| |
| |
|
|
| import qlib |
| import fire |
|
|
| from datetime import datetime |
| from qlib.constant import REG_CN |
| from qlib.data.dataset.handler import DataHandlerLP |
| from qlib.utils import init_instance_by_config |
| from qlib.utils.pickle_utils import restricted_pickle_load |
| from qlib.tests.data import GetData |
|
|
|
|
| class RollingDataWorkflow: |
| MARKET = "csi300" |
| start_time = "2010-01-01" |
| end_time = "2019-12-31" |
| rolling_cnt = 5 |
|
|
| def _init_qlib(self): |
| """initialize qlib""" |
| provider_uri = "~/.qlib/qlib_data/cn_data" |
| GetData().qlib_data(target_dir=provider_uri, region=REG_CN, exists_skip=True) |
| qlib.init(provider_uri=provider_uri, region=REG_CN) |
|
|
| def _dump_pre_handler(self, path): |
| handler_config = { |
| "class": "Alpha158", |
| "module_path": "qlib.contrib.data.handler", |
| "kwargs": { |
| "start_time": self.start_time, |
| "end_time": self.end_time, |
| "instruments": self.MARKET, |
| "infer_processors": [], |
| "learn_processors": [], |
| }, |
| } |
| pre_handler = init_instance_by_config(handler_config) |
| pre_handler.config(dump_all=True) |
| pre_handler.to_pickle(path) |
|
|
| def _load_pre_handler(self, path): |
| with open(path, "rb") as file_dataset: |
| pre_handler = restricted_pickle_load(file_dataset) |
| return pre_handler |
|
|
| def rolling_process(self): |
| self._init_qlib() |
| self._dump_pre_handler("pre_handler.pkl") |
| pre_handler = self._load_pre_handler("pre_handler.pkl") |
|
|
| train_start_time = (2010, 1, 1) |
| train_end_time = (2012, 12, 31) |
| valid_start_time = (2013, 1, 1) |
| valid_end_time = (2013, 12, 31) |
| test_start_time = (2014, 1, 1) |
| test_end_time = (2014, 12, 31) |
|
|
| dataset_config = { |
| "class": "DatasetH", |
| "module_path": "qlib.data.dataset", |
| "kwargs": { |
| "handler": { |
| "class": "RollingDataHandler", |
| "module_path": "rolling_handler", |
| "kwargs": { |
| "start_time": datetime(*train_start_time), |
| "end_time": datetime(*test_end_time), |
| "fit_start_time": datetime(*train_start_time), |
| "fit_end_time": datetime(*train_end_time), |
| "infer_processors": [ |
| {"class": "RobustZScoreNorm", "kwargs": {"fields_group": "feature"}}, |
| ], |
| "learn_processors": [ |
| {"class": "DropnaLabel"}, |
| {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}}, |
| ], |
| "data_loader_kwargs": { |
| "handler_config": pre_handler, |
| }, |
| }, |
| }, |
| "segments": { |
| "train": (datetime(*train_start_time), datetime(*train_end_time)), |
| "valid": (datetime(*valid_start_time), datetime(*valid_end_time)), |
| "test": (datetime(*test_start_time), datetime(*test_end_time)), |
| }, |
| }, |
| } |
|
|
| dataset = init_instance_by_config(dataset_config) |
|
|
| for rolling_offset in range(self.rolling_cnt): |
| print(f"===========rolling{rolling_offset} start===========") |
| if rolling_offset: |
| dataset.config( |
| handler_kwargs={ |
| "start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]), |
| "end_time": datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]), |
| "processor_kwargs": { |
| "fit_start_time": datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]), |
| "fit_end_time": datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]), |
| }, |
| }, |
| segments={ |
| "train": ( |
| datetime(train_start_time[0] + rolling_offset, *train_start_time[1:]), |
| datetime(train_end_time[0] + rolling_offset, *train_end_time[1:]), |
| ), |
| "valid": ( |
| datetime(valid_start_time[0] + rolling_offset, *valid_start_time[1:]), |
| datetime(valid_end_time[0] + rolling_offset, *valid_end_time[1:]), |
| ), |
| "test": ( |
| datetime(test_start_time[0] + rolling_offset, *test_start_time[1:]), |
| datetime(test_end_time[0] + rolling_offset, *test_end_time[1:]), |
| ), |
| }, |
| ) |
| dataset.setup_data( |
| handler_kwargs={ |
| "init_type": DataHandlerLP.IT_FIT_SEQ, |
| } |
| ) |
|
|
| dtrain, dvalid, dtest = dataset.prepare(["train", "valid", "test"]) |
| print(dtrain, dvalid, dtest) |
| |
| print(f"===========rolling{rolling_offset} end===========") |
|
|
|
|
| if __name__ == "__main__": |
| fire.Fire(RollingDataWorkflow) |
|
|
