我们从Python开源项目中，提取了以下23个代码示例，用于说明如何使用tushare.get_realtime_quotes()。
def loop(self,code): name=self.bases[self.bases['code']==code]['name'].values[0] print name while 1: time.sleep(2) df_t1=ts.get_realtime_quotes(code) v1=long(df_t1['volume'].values[0]) p1=float(df_t1['price'].values[0]) #print df_t1 time.sleep(2) df_t2=ts.get_realtime_quotes(code) v2=long(df_t2['volume'].values[0]) p2=float(df_t2['price'].values[0]) delta_v= (v2-v1)/100 #??? #???? price_v=p2-p1 if delta_v >1000: print datetime.datetime.now().strftime('%H:%M:%S') print "Big deal on %s" %name, print delta_v,'price diff',price_v
def break_ceil(self, code): print threading.current_thread().name while 1: #print code time.sleep(2) try: df = ts.get_realtime_quotes(code) except: time.sleep(5) continue v = long(df['b1_v'].values[0]) if v <= 1000: print datetime.datetime.now().strftime("%H:%M:%S") print u"?????????" print self.bases[self.bases['code']==code]['name'].values[0] if self.send==True: self.push_msg('break', 10, 10, 'down') #???????????????s
def break_ceil(code): while 1: time.sleep(2) try: df=ts.get_realtime_quotes(code) except: time.sleep(5) continue v=long(df['b1_v'].values[0]) print datetime.datetime.now().strftime("%H:%M:%S") print v #print type(v) if v<=10000 : print u"?????????" push_msg('break',10,10,'down') #???????????????
def GET_OPEN_DAY(self, context, security, ref=0): if ref==0: dfreal = ts.get_realtime_quotes(security) if dfreal.empty == True: return np.nan return float(dfreal['open'][0]) else: dataCount = ref + 1 df_data = ts.get_k_data(security, index=False, ktype='D').tail(dataCount) if df_data.empty == True: print "security:%s in context:%s NO GET_OPEN_DAY!" %(str(security),str(context)) return np.nan if len(df_data['open']) < ref: return np.nan return df_data['open'].values[-ref] # ???????????
def getStkListNowQuotes(self, stockList): ''' ???????? ''' atOneTime = 30 # ???????????? nowDict = {} while len(stockList) > atOneTime: thisTime = stockList[:atOneTime] stockList = stockList[atOneTime:] data = ts.get_realtime_quotes(thisTime) nowPrice = data['price'].values nowVolume = data['volume'].values for index,code in enumerate(thisTime): nowDict[code] = {'price':eval(nowPrice[index]), 'vol':eval(nowVolume[index])} # ????? if len(stockList)>0: data = ts.get_realtime_quotes(stockList) nowPrice = data['price'].values nowVolume = data['volume'].values for index,code in enumerate(stockList): nowDict[code] = {'price':eval(nowPrice[index]), 'vol':eval(nowVolume[index])} return nowDict
def stock(): time = datetime.datetime.now() # ?????? now = time.strftime('%H:%M:%S') data = ts.get_realtime_quotes(stock_symbol) # ?????? r1 = float(data['price']) r2 = str(stock_symbol) + ' ?????? ' + str(r1) content = now + '\n' + r2 itchat.send(content, toUserName='filehelper') print(content) # ????????????? if r1 <= float(price_low): itchat.send('????????', toUserName='filehelper') print('????????') elif r1 >= float(price_high): itchat.send('????????', toUserName='filehelper') print('????????') else: itchat.send('????', toUserName='filehelper') print('????') # ? 3 ?????
def stock(): time = datetime.datetime.now() # ?????? now = time.strftime('%H:%M:%S') data = ts.get_realtime_quotes(stock_symbol) # ?????? r1 = float(data['price']) r2 = str(stock_symbol) + ' ?????? ' + str(r1) content = now + '\n' + r2 itchat.send(content, toUserName='filehelper') print(content) # ????????????? if r1 <= float(price_low): itchat.send('????????', toUserName='filehelper') print('????????') elif r1 >= float(price_high): itchat.send('????????', toUserName='filehelper') print('????????') else: itchat.send('????', toUserName='filehelper') print('????') # ?????????
