量化交易机器人是什么？本质上，交易机器人是一个软件程序，能够间接与金融(通常应用API来获取和解释相干信息)进行交互，并且能够依据对市场数据的解释来公布交易订单。他们通过监控市场上的价格走势，并依据一套当时设置好的规定作出反应来做出这些决策。个别状况下，交易机器人会剖析市场上的交易数量、订单、价格和工夫等行为，并依据你的爱好来布局它们。
在策略设定好之后，机器人智能调配每一次进单条件，严格执行交易策略，交易策略，依据以后行情，开发对接v+hkkf5566实时进行云大数据调整。同时反对百种交易同时执行交易策略，每一个种类立线程，主动治理报价深度，策略计算，实时查看交易状况，实时执行。
// main.py import datetimeimport osimport reimport shutil from constant import Exchange import pandas as pd# pd.set_option('display.max_columns', None)# pd.set_option('display.max_rows', None) from base_dict import dateToContract, clear_off_trading class HftDataPath: @classmethod def data_path(cls, date_str: str, exchange: Exchange) -> str: path_str: str = '' if exchange == Exchange.DCE: path_str = f'{cls.source_data_dir()}/ht_dce_L2-{date_str}.log' elif exchange == Exchange.SHFE: pass elif exchange == Exchange.CZCE: pass elif exchange == Exchange.CFFEX: pass else: pass return path_str @classmethod def source_data_dir(cls) -> str: # return "C:/Users/tqz_trader/Desktop/hft_data_parser" return "E:\海通期货\大连L2\ITfuwu_dce_l2\ITfuwu_dce_l2_2021\ht_dce_L2-2021_05" @classmethod def output_data_dir(cls) -> str: # return "C:/Users/tqz_trader/Desktop/hft_data_parser/output_data" return f'{cls.source_data_dir()}/output_data' @classmethod def output_main_data_dir(cls) -> str: # return "C:/Users/tqz_trader/Desktop/hft_data_parser/output_main_data" return f'{cls.source_data_dir()}/output_main_data' def __init__(self, date_str: str, exchange: Exchange, filter_option: bool = True): path = HftDataPath.data_path(date_str=date_str, exchange=exchange) # path = f'ht_dce_L2-{date_str}.log' # path = f'test_{path}' assert os.path.exists(path), f'{path} not exists.' def split_by_symbol(self) -> {str: pd.DataFrame()}: assert self.__data is not None, f'__data is None' self.__rets_map: dict = {} for symbol in list(set(self.__data.Symbol.tolist())): sub_data = self.__data[self.__data['Symbol'] == symbol] sub_data.reset_index(inplace=True) del sub_data['index'] self.__rets_map[symbol] = sub_data return self def dump_to_csv(self): assert self.__rets_map is not None, f'__rets_map is None' # check output 目标目录 是否存在 target_dir = HftDataPath.output_data_dir() if os.path.exists(path=target_dir) is False: os.mkdir(target_dir) # check 种类&对应日期 目标目录 是否存在 symbol_dir_map: {str: str} = {} date_str = self.__date_str.replace("_", "") for symbol in self.rets_map().keys(): symbol_dir = f'{target_dir}/data{re.match(r"^[a-zA-Z]{1,3}", symbol).group()}00' if os.path.exists(path=symbol_dir) is False: os.mkdir(symbol_dir) symbol_date_dir = f'{symbol_dir}/{date_str}' if os.path.exists(path=symbol_date_dir) is False: os.mkdir(symbol_date_dir) symbol_dir_map[symbol] = symbol_date_dir # write to csv for symbol, symbol_df in self.rets_map().items(): target_path = f'{symbol_dir_map[symbol]}/{symbol}_{date_str}.csv' symbol_df.to_csv(target_path, index=False) def data(self) -> pd.DataFrame(): return self.__data def rets_map(self) -> {str: pd.DataFrame()}: return self.__rets_map def __reset_format(self): assert self.__data is not None, f'__data is None.' # Date self.__data['Date'] = self.__date_str.replace('_', '') # Time, Symbol self.__data.rename(columns={'UpdateTime': 'Time'}, inplace=True) self.__data.rename(columns={'ContractID': 'Symbol'}, inplace=True) # HighLimit, LowLimit self.__data['HighLimit'] = 1000000 self.__data['LowLimit'] = 0 # LocalTime, timestamp self.__data['LocalTime'] = self.__data.Time self.