class tdxApi: def __init__(self): self.ipset = [(v[1], v[2]) for v in hq_hosts] random.shuffle(self.ipset) self.ippool = AvailableIPPool(TdxHq_API, self.ipset) self.primary_ip, _ = self.ippool.sync_get_top_n(2) self.api = TdxHq_API(multithread=True, heartbeat=True, auto_retry=True) def connect(self): self.api.connect(self.primary_ip[0], self.primary_ip[1]) def release(self): self.api.disconnect() def getQuotes(self, stock_list): datas = self.api.get_security_quotes(stock_list) if datas is None: return None else: return self.api.to_df(datas) def getList(self, market, index): df = self.api.to_df(self.api.get_security_list(market, index)) return df def getCount(self, market=0): return self.api.get_security_count(market) def getMinQuotes(self, market, stock_code): datas = self.api.get_minute_time_data(market, stock_code) if datas is None: return None return self.api.to_df(datas) def getMinQuotesHis(self, market, stock_code, date): datas = self.api.get_history_minute_time_data(market, stock_code, date) if datas is None: return None return self.api.to_df(datas)
class TBStockData: __serverList = [] _bestIP = [] __bestIPFile = '' __tdx = None _lastBaseHistList = pd.DataFrame() _xdxrData = None def __init__(self, autoIP = False): self.__serverList = hq_hosts self.__bestIPFile = os.path.dirname(os.path.realpath(__file__)) + '/best.ip' if autoIP: self.getBestIP() else: if os.path.exists(self.__bestIPFile): with open(self.__bestIPFile, 'r') as f: data = f.read() self._bestIP = json.loads(data) def ping(self, ip, port): api = TdxHq_API() time1 = datetime.datetime.now() try: with api.connect(ip, int(port)): if len(api.get_security_list(0, 1)) > 800: return datetime.datetime.now() - time1 else: return datetime.timedelta(9, 9, 0) except: return datetime.timedelta(9, 9, 0) def getBestIP(self): pingTimeList = [self.ping(x[1], x[2]) for x in self.__serverList] self._bestIP = self.__serverList[pingTimeList.index(min(pingTimeList))] with open(self.__bestIPFile, 'w') as f: f.write(json.dumps(self._bestIP)) def showAllIP(self): for item in self.__serverList: print item[0],'\t', item[1], '\t', item[2] def _connect(self): if self.__tdx is None: if not self._bestIP: self.getBestIP() #self.__tdx = TdxHq_API(heartbeat=True, auto_retry=True) self.__tdx = TdxHq_API(auto_retry=True) self.__tdx.connect(self._bestIP[1], int(self._bestIP[2])) #计算量比 def _setVolRaito(self, row): date = row.name histList = self._lastBaseHistList[:date] if len(histList) < 6: return np.nan return round((histList['vol'].values[-1] / 240) / (histList[-6:-1]['vol'].sum() / 1200), 3) #计算各种指标 def getData(self, df = pd.DataFrame(), indexs=['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']): indexs = [x.lower() for x in indexs] histList = pd.DataFrame() if not df.empty: histList = df.copy() elif not self._lastBaseHistList.empty: histList = self._lastBaseHistList.copy() if histList.empty: return None dayKStatus = False try: if int(time.mktime(time.strptime(str(histList.index[-1]), "%Y-%m-%d %X"))) - int(time.mktime(time.strptime(str(histList.index[-2]), "%Y-%m-%d %X"))) > 43200: #日线以上行情 dayKStatus = True except: dayKStatus = True #计算涨幅 histList['p_change'] = histList['close'].pct_change().round(5) * 100 #量比 histList['vol_ratio'] = histList.apply(self._setVolRaito, axis=1) #振幅 histList['amp'] = ((histList['high'] - histList['low']) / histList.shift()['close'] * 100).round(3) #计算换手率 if self._xdxrData is None: xdxrData = self.getXdxr(str(histList['code'].values[0])) else: xdxrData = self._xdxrData info = xdxrData[xdxrData['liquidity_after'] > 0][['liquidity_after', 'shares_after']] if dayKStatus: startDate = str(histList.index[0])[0:10] endDate = str(histList.index[-1])[0:10] info1 = info[info.index <= startDate][-1:] info = info1.append(info[info.index >= startDate]).drop_duplicates() info = info.reindex(pd.date_range(info1.index[-1], endDate)) info = info.resample('1D').last().fillna(method='pad')[startDate:endDate] #info['date'] = info.index #info['date'] = info['date'].dt.strftime('%Y-%m-%d') #info = info.set_index('date') circulate = info['liquidity_after'] * 10000 capital = info['shares_after'] * 10000 else: circulate = info['liquidity_after'].values[-1] * 10000 capital = info['shares_after'].values[-1] * 10000 #histList['circulate'] = (circulate / 10000 / 10000).round(4) if 'turnover' in indexs and dayKStatus: histList['turnover'] = (histList['vol'] * 100 / circulate).round(5) * 100 histList['turnover5'] = talib.MA(histList['turnover'].values, timeperiod=5).round(3) #stockstats转换,主要是用来计算KDJ等相关指标 #用talib计算KDJ时会与现有软件偏差大 ss = StockDataFrame.retype(histList[['high','low','open','close']]) #MACD计算 if 'macd' in indexs: difList, deaList, macdList = talib.MACD(histList['close'].values, fastperiod=12, slowperiod=26, signalperiod=9) macdList = macdList * 2 histList['macd_dif'] = difList.round(3) histList['macd_dea'] = deaList.round(3) histList['macd_value'] = macdList.round(3) histList['macd_value_ma'] = 0 try: histList['macd_value_ma'] = talib.MA(histList['macd_value'].values, timeperiod=5).round(3) except: pass histList['macd_cross_status'] = 0 macdPosList = histList['macd_dif'] > histList['macd_dea'] histList.loc[macdPosList[(macdPosList == True) & (macdPosList.shift() == False)].index, 'macd_cross_status'] = 1 histList.loc[macdPosList[(macdPosList == False) & (macdPosList.shift() == True)].index, 'macd_cross_status'] = -1 #histList[['macd_cross_status']] = histList[['macd_cross_status']].fillna(method='pad') #KDJ计算 if 'kdj' in indexs: histList['kdj_k'] = ss['kdjk'].round(3) histList['kdj_d'] = ss['kdjd'].round(3) histList['kdj_j'] = ss['kdjj'].round(3) histList['kdj_cross_status'] = 0 kdjPosList = histList['kdj_k'] >= histList['kdj_d'] histList.loc[kdjPosList[(kdjPosList == True) & (kdjPosList.shift() == False)].index, 'kdj_cross_status'] = 1 histList.loc[kdjPosList[(kdjPosList == False) & (kdjPosList.shift() == True)].index, 'kdj_cross_status'] = -1 #histList[['kdj_cross_status']] = histList[['kdj_cross_status']].fillna(method='pad') #CCI计算 if 'cci' in indexs: histList['cci'] = ss['cci'].round(3) #ma相关计算 if 'ma' in indexs: histList['ma5'] = talib.MA(histList['close'].values, timeperiod=5).round(3) histList['ma10'] = talib.MA(histList['close'].values, timeperiod=10).round(3) histList['ma20'] = talib.MA(histList['close'].values, timeperiod=20).round(3) histList['ma30'] = talib.MA(histList['close'].values, timeperiod=30).round(3) histList['ma60'] = talib.MA(histList['close'].values, timeperiod=60).round(3) histList['ma240'] = talib.MA(histList['close'].values, timeperiod=240).round(3) histList[['ma5', 'ma10', 'ma20', 'ma30', 'ma60', 'ma240']] = histList[['ma5', 'ma10', 'ma20', 'ma30', 'ma60', 'ma240']].fillna(0) #成交量计算 if 'vol' in indexs: histList['vol5'] = talib.MA(histList['vol'].values, timeperiod=5).round(3) histList['vol10'] = talib.MA(histList['vol'].values, timeperiod=10).round(3) histList['vol20'] = talib.MA(histList['vol'].values, timeperiod=20).round(3) histList['vol_zoom'] = (histList['vol'] / histList['vol5'] * 1.0).round(3) histList['vol5_vol10_cross_status'] = 0 volumePosList = histList['vol5'] >= histList['vol10'] histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol5_vol10_cross_status'] = 1 histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol5_vol10_cross_status'] = -1 del volumePosList histList['vol5_vol20_cross_status'] = 0 volumePosList = histList['vol5'] >= histList['vol20'] histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol5_vol20_cross_status'] = 