Python爬取香港交易所HKEX沪深港通历史持股数据

爬取网站: 沪港通https://www.hkexnews.hk/sdw/search/mutualmarket.aspx?t=sh&t=sh

和深港通https://www.hkexnews.hk/sdw/search/mutualmarket.aspx?t=sh&t=sz

(url只是最后一个字母不一样)

# coding=utf-8

import pandas as pd
import numpy as np
import datetime
from bs4 import BeautifulSoup
import time
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By

def get_browser():
    chrome_options = Options()
    chrome_options.add_argument("--headless")
    br = webdriver.Chrome(options=chrome_options)
    return br


def get_shareholding():
    for exchange in ["sh", "sz"]:
        URL = (
            "http://www.hkexnews.hk/sdw/search/mutualmarket.aspx?t=" + exchange.lower()
        )
        browser = get_browser()
        browser.get(URL)

        today = datetime.date.today()
        start_date = today.replace(year=today.year - 1)
        end_date = today
        date_list = pd.date_range(
            start=start_date, end=end_date, freq="1D", closed="left"
        ).strftime("%Y/%m/%d")

        for date in date_list:
            try:
                js = "document.getElementById('txtShareholdingDate').value='{}';".format(
                    date
                )
                browser.execute_script(js)
                browser.find_element(By.ID, "txtShareholdingDate").click()
                browser.find_element(By.ID, "btnSearch").click()

                soup = BeautifulSoup(browser.page_source, "html.parser")
                data = []
                for tr in (
                    soup.find("table", {"id": "mutualmarket-result"})
                    .find("tbody")
                    .findAll("tr")
                ):
                    code = (
                        tr.find("td", {"class": "col-stock-code"})
                        .find("div", {"class": "mobile-list-body"})
                        .get_text()
                    )
                    name = (
                        tr.find("td", {"class": "col-stock-name"})
                        .find("div", {"class": "mobile-list-body"})
                        .get_text()
                    )
                    shareholding = (
                        tr.find("td", {"class": "col-shareholding"})
                        .find("div", {"class": "mobile-list-body"})
                        .get_text()
                    )
                    shareholding_percent = (
                        tr.find("td", {"class": "col-shareholding-percent"})
                        .find("div", {"class": "mobile-list-body"})
                        .get_text()
                    )

                    data.append([code, name, shareholding, shareholding_percent])
                df = pd.DataFrame(data, columns=["code", "name", "shareholding", "shareholding_percent"])

                df["Symbol"] = df["name"].apply(lambda x: x[-7:-1].replace("#", "0"))

                df["shareholding_percent"] = (
                    df["shareholding_percent"]
                    .apply(lambda x: x[:-1] if len(x) > 0 else np.nan)
                    .astype("float64")
                )
                df["shareholding"] = (
                    df["shareholding"]
                    .apply(lambda x: x.replace(",", ""))
                    .astype("float64")
                )

                date = date.replace("/", "-")
                df["Tradedate"] = date
                del df["code"], df["name"]

                import pdb; pdb.set_trace()

                time.sleep(2)
            except Exception as er:
                print(er)

    browser.close()
    browser.quit()


if __name__ == "__main__":
    get_shareholding()

注意:

1.这里用pdb打断了, 输出结果是pandas的dataframe类型, 一般都会导入到数据库, 这里就不做演示了, 公司都会有自己封装好的方法

2.这个网站提供的数据是当前时间往前推一个自然年的数据, 每天的数据, 有个SEARCH按钮可以手动选择,

这里用无界面Chrome浏览器 + 执行js脚本 实现这一操作

windows系统下 需要手动下载chromedriver.exe 版本号与本地浏览器匹配, 浏览器地址栏输入chrome://version/ 第一行就是版本号

exe本地路径通过webdriver的executable_path参数指定, 如果放在和模块同一目录下可以省略(这里就省略了)

下载地址 http://chromedriver.storage.googleapis.com/index.html 

3.这个网站的数据有些奇怪, 我已经做了一些特殊数据处理, 转化成常见的数据

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