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be4646b4b7
* Intermediate version * Fix yaml template & Successfully run rolling * Be compatible with benchmark * Get same results with previous linear model * Black formatting * Update black * Update the placeholder mechanism * Update CI * Update CI * Upgrade Black * Fix CI and simplify code * Fix CI * Move the data processing caching mechanism into utils. * Adjusting DDG-DA * Organize import
90 lines
3.5 KiB
Python
90 lines
3.5 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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# pylint: skip-file
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# flake8: noqa
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import yaml
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import copy
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import os
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class TunerConfigManager:
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def __init__(self, config_path):
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if not config_path:
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raise ValueError("Config path is invalid.")
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self.config_path = config_path
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with open(config_path) as fp:
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config = yaml.safe_load(fp)
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self.config = copy.deepcopy(config)
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self.pipeline_ex_config = PipelineExperimentConfig(config.get("experiment", dict()), self)
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self.pipeline_config = config.get("tuner_pipeline", list())
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self.optim_config = OptimizationConfig(config.get("optimization_criteria", dict()), self)
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self.time_config = config.get("time_period", dict())
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self.data_config = config.get("data", dict())
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self.backtest_config = config.get("backtest", dict())
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self.qlib_client_config = config.get("qlib_client", dict())
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class PipelineExperimentConfig:
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def __init__(self, config, TUNER_CONFIG_MANAGER):
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"""
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:param config: The config dict for tuner experiment
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:param TUNER_CONFIG_MANAGER: The tuner config manager
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"""
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self.name = config.get("name", "tuner_experiment")
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# The dir of the config
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self.global_dir = config.get("dir", os.path.dirname(TUNER_CONFIG_MANAGER.config_path))
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# The dir of the result of tuner experiment
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self.tuner_ex_dir = config.get("tuner_ex_dir", os.path.join(self.global_dir, self.name))
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if not os.path.exists(self.tuner_ex_dir):
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os.makedirs(self.tuner_ex_dir)
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# The dir of the results of all estimator experiments
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self.estimator_ex_dir = config.get("estimator_ex_dir", os.path.join(self.tuner_ex_dir, "estimator_experiment"))
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if not os.path.exists(self.estimator_ex_dir):
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os.makedirs(self.estimator_ex_dir)
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# Get the tuner type
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self.tuner_module_path = config.get("tuner_module_path", "qlib.contrib.tuner.tuner")
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self.tuner_class = config.get("tuner_class", "QLibTuner")
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# Save the tuner experiment for further view
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tuner_ex_config_path = os.path.join(self.tuner_ex_dir, "tuner_config.yaml")
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with open(tuner_ex_config_path, "w") as fp:
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yaml.dump(TUNER_CONFIG_MANAGER.config, fp)
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class OptimizationConfig:
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def __init__(self, config, TUNER_CONFIG_MANAGER):
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self.report_type = config.get("report_type", "pred_long")
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if self.report_type not in [
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"pred_long",
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"pred_long_short",
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"pred_short",
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"excess_return_without_cost",
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"excess_return_with_cost",
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"model",
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]:
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raise ValueError(
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"report_type should be one of pred_long, pred_long_short, pred_short, excess_return_without_cost, excess_return_with_cost and model"
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)
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self.report_factor = config.get("report_factor", "information_ratio")
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if self.report_factor not in [
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"annualized_return",
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"information_ratio",
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"max_drawdown",
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"mean",
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"std",
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"model_score",
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"model_pearsonr",
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]:
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raise ValueError(
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"report_factor should be one of annualized_return, information_ratio, max_drawdown, mean, std, model_pearsonr and model_score"
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)
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self.optim_type = config.get("optim_type", "max")
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if self.optim_type not in ["min", "max", "correlation"]:
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raise ValueError("optim_type should be min, max or correlation")
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