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
87 lines
2.9 KiB
Python
87 lines
2.9 KiB
Python
from data_loader import load_data
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from data_loader.process import check_data
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from reporting.saving import load_models
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from run_pipeline import run_pipeline
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from config.types import Config, RawConfig
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from config.presets import get_default_config
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from labeling.process import label_data
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import pandas as pd
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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_inference(preload_models: bool, fallback_raw_config: RawConfig):
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if preload_models:
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pipeline_outcome, config = load_models(None)
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else:
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pipeline_outcome, config = run_pipeline(
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project_name="price-prediction",
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with_wandb=False,
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raw_config=fallback_raw_config,
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)
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config.mode = "inference"
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__inference(config, pipeline_outcome)
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def __inference(config: Config, pipeline_outcome: PipelineOutcome):
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# 1. Load data, check for validity and process data
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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. Cancelling Inference."
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# 2. 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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inference_from: pd.Timestamp = X.index[len(X.index) - 1]
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# 3. Train directional models
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directional_training_outcome = train_directional_model(
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X=X,
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y=y,
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forward_returns=forward_returns,
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config=config,
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model=config.directional_model,
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transformations=config.transformations,
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from_index=inference_from,
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preloaded_training_step=pipeline_outcome.directional_training,
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)
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# 4. Run bet sizing on primary model's output
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bet_sizing_outcome = bet_sizing_with_meta_model(
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X=X,
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input_predictions=directional_training_outcome.predictions,
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y=y,
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forward_returns=forward_returns,
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model=config.meta_model,
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transformations=config.transformations,
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config=config,
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from_index=inference_from,
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transformations_over_time=pipeline_outcome.bet_sizing.transformations,
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preloaded_models=pipeline_outcome.bet_sizing.model_over_time,
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)
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return PipelineOutcome(directional_training_outcome, bet_sizing_outcome)
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if __name__ == "__main__":
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run_inference(preload_models=True, fallback_raw_config=get_default_config())
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