#!/usr/bin/env python # -*- coding:utf-8 -*- """ Date: 2024/3/20 16:00 Desc: 新浪财经-国内期货-实时数据获取 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_3 P.S. 注意采集速度, 容易封禁 IP, 如果不能访问请稍后再试 """ import json import time from functools import lru_cache import pandas as pd import requests import py_mini_racer from akshare.futures.cons import ( zh_subscribe_exchange_symbol_url, zh_match_main_contract_url, zh_match_main_contract_payload, ) from akshare.futures.futures_contract_detail import futures_contract_detail from akshare.utils import demjson @lru_cache() def futures_symbol_mark() -> pd.DataFrame: """ 期货的品种和代码映射 https://vip.stock.finance.sina.com.cn/quotes_service/view/js/qihuohangqing.js :return: 期货的品种和代码映射 :rtype: pandas.DataFrame """ url = ( "https://vip.stock.finance.sina.com.cn/quotes_service/view/js/qihuohangqing.js" ) r = requests.get(url) r.encoding = "gb2312" data_text = r.text raw_json = data_text[data_text.find("{") : data_text.find("}") + 1] data_json = demjson.decode(raw_json) czce_mark_list = [item[1] for item in data_json["czce"][1:]] dce_mark_list = [item[1] for item in data_json["dce"][1:]] shfe_mark_list = [item[1] for item in data_json["shfe"][1:]] cffex_mark_list = [item[1] for item in data_json["cffex"][1:]] gfex_mark_list = [item[1] for item in data_json["gfex"][1:]] all_mark_list = ( czce_mark_list + dce_mark_list + shfe_mark_list + cffex_mark_list + gfex_mark_list ) czce_market_name_list = [data_json["czce"][0]] * len(czce_mark_list) dce_market_name_list = [data_json["dce"][0]] * len(dce_mark_list) shfe_market_name_list = [data_json["shfe"][0]] * len(shfe_mark_list) cffex_market_name_list = [data_json["cffex"][0]] * len(cffex_mark_list) gfex_market_name_list = [data_json["gfex"][0]] * len(gfex_mark_list) all_market_name_list = ( czce_market_name_list + dce_market_name_list + shfe_market_name_list + cffex_market_name_list + gfex_market_name_list ) czce_symbol_list = [item[0] for item in data_json["czce"][1:]] dce_symbol_list = [item[0] for item in data_json["dce"][1:]] shfe_symbol_list = [item[0] for item in data_json["shfe"][1:]] cffex_symbol_list = [item[0] for item in data_json["cffex"][1:]] gfex_symbol_list = [item[0] for item in data_json["gfex"][1:]] all_symbol_list = ( czce_symbol_list + dce_symbol_list + shfe_symbol_list + cffex_symbol_list + gfex_symbol_list ) temp_df = pd.DataFrame([all_market_name_list, all_symbol_list, all_mark_list]).T temp_df.columns = [ "exchange", "symbol", "mark", ] return temp_df def futures_zh_realtime(symbol: str = "PTA") -> pd.DataFrame: """ 期货品种当前时刻所有可交易的合约实时数据 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1 :param symbol: 品种名称；可以通过 ak.futures_symbol_mark() 获取所有品种命名表 :type symbol: str :return: 期货品种当前时刻所有可交易的合约实时数据 :rtype: pandas.DataFrame """ _futures_symbol_mark_df = futures_symbol_mark() symbol_mark_map = dict( zip(_futures_symbol_mark_df["symbol"], _futures_symbol_mark_df["mark"]) ) url = "https://vip.stock.finance.sina.com.cn/quotes_service/api/json_v2.php/Market_Center.getHQFuturesData" params = { "page": "1", "sort": "position", "asc": "0", "node": symbol_mark_map[symbol], "base": "futures", } r = requests.get(url, params=params) data_json = r.json() temp_df = pd.DataFrame(data_json) temp_df["trade"] = pd.to_numeric(temp_df["trade"], errors="coerce") temp_df["settlement"] = pd.to_numeric(temp_df["settlement"], errors="coerce") temp_df["presettlement"] = pd.to_numeric(temp_df["presettlement"], errors="coerce") temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce") temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce") temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce") temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce") temp_df["bidprice1"] = pd.to_numeric(temp_df["bidprice1"], errors="coerce") temp_df["askprice1"] = pd.to_numeric(temp_df["askprice1"], errors="coerce") temp_df["bidvol1"] = pd.to_numeric(temp_df["bidvol1"], errors="coerce") temp_df["askvol1"] = pd.to_numeric(temp_df["askvol1"], errors="coerce") temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce") temp_df["position"] = pd.to_numeric(temp_df["position"], errors="coerce") temp_df["preclose"] = pd.to_numeric(temp_df["preclose"], errors="coerce") temp_df["changepercent"] = pd.to_numeric(temp_df["changepercent"], errors="coerce") temp_df["bid"] = pd.to_numeric(temp_df["bid"], errors="coerce") temp_df["ask"] = pd.to_numeric(temp_df["ask"], errors="coerce") temp_df["prevsettlement"] = pd.to_numeric( temp_df["prevsettlement"], errors="coerce" ) return