def get_realtime_quotes(code_list, open_only=False): import tushare as ts max_len = 800 loop_cnt = int(math.ceil(float(len(code_list)) / max_len)) total_df = reduce(lambda df1, df2: df1.append(df2), [ts.get_realtime_quotes([code for code in code_list[i::loop_cnt]]) for i in range(loop_cnt)]) total_df["is_index"] = False index_symbol = ["sh", "sz", "hs300", "sz50", "zxb", "cyb"] index_df = ts.get_realtime_quotes(index_symbol) index_df["code"] = index_symbol index_df["is_index"] = True total_df = total_df.append(index_df) total_df = total_df.set_index("code").sort_index() columns = set(total_df.columns) - set(["name", "time", "date"]) # columns = filter(lambda x: "_v" not in x, columns) for label in columns: total_df[label] = total_df[label].map(lambda x: 0 if str(x).strip() == "" else x) total_df[label] = total_df[label].astype(float) total_df["chg"] = total_df["price"] / total_df["pre_close"] - 1 total_df["order_book_id"] = total_df.index total_df["order_book_id"] = total_df["order_book_id"].apply(tushare_code_2_order_book_id) total_df["datetime"] = total_df["date"] + " " + total_df["time"] total_df["datetime"] = total_df["datetime"].apply(lambda x: convert_dt_to_int(datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S"))) total_df["close"] = total_df["price"] if open_only: total_df = total_df[total_df.open > 0] return total_df
def realtime(self, id): # all_stock=ts.get_today_all() # print all_stock df = ts.get_realtime_quotes(id) # print df[['time','name','price','bid','ask','volume']] # print df.head() # price_change = ts.get_today_ticks(id) # print price_change[['time','change','type','volume']] big_share = ts.get_sina_dd(id, date=self.date) print big_share[['time', 'code', 'price', 'preprice', 'volume', 'type']] print big_share.sort(columns='volume')
def realtime(self, id): # all_stock=ts.get_today_all() # print all_stock df = ts.get_realtime_quotes(id) # print df[['time','name','price','bid','ask','volume']] # print df.head() price_change = ts.get_today_ticks(id) print price_change[['time', 'change', 'type', 'volume']] big_share = ts.get_sina_dd(id, date='2016-04-01') print big_share[['time', 'code', 'price', 'preprice', 'volume', 'type']]
def meet_percent(code, percent_up, percent_down,type): try: df = ts.get_realtime_quotes(code) except Exception, e: print e time.sleep(5) return 0 real_price = df['price'].values[0] name = df['name'].values[0] real_price = float(real_price) pre_close = float(df['pre_close'].values[0]) real_percent = (real_price - pre_close) / pre_close * 100 # print percent # percent=df[''] # print type(real_price) if real_percent >= percent_up: print '%s percent higher than %.2f , %.2f' % (name, real_percent, real_price), if type=='msn': push_msg(name, real_price, real_price, 'up') return 1 elif type=='wechat': push_wechat(name, real_price, real_percent, 'down') return 1 if real_percent <= percent_down: print '%s percent lower than %.2f , %.2f' % (name, real_percent, real_price), print '%' if type=='mns': push_msg(name, real_price, real_percent, 'down') return 1 elif type=='wechat': push_wechat(name, real_price, real_percent, 'down') return 1 # ?????????
