mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +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
55 lines
2.6 KiB
Python
55 lines
2.6 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_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(project_name='price-prediction', with_wandb = False, sweep = False, raw_config=fallback_raw_config)
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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(config.event_filter, config.labeling, X, returns)
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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(X, y, forward_returns, config, config.directional_model, 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_outcome = bet_sizing_with_meta_model(X, directional_training_outcome.training.predictions, y, forward_returns, config.meta_model, config, 'meta', from_index = inference_from, transformations_over_time = pipeline_outcome.bet_sizing.meta_transformations, preloaded_models = pipeline_outcome.bet_sizing.meta_training.model_over_time)
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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_lightweight_ensemble_config()) |