def QA_fetch_get_stock_min(code, start, end, frequence='1min', ip=best_ip['stock'], port=7709): api = TdxHq_API() type_ = '' start_date = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) if str(frequence) in ['5', '5m', '5min', 'five']: frequence, type_ = 0, '5min' lens = 48 * lens elif str(frequence) in ['1', '1m', '1min', 'one']: frequence, type_ = 8, '1min' lens = 240 * lens elif str(frequence) in ['15', '15m', '15min', 'fifteen']: frequence, type_ = 1, '15min' lens = 16 * lens elif str(frequence) in ['30', '30m', '30min', 'half']: frequence, type_ = 2, '30min' lens = 8 * lens elif str(frequence) in ['60', '60m', '60min', '1h']: frequence, type_ = 3, '60min' lens = 4 * lens if lens > 20800: lens = 20800 with api.connect(ip, port): data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code( str(code)), str(code), (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) data = data\ .assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\ .drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\ .assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\ .assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\ .assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end] return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_index_day(code, start_date, end_date, frequence='day', ip=best_ip['stock'], port=7709): '指数日线' api = TdxHq_API() if frequence in ['day', 'd', 'D', 'DAY', 'Day']: frequence = 9 elif frequence in ['w', 'W', 'Week', 'week']: frequence = 5 elif frequence in ['month', 'M', 'm', 'Month']: frequence = 6 elif frequence in ['Q', 'Quarter', 'q']: frequence = 10 elif frequence in ['y', 'Y', 'year', 'Year']: frequence = 11 with api.connect(ip, port): start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) if str(code)[0] in ['5', '1']: # ETF data = pd.concat([api.to_df(api.get_security_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) else: data = pd.concat([api.to_df(api.get_index_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date] return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def test_getAPI(self): """pytdx connect """ from QUANTAXIS.QAUtil import QA_util_get_trade_gap from QUANTAXIS.QAFetch.base import _select_market_code code = '600000' frequence = 9 days = 365 * 1.2 start = datetime.datetime.now() - datetime.timedelta(days) end = datetime.datetime.now() - datetime.timedelta(0) api = TDX.tdxapi print(type(api)) if api.connect(TDX.ip, TDX.port, time_out=0.7): print(type(api)) start_date = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) self.assertTrue(lens > 10, "时间间隔太短:{}".format(lens)) alist = [ api.to_df( api.get_security_bars(frequence, _select_market_code(code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ] api.disconnect() data = pd.concat(alist, axis=0, sort=False) self.assertTrue(len(data) > 1, "返回值为空")
def QA_fetch_get_future_day(code, start_date, end_date, frequence='day', ip=best_ip['future'], port=7727): '期货数据 日线' apix = TdxExHq_API() start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) global extension_market_info extension_market_info = QA_fetch_get_future_list( ) if extension_market_info is None else extension_market_info with apix.connect(ip, port): code_market = extension_market_info.query('code=="{}"'.format(code)) data = pd.concat([ apix.to_df( apix.get_instrument_bars(_select_type(frequence), int(code_market.market), str(code), (int(lens / 700) - i) * 700, 700)) for i in range(int(lens / 700) + 1) ], axis=0) data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False) return data.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date].assign( date=data['date'].apply(lambda x: str(x)[0:10]))
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', frequence='day', ip=None, port=None): """获取日线及以上级别的数据 Arguments: code {str:6} -- code 是一个单独的code 6位长度的str start_date {str:10} -- 10位长度的日期 比如'2017-01-01' end_date {str:10} -- 10位长度的日期 比如'2018-01-01' Keyword Arguments: if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权 frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y ip {str} -- [description] (default: None) ip可以通过select_best_ip()函数重新获取 port {int} -- [description] (default: {None}) Returns: pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None Exception: 如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理 """ ip, port = get_mainmarket_ip(ip, port) api = TdxHq_API() with api.connect(ip, port, time_out=0.7): if frequence in ['day', 'd', 'D', 'DAY', 'Day']: frequence = 9 elif frequence in ['w', 'W', 'Week', 'week']: frequence = 5 elif frequence in ['month', 'M', 'm', 'Month']: frequence = 6 elif frequence in ['quarter', 'Q', 'Quarter', 'q']: frequence = 10 elif frequence in ['y', 'Y', 'year', 'Year']: frequence = 11 start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code( code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) # 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了 if len(data) < 1: return None data = data[data['open'] != 0] data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10])), code=str(code),\ date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False) data = data.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date] if if_fq in ['00','bfq']: return data else: print('CURRENTLY NOT SUPPORT REALTIME FUQUAN') return None
def QA_fetch_get_index_min(code, start, end, level='1min', ip=best_ip, port=7709): '指数分钟线' api = TdxHq_API() type_ = '' start_date = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) if str(level) in ['5', '5m', '5min', 'five']: level, type_ = 0, '5min' lens = 48 * lens elif str(level) in ['1', '1m', '1min', 'one']: level, type_ = 8, '1min' lens = 240 * lens elif str(level) in ['15', '15m', '15min', 'fifteen']: level, type_ = 1, '15min' lens = 16 * lens elif str(level) in ['30', '30m', '30min', 'half']: level, type_ = 2, '30min' lens = 8 * lens elif str(level) in ['60', '60m', '60min', '1h']: level, type_ = 3, '60min' lens = 4 * lens with api.connect(ip, port): if str(code)[0] in ['5', '1']: # ETF data = pd.concat([ api.to_df( api.get_security_bars( level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ], axis=0) else: data = pd.concat([ api.to_df( api.get_index_bars( level, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ], axis=0) data = data\ .assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\ .drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\ .assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\ .assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\ .assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end] # data return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_index_min(code, start, end, frequence='1min', ip=None, port=None): '指数分钟线' global best_ip if ip is None and port is None and best_ip['stock']['ip'] is None and best_ip['stock']['port'] is None: best_ip = select_best_ip() ip = best_ip['stock']['ip'] port = best_ip['stock']['port'] elif ip is None and port is None and best_ip['stock']['ip'] is not None and best_ip['stock']['port'] is not None: ip = best_ip['stock']['ip'] port = best_ip['stock']['port'] else: pass api = TdxHq_API() type_ = '' start_date = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) if str(frequence) in ['5', '5m', '5min', 'five']: frequence, type_ = 0, '5min' lens = 48 * lens elif str(frequence) in ['1', '1m', '1min', 'one']: frequence, type_ = 8, '1min' lens = 240 * lens elif str(frequence) in ['15', '15m', '15min', 'fifteen']: frequence, type_ = 1, '15min' lens = 16 * lens elif str(frequence) in ['30', '30m', '30min', 'half']: frequence, type_ = 2, '30min' lens = 8 * lens elif str(frequence) in ['60', '60m', '60min', '1h']: frequence, type_ = 3, '60min' lens = 4 * lens if lens > 20800: lens = 20800 with api.connect(ip, port): if str(code)[0] in ['5', '1']: # ETF data = pd.concat([api.to_df(api.get_security_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) else: data = pd.concat([api.to_df(api.get_index_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) data = data\ .assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\ .drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\ .assign(code=code)\ .assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\ .assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\ .assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end] # data return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_future_min(code, start, end, frequence='1min', ip=best_ip['future'], port=7727): '期货数据 分钟线' apix = TdxExHq_API() type_ = '' start_date = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) global extension_market_info extension_market_info = QA_fetch_get_future_list( ) if extension_market_info is None else extension_market_info if str(frequence) in ['5', '5m', '5min', 'five']: frequence, type_ = 0, '5min' lens = 48 * lens elif str(frequence) in ['1', '1m', '1min', 'one']: frequence, type_ = 8, '1min' lens = 240 * lens elif str(frequence) in ['15', '15m', '15min', 'fifteen']: frequence, type_ = 1, '15min' lens = 16 * lens elif str(frequence) in ['30', '30m', '30min', 'half']: frequence, type_ = 2, '30min' lens = 8 * lens elif str(frequence) in ['60', '60m', '60min', '1h']: frequence, type_ = 3, '60min' lens = 4 * lens if lens > 20800: lens = 20800 with apix.connect(ip, port): code_market = extension_market_info.query('code=="{}"'.format(code)) data = pd.concat([ apix.to_df( apix.get_instrument_bars(frequence, int(code_market.market), str(code), (int(lens / 700) - i) * 700, 700)) for i in range(int(lens / 700) + 1) ], axis=0) data = data\ .assign(datetime=pd.to_datetime(data['datetime']), code=str(code))\ .drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False)\ .assign(date=data['datetime'].apply(lambda x: str(x)[0:10]))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(x)))\ .assign(time_stamp=data['datetime'].apply(lambda x: QA_util_time_stamp(x)))\ .assign(type=type_).set_index('datetime', drop=False, inplace=False)[start:end] return data.assign(datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_index_day(code, start_date, end_date, frequence='day', ip=None, port=None): """指数日线 1- sh 0 -sz Arguments: code {[type]} -- [description] start_date {[type]} -- [description] end_date {[type]} -- [description] Keyword Arguments: frequence {str} -- [description] (default: {'day'}) ip {[type]} -- [description] (default: {None}) port {[type]} -- [description] (default: {None}) Returns: [type] -- [description] """ ip, port = get_mainmarket_ip(ip, port) api = TdxHq_API() if frequence in ['day', 'd', 'D', 'DAY', 'Day']: frequence = 9 elif frequence in ['w', 'W', 'Week', 'week']: frequence = 5 elif frequence in ['month', 'M', 'm', 'Month']: frequence = 6 elif frequence in ['Q', 'Quarter', 'q']: frequence = 10 elif frequence in ['y', 'Y', 'year', 'Year']: frequence = 11 with api.connect(ip, port): start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) if str(code)[0] in ['5', '1']: # ETF data = pd.concat([api.to_df(api.get_security_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) else: data = pd.concat([api.to_df(api.get_index_bars( frequence, 1 if str(code)[0] in ['0', '8', '9', '5'] else 0, code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .assign(code=code)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date] return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
def getMin(cls, code, start, end, if_fq='00', frequence=8): """获取分钟级别的数据 Args: code: 代码 6位长度的str start: 10位长度的日期字符串 比如'2017-01-01' end: 10位长度的日期字符串 比如'2018-01-01' if_fq: frequence: Returns: pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获 取了一天,而当天停牌,返回None """ # type_ = '' start = str(start)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start, today_) _, type_, multiplicator = cls.getReverseFrequence(frequence) lens = lens * multiplicator if lens > 20800: lens = 20800 with cls.tdxapi.connect(cls.ip, cls.port) as api: data = pd.concat([ api.to_df( api.get_security_bars( frequence, _select_market_code(str(code)), str(code), (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ], axis=0, sort=False) data = data \ .drop(['year', 'month', 'day', 'hour', 'minute'], axis=1, inplace=False) \ .assign(datetime=pd.to_datetime(data['datetime']), code=str(code), date=data['datetime'].apply(lambda x: str(x)[0:10]), date_stamp=data['datetime'].apply( lambda x: QA_util_date_stamp(x)), time_stamp=data['datetime'].apply( lambda x: QA_util_time_stamp(x)), type=type_).set_index('datetime', drop=False, inplace=False)[start:end] return data.assign( datetime=data['datetime'].apply(lambda x: str(x)))
