Python TdxHq_API.get_security_bars方法代码示例

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_index_min(code, start, end, level='1min', ip=best_ip, port=7709):
    api = TdxHq_API()
    type_ = ''
    if str(level) in ['5', '5m', '5min', 'five']:
        level, type_ = 0, '5min'
    elif str(level) in ['1', '1m', '1min', 'one']:
        level, type_ = 8, '1min'
    elif str(level) in ['15', '15m', '15min', 'fifteen']:
        level, type_ = 1, '15min'
    elif str(level) in ['30', '30m', '30min', 'half']:
        level, type_ = 2, '30min'
    elif str(level) in ['60', '60m', '60min', '1h']:
        level, type_ = 3, '60min'
    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, (25 - i) * 800, 800)) for i in range(26)], 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, (25 - i) * 800, 800)) for i in range(26)], 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)))

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
 def _get_bars(self, market, code, ktype):
     data = []
     tdx_market = self._trans_market(market)
     if tdx_market is None:
         print("tdx_market == None")
         return data
     
     tdx_ktype = self._trans_ktype(ktype)
     if tdx_ktype is None:
         print("tdx_ktype == None")
         return data
     
     try:
         ip = self.getParam('ip')
         port = self.getParam('port')
     except:
         ip = '119.147.212.81'
         port = 7709
         
     api = TdxHq_API(raise_exception=True)
     
     with api.connect(ip, port):
         if (market == 'SH' and code[:3] == '000') \
               or (market == 'SZ'  and code[:2] == '39'):
             for i in range(self._max[ktype]):
                 data += api.get_index_bars(tdx_ktype, tdx_market, code,
                                        (self._max[ktype]-1-i)*800,800)
         else:
             for i in range(self._max[ktype]):
                 data += api.get_security_bars(tdx_ktype, tdx_market, code,
                                             (self._max[ktype]-1-i)*800,800)
             
     return data

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_index_day(code, start_date, end_date, level='day', ip=best_ip, port=7709):
    '指数日线'
    api = TdxHq_API()
    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

    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, (25 - i) * 800, 800)) for i in range(26)], 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, (25 - i) * 800, 800)) for i in range(26)], 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]))

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
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)))

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_stock_latest(code, ip=best_ip, port=7709):
    code = [code] if isinstance(code, str) else code
    api = TdxHq_API(multithread=True)
    with api.connect(ip, port):
        data = pd.concat([api.to_df(api.get_security_bars(
            9, __select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
        return data\
            .assign(date=pd.to_datetime(data['datetime']
                                        .apply(lambda x: x[0:10])), date_stamp=data['datetime']
                    .apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
            .set_index('date', drop=False)\
            .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def ping(ip, port=7709, multithread=False):
    api = TdxHq_API(multithread=multithread)
    success = False
    starttime = time.time()
    try:
        with api.connect(ip, port, time_out=1):
            #x = api.get_security_count(0)
            #x = api.get_index_bars(7, 1, '000001', 800, 100)
            x = api.get_security_bars(7, 0, '000001', 800, 100)
            if x:
                success = True

    except Exception as e:
        success = False

    endtime = time.time()
    return (success, endtime - starttime, ip, port)

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_index_day(code, start_date, end_date, frequence='day', ip=None, port=None):
    '指数日线'
    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()
    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]))

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_security_bars(code, _type, lens, ip=None, port=None):
    """按bar长度推算数据

    Arguments:
        code {[type]} -- [description]
        _type {[type]} -- [description]
        lens {[type]} -- [description]

    Keyword Arguments:
        ip {[type]} -- [description] (default: {best_ip})
        port {[type]} -- [description] (default: {7709})

    Returns:
        [type] -- [description]
    """
    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()
    with api.connect(ip, port):
        data = pd.concat([api.to_df(api.get_security_bars(_select_type(_type), _select_market_code(
            code), code, (i - 1) * 800, 800)) for i in range(1, int(lens / 800) + 2)], 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).tail(lens)
        if data is not None:
            return data
        else:
            return None

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
def QA_fetch_get_stock_latest(code, ip=None, port=None):
    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
    code = [code] if isinstance(code, str) else code
    api = TdxHq_API(multithread=True)
    with api.connect(ip, port):
        data = pd.concat([api.to_df(api.get_security_bars(
            9, _select_market_code(item), item, 0, 1)).assign(code=item) for item in code], axis=0)
        return data\
            .assign(date=pd.to_datetime(data['datetime']
                                        .apply(lambda x: x[0:10])), date_stamp=data['datetime']
                    .apply(lambda x: QA_util_date_stamp(str(x[0:10]))))\
            .set_index('date', drop=False)\
            .drop(['year', 'month', 'day', 'hour', 'minute', 'datetime'], axis=1)

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
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
#.........这里部分代码省略.........

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
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
    """
    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()
    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
#.........这里部分代码省略.........

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import get_security_bars [as 别名]
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

        data = pd.concat([api.to_df(api.get_security_bars(level, __select_market_code(
            code), code, (9 - i) * 800, 800)) for i in range(10)], axis=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)\
                .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]))
        elif if_fq in ['01', 'qfq']:
            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)[::-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 ['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)
#.........这里部分代码省略.........