1 histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol5_vol20_cross_status'] = -1 del volumePosList histList['vol10_vol20_cross_status'] = 0 volumePosList = histList['vol10'] >= histList['vol20'] histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'vol10_vol20_cross_status'] = 1 histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'vol10_vol20_cross_status'] = -1 #histList[['vol5_vol10_cross_status', 'vol5_vol20_cross_status', 'vol10_vol20_cross_status']] = histList[['vol5_vol10_cross_status', 'vol5_vol20_cross_status', 'vol10_vol20_cross_status']].fillna(method='pad') #bbi计算 if 'bbi' in indexs: ma3 = talib.MA(histList['close'].values, timeperiod=3) ma6 = talib.MA(histList['close'].values, timeperiod=6) ma12 = talib.MA(histList['close'].values, timeperiod=12) ma24 = talib.MA(histList['close'].values, timeperiod=24) histList['bbi'] = (ma3 + ma6 + ma12 + ma24) / 4 histList['bbi'] = histList['bbi'].round(3) #SAR计算 if 'sar' in indexs: sarList = talib.SAR(histList['high'].values, histList['low'].values, acceleration=0.04, maximum=0.2) histList['sar'] = sarList.round(3) histList['sar_cross_status'] = 0 sarPosList = histList['close'] >= histList['sar'] histList.loc[sarPosList[(sarPosList == True) & (sarPosList.shift() == False)].index, 'sar_cross_status'] = 1 histList.loc[sarPosList[(sarPosList == False) & (sarPosList.shift() == True)].index, 'sar_cross_status'] = -1 #计算TRIX if 'trix' in indexs: histList['trix'] = np.nan histList['trma'] = np.nan histList['trix_diff'] = np.nan try: trix = talib.TRIX(histList['close'].values, 12) trma = talib.MA(trix, timeperiod=20) histList['trix'] = trix.round(3) histList['trma'] = trma.round(3) histList['trix_diff'] = histList['trix'] - histList['trma'] histList['trix_cross_status'] = 0 trixPosList = histList['trix'] >= histList['trma'] histList.loc[trixPosList[(trixPosList == True) & (trixPosList.shift() == False)].index, 'trix_cross_status'] = 1 histList.loc[trixPosList[(trixPosList == False) & (trixPosList.shift() == True)].index, 'trix_cross_status'] = -1 #histList[['trix_cross_status']] = histList[['trix_cross_status']].fillna(method='pad') except: pass if 'cyc' in indexs: avePrice = histList['amount'] / (histList['vol'] * 100) histList['cyc5'] = talib.MA(avePrice.values, timeperiod=5).round(3) histList['cyc13'] = talib.MA(avePrice.values, timeperiod=13).round(3) histList['cyc34'] = talib.MA(avePrice.values, timeperiod=34).round(3) #histList['cycx'] = talib.EMA(histList['close'].values, timeperiod=histList['vol'].values * 100 / circulate).round(3) histList['cyc5_cyc13_cross_status'] = 0 volumePosList = histList['cyc5'] >= histList['cyc13'] histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'cyc5_cyc13_cross_status'] = 1 histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'cyc5_cyc13_cross_status'] = -1 del volumePosList histList['cyc13_cyc34_cross_status'] = 0 volumePosList = histList['cyc13'] >= histList['cyc34'] histList.loc[volumePosList[(volumePosList == True) & (volumePosList.shift() == False)].index, 'cyc13_cyc34_cross_status'] = 1 histList.loc[volumePosList[(volumePosList == False) & (volumePosList.shift() == True)].index, 'cyc13_cyc34_cross_status'] = -1 del volumePosList if 'boll' in indexs: up, mid, low = talib.BBANDS( histList['close'].values, timeperiod=20, # number of non-biased standard deviations from the mean nbdevup=2, nbdevdn=2, # Moving average type: simple moving average here matype=0) histList['boll_up'] = up.round(3) histList['boll_mid'] = mid.round(3) histList['boll_low'] = low.round(3) return histList #整理开始,结束时间,并计算相差天数 def _getDate(self, start, end): if not end: end = time.strftime('%Y-%m-%d',time.localtime()) if not start: t = int(time.mktime(time.strptime(str(end), '%Y-%m-%d'))) - 86400 * 800 start = str(time.strftime('%Y-%m-%d',time.localtime(t))) startTimestamp = int(time.mktime(time.strptime(str(start), '%Y-%m-%d'))) endTimestamp = int(time.mktime(time.strptime(str(end), '%Y-%m-%d'))) diffDayNum = int((time.time() - startTimestamp) / 86400) if diffDayNum <= 0: diffDayNum = 1 return start, end, diffDayNum #得到市场代码 def getMarketCode(self, code): code = str(code) if code[0] in ['5', '6', '9'] or code[:3] in ["009", "126", "110", "201", "202", "203", "204"]: return 1 return 0 #时间整理 def _dateStamp(self, date): datestr = str(date)[0:10] date = time.mktime(time.strptime(datestr, '%Y-%m-%d')) return date #整理时间 def _timeStamp(self, _time): if len(str(_time)) == 10: # yyyy-mm-dd格式 return time.mktime(time.strptime(_time, '%Y-%m-%d')) elif len(str(_time)) == 16: # yyyy-mm-dd hh:mm格式 return time.mktime(time.strptime(_time, '%Y-%m-%d %H:%M')) else: timestr = str(_time)[0:19] return time.mktime(time.strptime(timestr, '%Y-%m-%d %H:%M:%S')) #得到除权信息 def getXdxr(self, code): self._connect() category = { '1': '除权除息', '2': '送配股上市', '3': '非流通股上市', '4': '未知股本变动', '5': '股本变化', '6': '增发新股', '7': '股份回购', '8': '增发新股上市', '9': '转配股上市', '10': '可转债上市', '11': '扩缩股', '12': '非流通股缩股', '13': '送认购权证', '14': '送认沽权证'} data = self.__tdx.to_df(self.__tdx.get_xdxr_info(self.getMarketCode(code), code)) if len(data) >= 1: data = data\ .assign(date=pd.to_datetime(data[['year', 'month', 'day']], format='%Y-%m-%d'))\ .drop(['year', 'month', 'day'], axis=1)\ .assign(category_meaning=data['category'].apply(lambda x: category[str(x)]))\ .assign(code=str(code))\ .rename(index=str, columns={'panhouliutong': 'liquidity_after', 'panqianliutong': 'liquidity_before', 'houzongguben': 'shares_after', 'qianzongguben': 'shares_before'})\ .set_index('date', drop=False, inplace=False) xdxrData = data.assign(date=data['date'].apply(lambda x: str(x)[0:10])) #xdxrData = xdxrData.set_index('date') self._xdxrData = xdxrData return xdxrData else: return None #得到股本 def getGuben(self, code): self._connect() if self._xdxrData is None: xdxrData = self.getXdxr(code) else: xdxrData = self._xdxrData info = xdxrData[xdxrData['liquidity_after'] > 0][['liquidity_after', 'shares_after']] circulate = info['liquidity_after'].values[-1] * 10000 capital = info['shares_after'].values[-1] * 10000 return capital,circulate #按天得到标准数据 ''' ktype = D(天)/W(周)/M(月)/Q(季)/Y(年) autype = bfq(不复权)/hfq(后复权)/qfq(前复权) ''' def getDays(self, code, ktype = 'D', start = '', end = '', autype = 'qfq', indexs = ['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']): startDate, endDate, diffDayNum = self._getDate(start, end) self._connect() ktypeCode = 9 if ktype.lower() == 'd': ktypeCode = 9 elif ktype.lower() == 'w': ktypeCode = 5 elif ktype.lower() == 'm': ktypeCode = 6 elif ktype.lower() == 'q': ktypeCode = 10 elif ktype.lower() == 'y': ktypeCode = 11 histList = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(ktypeCode, self.getMarketCode(code), code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) if histList.empty: return None histList = histList[histList['open'] != 0] histList = histList[histList['vol'] > 1] if not autype or autype == 'bfq': histList = histList.assign(date=histList['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10]))) histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp'], axis=1) histList = histList.set_index('date') histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 if indexs: return self.getData(indexs=indexs) else: return histList elif autype == 'qfq': bfqData = histList.assign(date=pd.to_datetime(histList['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\ .assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10]))) bfqData = bfqData.set_index('date') bfqData = bfqData.