# -*- 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/ """ 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) 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)