def stock_zh_a_daily(symbol: str='sz000613', adjust: str='qfq') -> pd.DataFrame:
    '\n    新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP\n    :param symbol: sh600000\n    :type symbol: str\n    :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子\n    :type adjust: str\n    :return: specific data\n    :rtype: pandas.DataFrame\n    '
    res = requests.get(zh_sina_a_stock_hist_url.format(symbol))
    js_code = execjs.compile(hk_js_decode)
    dict_list = js_code.call('d', res.text.split('=')[1].split(';')[0].replace('"', ''))
    data_df = pd.DataFrame(dict_list)
    data_df['date'] = data_df['date'].str.split('T', expand=True).iloc[:, 0]
    data_df.index = pd.to_datetime(data_df['date'])
    del data_df['date']
    data_df = data_df.astype('float')
    r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol))
    amount_data_json = demjson.decode(r.text[r.text.find('['):(r.text.rfind(']') + 1)])
    amount_data_df = pd.DataFrame(amount_data_json)
    amount_data_df.index = pd.to_datetime(amount_data_df.date)
    del amount_data_df['date']
    temp_df = pd.merge(data_df, amount_data_df, left_index=True, right_index=True, how='left')
    temp_df.fillna(method='ffill', inplace=True)
    temp_df = temp_df.astype(float)
    temp_df['amount'] = (temp_df['amount'] * 10000)
    temp_df['turnover'] = (temp_df['volume'] / temp_df['amount'])
    temp_df.columns = ['open', 'high', 'low', 'close', 'volume', 'outstanding_share', 'turnover']
    if (adjust == ''):
        return temp_df
    if (adjust == 'hfq'):
        res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
        hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        hfq_factor_df.columns = ['date', 'hfq_factor']
        hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
        del hfq_factor_df['date']
        temp_df = pd.merge(temp_df, hfq_factor_df, left_index=True, right_index=True, how='left')
        temp_df.fillna(method='ffill', inplace=True)
        temp_df = temp_df.astype(float)
        temp_df['open'] = (temp_df['open'] * temp_df['hfq_factor'])
        temp_df['high'] = (temp_df['high'] * temp_df['hfq_factor'])
        temp_df['close'] = (temp_df['close'] * temp_df['hfq_factor'])
        temp_df['low'] = (temp_df['low'] * temp_df['hfq_factor'])
        return temp_df.iloc[:, :(- 1)]
    if (adjust == 'qfq'):
        res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
        qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        qfq_factor_df.columns = ['date', 'qfq_factor']
        qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
        del qfq_factor_df['date']
        temp_df = pd.merge(temp_df, qfq_factor_df, left_index=True, right_index=True, how='left')
        temp_df.fillna(method='ffill', inplace=True)
        temp_df = temp_df.astype(float)
        temp_df['open'] = (temp_df['open'] / temp_df['qfq_factor'])
        temp_df['high'] = (temp_df['high'] / temp_df['qfq_factor'])
        temp_df['close'] = (temp_df['close'] / temp_df['qfq_factor'])
        temp_df['low'] = (temp_df['low'] / temp_df['qfq_factor'])
        return temp_df.iloc[:, :(- 1)]
    if (adjust == 'hfq-factor'):
        res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
        hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        hfq_factor_df.columns = ['date', 'hfq_factor']
        hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
        del hfq_factor_df['date']
        return hfq_factor_df
    if (adjust == 'qfq-factor'):
        res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
        qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        qfq_factor_df.columns = ['date', 'qfq_factor']
        qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
        del qfq_factor_df['date']
        return qfq_factor_df
新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP
:param symbol: sh600000
:type symbol: str
:param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子
