217 lines
8.6 KiB
Python
217 lines
8.6 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2022/11/19 12:00
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Desc: 新浪财经-龙虎榜
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https://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml
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"""
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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 tqdm import tqdm
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def stock_lhb_detail_daily_sina(
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trade_date: str = "20200730", symbol: str = "当日无价格涨跌幅限制的A股，出现异常波动停牌的股票"
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) -> pd.DataFrame:
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"""
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龙虎榜-每日详情
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http://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml
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:param trade_date: 交易日, e.g., trade_date="20200729"
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:type trade_date: str
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:param symbol: 指定标题
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:type symbol: str
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:return: 龙虎榜-每日详情
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:rtype: pandas.DataFrame
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"""
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trade_date = "-".join([trade_date[:4], trade_date[4:6], trade_date[6:]])
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vInvestConsult/kind/lhb/index.phtml"
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params = {"tradedate": trade_date}
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r = requests.get(url, params=params)
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soup = BeautifulSoup(r.text, "lxml")
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table_name_list = [
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item.get_text().strip()
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for item in soup.find_all(
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"span", attrs={"style": "font-weight:bold;font-size:14px;"}
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)
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if item.get_text().strip() != ""
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]
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if symbol == "返回当前交易日所有可查询的指标":
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return table_name_list
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else:
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position_num = table_name_list.index(symbol)
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if len(table_name_list) == position_num + 1:
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temp_df_1 = pd.read_html(r.text, flavor='bs4', header=1)[position_num].iloc[0:, :]
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temp_df_2 = pd.read_html(r.text, flavor='bs4', header=1)[position_num + 1].iloc[0:, :]
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temp_df_3 = pd.read_html(r.text, flavor='bs4', header=1)[position_num + 2].iloc[0:, :]
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temp_df = pd.concat([temp_df_1, temp_df_2, temp_df_3], ignore_index=True)
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else:
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temp_df = pd.read_html(r.text, flavor='bs4', header=1)[position_num].iloc[0:, :]
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temp_df["股票代码"] = temp_df["股票代码"].astype(str).str.zfill(6)
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del temp_df["查看详情"]
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temp_df.columns = ["序号", "股票代码", "股票名称", "收盘价", "对应值", "成交量", "成交额"]
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return temp_df
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def _find_last_page(url: str = None, recent_day: str = "60"):
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml"
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params = {
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"last": recent_day,
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"p": "1",
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}
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r = requests.get(url, params=params)
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soup = BeautifulSoup(r.text, "lxml")
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try:
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previous_page = int(soup.find_all(attrs={"class": "page"})[-2].text)
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except:
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previous_page = 1
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if previous_page != 1:
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while True:
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params = {
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"last": recent_day,
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"p": previous_page,
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}
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r = requests.get(url, params=params)
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soup = BeautifulSoup(r.text, "lxml")
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last_page = int(soup.find_all(attrs={"class": "page"})[-2].text)
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if last_page != previous_page:
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previous_page = last_page
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continue
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else:
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break
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return previous_page
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def stock_lhb_ggtj_sina(recent_day: str = "30") -> pd.DataFrame:
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"""
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龙虎榜-个股上榜统计
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http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml
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:param recent_day: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
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:type recent_day: str
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:return: 龙虎榜-每日详情
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:rtype: pandas.DataFrame
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"""
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/ggtj/index.phtml"
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last_page_num = _find_last_page(url, recent_day)
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big_df = pd.DataFrame()
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for page in tqdm(range(1, last_page_num + 1), leave=False):
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params = {
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"last": recent_day,
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"p": page,
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(r.text)[0].iloc[0:, :]
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
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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"]
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return big_df
