知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
134 lines
5.3 KiB
Python
134 lines
5.3 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2024/2/21 11:00
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Desc: 新浪财经-股票-机构持股
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https://vip.stock.finance.sina.com.cn/q/go.php/vComStockHold/kind/jgcg/index.phtml
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"""
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from io import StringIO
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import pandas as pd
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import requests
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from akshare.utils import demjson
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def stock_institute_hold(symbol: str = "20051") -> pd.DataFrame:
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"""
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新浪财经-股票-机构持股一览表
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https://vip.stock.finance.sina.com.cn/q/go.php/vComStockHold/kind/jgcg/index.phtml
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:param symbol: 从 2005 年开始, {"一季报":1, "中报":2 "三季报":3 "年报":4}, e.g., "20191", 其中的 1 表示一季报; "20193", 其中的 3 表示三季报;
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:type symbol: str
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:return: 机构持股一览表
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:rtype: pandas.DataFrame
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"""
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url = "https://vip.stock.finance.sina.com.cn/q/go.php/vComStockHold/kind/jgcg/index.phtml"
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params = {
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"p": "1",
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"num": "10000",
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"reportdate": symbol[:-1],
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"quarter": symbol[-1],
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(StringIO(r.text))[0]
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temp_df["证券代码"] = temp_df["证券代码"].astype(str).str.zfill(6)
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del temp_df["明细"]
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temp_df.columns = [
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"证券代码",
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"证券简称",
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"机构数",
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"机构数变化",
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"持股比例",
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"持股比例增幅",
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"占流通股比例",
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"占流通股比例增幅",
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]
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temp_df["机构数"] = pd.to_numeric(temp_df["机构数"], errors="coerce")
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temp_df["机构数变化"] = pd.to_numeric(temp_df["机构数变化"], errors="coerce")
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temp_df["持股比例"] = pd.to_numeric(temp_df["持股比例"], errors="coerce")
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temp_df["持股比例增幅"] = pd.to_numeric(temp_df["持股比例增幅"], errors="coerce")
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temp_df["占流通股比例"] = pd.to_numeric(temp_df["占流通股比例"], errors="coerce")
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temp_df["占流通股比例增幅"] = pd.to_numeric(
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temp_df["占流通股比例增幅"], errors="coerce"
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)
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return temp_df
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def stock_institute_hold_detail(
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stock: str = "600433", quarter: str = "20201"
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) -> pd.DataFrame:
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"""
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新浪财经-股票-机构持股详情
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https://vip.stock.finance.sina.com.cn/q/go.php/vComStockHold/kind/jgcg/index.phtml
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:param stock: 股票代码
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:type stock: str
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:param quarter: 从 2005 年开始, {"一季报":1, "中报":2 "三季报":3 "年报":4}, e.g., "20191", 其中的 1 表示一季报; "20193", 其中的 3 表示三季报;
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:type quarter: str
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:return: 指定股票和财报时间的机构持股数据
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:rtype: pandas.DataFrame
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"""
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url = "https://vip.stock.finance.sina.com.cn/q/api/jsonp.php/var%20details=/ComStockHoldService.getJGCGDetail"
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params = {
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"symbol": stock,
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"quarter": quarter,
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}
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r = requests.get(url, params=params)
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text_data = r.text
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json_data = demjson.decode(text_data[text_data.find("{") : -2])
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big_df = pd.DataFrame()
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for item in json_data["data"].keys():
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inner_temp_df = pd.DataFrame(json_data["data"][item]).T.iloc[:-1, :]
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inner_temp_df.reset_index(inplace=True)
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big_df = pd.concat(objs=[big_df, inner_temp_df], ignore_index=True)
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if not big_df.empty:
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big_df["index"] = big_df["index"].str.split("_", expand=True)[0]
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big_df.rename(columns={"index": "institute"}, inplace=True)
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big_df = big_df.iloc[:, :12]
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big_df.columns = [
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"持股机构类型",
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"持股机构代码",
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"持股机构简称",
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"持股机构全称",
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"持股数",
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"最新持股数",
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"持股比例",
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"最新持股比例",
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"占流通股比例",
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"最新占流通股比例",
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"持股比例增幅",
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"占流通股比例增幅",
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]
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big_df["持股机构类型"] = big_df["持股机构类型"].str.replace("fund", "基金")
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big_df["持股机构类型"] = big_df["持股机构类型"].str.replace(
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"socialSecurity", "全国社保"
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)
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big_df["持股机构类型"] = big_df["持股机构类型"].str.replace("qfii", "QFII")
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big_df["持股机构类型"] = big_df["持股机构类型"].str.replace("insurance", "保险")
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big_df["持股数"] = pd.to_numeric(big_df["持股数"], errors="coerce")
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big_df["最新持股数"] = pd.to_numeric(big_df["最新持股数"], errors="coerce")
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big_df["持股比例"] = pd.to_numeric(big_df["持股比例"], errors="coerce")
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big_df["最新持股比例"] = pd.to_numeric(big_df["最新持股比例"], errors="coerce")
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big_df["占流通股比例"] = pd.to_numeric(big_df["占流通股比例"], errors="coerce")
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big_df["最新占流通股比例"] = pd.to_numeric(
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big_df["最新占流通股比例"], errors="coerce"
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)
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big_df["持股比例增幅"] = pd.to_numeric(big_df["持股比例增幅"], errors="coerce")
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big_df["占流通股比例增幅"] = pd.to_numeric(
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big_df["占流通股比例增幅"], errors="coerce"
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)
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return big_df
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else:
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return pd.DataFrame()
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if __name__ == "__main__":
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stock_institute_hold_df = stock_institute_hold(symbol="20201")
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print(stock_institute_hold_df)
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stock_institute_hold_detail_df = stock_institute_hold_detail(
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stock="300003", quarter="20201"
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)
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print(stock_institute_hold_detail_df)
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