyState", "complete", '', "body", "onwheel", "mousewheel", 37, "rus", "\u2554\u0975", "chr", "ActiveXObject", "WeakMap", "aT1Kg", "i", 24, "\u2554\u253c\u254e\u2521\u254c\u2529", "\u2547\u0971\u094f\u09f6\u09b9\u0933\u099d", "Shockwav", "hockwave", "$cdc_asdjflasutopfhvcZLmcfl_", "webgl2", "2>n|", "plugins", "platform", "^Win64", "^Linux armv|Android", "^iPhone", "^MacIntel", !1, "getPlatform", "6Y,", 2333, "100", 12, "14", 10, 36, "01", "60", "\u2542\u096d\u095e\u09f0\u09a4\u0938", "j", 17, "Request", "prototype", "`z}lc", "error", "s", "r", "target", "\u255e", "A", "U", "193", "host", "$"],
|
||||
["se", "g@g?", Array, "*Y", Number, "^{.", "*}$", "und", "429", "496", "imeNow", "etti", "rg", "v", "hexin-v", Error, "L_%\\T8", ".baidu.", 1, "", Function, !0, " ", '', 0, 2, "#default#userData", "ydob", "^d", 89, "11111111", 8, "epytotorp", 10, 16, "\u2506\u2536\u2506\u2536\u2506", "14", 13, "10", "Syd", 44, "Domain", "serverT", '^"', "length", RegExp, "00", "tcejbo", "status_code", "n", 66, "\u2506\u2531\u2504\u2534", "htgnel", Date, "L%", 67, "5", "?)'", '', "[[?VS", "isT", "getMouseWhell", "}}", "TR", "ActiveXObject", "WE", "python", "Maxthon", 97, "chrome", "Ryp", "UBrowser", 54, !1, "ontouchstart", "\u254d\u0975\u0917\u09f2\u09be", "iso-8859-1", "defaultCharset", "^iPad", "getBrowserIndex", 256, "1", 5, "17", "Init", "XMLHttp", "tar", "allow", "@*", "?\\", "?", "\u2571\u2503\u256a\u2546\u2566\u2556\u2567\u2547\u2501\u2564\u2506\u2526\u2514\u2524\u2511\u2521\u2501\u2531\u2501\u253b\u250b\u253b\u2501\u2531\u2501\u2521\u2566\u252b\u257f", "den", "tia", 94, "ls", "\u2554\u2526\u2543", "_str", 37, "append", "Child", "\u255f", "\u2569\u0975\u094e\u09e5\u09a0\u092e\u09d1\u09ed\u09ce", "srcElement", "parentNode", "\u2543\u2522\u2545\u250b\u256a\u2507\u2562", "}*", "err", "or", "getAllResponseHeaders", "\\.?", "\\."]
|
||||
);
|
||||
return v
|
||||
}
|
||||
Reference in New Issue
Block a user