#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/7/22 18:30 Desc: 新浪财经-B股-实时行情数据和历史行情数据(包含前复权和后复权因子) https://finance.sina.com.cn/realstock/company/sh689009/nc.shtml """ import json import re from functools import lru_cache import pandas as pd import requests import py_mini_racer from akshare.stock.cons import ( zh_sina_a_stock_url, zh_sina_a_stock_hist_url, hk_js_decode, zh_sina_a_stock_hfq_url, zh_sina_a_stock_qfq_url, zh_sina_a_stock_amount_url, ) from akshare.utils import demjson @lru_cache() def _get_zh_b_page_count() -> int: """ 所有股票的总页数 https://vip.stock.finance.sina.com.cn/mkt/#hs_b :return: 需要采集的股票总页数 :rtype: int """ url = ( "https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/" "Market_Center.getHQNodeStockCount?node=hs_b" ) r = requests.get(url) page_count = int(re.findall(re.compile(r"\d+"), r.text)[0]) / 80 if isinstance(page_count, int): return page_count else: return int(page_count) + 1 def stock_zh_b_spot() -> pd.DataFrame: """ 新浪财经-所有 B 股的实时行情数据; 重复运行本函数会被新浪暂时封 IP https://vip.stock.finance.sina.com.cn/mkt/#hs_b :return: 所有股票的实时行情数据 :rtype: pandas.DataFrame """ page_count = _get_zh_b_page_count() zh_sina_stock_payload_copy = { "page": "1", "num": "80", "sort": "symbol", "asc": "1", "node": "hs_b", "symbol": "", "_s_r_a": "page", } big_df = pd.DataFrame() for page in range(1, page_count + 1): zh_sina_stock_payload_copy.update({"page": page}) r = requests.get(zh_sina_a_stock_url, params=zh_sina_stock_payload_copy) data_json = demjson.decode(r.text) big_df = pd.concat(objs=[big_df, pd.DataFrame(data_json)], ignore_index=True) big_df.columns = [ "代码", "_", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "成交量", "成交额", "_", "_", "_", "_", "_", "_", ] big_df = big_df[ [ "代码", "名称", "最新价", "涨跌额", "涨跌幅", "买入", "卖出", "昨收", "今开", "最高", "最低", "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") big_df["涨跌幅"] = pd.to_numeric(big_df["涨跌幅"], errors="coerce") big_df["买入"] = pd.to_numeric(big_df["买入"], errors="coerce") big_df["卖出"] = pd.to_numeric(big_df["卖出"], errors="coerce") big_df["昨收"] = pd.to_numeric(big_df["昨收"], errors="coerce") big_df["今开"] = pd.to_numeric(big_df["今开"], errors="coerce") big_df["最高"] = pd.to_numeric(big_df["最高"], errors="coerce") big_df["最低"] = pd.to_numeric(big_df["最低"], 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_zh_b_daily( symbol: str = "sh900901", start_date: str = "19900101", end_date: str = "21000118", adjust: str = "", ) -> pd.DataFrame: """ 新浪财经-B 股-个股的历史行情数据, 大量抓取容易封 IP https://finance.sina.com.cn/realstock/company/sh900901/nc.shtml :param start_date: 20201103; 开始日期 :type start_date: str :param end_date: 20201103; 结束日期 :type end_date: str :param symbol: sh600000 :type symbol: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; qfq-factor: 返回前复权因子 :type adjust: str :return: specific data :rtype: pandas.DataFrame """ def _fq_factor(method: str) -> pd.DataFrame: if method == "hfq": r = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame( eval(r.text.split("=")[1].split("\n")[0])["data"] ) if hfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] hfq_factor_df.reset_index(inplace=True) return hfq_factor_df else: r = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame( eval(r.text.split("=")[1].split("\n")[0])["data"] ) if qfq_factor_df.shape[0] == 0: raise ValueError("sina hfq factor not available") qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] qfq_factor_df.reset_index(inplace=True) return qfq_factor_df if adjust in ("hfq-factor", "qfq-factor"): return _fq_factor(adjust.split("-")[0]) r = requests.get(zh_sina_a_stock_hist_url.format(symbol)) js_code = py_mini_racer.MiniRacer() js_code.eval(hk_js_decode) dict_list = js_code.call( "d", r.text.split("=")[1].split(";")[0].replace('"', "") ) # 