github   

1. tgw 

   https://github.com/tgw2023/tgw

2. AmazingQuant

https://github.com/zhanggao2013/AmazingQuant

一、主要步骤

1. 登录tgw

2. 获取交易日历

3. 获取代码表

4. 获取全市场日K线数据

5. 保存数据到本地

讯享网

6. 读取数据

二、获取交易日历

使用上证指数日线行情的时间戳作为交易日历，方便补全停牌股票的数据。

import os import time import pandas as pd import tgw class MyLogSpi(tgw.ILogSpi): def __init__(self) -> None: super().__init__() pass def OnLog(self, level, log, len): if level > 1: # print("TGW log: level: {} log: {}".format(level, log.strip('\n').strip('\r'))) pass def OnLogon(self, data): print("TGW Logon information: : ") print("api_mode : ", data.api_mode) print("logon json : ", data.logon_json) class UpdateKlineData(object): def __init__(self, end_date): self.field = ['kline_time', 'open_price', 'high_price', 'low_price', 'close_price', 'volume_trade', 'value_trade'] self.end_date = end_date self.calendar = [] self.code_sh_list, self.code_sz_list = [], [] def get_calendar(self): index_kline = tgw.ReqKline() index_kline.cq_flag = 0 index_kline.auto_complete = 1 index_kline.cyc_type = tgw.MDDatatype.kDayKline index_kline.begin_date =  index_kline.end_date = self.end_date index_kline.begin_time = 930 index_kline.end_time = 1700 index_kline.security_code = '000001' index_kline.market_type = tgw.MarketType.kSSE index_kline_df, _ = tgw.QueryKline(index_kline) self.calendar = list(index_kline_df['kline_time']) return self.calendar 

三、获取代码表

获取最新的代码表，用上交所、深交所过滤市场类型，用细分项过滤出股票。

讯享网 def get_code_list(self): code_table_df, _ = tgw.QueryCodeTable(return_df_format=True) code_table_shsz_df = code_table_df[code_table_df['market_type'].isin([101, 102])] self.code_sh_list = list( code_table_shsz_df[code_table_shsz_df['security_type'].isin(['ASH', 'KSH'])]['security_code']) self.code_sz_list = list( code_table_shsz_df[code_table_shsz_df['security_type'].isin(['1', '2', '3'])]['security_code']) return self.code_sh_list, self.code_sz_list

四、获取日K线数据

获取日线数据，并修改格式，分为6个dataframe（'open_price', 'high_price', 'low_price', 'close_price', 'volume_trade', 'value_trade'），以时间戳为index，以股票代码为code。

这样格式的dataframe，非常适合talib等方式计算时序指标。

def get_kline_data(self): stock_kline = tgw.ReqKline() stock_kline.cq_flag = 0 stock_kline.auto_complete = 1 stock_kline.cyc_type = tgw.MDDatatype.kDayKline stock_kline.begin_date =  stock_kline.end_date =  stock_kline.begin_time = 930 stock_kline.end_time = 1700 # 获取深圳/上海股票的行情 stock_data_dict = {} for market_type in [tgw.MarketType.kSSE, tgw.MarketType.kSZSE]: stock_kline.market_type = tgw.MarketType.kSSE code_list = self.code_sh_list if market_type == tgw.MarketType.kSZSE: stock_kline.market_type = tgw.MarketType.kSZSE code_list = self.code_sz_list for code in code_list[:5]: stock_kline.security_code = code stock_data_df, _ = tgw.QueryKline(stock_kline) stock_data_df = stock_data_df[self.field] stock_data_df.set_index(["kline_time"], inplace=True) stock_data_df = stock_data_df.reindex(self.calendar).fillna(method='ffill') stock_data_dict[code] = stock_data_df return stock_data_dict

五、保存和读取数据

保存数据的格式为HDF5，优势如下：

1. 读取速度极快，远超csv、excel等文件格式；

2. 读取更方便为dataframe格式，相对于mysql等数据库都更方便。

讯享网class SaveGetData(object): def __init__(self, path): self.path = path def save_data_to_hdf5(self, data_name, input_data, is_append=False): if not os.path.exists(self.path): os.makedirs(self.path) input_data.to_hdf(path + data_name + '.h5', key=data_name, mode='w', append=is_append) def get_local_data(self, data_name): return pd.read_hdf(self.path + data_name+'.h5')

六、main程序

最后这些展示main函数，执行以上步骤：

if __name__ == '__main__': # 第一步：设置日志spi，保证有日志输出 log_spi = MyLogSpi() tgw.SetLogSpi(log_spi) # 第二步，登录 cfg = tgw.Cfg() cfg.server_vip = "*.*.*.*" cfg.server_port = 8* cfg.username = "*" # 账号 cfg.password = "z" # 密码 success = tgw.Login(cfg, tgw.ApiMode.kInternetMode, './') # 互联网模式初始化，可指定证书文件地址 if not success: print("login fail") exit(0) kline_object = UpdateKlineData() code_sh_list, code_sz_list = kline_object.get_code_list() calendar_index = kline_object.get_calendar() stock_data_dict = kline_object.get_kline_data() local_data_path = 'D://AmazingQuant//local_data//' folder_name = 'market_data' sub_folder_name = 'kline_daily' sub_sub_folder_name = 'a_share' path = local_data_path + folder_name + '//' + sub_folder_name + '//' + sub_sub_folder_name + '//' save_get_data = SaveGetData(path) field_data_dict = {} for i in kline_object.field: if i != 'kline_time': field_data_pd = pd.DataFrame({key: value[i] for key, value in stock_data_dict.items()}) field_data_dict[i] = field_data_pd save_get_data.save_data_to_hdf5(i, field_data_pd) close = save_get_data.get_local_data('close_price')