#!/usr/bin/env python # -*- coding:utf-8 -*- """ 所有 A 股的实时行情数据 Created on 2023/12/06 @author: Monday @group : waditu @contact: """ import time import pandas as pd import requests from tqdm import tqdm from typing import Optional import re from tushare.util.format_stock_code import symbol_verify from tushare.util.format_stock_code import format_stock_code, verify_stock_or_index from tushare.util.form_date import timestemp_to_time from tushare.stock import rtq_vars as rtqv from tushare.util.verify_token import require_permission from tushare.stock import cons as ct from tushare.stock.rtq_vars import ( zh_sina_a_stock_payload, zh_sina_a_stock_url, zh_sina_a_stock_count_url, zh_sina_a_stock_headers, zh_sina_a_stock_cookies, ) def _random(n=13): from random import randint start = 10 ** (n - 1) end = (10 ** n) - 1 return str(randint(start, end)) def _get_current_timestamp(): return str(int(time.time() * 1000)) @require_permission(event_name="realtime_quote", event_detail="个股实时交易数据") def realtime_quote(ts_code="688553.SH", src="sina", ): """ 获取实时交易数据 getting real time quotes data 用于跟踪交易情况（本次执行的结果-上一次执行的数据） Parameters ------ ts_code : string src : sina ，dc return ------- DataFrame 实时交易数据 属性:0：name，股票名字 1：open，今日开盘价 2：pre_close，昨日收盘价 3：price，当前价格 4：high，今日最高价 5：low，今日最低价 6：bid，竞买价，即“买一”报价 7：ask，竞卖价，即“卖一”报价 8：volumn，成交量 maybe you need do volumn/100 9：amount，成交金额（元 CNY） 10：b1_v，委买一（笔数 bid volume） 11：b1_p，委买一（价格 bid price） 12：b2_v，“买二” 13：b2_p，“买二” 14：b3_v，“买三” 15：b3_p，“买三” 16：b4_v，“买四” 17：b4_p，“买四” 18：b5_v，“买五” 19：b5_p，“买五” 20：a1_v，委卖一（笔数 ask volume） 21：a1_p，委卖一（价格 ask price） ... 30：date，日期； 31：time，时间； """ symbols = symbol_verify(ts_code) if src == "sina": return get_realtime_quotes_sina(symbols) else: return get_realtime_quotes_dc(symbols) sina_stock_code = {} def get_realtime_quotes_sina(symbol="688553"): # "688553" global sina_stock_code symbols = [] syms = [] for i in [j for j in symbol.split(",")]: s = i.split(".") sina_stock_code[s[0]] = s[1].upper() symbols.append(s[1].lower() + s[0]) syms.append(s[0]) # symbols = re.search(r"(\d+)", str(symbols), re.S | re.M).group(1) symbols_list = '' if isinstance(symbols, list) or isinstance(symbols, set) or \ isinstance(symbols, tuple) or isinstance(symbols, pd.Series): for code in symbols: symbols_list += code + ',' else: symbols_list = symbols symbols_list = symbols_list[:-1] if len(symbols_list) > 8 else symbols_list root_url = ct.LIVE_DATA_URL % (ct.P_TYPE['http'], ct.DOMAINS['sinahq'], _get_current_timestamp(), symbols_list) response = requests.get(root_url, headers={ "user-agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36 Edg/119.0.0.0", 'host': 'hq.sinajs.cn', 'referer': 'https://finance.sina.com.cn/' }) text = response.content.decode('GBK') reg = re.compile(r'\="(.*?)