def GET_ALL_SECURITIES(self,filtPaused=True, filtSt=True, filtMarketcap=0): l_stocks = self.__df_allsecurities__.index.get_values().tolist() print "l_stocks %s in all", len(l_stocks) if filtPaused or filtSt or filtMarketcap > 0: filtresult = [] for s in l_stocks: isfilted = False try: dfreal = ts.get_realtime_quotes(s) except Exception,e: print Exception,":",e dfreal = None if not dfreal is None and filtPaused: if float(str(dfreal['volume'][0])) == 0 and float(str(dfreal['bid'][0])) == 0 and float(str(dfreal['ask'][0])) == 0: isfilted = True if not dfreal is None and filtSt: if str(dfreal['name'][0]).find('ST') > -1: isfilted = True if filtMarketcap > 0: outstanding = self.GET_SECURITY_INFO(s)['outstanding'] price = self.GET_CLOSE_DAY(None, s) markCap = outstanding * price if markCap > filtMarketcap: print "security %s isfilted,markCap:%s" % (str(s),str(markCap)) isfilted = True if isfilted: print "security %s isfilted" % str(s) pass else: filtresult.append(s) l_stocks = filtresult print "l_stocks %s after filter", len(l_stocks) return l_stocks # ?????? #security
def GET_AMOUNT_DATA_DAY(self, context, security,isLastest=True,data={},dataCount=20): #TODO get_k_data has no amount data, get_h_data no support index try: df_data = ts.get_h_data(security, index=False) except Exception,e: print e df_data = None if df_data is None or df_data.empty == True: print "security:%s in context:%s NO GET_AMOUNT_DATA_DAY!" %(str(security),str(context)) return np.array([np.nan]) df_data = df_data.iloc[::-1].tail(dataCount) amountData = df_data['amount'].values try: dfreal = ts.get_realtime_quotes(security) except Exception,e: print Exception,":",e dfreal = None if not dfreal is None: data = dfreal.date[0] time = dfreal.time[0] dateStr = data + ' ' + time current_dt = self.__getdatetime__(dateStr) runtime = SecurityDataSrcBase.GET_RUN_MINUTES(TsContext(current_dt)) if runtime < 240: amountLast = float(dfreal.amount[0]) amountData = np.append(amountData, amountLast) return amountData # overide
def getStkSingleNowPrice(self, code, flag): # -> float flag: 'ask' ???;'bid' ???;'price' ?? ''' ????????????/??? ''' price = ts.get_realtime_quotes(code).T[0][flag] return round(float(price),2) # ??????
def get_today_open_price(code): ''' :param code:???? :return:????? ''' import tushare as ts return ts.get_realtime_quotes(code)['open']
def get_today_close_price(code): ''' :param code:???? :return: ????? ''' import tushare as ts return ts.get_realtime_quotes(code)['close']
def repo(self): security = '131810' quote_df = ts.get_realtime_quotes(security) order = { 'action': 'SELL', 'symbol': security, 'type': 'LIMIT', 'price': float(quote_df['bid'][0]), 'amountProportion': 'ALL' } for trader in self._traders.values(): try: trader.execute(**order) except: self._logger.exception('[%s] ?????', trader.id)
def __call__(self): df = ts.get_realtime_quotes(self._symbol) order = { 'action': 'SELL', 'symbol': self._symbol, 'type': 'LIMIT', 'price': float(df['bid'][0]), 'amountProportion': 'ALL' } for client_alias in self._client_aliases: try: client = self._client_aliases[client_alias] self._client.execute(client, **order) except: self._logger.exception('???[%s]?????', client_alias)
def get_stock_quotes(): # Get real time quotes of the stock list global sched_time while True: now = dt.datetime.now().time() #now = dt.time(9, 25, 1) if is_market_open(now): try: raw_data = ts.get_realtime_quotes(stock_list) raw_data = process_raw_data(raw_data) #print(raw_data) for index, row in raw_data.iterrows(): data_list[index] = pd.concat([data_list[index], raw_data[index:index + 1]]) #print(data_list[index]) print('Get real time quotes of stock list done!') time.sleep(seconds_per_tick_data) except KeyboardInterrupt: print('Interrupt occur!') break except Exception: print('Error in reading!') continue else: if now > market_close_time3: break else: sleep_time = (sched_time.hour - now.hour) * 60 * 60 + (sched_time.minute - now.minute) * 60 + (sched_time.second - now.second) print(sleep_time) time.sleep(sleep_time) save_to_csv(data_list)