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) xdxrData = self.getXdxr(code) if xdxrData is not None: info = xdxrData[xdxrData['category'] == 1] bfqData['if_trade'] = True data = pd.concat([bfqData, info[['category']] [bfqData.index[0]:]], axis=1) #data['date'] = data.index data['if_trade'].fillna(value=False, inplace=True) data = data.fillna(method='ffill') data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu']][bfqData.index[0]:]], axis=1) data = data.fillna(0) data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu'] * data['peigujia']) / (10 + data['peigu'] + data['songzhuangu']) data['adj'] = (data['preclose'].shift(-1) / data['close']).fillna(1)[::-1].cumprod() data['open'] = data['open'] * data['adj'] data['high'] = data['high'] * data['adj'] data['low'] = data['low'] * data['adj'] data['close'] = data['close'] * data['adj'] data['preclose'] = data['preclose'] * data['adj'] data = data[data['if_trade']] histList = data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category', 'preclose', 'date_stamp', 'adj'], axis=1) histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 if indexs: return self.getData(indexs=indexs) else: return histList else: bfqData['preclose'] = bfqData['close'].shift(1) bfqData['adj'] = 1 histList = bfqData.drop(['preclose', 'date_stamp', 'adj'], axis=1) histList = histList[startDate:endDate] self._lastBaseHistList = histList if indexs: return self.getData(indexs=indexs) else: return histList elif autype == 'hfq': xdxrData = self.getXdxr(code) info = xdxrData[xdxrData['category'] == 1] bfqData = histList.assign(date=histList['datetime'].apply(lambda x: x[0:10])).assign(code=str(code))\ .assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10]))) bfqData = bfqData.set_index('date') bfqData = bfqData.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfqData['if_trade'] = True data = pd.concat([bfqData, info[['category']] [bfqData.index[0]:]], axis=1) data['if_trade'].fillna(value=False, inplace=True) data = data.fillna(method='ffill') data = pd.concat([data, info[['fenhong', 'peigu', 'peigujia', 'songzhuangu']][bfqData.index[0]:]], axis=1) data = data.fillna(0) data['preclose'] = (data['close'].shift(1) * 10 - data['fenhong'] + data['peigu'] * data['peigujia']) / (10 + data['peigu'] + data['songzhuangu']) data['adj'] = (data['preclose'].shift(-1) / data['close']).fillna(1).cumprod() data['open'] = data['open'] / data['adj'] data['high'] = data['high'] / data['adj'] data['low'] = data['low'] / data['adj'] data['close'] = data['close'] / data['adj'] data['preclose'] = data['preclose'] / data['adj'] data = data[data['if_trade']] histList = data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category', 'preclose', 'date_stamp', 'adj'], axis=1) histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 if indexs: return self.getData(indexs=indexs) else: return histList #按分钟得到标准数据 ''' ktype = 1/5/15/30/60 分钟 ''' def getMins(self, code, ktype = 1, start = '', end = '', indexs=['vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']): startDate, endDate, diffDayNum = self._getDate(start, end) self._connect() ktypeCode = 8 if int(ktype) == 1: ktypeCode = 8 diffDayNum = 240 * diffDayNum elif int(ktype) == 5: ktypeCode = 0 diffDayNum = 48 * diffDayNum elif int(ktype) == 15: ktypeCode = 1 diffDayNum = 16 * diffDayNum elif int(ktype) == 30: ktypeCode = 2 diffDayNum = 8 * diffDayNum elif int(ktype) == 60: ktypeCode = 3 diffDayNum = 4 * diffDayNum if diffDayNum > 20800: diffDayNum = 20800 histList = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars(ktypeCode, self.getMarketCode( str(code)), str(code), (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) if histList.empty: return None histList = histList\ .assign(datetime=pd.to_datetime(histList['datetime']), code=str(code))\ .assign(date=histList['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=histList['datetime'].apply(lambda x: self._dateStamp(x)))\ .assign(time_stamp=histList['datetime'].apply(lambda x: self._timeStamp(x))) histList['date'] = histList['datetime'] histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'time_stamp'], axis=1) histList = histList.set_index('date') histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 histList['vol'] = histList['vol'] / 100.0 if indexs: return self.getData(indexs=indexs) else: return histList #按天得到指数日k线 ''' ktype = D(天)/W(周)/M(月)/Q(季)/Y(年) ''' def getIndexDays(self, code, ktype = 'D', start = '', end = '', indexs=['turnover', 'vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']): startDate, endDate, diffDayNum = self._getDate(start, end) self._connect() ktypeCode = 9 if ktype.lower() == 'd': ktypeCode = 9 elif ktype.lower() == 'w': ktypeCode = 5 elif ktype.lower() == 'm': ktypeCode = 6 elif ktype.lower() == 'q': ktypeCode = 10 elif ktype.lower() == 'y': ktypeCode = 11 if str(code)[0] in ['5', '1']: # ETF data = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars( ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) else: data = pd.concat([self.__tdx.to_df(self.__tdx.get_index_bars( ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) histList = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: self._dateStamp(str(x)[0:10])))\ .assign(code=code) if histList.empty: return None histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'up_count', 'down_count'], axis=1) histList = histList.set_index('date') histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 if indexs: return self.getData(indexs=indexs) else: return histList #按分钟得到标准数据 ''' ktype = 1/5/15/30/60 分钟 ''' def getIndexMins(self, code, ktype = 1, start = '', end = '', indexs=['vol', 'ma', 'macd', 'kdj', 'cci', 'bbi', 'sar', 'trix']): startDate, endDate, diffDayNum = self._getDate(start, end) self._connect() ktypeCode = 8 if int(ktype) == 1: ktypeCode = 8 diffDayNum = 240 * diffDayNum elif int(ktype) == 5: ktypeCode = 0 diffDayNum = 48 * diffDayNum elif int(ktype) == 15: ktypeCode = 1 diffDayNum = 16 * diffDayNum elif int(ktype) == 30: ktypeCode = 2 diffDayNum = 8 * diffDayNum elif int(ktype) == 60: ktypeCode = 3 diffDayNum = 4 * diffDayNum if diffDayNum > 20800: diffDayNum = 20800 if str(code)[0] in ['5', '1']: # ETF data = pd.concat([self.__tdx.to_df(self.__tdx.get_security_bars( ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) else: data = pd.concat([self.__tdx.to_df(self.__tdx.get_index_bars( ktypeCode, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(diffDayNum / 800) - i) * 800, 800)) for i in range(int(diffDayNum / 800) + 1)], axis=0) histList = data.assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\ .assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=data['datetime'].apply(lambda x: self._dateStamp(x)))\ .assign(time_stamp=data['datetime'].apply(lambda x: self._timeStamp(x))) if histList.empty: return None histList['date'] = histList['datetime'] histList = histList.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime', 'date_stamp', 'time_stamp', 'up_count', 'down_count'], axis=1) histList = histList.set_index('date') histList = histList[startDate:endDate] self._lastBaseHistList = histList histList['p_change'] = histList['close'].pct_change().round(5) * 100 if indexs: return self.getData(indexs=indexs) else: return histList #实时逐笔 ''' 0买 1卖 2中性 ''' def getRealtimeTransaction(self, code): self._connect() try: data = pd.concat([self.