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78 lines
2.4 KiB
Python
78 lines
2.4 KiB
Python
import qlib
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from qlib.model.trainer import task_train
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from qlib.workflow.task.update import ModelUpdater
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from qlib.config import REG_CN
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import fire
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data_handler_config = {
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"start_time": "2008-01-01",
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"end_time": "2020-08-01",
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"fit_start_time": "2008-01-01",
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"fit_end_time": "2014-12-31",
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"instruments": "csi100",
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}
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task = {
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"model": {
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"class": "LGBModel",
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"module_path": "qlib.contrib.model.gbdt",
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"kwargs": {
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"loss": "mse",
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"colsample_bytree": 0.8879,
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"learning_rate": 0.0421,
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"subsample": 0.8789,
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"lambda_l1": 205.6999,
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"lambda_l2": 580.9768,
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"max_depth": 8,
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"num_leaves": 210,
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"num_threads": 20,
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},
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},
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"dataset": {
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"class": "DatasetH",
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"module_path": "qlib.data.dataset",
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"kwargs": {
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"handler": {
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"class": "Alpha158",
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"module_path": "qlib.contrib.data.handler",
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"kwargs": data_handler_config,
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},
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"segments": {
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"train": ("2008-01-01", "2014-12-31"),
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"valid": ("2015-01-01", "2016-12-31"),
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"test": ("2017-01-01", "2020-08-01"),
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},
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},
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},
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"record": {"class": "SignalRecord", "module_path": "qlib.workflow.record_temp",},
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}
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provider_uri = "~/.qlib/qlib_data/cn_data" # target_dir
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def first_train(experiment_name="online_svr"):
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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model_updater = ModelUpdater(experiment_name)
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rid = task_train(task_config=task, experiment_name=experiment_name)
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model_updater.reset_online_model(rid)
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def update_online_pred(experiment_name="online_svr"):
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qlib.init(provider_uri=provider_uri, region=REG_CN)
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model_updater = ModelUpdater(experiment_name)
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print("Here are the online models waiting for update:")
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for rid, rec in model_updater.list_online_model().items():
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print(rid)
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model_updater.update_online_pred()
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if __name__ == '__main__':
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fire.Fire()
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# to train a model and set it to online model, use the command below
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# python update_online_pred.py first_train
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# to update online predictions once a day, use the command below
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# python update_online_pred.py update_online_pred
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