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def stock_lhb_yytj_sina(recent_day: str = "5") -> pd.DataFrame:
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"""
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龙虎榜-营业部上榜统计
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http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/yytj/index.phtml
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:param recent_day: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
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:type recent_day: str
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:return: 龙虎榜-营业部上榜统计
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:rtype: pandas.DataFrame
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"""
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/yytj/index.phtml"
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last_page_num = _find_last_page(url, recent_day)
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big_df = pd.DataFrame()
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for page in tqdm(range(1, last_page_num + 1), leave=False):
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params = {
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"last": "5",
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"p": page,
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(r.text)[0].iloc[0:, :]
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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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"]
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big_df['上榜次数'] = pd.to_numeric(big_df['上榜次数'], errors="coerce")
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big_df['买入席位数'] = pd.to_numeric(big_df['买入席位数'], errors="coerce")
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big_df['卖出席位数'] = pd.to_numeric(big_df['卖出席位数'], errors="coerce")
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return big_df
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def stock_lhb_jgzz_sina(recent_day: str = "5") -> pd.DataFrame:
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"""
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龙虎榜-机构席位追踪
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http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgzz/index.phtml
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:param recent_day: choice of {"5": 最近 5 天; "10": 最近 10 天; "30": 最近 30 天; "60": 最近 60 天;}
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:type recent_day: str
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:return: 龙虎榜-机构席位追踪
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:rtype: pandas.DataFrame
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"""
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgzz/index.phtml"
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last_page_num = _find_last_page(url, recent_day)
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big_df = pd.DataFrame()
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for page in tqdm(range(1, last_page_num + 1), leave=False):
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params = {
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"last": recent_day,
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"p": page,
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(r.text)[0].iloc[0:, :]
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
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del big_df["当前价"]
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del big_df["涨跌幅"]
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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"]
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big_df['买入次数'] = pd.to_numeric(big_df['买入次数'], errors="coerce")
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big_df['卖出次数'] = pd.to_numeric(big_df['卖出次数'], errors="coerce")
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return big_df
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def stock_lhb_jgmx_sina() -> pd.DataFrame:
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"""
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龙虎榜-机构席位成交明细
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http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgmx/index.phtml
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:return: 龙虎榜-机构席位成交明细
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:rtype: pandas.DataFrame
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"""
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url = "http://vip.stock.finance.sina.com.cn/q/go.php/vLHBData/kind/jgmx/index.phtml"
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params = {
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"p": "1",
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}
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r = requests.get(url, params=params)
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soup = BeautifulSoup(r.text, "lxml")
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try:
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last_page_num = int(soup.find_all(attrs={"class": "page"})[-2].text)
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except:
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last_page_num = 1
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big_df = pd.DataFrame()
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for page in tqdm(range(1, last_page_num + 1), leave=False):
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params = {
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"p": page,
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}
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r = requests.get(url, params=params)
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temp_df = pd.read_html(r.text)[0].iloc[0:, :]
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big_df = pd.concat([big_df, temp_df], ignore_index=True)
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big_df["股票代码"] = big_df["股票代码"].astype(str).str.zfill(6)
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return big_df
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if __name__ == "__main__":
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indicator_name_list = stock_lhb_detail_daily_sina(
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trade_date="20221118", symbol="返回当前交易日所有可查询的指标"
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)
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print(indicator_name_list)
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stock_lhb_detail_daily_sina_df = stock_lhb_detail_daily_sina(
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trade_date="20221118", symbol="换手率达20%的证券"
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)
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print(stock_lhb_detail_daily_sina_df)
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stock_lhb_ggtj_sina_df = stock_lhb_ggtj_sina(recent_day="60")
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print(stock_lhb_ggtj_sina_df)
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stock_lhb_yytj_sina_df = stock_lhb_yytj_sina(recent_day="60")
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print(stock_lhb_yytj_sina_df)
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stock_lhb_jgzz_sina_df = stock_lhb_jgzz_sina(recent_day="30")
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print(stock_lhb_jgzz_sina_df)
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stock_lhb_jgmx_sina_df = stock_lhb_jgmx_sina()
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print(stock_lhb_jgmx_sina_df)
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