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92 lines
3.0 KiB
Python
92 lines
3.0 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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"""
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This example shows how OnlineTool works when we need update prediction.
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There are two parts including first_train and update_online_pred.
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Firstly, we will finish the training and set the trained models to the `online` models.
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Next, we will finish updating online predictions.
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"""
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import fire
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import qlib
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from qlib.config import REG_CN
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from qlib.model.trainer import task_train
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from qlib.workflow.online.utils import OnlineToolR
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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": {
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"class": "SignalRecord",
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"module_path": "qlib.workflow.record_temp",
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},
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}
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class UpdatePredExample:
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def __init__(
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self, provider_uri="~/.qlib/qlib_data/cn_data", region=REG_CN, experiment_name="online_srv", task_config=task
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):
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qlib.init(provider_uri=provider_uri, region=region)
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self.experiment_name = experiment_name
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self.online_tool = OnlineToolR(self.experiment_name)
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self.task_config = task_config
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def first_train(self):
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rec = task_train(self.task_config, experiment_name=self.experiment_name)
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self.online_tool.reset_online_tag(rec) # set to online model
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def update_online_pred(self):
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self.online_tool.update_online_pred()
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def main(self):
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self.first_train()
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self.update_online_pred()
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if __name__ == "__main__":
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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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## to see the whole process with your own parameters, use the command below
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# python update_online_pred.py main --experiment_name="your_exp_name"
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fire.Fire(UpdatePredExample)
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