def get_realtime_quotes(code_list, open_only=False): import tushare as ts max_len = 800 loop_cnt = int(math.ceil(float(len(code_list)) / max_len)) total_df = reduce(lambda df1, df2: df1.append(df2), [ts.get_realtime_quotes([code for code in code_list[i::loop_cnt]]) for i in range(loop_cnt)]) total_df["is_index"] = False index_symbol = ["sh", "sz", "hs300", "sz50", "zxb", "cyb"] index_df = ts.get_realtime_quotes(index_symbol) index_df["code"] = index_symbol index_df["is_index"] = True total_df = total_df.append(index_df) columns = set(total_df.columns) - set(["name", "time", "date", "code"]) # columns = filter(lambda x: "_v" not in x, columns) for label in columns: total_df[label] = total_df[label].map(lambda x: 0 if str(x).strip() == "" else x) total_df[label] = total_df[label].astype(float) total_df["chg"] = total_df["price"] / total_df["pre_close"] - 1 total_df["order_book_id"] = total_df["code"] total_df["order_book_id"] = total_df["order_book_id"].apply(tushare_code_2_order_book_id) total_df = total_df.set_index("order_book_id").sort_index() total_df["datetime"] = total_df["date"] + " " + total_df["time"] total_df["datetime"] = total_df["datetime"].apply(lambda x: convert_dt_to_int(datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S"))) total_df["close"] = total_df["price"] total_df["last"] = total_df["price"] total_df["limit_up"] = total_df.apply(lambda row: row.pre_close * (1.1 if "ST" not in row["name"] else 1.05), axis=1).round(2) total_df["limit_down"] = total_df.apply(lambda row: row.pre_close * (0.9 if "ST" not in row["name"] else 0.95), axis=1).round(2) if open_only: total_df = total_df[total_df.open > 0] return total_df
def get_realtime_quotes(order_book_id_list, open_only=False, include_limit=False): import tushare as ts code_list = [order_book_id_2_tushare_code(code) for code in order_book_id_list] max_len = 800 loop_cnt = int(math.ceil(float(len(code_list)) / max_len)) total_df = reduce(lambda df1, df2: df1.append(df2), [ts.get_realtime_quotes([code for code in code_list[i::loop_cnt]]) for i in range(loop_cnt)]) total_df["is_index"] = False index_symbol = ["sh", "sz", "hs300", "sz50", "zxb", "cyb"] index_df = ts.get_realtime_quotes(index_symbol) index_df["code"] = index_symbol index_df["is_index"] = True total_df = total_df.append(index_df) columns = set(total_df.columns) - set(["name", "time", "date", "code"]) # columns = filter(lambda x: "_v" not in x, columns) for label in columns: total_df[label] = total_df[label].map(lambda x: 0 if str(x).strip() == "" else x) total_df[label] = total_df[label].astype(float) total_df["chg"] = total_df["price"] / total_df["pre_close"] - 1 total_df["order_book_id"] = total_df["code"] total_df["order_book_id"] = total_df["order_book_id"].apply(tushare_code_2_order_book_id) total_df = total_df.set_index("order_book_id").sort_index() total_df["order_book_id"] = total_df.index total_df["datetime"] = total_df["date"] + " " + total_df["time"] # total_df["datetime"] = total_df["datetime"].apply( # lambda x: convert_dt_to_int(datetime.datetime.strptime(x, "%Y-%m-%d %H:%M:%S"))) total_df["close"] = total_df["price"] total_df["last"] = total_df["price"] total_df = total_df.rename(columns={ "{}{}_p".format(base_name, i): "{}{}".format(base_name, i) for i in range(1, 6) for base_name in ["a", "b"] }) total_df = total_df.rename(columns={"pre_close": "prev_close"}) del total_df["code"] del total_df["is_index"] del total_df["date"] del total_df["time"] if include_limit: total_df["limit_up"] = total_df.apply( lambda row: row.prev_close * (1.1 if "ST" not in row["name"] else 1.05), axis=1).round(2) total_df["limit_down"] = total_df.apply( lambda row: row.prev_close * (0.9 if "ST" not in row["name"] else 0.95), axis=1).round(2) if open_only: total_df = total_df[total_df.open > 0] return total_df