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', frequence='day', ip=best_ip['stock'], port=7709): """获取日线及以上级别的数据 Arguments: code {str:6} -- code 是一个单独的code 6位长度的str start_date {str:10} -- 10位长度的日期 比如'2017-01-01' end_date {str:10} -- 10位长度的日期 比如'2018-01-01' Keyword Arguments: if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权 frequency {str} -- day/week/month/quarter/year 也可以是简写 D/W/M/Q/Y ip {str} -- [description] (default: best_ip['stock']) ip可以通过select_best_ip()函数重新获取 port {int} -- [description] (default: {7709}) Returns: pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获取了一天,而当天停牌,返回None Exception: 如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理 """ api = TdxHq_API() with api.connect(ip, port, time_out=0.7): if frequence in ['day', 'd', 'D', 'DAY', 'Day']: frequence = 9 elif frequence in ['w', 'W', 'Week', 'week']: frequence = 5 elif frequence in ['month', 'M', 'm', 'Month']: frequence = 6 elif frequence in ['quarter', 'Q', 'Quarter', 'q']: frequence = 10 elif frequence in ['y', 'Y', 'year', 'Year']: frequence = 11 start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) data = pd.concat([api.to_df(api.get_security_bars(frequence, _select_market_code( code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1)], axis=0) # 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了 if len(data) < 1: return None data = data[data['open'] != 0] if if_fq in ['00', 'bfq']: data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False) return data.drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date].assign(date=data['date'].apply(lambda x: str(x)[0:10])) elif if_fq in ['01', 'qfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) bfq_data = data.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False) bfq_data = bfq_data.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) # if xdxr_data is not None: info = xdxr_data[xdxr_data['category'] == 1] bfq_data['if_trade'] = True data = pd.concat([bfq_data, info[['category']] [bfq_data.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']][bfq_data.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']] return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date] else: bfq_data['preclose'] = bfq_data['close'].shift(1) bfq_data['adj'] = 1 return bfq_data[start_date:end_date] elif if_fq in ['03', 'ddqfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat([bfq_data, info[['category']] [bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']] return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date] elif if_fq in ['02', 'hfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat([bfq_data, info[['category']] [bfq_data.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']][bfq_data.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']] return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date] elif if_fq in ['04', 'ddhfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat([bfq_data, info[['category']] [bfq_data.index[0]:end_date]], 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']][bfq_data.index[0]:end_date]], 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']] return data.drop(['fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category'], axis=1)[data['open'] != 0].assign(date=data['date'].apply(lambda x: str(x)[0:10]))[start_date:end_date]
def QA_fetch_get_stock_day(code, start_date, end_date, if_fq='00', level='day', ip=best_ip, port=7709): api = TdxHq_API() with api.connect(ip, port): if level in ['day', 'd', 'D', 'DAY', 'Day']: level = 9 elif level in ['w', 'W', 'Week', 'week']: level = 5 elif level in ['month', 'M', 'm', 'Month']: level = 6 elif level in ['Q', 'Quarter', 'q']: level = 10 elif level in ['y', 'Y', 'year', 'Year']: level = 11 start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) data = pd.concat([ api.to_df( api.get_security_bars(level, __select_market_code(code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ], axis=0) data = data[data['open'] != 0] if if_fq in ['00', 'bfq']: data = data.assign(date=data['datetime'].apply(lambda x: str(x[0:10]))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False) return data.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date].assign( date=data['date'].apply(lambda x: str(x)[0:10])) elif if_fq in ['01', 'qfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) bfq_data = data.assign(date=pd.to_datetime(data['datetime'].apply(lambda x: str(x[0:10])))).assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10]))).set_index('date', drop=False, inplace=False) bfq_data = bfq_data.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) # if xdxr_data is not None: info = xdxr_data[xdxr_data['category'] == 1] bfq_data['if_trade'] = True data = pd.concat( [bfq_data, info[['category']][bfq_data.