temp_df def zh_subscribe_exchange_symbol(symbol: str = "cffex") -> pd.DataFrame: """ 交易所具体的可交易品种 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1 :param symbol: choice of {'czce', 'dce', 'shfe', 'cffex', 'gfex'} :type symbol: str :return: 交易所具体的可交易品种 :rtype: dict """ r = requests.get(zh_subscribe_exchange_symbol_url) r.encoding = "gbk" data_text = r.text data_json = demjson.decode( data_text[data_text.find("{") : data_text.find("};") + 1] ) if symbol == "czce": data_json["czce"].remove("郑州商品交易所") return pd.DataFrame(data_json["czce"]) if symbol == "dce": data_json["dce"].remove("大连商品交易所") return pd.DataFrame(data_json["dce"]) if symbol == "shfe": data_json["shfe"].remove("上海期货交易所") return pd.DataFrame(data_json["shfe"]) if symbol == "cffex": data_json["cffex"].remove("中国金融期货交易所") return pd.DataFrame(data_json["cffex"]) if symbol == "gfex": data_json["gfex"].remove("广州期货交易所") return pd.DataFrame(data_json["gfex"]) def match_main_contract(symbol: str = "cffex") -> str: """ 新浪财经-期货-主力合约 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1 :param symbol: choice of {'czce', 'dce', 'shfe', 'cffex', 'gfex'} :type symbol: str :return: 主力合约的字符串 :rtype: str """ subscribe_exchange_list = [] exchange_symbol_list = zh_subscribe_exchange_symbol(symbol).iloc[:, 1].tolist() for item in exchange_symbol_list: # item = 'sngz_qh' zh_match_main_contract_payload.update({"node": item}) res = requests.get( zh_match_main_contract_url, params=zh_match_main_contract_payload ) data_json = demjson.decode(res.text) data_df = pd.DataFrame(data_json) try: main_contract = data_df[data_df.iloc[:, 3:].duplicated()] print(main_contract["symbol"].values[0]) subscribe_exchange_list.append(main_contract["symbol"].values[0]) except: # noqa: E722 if len(data_df) == 1: subscribe_exchange_list.append(data_df["symbol"].values[0]) print(data_df["symbol"].values[0]) else: print(item, "无主力合约") continue print(f"{symbol}主力合约获取成功") return ",".join([item for item in subscribe_exchange_list]) def futures_zh_spot( symbol: str = "V2309", market: str = "CF", adjust: str = "0", ) -> pd.DataFrame: """ 期货的实时行情数据 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_1 :param symbol: 合约名称的字符串组合 :type symbol: str :param market: CF 为商品期货 :type market: str :param adjust: '1' or '0'；字符串的 0 或 1；返回合约、交易所和最小变动单位的实时数据, 返回数据会变慢 :type adjust: str :return: 期货的实时行情数据 :rtype: pandas.DataFrame """ file_data = "Math.round(Math.random() * 2147483648).toString(16)" ctx = py_mini_racer.MiniRacer() rn_code = ctx.eval(file_data) subscribe_list = ",".join(["nf_" + item.strip() for item in symbol.split(",")]) url = f"https://hq.sinajs.cn/rn={rn_code}&list={subscribe_list}" headers = { "Accept": "*/*", "Accept-Encoding": "gzip, deflate", "Accept-Language": "zh-CN,zh;q=0.9,en;q=0.8", "Cache-Control": "no-cache", "Host": "hq.sinajs.cn", "Pragma": "no-cache", "Proxy-Connection": "keep-alive", "Referer": "https://vip.stock.finance.sina.com.cn/", "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " "Chrome/97.0.4692.71 Safari/537.36", } r = requests.get(url, headers=headers) data_df = pd.DataFrame( [ item.strip().split("=")[1].split(",") for item in r.text.split(";") if item.strip() != "" ] ) data_df.iloc[:, 0] = data_df.iloc[:, 0].str.replace('"', "") data_df.iloc[:, -1] = data_df.iloc[:, -1].str.replace('"', "") if adjust == "1": contract_name_list = [item.split("_")[1] for item in subscribe_list.split(",")] contract_min_list = [] contract_exchange_list = [] for contract_name in contract_name_list: temp_df = futures_contract_detail(symbol=contract_name) exchange_name = temp_df[temp_df["item"] == "上市交易所"]["value"].values[0] contract_exchange_list.append(exchange_name) contract_min = temp_df[temp_df["item"] == "最小变动价位"]["value"].values[0] contract_min_list.append(contract_min) if market == "CF": data_df.columns = [ "symbol", "time", "open", "high", "low", "last_close", "bid_price", "ask_price", "current_price", "avg_price", "last_settle_price", "buy_vol", "sell_vol", "hold", "volume", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] data_df = data_df[ [ "symbol", "time", "open", "high", "low", "current_price", "bid_price", "ask_price", "buy_vol", "sell_vol", "hold", "volume", "avg_price", "last_close", "last_settle_price", ] ] data_df["exchange"] = contract_exchange_list data_df["contract"] = contract_name_list data_df["contract_min_change"] = contract_min_list data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce") data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce") data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce") data_df["current_price"] = pd.to_numeric( data_df["current_price"], errors="coerce" ) data_df["bid_price"] = pd.to_numeric(data_df["bid_price"], errors="coerce") data_df["ask_price"] = pd.to_numeric(data_df["ask_price"], errors="coerce") data_df["buy_vol"] = pd.to_numeric(data_df["buy_vol"], errors="coerce") data_df["sell_vol"] = pd.to_numeric(data_df["sell_vol"], errors="coerce") data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce") data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce") data_df["avg_price"] = pd.to_numeric(data_df["avg_price"], errors="coerce") data_df["last_close"] = pd.to_numeric( data_df["last_close"], errors="coerce" ) data_df["last_settle_price"] = pd.to_numeric( data_df["last_settle_price"], errors="coerce" ) data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True) return data_df else: data_df.columns = [ "open", "high", "low", "current_price", "volume", "amount", "hold", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "__", "time", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "symbol", ] data_df = data_df[ [ "symbol", "time", "open", "high", "low", "current_price", "hold", "volume", "amount", ] ] data_df["exchange"] = contract_exchange_list data_df["contract"] = contract_name_list data_df["contract_min_change"] = contract_min_list data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce") data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce") data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce") data_df["current_price"] = pd.to_numeric( data_df["current_price"], errors="coerce" ) data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce") data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce") data_df["amount"] = pd.to_numeric(data_df["amount"], errors="coerce") data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True) return data_df else: if market == "CF": # 此处由于 20220601 接口变动，增加了字段，此处增加异常判断，except 后为新代码 try: data_df.columns = [ "symbol", "time", "open", "high", "low", "last_close", "bid_price", "ask_price", "current_price", "avg_price", "last_settle_price", "buy_vol", "sell_vol", "hold", "volume", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] except: # noqa: E722 data_df.columns = [ "symbol", "time", "open", "high", "low", "last_close", "bid_price", "ask_price", "current_price", "avg_price", "last_settle_price", "buy_vol", "sell_vol", "hold", "volume", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", ] data_df = data_df[ [ "symbol", "time", "open", "high", "low", "current_price", "bid_price", "ask_price", "buy_vol", "sell_vol", "hold", "volume", "avg_price", "last_close", "last_settle_price", ] ] data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce") data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce") data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce") data_df["current_price"] = pd.to_numeric( data_df["current_price"], errors="coerce" ) data_df["bid_price"] = pd.to_numeric(data_df["bid_price"], errors="coerce") data_df["ask_price"] = pd.to_numeric(data_df["ask_price"], errors="coerce") data_df["buy_vol"] = pd.to_numeric(data_df["buy_vol"], errors="coerce") data_df["sell_vol"] = pd.to_numeric(data_df["sell_vol"], errors="coerce") data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce") data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce") data_df["avg_price"] = pd.to_numeric(data_df["avg_price"], errors="coerce") data_df["last_close"] = pd.to_numeric( data_df["last_close"], errors="coerce" ) data_df["last_settle_price"] = pd.to_numeric( data_df["last_settle_price"], errors="coerce" ) data_df.dropna(subset=["current_price"], ignore_index=True, inplace=True) return data_df else: data_df.columns = [ "open", "high", "low", "current_price", "volume", "amount", "hold", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "__", "time", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "_", "symbol", ] data_df = data_df[ [ "symbol", "time", "open", "high", "low", "current_price", "hold", "volume", "amount", ] ] data_df["open"] = pd.to_numeric(data_df["open"], errors="coerce") data_df["high"] = pd.to_numeric(data_df["high"], errors="coerce") data_df["low"] = pd.to_numeric(data_df["low"], errors="coerce") data_df["current_price"] = pd.to_numeric( data_df["current_price"], errors="coerce" ) data_df["hold"] = pd.to_numeric(data_df["hold"], errors="coerce") data_df["volume"] = pd.to_numeric(data_df["volume"], errors="coerce") data_df["amount"] = pd.to_numeric(data_df["amount"], errors="coerce") data_df.dropna(subset=["current_price"], inplace=True, ignore_index=True) return data_df def futures_zh_minute_sina(symbol: str = "IF2008", period: str = "1") -> pd.DataFrame: """ 中国各品种期货分钟频率数据 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_3 :param symbol: 可以通过 match_main_contract(symbol="cffex") 获取, 或者访问网页获取 :type symbol: str :param period: choice of {"1": "1分钟", "5": "5分钟", "15": "15分钟", "30": "30分钟", "60": "60分钟"} :type period: str :return: 指定 symbol 和 period 的数据 :rtype: pandas.DataFrame """ url = "https://stock2.finance.sina.com.cn/futures/api/jsonp.php/=/InnerFuturesNewService.getFewMinLine" params = { "symbol": symbol, "type": period, } r = requests.get(url, params=params) temp_df = pd.DataFrame(json.loads(r.text.split("=(")[1].split(");")[0])) temp_df.columns = [ "datetime", "open", "high", "low", "close", "volume", "hold", ] temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce") temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce") temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce") temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce") temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce") temp_df["hold"] = pd.to_numeric(temp_df["hold"], errors="coerce") return temp_df def futures_zh_daily_sina(symbol: str = "RB0") -> pd.DataFrame: """ 中国各品种期货日频率数据 https://finance.sina.com.cn/futures/quotes/V2105.shtml :param symbol: 可以通过 match_main_contract(symbol="cffex") 获取, 或者访问网页获取 :type symbol: str :return: 指定 symbol 的数据 :rtype: pandas.DataFrame """ date = "20210412" url = ( "https://stock2.finance.sina.com.cn/futures/api/jsonp.php/var%20_V21052021_4_12=" "/InnerFuturesNewService.getDailyKLine" ) params = { "symbol": symbol, "type": "_".join([date[:4], date[4:6], date[6:]]), } r = requests.get(url, params=params) temp_df = pd.DataFrame(json.loads(r.text.split("=(")[1].split(");")[0])) temp_df.columns = [ "date", "open", "high", "low", "close", "volume", "hold", "settle", ] temp_df["open"] = pd.to_numeric(temp_df["open"], errors="coerce") temp_df["high"] = pd.to_numeric(temp_df["high"], errors="coerce") temp_df["low"] = pd.to_numeric(temp_df["low"], errors="coerce") temp_df["close"] = pd.to_numeric(temp_df["close"], errors="coerce") temp_df["volume"] = pd.to_numeric(temp_df["volume"], errors="coerce") temp_df["hold"] = pd.to_numeric(temp_df["hold"], errors="coerce") temp_df["settle"] = pd.to_numeric(temp_df["settle"], errors="coerce") return temp_df if __name__ == "__main__": match_main_contract_df = match_main_contract(symbol="gfex") print(match_main_contract_df) futures_zh_spot_df = futures_zh_spot(symbol="V2405,V2409", market="CF", adjust="0") print(futures_zh_spot_df) futures_zh_spot_df = futures_zh_spot(symbol="V2405", market="CF", adjust="0") print(futures_zh_spot_df) futures_symbol_mark_df = futures_symbol_mark() print(futures_symbol_mark_df) futures_zh_realtime_df = futures_zh_realtime(symbol="工业硅") print(futures_zh_realtime_df) futures_zh_minute_sina_df = futures_zh_minute_sina(symbol="RB0", period="1") print(futures_zh_minute_sina_df) futures_zh_daily_sina_df = futures_zh_daily_sina(symbol="RB0") print(futures_zh_daily_sina_df) futures_zh_daily_sina_df = futures_zh_daily_sina(symbol="RB2410") print(futures_zh_daily_sina_df) dce_text = match_main_contract(symbol="dce") czce_text = match_main_contract(symbol="czce") shfe_text = match_main_contract(symbol="shfe") gfex_text = match_main_contract(symbol="gfex") while True: time.sleep(3) futures_zh_spot_df = futures_zh_spot( symbol=",".join([dce_text, czce_text, shfe_text, gfex_text]), market="CF", adjust="0", ) print(futures_zh_spot_df)