64 lines
2.1 KiB
Python
64 lines
2.1 KiB
Python
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#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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import qlib
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from qlib.utils import init_instance_by_config
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from qlib.workflow import R
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from qlib.utils import flatten_dict
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from qlib.log import set_log_basic_config
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def update_gpu(config, gpu):
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config = config.copy()
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if "task" in config and "GPU" in config["task"]["model"]:
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config["task"]["model"]["GPU"] = gpu
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elif "model" in config and "GPU" in config["model"]:
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config["model"]["GPU"] = gpu
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elif "GPU" in config:
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config["GPU"] = gpu
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return config
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def update_market(config, market):
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config = config.copy()
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config["market"] = market
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config["data_handler_config"]["instruments"] = market
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return config
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def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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# model initiaiton
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print("")
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print("[{:}] - [{:}]: {:}".format(experiment_name, recorder_name, uri))
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print("dataset={:}".format(dataset))
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model = init_instance_by_config(task_config["model"])
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# start exp
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with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri):
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log_file = R.get_recorder().root_uri / "{:}.log".format(experiment_name)
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set_log_basic_config(log_file)
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# train model
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R.log_params(**flatten_dict(task_config))
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model.fit(dataset)
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recorder = R.get_recorder()
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R.save_objects(**{"model.pkl": model})
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# generate records: prediction, backtest, and analysis
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for record in task_config["record"]:
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record = record.copy()
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if record["class"] == "SignalRecord":
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srconf = {"model": model, "dataset": dataset, "recorder": recorder}
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record["kwargs"].update(srconf)
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sr = init_instance_by_config(record)
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sr.generate()
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else:
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rconf = {"recorder": recorder}
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record["kwargs"].update(rconf)
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ar = init_instance_by_config(record)
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ar.generate()
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