__data['LocalTime'] = self.__data.LocalTime.str.replace(':', '', regex=True) self.__data['LocalTime'] = self.__data.LocalTime.str.replace('.', '', regex=True) self.__data['timestamp'] = self.__data.LocalTime # LocalNS self.__data["LocalNS"] = pd.to_datetime(self.__date_str.replace('_', '-') + ' ' + self.__data['Time']).apply( lambda x: x.value) class HftDataManager: @classmethod def parser(cls, start_date_str: str, end_date_str: str, exchange: Exchange): """ clear source hft data :param start_date_str: begin date of source file :param end_date_str: end date of source file :param exchange: enum of exchang """ start_date, end_date = datetime.datetime.strptime(start_date_str, '%Y_%m_%d').date(), datetime.datetime.strptime(end_date_str, '%Y_%m_%d').date() while True: if start_date > end_date: break format_date_str = str(start_date).replace("-", "_") if os.path.exists(path=HftDataPath.data_path(date_str=format_date_str, exchange=exchange)): HftDataOperator(date_str=format_date_str, exchange=exchange).split_by_symbol().dump_to_csv() print(f'date({start_date}) parser over.') start_date += datetime.timedelta(days=1) @classmethod def refresh_main_contracts_dir(cls, target_contracts: list, start_date_str: str, end_date_str: str): """ refresh main contract's dir :param target_contracts: target contracts which need refresh :param start_date_str: begin date of source file :param end_date_str: end date of source file """ source_dir = HftDataPath.output_data_dir() assert os.path.exists(path=source_dir), f'{source_dir} not exist.' target_dir = HftDataPath.output_main_data_dir() if os.path.exists(path=target_dir) is True: shutil.rmtree(target_dir) os.mkdir(target_dir) for contract in target_contracts: start_date, end_date = datetime.datetime.strptime(start_date_str,'%Y_%m_%d').date(), datetime.datetime.strptime(end_date_str, '%Y_%m_%d').date() source_sub_dir = f'{source_dir}/data{contract}' target_sub_dir = f'{target_dir}/data{contract}' if os.path.exists(path=target_sub_dir) is False: os.mkdir(target_sub_dir) while True: if start_date > end_date: break format_date_str = str(start_date).replace('-', '') main_contract = dateToContract(instrument=contract, date=format_date_str) source_main_contract_dir = f'{source_sub_dir}/{format_date_str}' target_main_contract_dir = f'{target_sub_dir}/{format_date_str}' if os.path.exists(path=source_main_contract_dir): os.mkdir(target_main_contract_dir) source_main_contract_path = f'{source_main_contract_dir}/{main_contract}_{format_date_str}.csv' target_main_contract_path = f'{target_main_contract_dir}/{main_contract}_{format_date_str}.csv' if os.path.exists(path=source_main_contract_path): shutil.copy(src=source_main_contract_path, dst=target_main_contract_path) # clear main contract data. target_df = pd.read_csv(target_main_contract_path) clear_off_trading(target_df, "timestamp") target_df = target_df.drop_duplicates(subset='timestamp', keep='first', inplace=False) target_df = target_df.reset_index(drop=True) target_df.to_csv(target_main_contract_path, index=False) start_date += datetime.timedelta(days=1) if __name__ == '__main__': _start_date_str = "2021_05_14" _end_date_str = "2021_05_24" HftDataManager.parser(start_date_str=_start_date_str, end_date_str=_end_date_str, exchange=Exchange.DCE) # HftDataManager.refresh_main_contracts_dir( # target_contracts=['v00', 'eg00'], # start_date_str=_start_date_str, # end_date_str=_end_date_str # )