__tdx.to_df(self.__tdx.get_transaction_data( self.getMarketCode(str(code)), code, (2 - i) * 2000, 2000)) for i in range(3)], axis=0) if 'value' in data.columns: data = data.drop(['value'], axis=1) data = data.dropna() day = datetime.date.today() histList = data.assign(date=str(day)).assign(datetime=pd.to_datetime(data['time'].apply(lambda x: str(day) + ' ' + str(x))))\ .assign(code=str(code)).assign(order=range(len(data.index))) histList['money'] = histList['price'] * histList['vol'] * 100 histList['type'] = histList['buyorsell'] histList['type'].replace([0,1,2], ['B','S','N'], inplace = True) histList = histList.drop(['order', 'buyorsell'], axis=1).reset_index() return histList except: return None #历史逐笔 ''' 0买 1卖 2中性 ''' def getHistoryTransaction(self, code, date): self._connect() try: data = pd.concat([self.__tdx.to_df(self.__tdx.get_history_transaction_data( self.getMarketCode(str(code)), code, (2 - i) * 2000, 2000, int(str(date).replace('-', '')))) for i in range(3)], axis=0) if 'value' in data.columns: data = data.drop(['value'], axis=1) data = data.dropna() #day = datetime.date.today() day = date histList = data.assign(date=str(day)).assign(datetime=pd.to_datetime(data['time'].apply(lambda x: str(day) + ' ' + str(x))))\ .assign(code=str(code)).assign(order=range(len(data.index))) histList['money'] = histList['price'] * histList['vol'] * 100 histList['type'] = histList['buyorsell'] histList['type'].replace([0,1,2], ['B','S','N'], inplace = True) histList = histList.drop(['order', 'buyorsell'], axis=1).reset_index() return histList except: return None #实时分时数据 def getRealtimeMinuteTime(self, code): self._connect() date = str(time.strftime('%Y-%m-%d',time.localtime())) morningData = pd.date_range(start=str(date) + ' 09:31', end=str(date) + ' 11:30', freq = 'min') morningDF = pd.DataFrame(index=morningData) afternoonData = pd.date_range(start=str(date) + ' 13:01',end=str(date) + ' 15:00', freq = 'min') afternoonDF = pd.DataFrame(index=afternoonData) timeData = morningDF.append(afternoonDF) histList = self.__tdx.to_df(self.__tdx.get_minute_time_data( self.getMarketCode(str(code)), code)) #非标准均价计算 money = histList['price'] * histList['vol'] * 100 histList['money'] = money.round(2) totalMoney = money.cumsum() totalVol = histList['vol'].cumsum() histList['ave'] = totalMoney / (totalVol * 100) histList['ave'] = histList['ave'].round(3) histList['datetime'] = timeData.index[0:len(histList)] histList['date'] = histList['datetime'].apply(lambda x: x.strftime('%Y-%m-%d')) histList['time'] = histList['datetime'].apply(lambda x: x.strftime('%H:%M')) histList = histList.reset_index() return histList #历史分时数据 def getHistoryMinuteTime(self, code, date): self._connect() morningData = pd.date_range(start=str(date) + ' 09:31', end=str(date) + ' 11:30', freq = 'min') morningDF = pd.DataFrame(index=morningData) afternoonData = pd.date_range(start=str(date) + ' 13:01',end=str(date) + ' 15:00', freq = 'min') afternoonDF = pd.DataFrame(index=afternoonData) timeData = morningDF.append(afternoonDF) histList = self.__tdx.to_df(self.__tdx.get_history_minute_time_data( self.getMarketCode(str(code)), code, int(str(date).replace('-', '')))) #非标准均价计算 money = histList['price'] * histList['vol'] * 100 histList['money'] = money.round(2) totalMoney = money.cumsum() totalVol = histList['vol'].cumsum() histList['ave'] = totalMoney / (totalVol * 100) histList['ave'] = histList['ave'].round(3) histList['datetime'] = timeData.index[0:len(histList)] histList['date'] = histList['datetime'].apply(lambda x: x.strftime('%Y-%m-%d')) histList['time'] = histList['datetime'].apply(lambda x: x.strftime('%H:%M')) histList = histList.reset_index() return histList #实时报价(五档行情) ''' market => 市场 active1 => 活跃度 price => 现价 last_close => 昨收 open => 开盘 high => 最高 low => 最低 reversed_bytes0 => 保留 reversed_bytes1 => 保留 vol => 总量 cur_vol => 现量 amount => 总金额 s_vol => 内盘 b_vol => 外盘 reversed_bytes2 => 保留 reversed_bytes3 => 保留 bid1 => 买一价 ask1 => 卖一价 bid_vol1 => 买一量 ask_vol1 => 卖一量 bid2 => 买二价 ask2 => 卖二价 bid_vol2 => 买二量 ask_vol2 => 卖二量 bid3 => 买三价 ask3 => 卖三价 bid_vol3 => 买三量 ask_vol3 => 卖三量 bid4 => 买四价 ask4 => 卖四价 bid_vol4 => 买四量 ask_vol4 => 卖四量 bid5 => 买五价 ask5 => 卖五价 bid_vol5 => 买五量 ask_vol5 => 卖五量 reversed_bytes4 => 保留 reversed_bytes5 => 保留 reversed_bytes6 => 保留 reversed_bytes7 => 保留 reversed_bytes8 => 保留 reversed_bytes9 => 涨速 active2 => 活跃度 ''' def getRealtimeQuotes(self, codeList): self._connect() itemList = [] for item in codeList: itemList.append((self.getMarketCode(item), item)) histList = self.__tdx.to_df(self.__tdx.get_security_quotes(itemList)) histList = histList.set_index('code') return histList #计算指定日期成交量细节 def getVolAnalysis(self, code, date): self._connect() if str(time.strftime('%Y-%m-%d',time.localtime())) == str(date): if int(time.strftime('%H%M',time.localtime())) > 1600: volList = self.getHistoryTransaction(code, date) else: volList = self.getRealtimeTransaction(code) else: volList = self.getHistoryTransaction(code, date) if volList is None: return None guben,circulate = self.getGuben(code) if not self._lastBaseHistList.empty: histList = self._lastBaseHistList.copy() else: histList = self.getDays(code, end=date, indexs=[]) #涨停单数量 limitVol = round(histList[-5:]['vol'].mean() * 0.0618) #超大单,先转成市值,再转回成手数 superVol = float(circulate) * float(histList['close'].values[-1]) * 0.000618 / float(histList['close'].values[-1]) / 100 #大单 bigVol = round(superVol * 0.518) #中单 middleVol = round(superVol * 0.382) #小单 smallVol = round(superVol * 0.191) #买单统计 buyVolList = volList[volList['type'] == 'B'] totalBuyVolNum = buyVolList['vol'].sum() mainBuyVolNum = buyVolList[buyVolList['vol'] >= bigVol]['vol'].sum() limitBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] >= limitVol)]['vol'].sum() / limitVol) superBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < limitVol) & (buyVolList['vol'] >= superVol)]['vol'].sum() / superVol) bigBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < superVol) & (buyVolList['vol'] >= bigVol)]['vol'].sum() / bigVol) middleBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < bigVol) & (buyVolList['vol'] >= middleVol)]['vol'].sum() / middleVol) smallBuyVolNum = math.ceil(buyVolList[(buyVolList['vol'] < middleVol) & (buyVolList['vol'] >= smallVol)]['vol'].sum() / smallVol) microBuyVolNum = len(buyVolList[(buyVolList['vol'] < smallVol)]) #print limitBuyVolNum,superBuyVolNum,bigBuyVolNum,middleBuyVolNum,smallBuyVolNum,microBuyVolNum #卖单统计 sellVolList = volList[volList['type'] == 'S'] totalSellVolNum = sellVolList['vol'].sum() mainSellVolNum = sellVolList[sellVolList['vol'] >= bigVol]['vol'].sum() limitSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] >= limitVol)]['vol'].sum() / limitVol) superSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < limitVol) & (sellVolList['vol'] >= superVol)]['vol'].sum() / superVol) bigSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < superVol) & (sellVolList['vol'] >= bigVol)]['vol'].sum() / bigVol) middleSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < bigVol) & (sellVolList['vol'] >= middleVol)]['vol'].sum() / middleVol) smallSellVolNum = math.ceil(sellVolList[(sellVolList['vol'] < middleVol) & (sellVolList['vol'] >= smallVol)]['vol'].sum() / smallVol) microSellVolNum = len(sellVolList[(sellVolList['vol'] < smallVol)]) #print limitSellVolNum,superSellVolNum,bigSellVolNum,middleSellVolNum,smallSellVolNum,microSellVolNum #计算吸筹线 #主力标准吸筹金额 mainBaseMoney = round(histList['close'].values[-1] * circulate * 0.001 / 10000 / 10000, 4) #主力强力吸筹金额 mainBigMoney = round(histList['close'].values[-1] * circulate * 0.003 / 10000 / 10000, 4) #资金统计 totalMoney = round(volList['money'].sum() / 10000 / 10000, 4) totalBuyMoney = round(buyVolList['money'].sum() / 10000 / 10000, 4) totalSellMoney = round(sellVolList['money'].sum() / 10000 / 10000, 4) totalAbsMoney = round(totalBuyMoney - totalSellMoney, 3) mainMoney = round(volList[volList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4) mainBuyMoney = round(buyVolList[buyVolList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4) mainSellMoney = round(sellVolList[sellVolList['vol'] >= bigVol]['money'].sum() / 10000 / 10000, 4) mainAbsMoney = round(mainBuyMoney - mainSellMoney, 3) mainRate = 0 try: mainRate = round((mainBuyMoney + mainSellMoney) / totalMoney * 100, 2) except: pass mainBuyRate = 0 try: mainBuyRate = round(mainBuyMoney / (mainBuyMoney + mainSellMoney) * 100, 2) except: pass #print totalAbsMoney,mainAbsMoney,totalMoney,totalBuyMoney,totalSellMoney,mainBuyMoney,mainSellMoney,mainRate,mainBuyRate #成交笔数 volNum = len(volList) #平均每笔交易价格 aveTradePrice = round(totalMoney / volNum * 10000 * 10000, 2) #平均每股买价格 avePerShareBuyPrice = 0 try: avePerShareBuyPrice = round(totalBuyMoney * 10000 * 10000 / (totalBuyVolNum * 100), 3) except: pass #主力平均每股买价格 mainAvePerShareBuyPrice = 0 try: mainAvePerShareBuyPrice = round(mainBuyMoney * 10000 * 10000 / (mainBuyVolNum * 100), 3) except: pass #平均每股卖价格 avePerShareSellPrice = 0 try: avePerShareSellPrice = round(totalSellMoney * 10000 * 10000 / (totalSellVolNum * 100), 3) except: pass #主力平均每股卖价格 mainAvePerShareSellPrice = 0 try: mainAvePerShareSellPrice = round(mainSellMoney * 10000 * 10000 / (mainSellVolNum * 100), 3) except: pass #print totalMoney,volNum,aveVolPrice * 10000 * 10000 statData = {} statData['limit_buy_vol_num'] = limitBuyVolNum statData['super_buy_vol_num'] = superBuyVolNum statData['big_buy_vol_num'] = bigBuyVolNum statData['middle_buy_vol_num'] = middleBuyVolNum statData['small_buy_vol_num'] = smallBuyVolNum statData['micro_buy_vol_num'] = microBuyVolNum statData['limit_sell_vol_num'] = limitSellVolNum statData['super_sell_vol_num'] = superSellVolNum statData['big_sell_vol_num'] = bigSellVolNum statData['middle_sell_vol_num'] = middleSellVolNum statData['small_sell_vol_num'] = smallSellVolNum statData['micro_sell_vol_num'] = microSellVolNum statData['total_abs_money'] = totalAbsMoney statData['main_abs_money'] = mainAbsMoney statData['total_money'] = totalMoney statData['total_buy_money'] = totalBuyMoney statData['total_sell_money'] = totalSellMoney statData['main_money'] = mainMoney statData['main_buy_money'] = mainBuyMoney statData['main_sell_money'] = mainSellMoney statData['main_rate'] = mainRate statData['main_buy_rate'] = mainBuyRate statData['trade_num'] = volNum statData['vol_num'] = volList['vol'].sum() statData['ave_trade_price'] = aveTradePrice statData['main_base_money'] = mainBaseMoney statData['main_big_money'] = mainBigMoney statData['ave_per_share_buy_price'] = avePerShareBuyPrice statData['ave_per_share_sell_price'] = avePerShareSellPrice statData['main_ave_per_share_buy_price'] = mainAvePerShareBuyPrice statData['main_ave_per_share_sell_price'] = mainAvePerShareSellPrice statData['circulate_money'] = round(circulate * histList['close'].values[-1] / 10000 / 10000, 4) return statData #输出ebk文件 def outputEbk(self, stockList, ebkPath = ''): if len(ebkPath) <= 0: ebkPath = os.getcwd() + '/' + sys.argv[0][0:-3] + '.' + str(time.strftime('%Y%m%d',time.localtime())) + '.ebk' if not isinstance(stockList,list): return False fp = open(ebkPath, "a") fp.write('\r\n') #ebk第一行为空行 for code in stockList: if self.getMarketCode(code) == 1: fp.write('1' + code) else: fp.write('0' + code) fp.write('\r\n') fp.close() return True #输出sel文件 def outputSel(self, stockList, selPath = ''): import struct if len(selPath) <= 0: selPath = os.getcwd() + '/' + sys.argv[0][0:-3] + '.' + str(time.strftime('%Y%m%d',time.localtime())) + '.sel' if not isinstance(stockList,list): return False stocks = [] for code in stockList: if self.getMarketCode(code) == 1: stocks.append('\x07\x11' + code) else: stocks.append('\x07\x21' + code) with open(selPath, 'ab') as fp: data = struct.pack('H', len(stocks)).decode() + ''.join(stocks) fp.write(data.encode()) return True #ebk to sel def ebk2sel(self, ebkPath): import struct if not os.path.exists(ebkPath): return False selPath = ebkPath.replace('.ebk', '.sel') stocks = [] with open(ebkPath, 'r') as ebkfp: for code in ebkfp: code = code.strip() if len(code) > 0: if self.getMarketCode(code[1:]) == 1: stocks.append('\x07\x11' + code[1:]) else: stocks.append('\x07\x21' + code[1:]) with open(selPath, 'wb') as selfp: data = struct.pack('H', len(stocks)).decode() + ''.join(stocks) selfp.write(data.encode()) return True #sel to ebk def sel2ebk(self, selPath): import struct if not os.path.exists(selPath): return False ebkPath = selPath.replace('.sel', '.ebk') with open(selPath, 'rb') as selfp: ebkfp = open(ebkPath, "a") cnt = struct.unpack('<H', selfp.read(2))[0] for _ in range(cnt): data = selfp.readline(8).decode() exch = '1' if data[1] == '\x11' else '0' code = exch + data[2:] ebkfp.write(code + '\r\n') ebkfp.close() return True
from pytdx.hq import TdxHq_API api = TdxHq_API() with api.connect('119.147.212.81', 7709): df = api.to_df(api.get_security_quotes([(0, '000001'), (1, '600300')])) print(df.to_json()) data = api.get_minute_time_data(1, '600300') print(data) list_df = api.to_df(api.get_security_list(0, 300)) print(list_df.to_json())
class StdQuotes(object): """股票市场实时行情""" bestip = ('47.103.48.45', 7709) def __init__(self, **kwargs): try: default = settings.get('SERVER').get('HQ')[0] self.bestip = config.get('BESTIP').get('HQ', default) except ValueError: self.config = None self.client = TdxHq_API(**kwargs) def traffic(self): with self.client.connect(*self.bestip): return self.client.get_traffic_stats() def quotes(self, symbol=[]): ''' 获取实时日行情数据 :param symbol: 股票代码 :return: pd.dataFrame or None ''' logger.debug(type(logger)) if type(symbol) is str: symbol = [symbol] with self.client.connect(*self.bestip): symbol = get_stock_markets(symbol) result = self.client.get_security_quotes(symbol) return to_data(result) def bars(self, symbol='000001', frequency='9', start='0', offset='100'): ''' 获取实时日K线数据 :param symbol: 股票代码 :param frequency: 数据类别 :param market: 证券市场 :param start: 开始位置 :param offset: 每次获取条数 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_security_bars(int(frequency), int(market), str(symbol), int(start), int(offset)) return to_data(result) def stock_count(self, market=MARKET_SH): ''' 获取市场股票数量 :param market: 股票市场代码 sh 上海, sz 深圳 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): result = self.client.get_security_count(market=market) return result def stocks(self, market=MARKET_SH): ''' 获取股票列表 :param market: :return: ''' with self.client.connect(*self.bestip): counts = self.client.get_security_count(market=market) stocks = None for start in tqdm(range(0, counts, 1000)): result = self.client.get_security_list(market=market, start=start) stocks = pandas.concat( [stocks, to_data(result)], ignore_index=True) if start > 1 else to_data(result) return stocks def index_bars(self, symbol='000001', frequency='9', start='0', offset='100'): ''' 获取指数k线 :param symbol: :param frequency: :param start: :param offset: :return: ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_index_bars(frequency=frequency, market=market, code=symbol, start=start, count=offset) return to_data(result) def minute(self, symbol=''): ''' 获取实时分时数据 :param market: 证券市场 :param symbol: 股票代码 :return: pd.DataFrame ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_minute_time_data(market=market, code=symbol) return to_data(result) def minutes(self, symbol='', date='20191023'): ''' 分时历史数据 :param market: :param symbol: :param date: :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_history_minute_time_data(market=market, code=symbol, date=date) return to_data(result) def transaction(self, symbol='', start=0, offset=10): ''' 查询分笔成交 :param market: 市场代码 :param symbol: 股票代码 :param start: 起始位置 :param offset: 请求数量 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_transaction_data(int(market), symbol, int(start), int(offset)) return to_data(result) def transactions(self, symbol='', start=0, offset=10, date='20170209'): ''' 查询历史分笔成交 参数:市场代码, 股票代码,起始位置,日期 数量 如: 0,000001,0,10,20170209 :param symbol: 股票代码 :param start: 起始位置 :param offset: 数量 :param date: 日期 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol, string=False) result = self.client.get_history_transaction_data(market=market, code=symbol, start=start, count=offset, date=date) return to_data(result) def F10C(self, symbol=''): ''' 查询公司信息目录 :param market: 市场代码 :param symbol: 股票代码 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_company_info_category(int(market), symbol) return result def F10(self, symbol='', name=''): ''' 读取公司信息详情 :param name: 公司 F10 标题 :param symbol: 股票代码 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): result = {} market = get_stock_market(symbol, string=False) frequency = self.client.get_company_info_category( int(market), symbol) if name: for x in frequency: if x['name'] == name: return self.client.get_company_info_content( market=market, code=symbol, filename=x['filename'], start=x['start'], length=x['length']) for x in frequency: result[x['name']] = self.client.get_company_info_content( market=market, code=symbol, filename=x['filename'], start=x['start'], length=x['length']) else: pass return result def xdxr(self, symbol=''): ''' 读取除权除息信息 :param market: 市场代码 :param symbol: 股票代码 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_xdxr_info(int(market), symbol) return to_data(result) def finance(self, symbol='000001'): ''' 读取财务信息 :param symbol: :return: ''' with self.client.connect(*self.bestip): market = get_stock_market(symbol) result = self.client.get_finance_info(market=market, code=symbol) return to_data(result) def k(self, symbol='', begin=None, end=None): ''' 读取k线信息 :param symbol: :param begin: 开始日期 :param end: 截止日期 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): result = self.client.get_k_data(symbol, begin, end) return to_data(result) def index(self, symbol='000001', market=MARKET_SH, frequency='9', start=1, offset=2): ''' 获取指数k线 K线种类: - 0 5分钟K线 - 1 15分钟K线 - 2 30分钟K线 - 3 1小时K线 - 4 日K线 - 5 周K线 - 6 月K线 - 7 1分钟 - 8 1分钟K线 - 9 日K线 - 10 季K线 - 11 年K线 :param symbol: 股票代码 :param frequency: 数据类别 :param market: 证券市场 :param start: 开始位置 :param offset: 每次获取条数 :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): result = self.client.get_index_bars(int(frequency), int(market), str(symbol), int(start), int(offset)) return to_data(result) def block(self, tofile="block.dat"): ''' 获取证券板块信息 :param tofile: :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): result = self.client.get_and_parse_block_info(tofile) return to_data(result)
def test_all_functions(multithread, heartbeat, auto_retry, raise_exception): api = TdxHq_API(multithread=multithread, heartbeat=heartbeat, auto_retry=auto_retry, raise_exception=raise_exception) with api.connect(time_out=30): log.info("获取股票行情") stocks = api.get_security_quotes([(0, "000001"), (1, "600300")]) assert stocks is not None assert type(stocks) is list # 方法2 stocks = api.get_security_quotes(0, "000001") assert stocks is not None assert type(stocks) is list # 方法3 stocks = api.get_security_quotes((0, "000001")) assert stocks is not None assert type(stocks) is list log.info("获取k线") data = api.get_security_bars(9, 0, '000001', 4, 3) assert data is not None assert type(data) is list assert len(data) == 3 log.info("获取 深市 股票数量") assert api.get_security_count(0) > 0 log.info("获取股票列表") stocks = api.get_security_list(1, 0) assert stocks is not None assert type(stocks) is list assert len(stocks) > 0 log.info("获取指数k线") data = api.get_index_bars(9, 1, '000001', 1, 2) assert data is not None assert type(data) is list assert len(data) == 2 log.info("查询分时行情") data = api.get_minute_time_data(TDXParams.MARKET_SH, '600300') assert data is not None log.info("查询历史分时行情") data = api.get_history_minute_time_data( TDXParams.MARKET_SH, '600300', 20161209) assert data is not None assert type(data) is list assert len(data) > 0 log.info("查询分时成交") data = api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0, 30) assert data is not None assert type(data) is list log.info("查询历史分时成交") data = api.get_history_transaction_data( TDXParams.MARKET_SZ, '000001', 0, 10, 20170209) assert data is not None assert type(data) is list assert len(data) == 10 log.info("查询公司信息目录") data = api.get_company_info_category(TDXParams.MARKET_SZ, '000001') assert data is not None assert type(data) is list assert len(data) > 0 start = data[0]['start'] length = data[0]['length'] log.info("读取公司信息-最新提示") data = api.get_company_info_content( 0, '000001', '000001.txt', start, length) assert data is not None assert len(data) > 0 log.info("读取除权除息信息") data = api.get_xdxr_info(1, '600300') assert data is not None assert type(data) is list assert len(data) > 0 log.info("读取财务信息") data = api.get_finance_info(0, '000001') assert data is not None assert type(data) is OrderedDict assert len(data) > 0 log.info("日线级别k线获取函数") data = api.get_k_data('000001', '2017-07-01', '2017-07-10') assert type(data) is pd.DataFrame assert len(data) == 6 log.info("获取板块信息") data = api.get_and_parse_block_info(TDXParams.BLOCK_FG) assert data is not None assert type(data) is list assert len(data) > 0
class StdQuotes(object): """股票市场实时行情""" # __slots__ = def __init__(self, **kwargs): self.config = None try: self.config = json.loads( os.path.join(os.environ['HOME'], '.mootdx/config.josn')) except Exception as e: self.config = None self.client = TdxHq_API(**kwargs) if not self.config: self.bestip = os.environ.setdefault("MOOTDX_SERVER", '202.108.253.131:7709') self.bestip = self.bestip.split(':') self.bestip[1] = int(self.bestip[1]) else: self.bestip = self.config.get('SERVER') # K线 def bars(self, symbol='000001', category='9', start='0', offset='100'): ''' 获取实时日K线数据 :param symbol: 股票代码 :param category: 数据类别 :param market: 证券市场 :param start: 开始位置 :param offset: 每次获取条数 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_security_bars(int(category), int(market), str(symbol), int(start), int(offset)) return self.client.to_df(data) # 分时数据 def minute(self, symbol=''): ''' 获取实时分时数据 :param market: 证券市场 :param symbol: 股票代码 :return: pd.DataFrame ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_minute_time_data(int(market), symbol) return self.client.to_df(data) # 分时历史数据 def minute_his(self, symbol='', datetime='20161209'): ''' 分时历史数据 :param market: :param symbol: :param datetime: :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_history_minute_time_data( int(market), symbol, datetime) return