mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
b5ddee8dce
* feat(HPO): added `run_hpo` script * fix(Linter): ran * feat(HPO): removed any reference to sweep (superseeded by optuna) * fix(HPO): optimize for sharpe * fix(Config): removed glassnode data, save trials from hpo * feat(Labelling): added three-balanced method works again * fix(BetSizing): set the correct class labels * fix(HPO): powerset should return what's expected, added two new normalization methods * fix(Linter): ran * fix(DataLoader): sort the dataframe when fetching data * fix(Config): only take z-score of other assets
32 lines
1.0 KiB
Python
32 lines
1.0 KiB
Python
from run_pipeline import run_training, setup_config
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from config.types import RawConfig
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from data_loader.collections import data_collections
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from reporting.reporting import report_results
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from config.presets import get_default_config
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def run_multi_asset_pipeline(
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project_name: str, with_wandb: bool, raw_config: RawConfig
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):
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collection = data_collections["fivemin_crypto"]
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for asset in collection:
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print(f"# Predicting asset: {asset.file_name}\n")
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raw_config.target_asset = asset.file_name
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wandb, config = setup_config(project_name, with_wandb, raw_config)
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pipeline_outcome = run_training(config)
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report_results(
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pipeline_outcome.directional_training.training.stats,
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pipeline_outcome.get_output_stats(),
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pipeline_outcome.get_output_weights(),
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config,
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wandb,
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)
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if __name__ == "__main__":
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run_multi_asset_pipeline(
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project_name="price-prediction",
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with_wandb=False,
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raw_config=get_default_config(),
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)
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