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' ]][bfq_data.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']] return data.drop( [ 'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category' ], axis=1)[data['open'] != 0].assign(date=data['date'].apply( lambda x: str(x)[0:10]))[start_date:end_date] else: bfq_data['preclose'] = bfq_data['close'].shift(1) bfq_data['adj'] = 1 return bfq_data[start_date:end_date] elif if_fq in ['03', 'ddqfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat( [bfq_data, info[['category']][bfq_data.index[0]:end_date]], 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' ]][bfq_data.index[0]:end_date] ], 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']] return data.drop( [ 'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category' ], axis=1)[data['open'] != 0].assign(date=data['date'].apply( lambda x: str(x)[0:10]))[start_date:end_date] elif if_fq in ['02', 'hfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat( [bfq_data, info[['category']][bfq_data.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' ]][bfq_data.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']] return data.drop( [ 'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category' ], axis=1)[data['open'] != 0].assign(date=data['date'].apply( lambda x: str(x)[0:10]))[start_date:end_date] elif if_fq in ['04', 'ddhfq']: xdxr_data = QA_fetch_get_stock_xdxr(code) info = xdxr_data[xdxr_data['category'] == 1] bfq_data = data\ .assign(date=pd.to_datetime(data['datetime'].apply(lambda x: x[0:10])))\ .assign(code=str(code))\ .assign(date_stamp=data['datetime'].apply(lambda x: QA_util_date_stamp(str(x)[0:10])))\ .set_index('date', drop=False, inplace=False)\ .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1) bfq_data['if_trade'] = True data = pd.concat( [bfq_data, info[['category']][bfq_data.index[0]:end_date]], 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' ]][bfq_data.index[0]:end_date] ], 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']] return data.drop( [ 'fenhong', 'peigu', 'peigujia', 'songzhuangu', 'if_trade', 'category' ], axis=1)[data['open'] != 0].assign(date=data['date'].apply( lambda x: str(x)[0:10]))[start_date:end_date]
def getDay(cls, code, start_date, end_date, if_fq='00', frequence=9): """获取日线及以上级别的数据 Args: code {str:6} -- code 是一个单独的code 6位长度的str start_date {str:10} -- 10位长度的日期 比如'2017-01-01' end_date {str:10} -- 10位长度的日期 比如'2018-01-01' if_fq {str} -- '00'/'bfq' -- 不复权 '01'/'qfq' -- 前复权 '02'/'hfq' -- 后复权 '03'/'ddqfq' -- 定点前复权 '04'/'ddhfq' --定点后复权 frequency {int} -- K线周期 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 ip {str} -- [description] (default: None) ip可以通过select_best_ip()函数重新获取 port {int} -- [description] (default: {None}) Returns: pd.DataFrame/None -- 返回的是dataframe,如果出错比如只获 取了一天,而当天停牌,返回None Exception: 如果出现网络问题/服务器拒绝, 会出现socket:time out 尝试再次获取/更换ip即可, 本函数不做处理 """ try: with cls.tdxapi.connect(cls.ip, cls.port, time_out=0.7) as api: start_date = str(start_date)[0:10] today_ = datetime.date.today() lens = QA_util_get_trade_gap(start_date, today_) data = pd.concat([ api.to_df( api.get_security_bars( frequence, _select_market_code(code), code, (int(lens / 800) - i) * 800, 800)) for i in range(int(lens / 800) + 1) ], axis=0, sort=False) # 这里的问题是: 如果只取了一天的股票,而当天停牌, 那么就直接返回None了 if len(data) < 1: return None data = data[data['open'] != 0] data = data.assign( date=data['datetime'].apply(lambda x: str(x[0:10])), code=str(code), date_stamp=data['datetime'].apply( lambda x: QA_util_date_stamp(str(x)[0:10]))) \ .set_index('date', drop=False, inplace=False) end_date = str(end_date)[0:10] data = data.drop( ['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)[start_date:end_date] if if_fq in ['00', 'bfq']: return data else: print('CURRENTLY NOT SUPPORT REALTIME FUQUAN') return None # xdxr = QA_fetch_get_stock_xdxr(code) # if if_fq in ['01','qfq']: # return QA_data_make_qfq(data,xdxr) # elif if_fq in ['02','hfq']: # return QA_data_make_hfq(data,xdxr) except Exception as e: if isinstance(e, TypeError): print( '1、Tushare内置的pytdx版本和QUANTAXIS使用的pytdx 版本不同, 请重新安装pytdx以解决此问题.{}:{}' .format(cls.ip, cls.port)) print('pip uninstall pytdx\npip install pytdx') print('2、或者此时间段无数据。') else: print(e)