Python TdxHq_API.to_df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 to_df [as 别名]
def QA_fetch_get_stock_block(ip=None, port=None):
    '板块数据'
    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_and_parse_block_info("block_gn.dat")).assign(type='gn'),
                          api.to_df(api.get_and_parse_block_info(
                              "block.dat")).assign(type='yb'),
                          api.to_df(api.get_and_parse_block_info(
                              "block_zs.dat")).assign(type='zs'),
                          api.to_df(api.get_and_parse_block_info("block_fg.dat")).assign(type='fg')])

        if len(data) > 10:
            return data.assign(source='tdx').drop(['block_type', 'code_index'], axis=1).set_index('code', drop=False, inplace=False).drop_duplicates()
        else:
            QA_util_log_info('Wrong with fetch block ')

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import to_df [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 to_df [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 to_df [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 to_df [as 别名]
def QA_fetch_get_stock_info(code, ip=None, port=None):
    '股票基本信息'
    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()
    market_code = _select_market_code(code)
    with api.connect(ip, port):
        return api.to_df(api.get_finance_info(market_code, code))

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import to_df [as 别名]
def QA_fetch_get_stock_transaction_realtime(code, ip=None, port=None):
    '实时分笔成交 包含集合竞价 buyorsell 1--sell 0--buy 2--盘前'
    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()
    try:
        with api.connect(ip, port):
            data = pd.DataFrame()
            data = pd.concat([api.to_df(api.get_transaction_data(
                _select_market_code(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()
            return 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))).set_index('datetime', drop=False, inplace=False)
    except:
        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 to_df [as 别名]
def QA_fetch_get_stock_xdxr(code, ip=None, port=None):
    '除权除息'
    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()
    market_code = _select_market_code(code)
    with api.connect(ip, port):
        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 = api.to_df(api.get_xdxr_info(market_code, code))
        if len(data) >= 1:
            data = data\
                .assign(date=pd.to_datetime(data[['year', 'month', 'day']]))\
                .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)
            return data.assign(date=data['date'].apply(lambda x: str(x)[0:10]))
        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 to_df [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 to_df [as 别名]
def QA_fetch_get_stock_realtime(code=['000001', '000002'], ip=best_ip, port=7709):
    api = TdxHq_API()
    __data = pd.DataFrame()
    with api.connect(ip, port):
        code = [code] if type(code) is str else code
        for id_ in range(int(len(code) / 80) + 1):
            __data = __data.append(api.to_df(api.get_security_quotes(
                [(__select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
            __data['datetime'] = datetime.datetime.now()
        data = __data[['datetime', 'code', 'open', 'high', 'low', 'price']]
        return data.set_index('code', drop=False, inplace=False)

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import to_df [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 to_df [as 别名]
def QA_fetch_get_stock_list(type_='stock', ip=best_ip, port=7709):

    api = TdxHq_API()
    with api.connect(ip, port):
        data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
            ['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
        if type_ in ['stock', 'gp']:
            return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 <= 30][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 != 2],
                              data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 100000 == 6]]).assign(code=data['code'].apply(lambda x: str(x)))
        elif type_ in ['index', 'zs']:

            return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 >= 399],
                              data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 1000 == 0]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))

        elif type_ in ['etf', 'ETF']:
            return pd.concat([data[data['sse'] == 'sz'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 15],
                              data[data['sse'] == 'sh'][data.assign(code=data['code'].apply(lambda x: int(x)))['code'] // 10000 == 51]]).sort_index().assign(code=data['code'].apply(lambda x: str(x)))

        else:
            return data.assign(code=data['code'].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 to_df [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 to_df [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 to_df [as 别名]
def QA_fetch_depth_market_data(code=['000001', '000002'], 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
    api = TdxHq_API()
    __data = pd.DataFrame()
    with api.connect(ip, port):
        code = [code] if type(code) is str else code
        for id_ in range(int(len(code) / 80) + 1):
            __data = __data.append(api.to_df(api.get_security_quotes(
                [(_select_market_code(x), x) for x in code[80 * id_:80 * (id_ + 1)]])))
            __data['datetime'] = datetime.datetime.now()
        data = __data[['datetime', 'active1', 'active2', 'last_close', 'code', 'open', 'high', 'low', 'price', 'cur_vol',
                       's_vol', 'b_vol', 'vol', 'ask1', 'ask_vol1', 'bid1', 'bid_vol1', 'ask2', 'ask_vol2',
                       'bid2', 'bid_vol2', 'ask3', 'ask_vol3', 'bid3', 'bid_vol3', 'ask4',
                       'ask_vol4', 'bid4', 'bid_vol4', 'ask5', 'ask_vol5', 'bid5', 'bid_vol5']]
        return data.set_index(['datetime', 'code'], drop=False, inplace=False)

# 需要导入模块: from pytdx.hq import TdxHq_API [as 别名]
# 或者: from pytdx.hq.TdxHq_API import to_df [as 别名]
def QA_fetch_get_stock_list(type_='stock', 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
    api = TdxHq_API()
    with api.connect(ip, port):
        data = pd.concat([pd.concat([api.to_df(api.get_security_list(j, i * 1000)).assign(sse='sz' if j == 0 else 'sh').set_index(
            ['code', 'sse'], drop=False) for i in range(int(api.get_security_count(j) / 1000) + 1)], axis=0) for j in range(2)], axis=0)
        #data.code = data.code.apply(int)
        sz = data.query('sse=="sz"')
        sh = data.query('sse=="sh"')

        sz = sz.assign(sec=sz.code.apply(for_sz))
        sh = sh.assign(sec=sh.code.apply(for_sh))

        if type_ in ['stock', 'gp']:

            return pd.concat([sz, sh]).query('sec=="stock_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))

        elif type_ in ['index', 'zs']:

            return pd.concat([sz, sh]).query('sec=="index_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))
            #.assign(szm=data['name'].apply(lambda x: ''.join([y[0] for y in lazy_pinyin(x)])))\
            #.assign(quanpin=data['name'].apply(lambda x: ''.join(lazy_pinyin(x))))
        elif type_ in ['etf', 'ETF']:
            return pd.concat([sz, sh]).query('sec=="etf_cn"').sort_index().assign(name=data['name'].apply(lambda x: str(x)[0:6]))

        else:
            return data.assign(code=data['code'].apply(lambda x: str(x))).assign(name=data['name'].apply(lambda x: str(x)[0:6]))