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77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
import qlib
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from qlib.config import REG_CN
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from qlib.utils import exists_qlib_data, init_instance_by_config
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import optuna
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provider_uri = "~/.qlib/qlib_data/cn_data"
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if not exists_qlib_data(provider_uri):
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print(f"Qlib data is not found in {provider_uri}")
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sys.path.append(str(scripts_dir))
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from get_data import GetData
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GetData().qlib_data(target_dir=provider_uri, region="cn")
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qlib.init(provider_uri=provider_uri, region="cn")
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market = "csi300"
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benchmark = "SH000300"
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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": market,
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}
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dataset_task = {
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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": "Alpha360",
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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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}
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dataset = init_instance_by_config(dataset_task["dataset"])
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def objective(trial):
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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": trial.suggest_uniform("colsample_bytree", 0.5, 1),
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"learning_rate": trial.suggest_uniform("learning_rate", 0, 1),
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"subsample": trial.suggest_uniform("subsample", 0, 1),
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"lambda_l1": trial.suggest_loguniform("lambda_l1", 1e-8, 1e4),
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"lambda_l2": trial.suggest_loguniform("lambda_l2", 1e-8, 1e4),
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"max_depth": 10,
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"num_leaves": trial.suggest_int("num_leaves", 1, 1024),
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"feature_fraction": trial.suggest_uniform("feature_fraction", 0.4, 1.0),
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"bagging_fraction": trial.suggest_uniform("bagging_fraction", 0.4, 1.0),
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"bagging_freq": trial.suggest_int("bagging_freq", 1, 7),
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"min_data_in_leaf": trial.suggest_int("min_data_in_leaf", 1, 50),
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"min_child_samples": trial.suggest_int("min_child_samples", 5, 100),
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},
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},
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}
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evals_result = dict()
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model = init_instance_by_config(task["model"])
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model.fit(dataset, evals_result=evals_result)
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return min(evals_result["valid"])
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study = optuna.Study(study_name="LGBM_360", storage="sqlite:///db.sqlite3")
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study.optimize(objective, n_jobs=6)
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