\";') data = reg.findall(text) data_list = [] syms_list = [] for index, row in enumerate(data): if len(row) > 1: data_list.append([astr for astr in row.split(',')[:33]]) syms_list.append(syms[index]) if len(syms_list) == 0: return None df = pd.DataFrame(data_list, columns=ct.LIVE_DATA_COLS) df = df.drop('s', axis=1) df['code'] = syms_list ls = [cls for cls in df.columns if '_v' in cls] for txt in ls: df[txt] = df[txt].map(lambda x: x[:-2]) df.columns = rtqv.LIVE_DATA_COLS df["TS_CODE"] = df["TS_CODE"].apply(format_sina_stock_code) df["DATE"] = df["DATE"].apply(format_date_str) new_order = rtqv.LIVE_DATA_COLS_REINDEX df = df[new_order] # 指定要转换为 float 类型的列 cols_to_convert = ['OPEN', 'PRE_CLOSE', 'PRICE', 'HIGH', 'LOW', 'BID', 'ASK', 'VOLUME', 'AMOUNT', 'B1_V', 'B1_P', 'B2_V', 'B2_P', 'B3_V', 'B3_P', 'B4_V', 'B4_P', 'B5_V', 'B5_P', 'A1_V', 'A1_P', 'A2_V', 'A2_P', 'A3_V', 'A3_P', 'A4_V', 'A4_P', 'A5_V', 'A5_P'] # 使用 to_numeric() 方法将指定的列转换为 float 类型，并将非数值类型的数据转换为 NaN df[cols_to_convert] = df[cols_to_convert].apply(pd.to_numeric, errors='coerce') # 使用 fillna() 方法将 NaN 值替换为 0 df_filled = df.fillna(0) return df_filled def format_sina_stock_code(x): ts_code = f"{x}.{sina_stock_code[x]}" return ts_code def format_date_str(date_str): return date_str.replace("-", "") @require_permission(event_name="realtime_list", event_detail="A股所有实时交易数据") def realtime_list(src: Optional[str] = None, interval: Optional[int] = 3, page_count: Optional[int] = None, proxies: Optional[dict] = {}) -> pd.DataFrame: """ 沪深京 A 股 all -实时行情 @param src: 数据源 新浪sina |东方财富 dc @param interval: 分页采集时间间隔（默认3秒翻译一夜） @param page_count: 限制抓取的页数（仅对新浪有效） @param proxies: 设置代理 防止被封禁 @return: 按涨跌幅 倒序排序 ------- DataFrame 实时交易数据 东方财富： 2、代码:TS_CODE 3、名称:NAME 4、最新价:CLOSE 5、涨跌幅:PCT_CHANGE 6、涨跌额:CHANGE 7、成交量:VOLUME 8、成交额:AMOUNT 9、振幅:SWING 10、最高:HIGH 11、最低:LOW 12、今开:OPEN 13、昨收:PRICE 14、量比:VOL_RATIO 15、换手率:TURNOVER_RATE 16、市盈率-动态:PE 17、市净率:PB 18、总市值:TOTAL_MV 19、流通市值:FLOAT_MV 20、涨速:RISE 21、5分钟涨跌:5MIN 22、60日涨跌幅:60DAY 23、年初至今涨跌幅:1YEAR 新浪财经： 1、代码:TS_CODE 2、名称:NAME 3、最新价:CLOSE 4、涨跌额:CHANGE 5、涨跌幅:PCT_CHANGE 6、买入:BUY 7、卖出:SALE 8、昨收:CLOSE 9、今开:OPEN 10、最高:HIGH 11、最低:LOW 12、成交量:VOLUME 13、成交额:AMOUNT 14、时间戳:TIME """ if src == "dc": return get_stock_all_a_dc(page_count, proxies) elif src == "sina": return get_stock_all_a_sina(interval, page_count, proxies) else: return get_stock_all_a_dc(page_count, proxies) def get_stock_all_a_dc(page_count: Optional[int] = None, proxies: Optional[dict] = {}) -> pd.DataFrame: """ 东方财富网-沪深京 A 股-实时行情 https://quote.eastmoney.com/center/gridlist.html#hs_a_board :return: 实时行情 :rtype: pandas.DataFrame 1、序号:RANK 2、代码:TS_CODE 3、名称:NAME 4、最新价:PRICE 5、涨跌幅:PCT_CHANGE 6、涨跌额:CHANGE 7、成交量:VOLUME 8、成交额:AMOUNT 9、振幅:SWING 10、最高:HIGH 11、最低:LOW 12、今开:OPEN 13、昨收:CLOSE 14、量比:VOL_RATIO 15、换手率:TURNOVER_RATE 16、市盈率-动态:PE 17、市净率:PB 18、总市值:TOTAL_MV 19、流通市值:FLOAT_MV 20、涨速:RISE 21、5分钟涨跌:5MIN 22、60日涨跌幅:60DAY 23、年初至今涨跌幅:1YEAR """ dfs = [] for page in range(1,500): url = "http://82.push2.eastmoney.com/api/qt/clist/get" params = { "pn": page, "pz": "200", "po": "1", "np": "1", "ut": "bd1d9ddb04089700cf9c27f6f7426281", "fltt": "2", "invt": "2", "fid": "f3", "fs": "m:0 t:6,m:0 t:80,m:1 t:2,m:1 t:23,m:0 t:81 s:2048", "fields": "f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f12,f13,f14,f15,f16,f17,f18,f20,f21,f23,f24,f25,f22,f11,f62,f128,f136,f115,f152", "_": "1623833739532", } if page_count: params["pz"] = 20 r = requests.get(url, params=params, proxies=proxies) data_json = r.json() if not data_json["data"]: break # return pd.DataFrame() temp_df = pd.DataFrame(data_json["data"]["diff"]) temp_df.columns = [ "_", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "换手率", "市盈率-动态", "量比", "5分钟涨跌", "代码", "_", "名称", "最高", "最低", "今开", "昨收", "总市值", "流通市值", "涨速", "市净率", "60日涨跌幅", "年初至今涨跌幅", "-", "-", "-", "-", "-", "-", "-", ] temp_df.reset_index(inplace=True) # temp_df["index"] = temp_df.index + 1 # temp_df.rename(columns={"index": "序号"}, inplace=True) temp_df = temp_df[ [ # "序号", "代码", "名称", "最新价", "涨跌幅", "涨跌额", "成交量", "成交额", "振幅", "最高", "最低", "今开", "昨收", "量比", "换手率", "市盈率-动态", "市净率", "总市值", "流通市值", "涨速", "5分钟涨跌", "60日涨跌幅", "年初至今涨跌幅", ] ] temp_df["代码"] = temp_df["代码"].apply(format_stock_code) temp_df["最新价"] = pd.to_numeric(temp_df["最新价"], errors="coerce") temp_df["涨跌幅"] = pd.to_numeric(temp_df["涨跌幅"], errors="coerce") temp_df["涨跌额"] = pd.to_numeric(temp_df["涨跌额"], errors="coerce") temp_df["成交量"] = pd.to_numeric(temp_df["成交量"], errors="coerce") temp_df["成交额"] = pd.to_numeric(temp_df["成交额"], errors="coerce") temp_df["振幅"] = pd.to_numeric(temp_df["振幅"], errors="coerce") temp_df["最高"] = pd.to_numeric(temp_df["最高"], errors="coerce") temp_df["最低"] = pd.to_numeric(temp_df["最低"], errors="coerce") temp_df["今开"] = pd.to_numeric(temp_df["今开"], errors="coerce") temp_df["昨收"] = pd.to_numeric(temp_df["昨收"], errors="coerce") temp_df["量比"] = pd.to_numeric(temp_df["量比"], errors="coerce") temp_df["换手率"] = pd.to_numeric(temp_df["换手率"], errors="coerce") temp_df["市盈率-动态"] = pd.to_numeric(temp_df["市盈率-动态"], errors="coerce") temp_df["市净率"] = pd.to_numeric(temp_df["市净率"], errors="coerce") temp_df["总市值"] = pd.to_numeric(temp_df["总市值"], errors="coerce") temp_df["流通市值"] = pd.to_numeric(temp_df["流通市值"], errors="coerce") temp_df["涨速"] = pd.to_numeric(temp_df["涨速"], errors="coerce") temp_df["5分钟涨跌"] = pd.to_numeric(temp_df["5分钟涨跌"], errors="coerce") temp_df["60日涨跌幅"] = pd.to_numeric(temp_df["60日涨跌幅"], errors="coerce") temp_df["年初至今涨跌幅"] = pd.to_numeric(temp_df["年初至今涨跌幅"], errors="coerce") temp_df.columns = [ # "RANK", "TS_CODE", "NAME", "PRICE", "PCT_CHANGE", "CHANGE", "VOLUME", "AMOUNT", "SWING", "HIGH", "LOW", "OPEN", "CLOSE", "VOL_RATIO", "TURNOVER_RATE", "PE", "PB", "TOTAL_MV", "FLOAT_MV", "RISE", "5MIN", "60DAY", "1YEAR", ] # 指定要转换为 float 类型的列 cols_to_convert = ['PRICE', 'PCT_CHANGE', 'CHANGE', "VOLUME", "AMOUNT", "SWING", 'HIGH', "LOW", "OPEN", "CLOSE", "VOL_RATIO", "TURNOVER_RATE", "PE", "PB", "TOTAL_MV", "FLOAT_MV", "RISE", "5MIN", "60DAY", "1YEAR" ] # 使用 to_numeric() 方法将指定的列转换为 float 类型，并将非数值类型的数据转换为 NaN temp_df[cols_to_convert] = temp_df[cols_to_convert].apply(pd.to_numeric, errors='coerce') dfs.append( temp_df ) if page_count: break result_df = pd.concat(dfs, ignore_index=True) # 使用 fillna() 方法将 NaN 值替换为 0 df_filled = result_df.fillna(0) df_sorted = df_filled.sort_values(by='PCT_CHANGE', ascending=False).reset_index(drop=True) return df_sorted def _get_zh_a_page_count() -> int: """ 所有股票的总页数 https://vip.stock.finance.sina.com.cn/mkt/#hs_a :return: 需要采集的股票总页数 :rtype: int """ res = requests.get(zh_sina_a_stock_count_url) page_count = int(re.findall(re.compile(r"\d+"), res.text)[0]) / 80 if isinstance(page_count, int): return page_count else: return int(page_count) + 1 def get_stock_all_a_sina(interval: Optional[int] = 3, page_count: Optional[int] = None, proxies: Optional[dict] = {}) -> pd.DataFrame: """ 新浪财经-所有 A 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_a :param interval:请求间隔时间 :param page_count:限制抓取页数 :param proxies: 代理ip {} :return: 所有股票的实时行情数据 :rtype: pandas.DataFrame 1、代码:TS_CODE 2、名称:NAME 3、最新价:PRICE 4、涨跌额:CHANGE 5、涨跌幅:PCT_CHANGE 6、买入:BUY 7、卖出:SALE 8、昨收:CLOSE 9、今开:OPEN 10、最高:HIGH 11、最低:LOW 12、成交量:VOLUME 13、成交额:AMOUNT 14、时间戳:TIME """ big_df = pd.DataFrame() if not page_count: page_count = _get_zh_a_page_count() zh_sina_stock_payload_copy = zh_sina_a_stock_payload.copy() for page in tqdm( range(1, page_count + 1), leave=False, desc="Please wait for a moment" ): zh_sina_stock_payload_copy.update({"page": page}) r = requests.get( zh_sina_a_stock_url, headers=zh_sina_a_stock_headers, cookies=zh_sina_a_stock_cookies, params=zh_sina_stock_payload_copy, proxies=proxies, ) data_json = r.json() big_df = pd.concat( [big_df, pd.DataFrame(data_json)], ignore_index=True ) time.sleep(interval) big_df = big_df.astype( { "trade": "float", "pricechange": "float", "changepercent": "float", "buy": "float", "sell": "float", "settlement": "float", "open": "float", "high": "float", "low": "float", "volume": "float", "amount": "float", "per": "float", "pb": "float", "mktcap": "float", "nmc": "float", "turnoverratio": "float", } ) 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", "时间戳", "_", "_", "_", "_", "_", ] 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", "时间戳", ] ] big_df["代码"] = big_df["代码"].apply(format_stock_code) big_df.columns = [ "TS_CODE", "NAME", "PRICE", "CHANGE", "PCT_CHANGE", "BUY", "SALE", "CLOSE", "OPEN", "HIGH", "LOW", "VOLUME", "AMOUNT", "TIME", ] df_sorted = big_df.sort_values(by='PCT_CHANGE', ascending=False).reset_index(drop=True) return df_sorted def get_realtime_quotes_dc(symbols="688553"): """ https://quote.eastmoney.com/sh601096.html """ symbols = str(symbols).split(",")[0] url = "https://push2.eastmoney.com/api/qt/stock/get" symbol = re.search(r"(\d+)", symbols, re.S | re.M).group(1) # print(symbol) params = { "invt": "2", "fltt": "1", # "cb": "jQuery35108939078769986013_1701853424476", "fields": "f58,f734,f107,f57,f43,f59,f169,f301,f60,f170,f152,f177,f111,f46,f44,f45,f47,f260,f48,f261,f279,f277,f278,f288,f19,f17,f531,f15,f13,f11,f20,f18,f16,f14,f12,f39,f37,f35,f33,f31,f40,f38,f36,f34,f32,f211,f212,f213,f214,f215,f210,f209,f208,f207,f206,f161,f49,f171,f50,f86,f84,f85,f168,f108,f116,f167,f164,f162,f163,f92,f71,f117,f292,f51,f52,f191,f192,f262,f294,f295,f748,f747", "secid": f"0.{symbol}", "ut": "fa5fd1943c7b386f172d6893dbfba10b", "wbp2u": "|0|0|0|web", "_": _get_current_timestamp() } if not verify_stock_or_index(symbols): params["secid"] = f"1.