self.client.to_df(data) def trans(self, symbol='', start=0, offset=10): ''' 查询分笔成交 :param market: 市场代码 :param symbol: 股票代码 :param start: 起始位置 :param offset: 请求数量 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_transaction_data(int(market), symbol, int(start), int(market)) return self.client.to_df(data) def trans_his(self, symbol='', start=0, offset=10, date=''): ''' 查询历史分笔成交 :param market: 市场代码 :param symbol: 股票代码 :param start: 起始位置 :param offset: 数量 :param date: 日期 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_history_transaction_data( int(market), symbol, int(start), int(offset), date) return self.client.to_df(data) def company(self, symbol='', detail='category', *args, **kwargs): ''' 企业信息获取 :param symbol: :param detail: :param args: :param kwargs: :return: ''' pass def company_category(self, symbol=''): ''' 查询公司信息目录 :param market: 市场代码 :param symbol: 股票代码 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_company_info_category(int(market), symbol) return self.client.to_df(data) def company_content(self, symbol='', file='', start=0, offset=10): ''' 读取公司信息详情 :param market: 市场代码 :param symbol: 股票代码 :param file: 文件名 :param start: 起始位置 :param offset: 数量 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_company_info_content( int(market), symbol, file, int(start), int(offset)) return self.client.to_df(data) def xdxr(self, symbol=''): ''' 读取除权除息信息 :param market: 市场代码 :param symbol: 股票代码 :return: pd.dataFrame or None ''' market = get_stock_market(symbol) with self.client.connect(*self.bestip): data = self.client.get_xdxr_info(int(market), symbol) return self.client.to_df(data) def k(self, symbol='', begin=None, end=None): ''' 读取k线信息 :param symbol: :param begin: :param end: :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): data = self.client.get_k_data(symbol, begin, end) return data def index(self, symbol='000001', market='sh', category='9', start='0', offset='100'): ''' 获取指数k线 K线种类: - 0 5分钟K线 - 1 15分钟K线 - 2 30分钟K线 - 3 1小时K线 - 4 日K线 - 5 周K线 - 6 月K线 - 7 1分钟 - 8 1分钟K线 - 9 日K线 - 10 季K线 - 11 年K线 :param symbol: 股票代码 :param category: 数据类别 :param market: 证券市场 :param start: 开始位置 :param offset: 每次获取条数 :return: pd.dataFrame or None ''' market = 1 if market == 'sh' else 0 with self.client.connect(*self.bestip): data = self.client.get_index_bars(int(category), int(market), str(symbol), int(start), int(offset)) return self.client.to_df(data) def block(self, tofile="block.dat"): ''' 获取证券板块信息 :param tofile: :return: pd.dataFrame or None ''' with self.client.connect(*self.bestip): data = self.client.get_and_parse_block_info(tofile) return self.client.to_df(data) def batch(self, method='', offset=100, *args, **kwargs): ''' 批量下载相关数据 :param method: :param offset: :return: ''' pass
class TdxHelper: ip_list = [{ 'ip': '119.147.212.81', 'port': 7709 }, { 'ip': '60.12.136.250', 'port': 7709 }] def __init__(self): #连接tdx接口 self.api = TdxHq_API() if not self.api.connect('60.12.136.250', 7709): print("服务器连接失败!") # pandas数据显示设置 pd.set_option('display.max_columns', None) # 显示所有列 #pd.set_option('display.max_rows', None) # 显示所有行 # mysql对象 self.mysql = mysqlHelper(config.mysql_host, config.mysql_username, bluedothe.mysql_password, config.mysql_dbname) # pandas的mysql对象 self.engine = create_engine( f'mysql+pymysql://{config.mysql_username}:{bluedothe.mysql_password}@{config.mysql_host}/{config.mysql_dbname}?charset=utf8' ) #断开tdx接口连接 def close_connect(self): self.api.disconnect() #获取k线,最后一个参数day,说明需要获取的数量,本接口只获取从最近交易日往前的数据 #输入参数:五个参数分别为:category(k线),市场代码(0:深圳,1:上海),股票代码,开始位置(从最近交易日向前取,0表示最近交易日),返回的记录条数 #K线种类: 0 5分钟K线; 1 15分钟K线; 2 30分钟K线; 3 1小时K线; 4 日K线;5 周K线;6 月K线;7 1分钟;8 1分钟K线; 9 日K线;10 季K线;11 年K线 #返回值:open,close,high,low,vol,amount,year,month,day,hour,minute,datetime # csv格式:code,ts_code,trade_date(缩写),trade_time,time_index,open,high,low,close,amount,volume def get_security_bars(self, category, market, code, start=0, count=240): dict = {0: 'SZ', 1: 'SH'} ts_code = code + "." + dict[market] order = [ 'code', 'ts_code', 'trade_date', 'trade_time', 'time_index', 'open', 'high', 'low', 'close', 'amount', 'volume' ] #df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list df = self.api.to_df( self.api.get_security_bars(category, market, code, start, count)) # 返回DataFrame if df.empty: return df df.insert(0, 'ts_code', ts_code) df.insert(0, 'code', code) df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19]) df['time_index'] = df['trade_time'].apply( lambda x: datatime_util.stockTradeTime2Index(x)) df['trade_date'] = df['datetime'].apply( lambda x: (str(x)[0:10]).replace('-', '')) df.rename(columns={'vol': 'volume'}, inplace=True) df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1, inplace=True) df['volume'] = df['volume'].apply(lambda x: int(x)) #取整 df.loc[df['amount'] == 5.877471754111438e-39, 'amount'] = 0 #列值根据条件筛选后修改为0 df = df[order] filename = config.tdx_csv_minline1_all + ts_code + ".csv" if os.path.isfile(filename): df.to_csv(filename, index=False, mode='a', header=False, sep=',', encoding="utf_8_sig") else: df.to_csv(filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig") print("新增加的一分钟all股票数据:", filename) # 获取1分钟k线,最后一个参数说明需要获取的数量,本接口只获取从最近交易日往前的数据 # 输入参数:五个参数分别为:category(k线),市场代码(0:深圳,1:上海),股票代码,开始位置(从最近交易日向前取,0表示最近交易日),返回的记录条数 # K线种类: 0 5分钟K线; 1 15分钟K线; 2 30分钟K线; 3 1小时K线; 4 日K线;5 周K线;6 月K线;7 1分钟;8 1分钟K线; 9 日K线;10 季K线;11 年K线 # 返回值:open,close,high,low,vol,amount,year,month,day,hour,minute,datetime # csv格式:code,ts_code,trade_date(缩写),trade_time,time_index,open,high,low,close,amount,volume def get_security_bars_minute1(self, category, market, code, start, count): dict = {0: 'SZ', 1: 'SH'} ts_code = code + "." + dict[market] order = [ 'code', 'ts_code', 'trade_date', 'trade_time', 'time_index', 'open', 'high', 'low', 'close', 'amount', 'volume' ] # df = self.api.get_security_bars(9, 0, '000001', 0, 10) # 返回普通list df = self.api.to_df( self.api.get_security_bars(category, market, code, start, count)) # 返回DataFrame if df.empty: return df.insert(0, 'ts_code', ts_code) df.insert(0, 'code', code) df['trade_time'] = df['datetime'].apply(lambda x: str(x)[11:19]) df['time_index'] = df['trade_time'].apply( lambda x: datatime_util.stockTradeTime2Index(x)) df['trade_date'] = df['datetime'].apply( lambda x: (str(x)[0:10]).replace('-', '')) df.rename(columns={'vol': 'volume'}, inplace=True) df.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1, inplace=True) df['volume'] = df['volume'].apply(lambda x: int(x)) # 取整 df.loc[df['amount'] == 5.877471754111438e-39, 'amount'] = 0 # 列值根据条件筛选后修改为0 df = df[order] #过滤掉停牌的数据,在tdx中,停牌股票也能取到数据,价格是前一交易日的收盘价,所以只能用成交量或成交金额为0来判断 #1按日期分组后取出成交量为0的日期;2循环过滤掉成交量为0的日期的数据。 