执行js解密代码 data_df = pd.DataFrame(dict_list) data_df.index = pd.to_datetime(data_df["date"]).dt.date del data_df["date"] del data_df["amount"] del data_df["prevclose"] data_df = data_df.astype("float") r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol)) amount_data_json = demjson.decode(r.text[r.text.find("[") : r.text.rfind("]") + 1]) amount_data_df = pd.DataFrame(amount_data_json) amount_data_df.index = pd.to_datetime(amount_data_df.date) del amount_data_df["date"] temp_df = pd.merge( data_df, amount_data_df, left_index=True, right_index=True, how="outer" ) temp_df.ffill(inplace=True) temp_df = temp_df.astype(float) temp_df["amount"] = temp_df["amount"] * 10000 temp_df["turnover"] = temp_df["volume"] / temp_df["amount"] temp_df.columns = [ "open", "high", "low", "close", "volume", "outstanding_share", "turnover", ] if adjust == "": temp_df = temp_df[start_date:end_date] temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.drop_duplicates(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "hfq": r = requests.get(zh_sina_a_stock_hfq_url.format(symbol)) hfq_factor_df = pd.DataFrame(eval(r.text.split("=")[1].split("\n")[0])["data"]) hfq_factor_df.columns = ["date", "hfq_factor"] hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date) del hfq_factor_df["date"] temp_df = pd.merge( temp_df, hfq_factor_df, left_index=True, right_index=True, how="outer" ) temp_df.ffill(inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] * temp_df["hfq_factor"] temp_df["high"] = temp_df["high"] * temp_df["hfq_factor"] temp_df["close"] = temp_df["close"] * temp_df["hfq_factor"] temp_df["low"] = temp_df["low"] * temp_df["hfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df if adjust == "qfq": r = requests.get(zh_sina_a_stock_qfq_url.format(symbol)) qfq_factor_df = pd.DataFrame(eval(r.text.split("=")[1].split("\n")[0])["data"]) qfq_factor_df.columns = ["date", "qfq_factor"] qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date) del qfq_factor_df["date"] temp_df = pd.merge( temp_df, qfq_factor_df, left_index=True, right_index=True, how="outer" ) temp_df.ffill(inplace=True) temp_df = temp_df.astype(float) temp_df.dropna(inplace=True) temp_df.drop_duplicates( subset=["open", "high", "low", "close", "volume"], inplace=True ) temp_df["open"] = temp_df["open"] / temp_df["qfq_factor"] temp_df["high"] = temp_df["high"] / temp_df["qfq_factor"] temp_df["close"] = temp_df["close"] / temp_df["qfq_factor"] temp_df["low"] = temp_df["low"] / temp_df["qfq_factor"] temp_df = temp_df.iloc[:, :-1] temp_df = temp_df[start_date:end_date] temp_df["open"] = round(temp_df["open"], 2) temp_df["high"] = round(temp_df["high"], 2) temp_df["low"] = round(temp_df["low"], 2) temp_df["close"] = round(temp_df["close"], 2) temp_df.dropna(inplace=True) temp_df.reset_index(inplace=True) return temp_df def stock_zh_b_minute( symbol: str = "sh900901", period: str = "1", adjust: str = "" ) -> pd.DataFrame: """ 股票及股票指数历史行情数据-分钟数据 https://finance.sina.com.cn/realstock/company/sh900901/nc.shtml :param symbol: sh900901 :type symbol: str :param period: 1, 5, 15, 30, 60 分钟的数据 :type period: str :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; :type adjust: str :return: specific data :rtype: pandas.DataFrame """ url = ( "https://quotes.sina.cn/cn/api/jsonp_v2.php/=/CN_MarketDataService.getKLineData" ) params = { "symbol": symbol, "scale": period, "datalen": "1970", } r = requests.get(url, params=params) temp_df = pd.DataFrame(json.loads(r.text.split("=(")[1].split(");")[0])).iloc[:, :6] if temp_df.empty: return pd.DataFrame() try: stock_zh_b_daily(symbol=symbol, adjust="qfq") except: # noqa: E722 return temp_df if adjust == "": return temp_df if adjust == "qfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_b_daily_qfq_df = stock_zh_b_daily(symbol=symbol, adjust="qfq") stock_zh_b_daily_qfq_df.index = pd.to_datetime(stock_zh_b_daily_qfq_df["date"]) result_df = stock_zh_b_daily_qfq_df.iloc[-len(need_df) :, :]["close"].astype( float ) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge(temp_df, result_df, left_index=True, right_index=True) merged_df["open"] = merged_df["open"].astype(float) * merged_df["close_y"] merged_df["high"] = merged_df["high"].astype(float) * merged_df["close_y"] merged_df["low"] = merged_df["low"].astype(float) * merged_df["close_y"] merged_df["close"] = merged_df["close_x"].astype(float) * merged_df["close_y"] temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df if adjust == "hfq": temp_df[["date", "time"]] = temp_df["day"].str.split(" ", expand=True) # 处理没有最后一分钟的情况 need_df = temp_df[ [ True if "09:31:00" <= item <= "15:00:00" else False for item in temp_df["time"] ] ] need_df.drop_duplicates(subset=["date"], keep="last", inplace=True) need_df.index = pd.to_datetime(need_df["date"]) stock_zh_b_daily_hfq_df = stock_zh_b_daily(symbol=symbol, adjust="hfq") stock_zh_b_daily_hfq_df.index = pd.to_datetime(stock_zh_b_daily_hfq_df["date"]) result_df = stock_zh_b_daily_hfq_df.iloc[-len(need_df) :, :]["close"].astype( float ) / need_df["close"].astype(float) temp_df.index = pd.to_datetime(temp_df["date"]) merged_df = pd.merge(temp_df, result_df, left_index=True, right_index=True) merged_df["open"] = merged_df["open"].astype(float) * merged_df["close_y"] merged_df["high"] = merged_df["high"].astype(float) * merged_df["close_y"] merged_df["low"] = merged_df["low"].astype(float) * merged_df["close_y"] merged_df["close"] = merged_df["close_x"].astype(float) * merged_df["close_y"] temp_df = merged_df[["day", "open", "high", "low", "close", "volume"]] temp_df.reset_index(drop=True, inplace=True) return temp_df if __name__ == "__main__": stock_zh_b_daily_hfq_df_one = stock_zh_b_daily( symbol="sh900901", start_date="20071103", end_date="20240916", adjust="" ) print(stock_zh_b_daily_hfq_df_one) stock_zh_b_daily_hfq_df_three = stock_zh_b_daily( symbol="sh900901", start_date="19900103", end_date="20240722", adjust="hfq" ) print(stock_zh_b_daily_hfq_df_three) stock_zh_b_daily_hfq_df_two = stock_zh_b_daily( symbol="sh900901", start_date="20101103", end_date="20210510" ) print(stock_zh_b_daily_hfq_df_two) qfq_factor_df = stock_zh_b_daily(symbol="sh900901", adjust="qfq-factor") print(qfq_factor_df) hfq_factor_df = stock_zh_b_daily(symbol="sh900901", adjust="hfq-factor") print(hfq_factor_df) stock_zh_b_daily_hfq_factor_df = stock_zh_b_daily( symbol="sh900901", adjust="hfq-factor" ) print(stock_zh_b_daily_hfq_factor_df) stock_zh_b_daily_df = stock_zh_b_daily(symbol="sh900901") print(stock_zh_b_daily_df) stock_zh_b_spot_df = stock_zh_b_spot() print(stock_zh_b_spot_df) stock_zh_b_minute_df = stock_zh_b_minute(symbol="sh900901", period="5", adjust="") print(stock_zh_b_minute_df) stock_zh_b_minute_df = stock_zh_b_minute(symbol="sh900901", period="1", adjust="") print(stock_zh_b_minute_df) stock_zh_b_daily_qfq_df = stock_zh_b_daily( symbol="sh900901", start_date="20101103", end_date="20201116", adjust="qfq" ) print(stock_zh_b_daily_qfq_df) stock_zh_b_daily_hfq_df = stock_zh_b_daily( symbol="sh900901", start_date="20201103", end_date="20201116", adjust="hfq" ) print(stock_zh_b_daily_hfq_df) qfq_factor_df = stock_zh_b_daily(symbol="sh900901", adjust="qfq-factor") print(qfq_factor_df) hfq_factor_df = stock_zh_b_daily(symbol="sh900901", adjust="hfq-factor") print(hfq_factor_df) stock_zh_b_minute_df = stock_zh_b_minute( symbol="sh900901", period="1", adjust="qfq" ) print(stock_zh_b_minute_df)