{symbol}" # print(params["secid"]) response = requests.get(url, headers=rtqv.dc_cookies, cookies=rtqv.dc_headers, params=params) data_info = response.json()["data"] if not data_info: return pd.DataFrame() name = data_info["f58"] open = data_info["f46"] # / 100 high = data_info["f44"] # / 100 pre_close = data_info["f60"] # / 100 low = data_info["f45"] # / 100 price = data_info["f43"] # / 100 if data_info["f43"] != "-" else "" b5_v = format_str_to_float(data_info["f12"]) b5_p = data_info["f11"] # / 100 if data_info["f11"] != "-" else "" b4_v = format_str_to_float(data_info["f14"]) b4_p = data_info["f13"] # / 100 if data_info["f13"] != "-" else "" b3_v = format_str_to_float(data_info["f16"]) b3_p = data_info["f15"] # / 100 if data_info["f15"] != "-" else "" b2_v = format_str_to_float(data_info["f18"]) b2_p = data_info["f17"] # / 100 if data_info["f17"] != "-" else "" b1_v = format_str_to_float(data_info["f20"]) b1_p = data_info["f19"] # / 100 if data_info["f19"] != "-" else "" a5_v = format_str_to_float(data_info["f32"]) a5_p = data_info["f31"] # / 100 if data_info["f31"] != "-" else "" a4_v = format_str_to_float(data_info["f34"]) a4_p = data_info["f33"] # / 100 if data_info["f33"] != "-" else "" a3_v = format_str_to_float(data_info["f36"]) a3_p = data_info["f35"] # / 100 if data_info["f35"] != "-" else "" a2_v = format_str_to_float(data_info["f38"]) a2_p = data_info["f37"] # / 100 if data_info["f38"] != "-" else "" a1_v = format_str_to_float(data_info["f40"]) a1_p = data_info["f39"] # / 100 if data_info["f39"] != "-" else "" date_time = timestemp_to_time(data_info["f86"]) date = date_time[0:10] times = date_time[10:] volume = format_str_to_float(data_info["f47"]) amount = format_str_to_float(data_info["f48"]) bid = format_str_to_float(data_info["f19"]) ask = format_str_to_float(data_info["f39"]) code = symbols data_list = [[name, open, pre_close, price, high, low, bid, ask, volume, amount, b1_v, b1_p, b2_v, b2_p, b3_v, b3_p, b4_v, b4_p, b5_v, b5_p, a1_v, a1_p, a2_v, a2_p, a3_v, a3_p, a4_v, a4_p, a5_v, a5_p, date, times, code]] df = pd.DataFrame(data_list, columns=rtqv.LIVE_DATA_COLS) df["DATE"] = df["DATE"].apply(format_date_str) df["ASK"] = df["ASK"].apply(format_dc_str) df["OPEN"] = df["OPEN"].apply(format_dc_str) df["HIGH"] = df["HIGH"].apply(format_dc_str) df["LOW"] = df["LOW"].apply(format_dc_str) df["PRE_CLOSE"] = df["PRE_CLOSE"].apply(format_dc_str) df["BID"] = df["BID"].apply(format_dc_str) df["A1_P"] = df["A1_P"].apply(format_dc_str) df["A2_P"] = df["A2_P"].apply(format_dc_str) df["A3_P"] = df["A3_P"].apply(format_dc_str) df["A4_P"] = df["A4_P"].apply(format_dc_str) df["A5_P"] = df["A5_P"].apply(format_dc_str) df["PRICE"] = df["PRICE"].apply(format_dc_str) df["B1_P"] = df["B1_P"].apply(format_dc_str) df["B2_P"] = df["B2_P"].apply(format_dc_str) df["B3_P"] = df["B3_P"].apply(format_dc_str) df["B4_P"] = df["B4_P"].apply(format_dc_str) df["B5_P"] = df["B5_P"].apply(format_dc_str) new_order = rtqv.LIVE_DATA_COLS_REINDEX df = df[new_order] return df def format_dc_str(x): return float(x / 100) if x != "-" else 0 def format_str_to_float(x): try: return float(x) if x != "" else 0 except: return 0 if __name__ == '__main__': # df = realtime_quote(ts_code="000688.SH,000010.SH,000012.SH,399005.SZ", src="sina") # df = realtime_list(src="dc", page_count=1) df = realtime_list(src="sina") print(df) # ts_code = '399005.SZ' # ts_code = '000001.SZ' # # ts_code = '836149.BJ' # # ts_code = '600000.SH' # # ts_code = '000001.SH' # # ts_code = '000010.SH' # ts_code = '600148.SH' # df = realtime_quote(src="dc", ts_code=ts_code) # print(df)