mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 19:27:47 +00:00
Mark Aron Szulyovszky
3eb3ea94e3
* refactor(Training): added InferenceResult & TrainedModel types * refactor(Pipeline): introduced TrainingOutcome, BetSizingWithMetaOutcome, etc. * fix(Pipeline): getting it to compile * refactor(WalkForward): separate preprocessing step * feat(Pipeline): separate out transformations processing step * refactor(Pipeline): use the Directional model terminology, put bet_sizing into pipeline instead of hiding it in a step * refactor(WalkForward): moved functions to separate folder * fix(WalkForward): use sparse array to store models, process transformations in parallel (lot faster) * fix(Tests): and evaluation * fix(Tests): for realz * fix(Inference): preloading everything now, renamed primary models to directional models * fix(BetSizing): was running transformations on the wrong data, oops * fix(BetSizing): concatenated on the wrong axis accidentally * fix(Reporting): able to use the new Stats type * fix(BetSizing): renamed int column names * fix(Portfolio): name the column properly * fix(Reporting): rename the correct Series, lol * fix(Inference): walk_forwad_inference() can deal with models not being aligned with the starting index * fix(WalkForward): accidentally using the wrong index * fix(WalkForward): use the correct indicies to fetch last model/transformations * fix(CI): changed the name of the results
63 lines
3.5 KiB
Python
63 lines
3.5 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_dev_config, get_default_ensemble_config, get_lightweight_ensemble_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_models
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from training.bet_sizing import bet_sizing_with_meta_models
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from training.ensemble import ensemble_weights
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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(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=fallback_raw_config)
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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, forward_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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events, X, y, forward_returns = label_data(config.event_filter, config.labeling, X, returns, forward_returns)
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inference_from: pd.Timestamp = X.index[len(X.index) - 2]
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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(config.event_filter, config.labeling, X, returns, forward_returns)
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# 3. Train directional models
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directional_training_outcome = train_directional_models(X, y, forward_returns, config, config.directional_models, from_index = inference_from, preloaded_training_step = pipeline_outcome.directional_training)
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# 4. Run bet sizing on primary model's output
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bet_sizing_outcomes = [bet_sizing_with_meta_models(X, training_outcome.predictions, y, forward_returns, config.meta_models, config, 'meta', from_index = inference_from, transformations_over_time = preloaded_outcome.meta_transformations, preloaded_models = [b.model_over_time for b in preloaded_outcome.meta_training]) for training_outcome, preloaded_outcome in zip(directional_training_outcome.training, pipeline_outcome.bet_sizing)]
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# 4. Ensemble weights
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ensemble_outcome = ensemble_weights([o.weights for o in bet_sizing_outcomes], forward_returns, y, config.no_of_classes)
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# 5. (Optional) Additional bet sizing on top of the ensembled weights
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ensemble_bet_sizing_outcome = bet_sizing_with_meta_models(X, ensemble_outcome.weights, y, forward_returns, config.meta_models, config, 'ensemble', from_index = inference_from, transformations_over_time = pipeline_outcome.secondary_bet_sizing.meta_transformations, preloaded_models= [b.model_over_time for b in pipeline_outcome.secondary_bet_sizing.meta_training]) if len(config.meta_models) > 0 else None
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return PipelineOutcome(directional_training_outcome, bet_sizing_outcomes, ensemble_outcome, ensemble_bet_sizing_outcome)
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if __name__ == '__main__':
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run_inference(preload_models=True, fallback_raw_config=get_lightweight_ensemble_config()) |