mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
5482e3fc95
* refactor(Training): remove non-expanding window option * feat(Config): added `start_date` property * fix(Inference): added start_date here as well * fix(Linter): ran
111 lines
3.2 KiB
Python
111 lines
3.2 KiB
Python
from typing import Optional
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from config.types import Config, RawConfig
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from config.preprocess import preprocess_config
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from config.presets import get_default_config
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from data_loader.load import load_data
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from data_loader.process import check_data
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from labeling.process import label_data
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from reporting.wandb import launch_wandb, override_config_with_wandb_values
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from reporting.reporting import report_results
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from reporting.saving import save_models
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from training.directional_training import train_directional_model
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from training.bet_sizing import bet_sizing_with_meta_model
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from training.types import PipelineOutcome
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def run_pipeline(
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project_name: str, with_wandb: bool, raw_config: RawConfig
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) -> tuple[PipelineOutcome, Config]:
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wandb, config = setup_config(project_name, with_wandb, raw_config)
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outcome = run_training(config)
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report_results(
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outcome.directional_training.stats,
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outcome.get_output_stats(),
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outcome.get_output_weights(),
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config,
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wandb,
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)
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if config.save_models:
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save_models(outcome, config)
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return outcome, config
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def setup_config(
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project_name: str, with_wandb: bool, raw_config: RawConfig
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) -> tuple[Optional[object], Config]:
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wandb = None
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if with_wandb:
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wandb = launch_wandb(project_name=project_name, default_config=raw_config)
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raw_config = override_config_with_wandb_values(wandb, raw_config)
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config = preprocess_config(raw_config)
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return wandb, config
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def run_training(config: Config) -> PipelineOutcome:
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print("---> Load data, check for validity")
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X, returns = load_data(
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assets=config.assets,
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other_assets=config.other_assets,
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exogenous_data=config.exogenous_data,
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target_asset=config.target_asset,
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load_non_target_asset=config.load_non_target_asset,
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own_features=config.own_features,
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other_features=config.other_features,
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exogenous_features=config.exogenous_features,
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start_date=config.start_date,
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)
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assert check_data(X, config) == True, "Data is not valid."
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print("---> Filter for significant events when we want to trade, and label data")
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events, X, y, forward_returns = label_data(
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event_filter=config.event_filter,
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event_labeller=config.labeling,
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X=X,
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returns=returns,
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remove_overlapping_events=config.remove_overlapping_events,
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)
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print("---> Train directional models")
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directional_training_outcome = train_directional_model(
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X,
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y,
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forward_returns,
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config,
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config.directional_model,
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config.transformations,
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from_index=None,
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preloaded_training_step=None,
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)
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print("---> Run bet sizing on directional model's output")
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bet_sizing_outcomes = bet_sizing_with_meta_model(
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X,
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directional_training_outcome.predictions,
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y,
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forward_returns,
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config.meta_model,
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config.transformations,
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config,
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None,
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None,
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None,
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)
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return PipelineOutcome(directional_training_outcome, bet_sizing_outcomes)
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if __name__ == "__main__":
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run_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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