dfg = df.groupby(by='trade_date').mean() #分组 dfg['trade_date'] = dfg.index dfg = dfg[dfg.volume == 0] #条件过滤,保留满足条件的数据 for trade_date in dfg['trade_date'].values: df = df[(df['trade_date'] != trade_date)] # 每个条件要用括号()括起来 return df #可以获取多只股票的行情信息 #返回值:market,code,active1,price,last_close,open,high,low,reversed_bytes0,reversed_bytes1,vol,cur_vol,amount,s_vol, #reversed_bytes2,reversed_bytes3,bid1,ask1,bid_vol1,ask_vol1,bid2,ask2,bid_vol2,ask_vol2,bid3,ask3,bid_vol3,ask_vol3,bid4, #ask4,bid_vol4,ask_vol4,bid5,ask5,bid_vol5,ask_vol5,reversed_bytes4,reversed_bytes5,reversed_bytes6,reversed_bytes7, #reversed_bytes8,reversed_bytes9,active2 def get_security_quotes(self): df = self.api.to_df( self.api.get_security_quotes([(0, '000001'), (1, '600300')])) print(df) # 获取市场股票数量 #返回值:value def get_security_count(self): df = self.api.to_df(self.api.get_security_count(0)) #0 - 深圳, 1 - 上海 print(df) # 获取股票列表,返回值里面除了股票,还有国债等 #返回值:code,volunit,decimal_point,name,pre_close def get_security_list(self): df = self.api.to_df(self.api.get_security_list( 0, 10000)) # 市场代码, 起始位置 如: 0,0 或 1,100 print(df) # 获取指数k线 #输入参数同股票k线接口 # 返回值:open,close,high,low,vol,amount,year,month,day,hour,minute,datetime,up_count down_count def get_index_bars(self): index_dict_cn = { "上证指数": "999999", "深证成指": "399001", "中小板指": "399005", "创业板指": "399006", "深证综指": "399106", "上证50": "000016", "沪深300": "000300" } index_dict = { "sh": "999999", "sz": "399001", "zxb": "399005", "cyb": "399006", "szz": "399106", "sz50": "000016", "hs300": "000300" } for key in index_dict.keys(): df = self.api.to_df( self.api.get_index_bars(9, 1, index_dict[key], 0, 2)) print(df) # 查询分时行情,最近交易日的数据,一分钟一条记录 #返回值:price,vol def get_minute_time_data(self): df = self.api.to_df(self.api.get_minute_time_data( 1, '600300')) #市场代码, 股票代码 print(df) # 查询历史分时行情 # 返回值:price,vol def get_history_minute_time_data(self): df = self.api.to_df( self.api.get_history_minute_time_data(TDXParams.MARKET_SH, '603887', 20200420)) #市场代码, 股票代码,时间 print(df) # 查询分笔成交,最近交易日数据 #返回值:time,price,vol,num,buyorsell def get_transaction_data(self): df = self.api.to_df( self.api.get_transaction_data(TDXParams.MARKET_SZ, '000001', 0, 30)) #市场代码, 股票代码,起始位置, 数量 print(df) # 查询历史分笔成交 #返回值:time,price,vol,buyorsell def get_history_transaction_data(self): df = self.api.to_df( self.api.get_history_transaction_data( TDXParams.MARKET_SZ, '000001', 0, 10, 20170209)) #市场代码, 股票代码,起始位置,日期 数量 print(df) # 查询公司信息目录,返回的不是具体数据 #返回值:name,filename,start,length def get_company_info_category(self): df = self.api.to_df( self.api.get_company_info_category(TDXParams.MARKET_SZ, '000001')) #市场代码, 股票代码 print(df) # 读取公司信息详情 #返回值:value def get_company_info_content(self): df = self.api.to_df( self.api.get_company_info_content( 0, '000001', '000001.txt', 0, 1000)) #市场代码, 股票代码, 文件名, 起始位置, 数量 print(df) # 读取除权除息信息 #返回值:year,month,day,category,name,fenhong,peigujia,songzhuangu,peigu def get_xdxr_info(self): df = self.api.to_df(self.api.get_xdxr_info(1, '600300')) #市场代码, 股票代码 print(df) # 读取财务信息 #返回值:market,code,liutongguben,province,industry,updated_date,ipo_date,zongguben,guojiagu,faqirenfarengu,farengu,bgu,hgu,zhigonggu, #zongzichan,liudongzichan,gudingzichan,wuxingzichan,gudongrenshu,liudongfuzhai,changqifuzhai,zibengongjijin,jingzichan,zhuyingshouru, #zhuyinglirun,yingshouzhangkuan,yingyelirun,touzishouyu,jingyingxianjinliu,zongxianjinliu,cunhuo,lirunzonghe,shuihoulirun,jinglirun,weifenlirun,baoliu1,baoliu2 def get_finance_info(self): df = self.api.to_df(self.api.get_finance_info(1, '600300')) #市场代码, 股票代码 print(df) # 读取k线信息 # 返回值:value def get_k_data(self): df = self.api.to_df( self.api.get_k_data('600300', '2017-07-03', '2017-07-10')) #股票代码, 开始时间, 结束时间 print(df) # 读取板块信息 #返回值:blockname, block_type, code_index, code """ BLOCK_SZ = "block_zs.dat";BLOCK_FG = "block_fg.dat";BLOCK_GN = "block_gn.dat";BLOCK_DEFAULT = "block.dat" """ def get_and_parse_block_info(self): ##指数板块 风格板块 概念板块 一般板块 block_filename = [ "block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat" ] for block in block_filename: df = self.api.to_df( self.api.get_and_parse_block_info(block)) #板块文件名称 filename = config.tdx_csv_block + block[0:-4] + ".csv" if os.path.isfile(filename): os.remove(filename) df.to_csv(filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig") else: df.to_csv(filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig") # 读取板块信息,多个类型封装到一个df对象中返回 # 返回值:data_source, block_category, block_type, block_name, block_code, ts_code, create_time def update_block_member(self): ##指数板块 风格板块 概念板块 一般板块 #block_filename = ["block_zs.dat", "block_fg.dat", "block_gn.dat", "block.dat"] block_filename = ["block_zs.dat", "block_fg.dat", "block_gn.dat"] #block.dat中的数据都包含在其他版块里了,这个可以去掉 data_source = "tdx" dfall = None for block in block_filename: df = self.api.to_df( self.api.get_and_parse_block_info(block)) # 板块文件名称 df['data_source'] = data_source if block == "block.dat": df['block_category'] = data_source + ".yb" else: df['block_category'] = data_source + "." + block[6:8] df['block_type'] = df['block_type'].map(lambda x: str(x)) df['block_type'] = df['block_category'].str.cat( df['block_type'], sep=".") #, sep = "." df['block_code'] = "" #使用pd直接插入到数据库时,字段不能是None值 df['ts_code'] = df['code'].apply(lambda x: x + ".SH" if x[0:1] == "6" else x + ".SZ") if (dfall is not None) and (not dfall.empty): dfall = dfall.append(df, ignore_index=True) else: dfall = df if (dfall is None) or (dfall.empty): return None dfall.rename(columns={'blockname': 'block_name'}, inplace=True) dfall['create_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time())) dfall = dfall[[ 'data_source', 'block_category', 'block_type', 'block_name', 'block_code', 'ts_code', 'create_time' ]] #列重排序 #分组统计 dfg = dfall.groupby(by=[ 'data_source', 'block_category', 'block_type', 'block_name', 'block_code' ], as_index=False).count() # 分组求每组数量 dfg.rename(columns={'ts_code': 'member_count'}, inplace=True) #ts_code列数值为汇总值,需要重命名 dfg['create_time'] = time.strftime( '%Y-%m-%d %H:%M:%S', time.localtime(time.time())) #create_time列数值为汇总值,需要重新赋值 delete_condition = f"data_source = '{data_source}'" mysql_script.df2db_update(delete_condition=delete_condition, block_basic_df=dfg, block_member_df=dfall) return (len(dfg), len(dfall)) #获取一段时间的1分钟数据,因为每次调用接口只能返回3天的分钟数据(240*3),需要分多次调用 #返回值:0没有提取到数据;1提取到数据 def get_minute1_data(self, category, market, code, start_date, end_date): init_start_date = start_date.replace('-', '') init_end_date = end_date.replace('-', '') day = datatime_util.diffrentPeriod(datatime_util.DAILY, start_date, end_date) df = self.get_security_bars_minute1(category, market, code, 0, 240 * 3) # 返回DataFrame if df is None or df.empty: print('{0}没有交易数据'.format(code)) return 0 print(market, '--', code, '--', start_date, '--', end_date) #print("最大值:",df.groupby('datetime').max()) #print(df.describe()) #df数据统计 data_start_date = df.min()['trade_date'] data_end_date = df.max()['trade_date'] start_date = start_date.replace('-', '') end_date = end_date.replace('-', '') if data_end_date < start_date or end_date < data_start_date: print("采集时间在数据范围之外,退出函数") return 0 elif end_date > data_end_date: end_date = data_end_date if start_date < data_start_date: #最近三天的数据中,去掉无用的数据后即是最终数据 #需要取的数据还有三天前的数据,需要继续向前取 n = (day - 3) // 3 m = (day - 3) % 3 for i in range(0, n): dfn = self.get_security_bars_minute1(category, market, code, 240 * 3 * (i + 1), 240 * 3) # 返回DataFrame if (dfn is not None) and (not dfn.empty): df = dfn.append(df, ignore_index=True) if m > 0: dfn = self.get_security_bars_minute1(category, market, code, 240 * 3 * (n + 1), 240 * m) if (dfn is not None) and (not dfn.empty): df = dfn.append(df, ignore_index=True) df = df.sort_values(by=['trade_date', 'time_index'], axis=0, ascending=True) #过滤掉start_date, end_date之外的数据 df = df[(df['trade_date'] >= str(init_start_date)) & (df['trade_date'] <= str(init_end_date))] #每个条件要用括号()括起来 dict = {0: 'SZ', 1: 'SH'} ts_code = code + "." + dict[market] filename = config.tdx_csv_minline1_all + ts_code + ".csv" if os.path.isfile(filename): df.to_csv(filename, index=False, mode='a', header=False, sep=',', encoding="utf_8_sig") print("更新一分钟all股票数据:", filename) else: df.to_csv(filename, index=False, mode='w', header=True, sep=',', encoding="utf_8_sig") print("新增加的一分钟all股票数据:", filename)