:type adjust: str
:return: specific data
:rtype: pandas.DataFrame
def stock_zh_a_daily(symbol: str='sz000613', adjust: str='qfq') -> pd.DataFrame:
    '\n    新浪财经-A股-个股的历史行情数据, 大量抓取容易封IP\n    :param symbol: sh600000\n    :type symbol: str\n    :param adjust: 默认为空: 返回不复权的数据; qfq: 返回前复权后的数据; hfq: 返回后复权后的数据; hfq-factor: 返回后复权因子; hfq-factor: 返回前复权因子\n    :type adjust: str\n    :return: specific data\n    :rtype: pandas.DataFrame\n    '
    res = requests.get(zh_sina_a_stock_hist_url.format(symbol))
    js_code = execjs.compile(hk_js_decode)
    dict_list = js_code.call('d', res.text.split('=')[1].split(';')[0].replace('"', ))
    data_df = pd.DataFrame(dict_list)
    data_df['date'] = data_df['date'].str.split('T', expand=True).iloc[:, 0]
    data_df.index = pd.to_datetime(data_df['date'])
    del data_df['date']
    data_df = data_df.astype('float')
    r = requests.get(zh_sina_a_stock_amount_url.format(symbol, symbol))
    amount_data_json = demjson.decode(r.text[r.text.find('['):(r.text.rfind(']') + 1)])
    amount_data_df = pd.DataFrame(amount_data_json)
    amount_data_df.index = pd.to_datetime(amount_data_df.date)
    del amount_data_df['date']
    temp_df = pd.merge(data_df, amount_data_df, left_index=True, right_index=True, how='left')
    temp_df.fillna(method='ffill', inplace=True)
    temp_df = temp_df.astype(float)
    temp_df['amount'] = (temp_df['amount'] * 10000)
    temp_df['turnover'] = (temp_df['volume'] / temp_df['amount'])
    temp_df.columns = ['open', 'high', 'low', 'close', 'volume', 'outstanding_share', 'turnover']
    if (adjust == ):
        return temp_df
    if (adjust == 'hfq'):
        res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
        hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        hfq_factor_df.columns = ['date', 'hfq_factor']
        hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
        del hfq_factor_df['date']
        temp_df = pd.merge(temp_df, hfq_factor_df, left_index=True, right_index=True, how='left')
        temp_df.fillna(method='ffill', inplace=True)
        temp_df = temp_df.astype(float)
        temp_df['open'] = (temp_df['open'] * temp_df['hfq_factor'])
        temp_df['high'] = (temp_df['high'] * temp_df['hfq_factor'])
        temp_df['close'] = (temp_df['close'] * temp_df['hfq_factor'])
        temp_df['low'] = (temp_df['low'] * temp_df['hfq_factor'])
        return temp_df.iloc[:, :(- 1)]
    if (adjust == 'qfq'):
        res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
        qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        qfq_factor_df.columns = ['date', 'qfq_factor']
        qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
        del qfq_factor_df['date']
        temp_df = pd.merge(temp_df, qfq_factor_df, left_index=True, right_index=True, how='left')
        temp_df.fillna(method='ffill', inplace=True)
        temp_df = temp_df.astype(float)
        temp_df['open'] = (temp_df['open'] / temp_df['qfq_factor'])
        temp_df['high'] = (temp_df['high'] / temp_df['qfq_factor'])
        temp_df['close'] = (temp_df['close'] / temp_df['qfq_factor'])
        temp_df['low'] = (temp_df['low'] / temp_df['qfq_factor'])
        return temp_df.iloc[:, :(- 1)]
    if (adjust == 'hfq-factor'):
        res = requests.get(zh_sina_a_stock_hfq_url.format(symbol))
        hfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        hfq_factor_df.columns = ['date', 'hfq_factor']
        hfq_factor_df.index = pd.to_datetime(hfq_factor_df.date)
        del hfq_factor_df['date']
        return hfq_factor_df
    if (adjust == 'qfq-factor'):
        res = requests.get(zh_sina_a_stock_qfq_url.format(symbol))
        qfq_factor_df = pd.DataFrame(eval(res.text.split('=')[1].split('\n')[0])['data'])
        qfq_factor_df.columns = ['date', 'qfq_factor']
        qfq_factor_df.index = pd.to_datetime(qfq_factor_df.date)
        del qfq_factor_df['date']
        return qfq_factor_df