mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
9d47ee942d
* feat(Project): use 5 minute data, running training in parallel, sped up cusum filter by 10x with numba * fix(WalkForward): inference mini-batch parallelization * fix(WalkForward): don't use the parallel version of any of the functions * feat(CI): download the data required * fix(Project): 5min_crypto folder added * fix(Evaluate): make sure we have numerical stability in returns * feat(Models): use SKLearn models directly to enable composability * feat(Inference): batched inference now working, added forecasting_horizon * fix(Inference): works again * fix(Inference) * chore(Models): remove unused Ensemble model * fix(Labeller): don't just forward shift returns, also take the sum of the data happened until then * Update test.yml
71 lines
3.0 KiB
Python
71 lines
3.0 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_ensemble_config, get_lightweight_ensemble_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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import ray
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ray.init()
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def run_pipeline(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> tuple[PipelineOutcome, Config]:
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wandb, config = __setup_config(project_name, with_wandb, sweep, raw_config)
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pipeline_outcome = __run_training(config)
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report_results(pipeline_outcome.directional_training.training.stats, pipeline_outcome.get_output_stats(), pipeline_outcome.get_output_weights(), config, wandb, sweep)
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save_models(pipeline_outcome, config)
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return pipeline_outcome, config
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def __setup_config(project_name:str, with_wandb: bool, sweep: bool, raw_config: RawConfig) -> 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, sweep=sweep)
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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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)
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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(config.event_filter, config.labeling, X, returns)
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print("---> Train directional models")
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directional_training_outcome = train_directional_model(X, y, forward_returns, config, config.directional_model, from_index = None, preloaded_training_step = None)
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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(X, directional_training_outcome.training.predictions, y, forward_returns, config.meta_model, config, 'meta', None, None, None)
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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(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=get_lightweight_ensemble_config()) |