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
44 lines
1.1 KiB
Python
44 lines
1.1 KiB
Python
import pandas as pd
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from dataclasses import dataclass
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from typing import Optional, Dict
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PredictionsSeries = pd.Series
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WeightsSeries = pd.Series
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ProbabilitiesDataFrame = pd.DataFrame
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Stats = Dict[str, float]
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ModelOverTime = pd.Series
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TransformationsOverTime = list[pd.Series]
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@dataclass
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class TrainingOutcome:
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model_id: str
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predictions: PredictionsSeries
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probabilities: ProbabilitiesDataFrame
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stats: Optional[Stats]
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model_over_time: ModelOverTime
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@dataclass
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class BetSizingWithMetaOutcome:
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model_id: str
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meta_training: TrainingOutcome
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meta_transformations: TransformationsOverTime
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weights: WeightsSeries
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stats: Optional[Stats]
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@dataclass
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class DirectionalTrainingOutcome:
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training: TrainingOutcome
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transformations: TransformationsOverTime
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@dataclass
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class PipelineOutcome:
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directional_training: DirectionalTrainingOutcome
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bet_sizing: BetSizingWithMetaOutcome
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def get_output_weights(self) -> WeightsSeries:
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return self.bet_sizing.weights
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def get_output_stats(self) -> Stats:
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return self.bet_sizing.stats |