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
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
from pydantic import BaseModel, validator
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from typing import Literal, Optional
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from labeling.types import EventFilter
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from models.base import Model
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from data_loader.types import DataCollection, DataSource
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from feature_extractors.types import FeatureExtractor
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from labeling.types import EventFilter, EventLabeller
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from sklearn.base import BaseEstimator
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from dataclasses import dataclass
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from transformations.base import Transformation
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# RawConfig is needed to ensure we can declare config presets here with static typing, we then convert it to Config
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class RawConfig(BaseModel):
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dimensionality_reduction_ratio: float
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n_features_to_select: int
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initial_window_size: int
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retrain_every: int
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scaler: Literal["normalize", "minmax", "standardize", "robust"]
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assets: list[str]
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target_asset: str
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other_assets: list[str]
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exogenous_data: list[str]
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load_non_target_asset: bool
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own_features: list[str]
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other_features: list[str]
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exogenous_features: list[str]
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event_filter: Literal["none", "cusum_vol", "cusum_fixed"]
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event_filter_multiplier: float
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remove_overlapping_events: bool
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labeling: Literal["two_class", "three_class_balanced", "three_class_imbalanced"]
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forecasting_horizon: int
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transaction_costs: float
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save_models: bool
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ensembling_method: Literal["voting_soft", "stacking"]
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directional_models: list[str]
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meta_models: list[str]
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@dataclass
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class Config:
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initial_window_size: int
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retrain_every: int
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assets: DataCollection
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target_asset: DataSource
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other_assets: DataCollection
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exogenous_data: DataCollection
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load_non_target_asset: bool
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own_features: list[tuple[str, FeatureExtractor, list[int]]]
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other_features: list[tuple[str, FeatureExtractor, list[int]]]
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exogenous_features: list[tuple[str, FeatureExtractor, list[int]]]
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event_filter: EventFilter
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remove_overlapping_events: bool
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labeling: EventLabeller
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forecasting_horizon: int
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transaction_costs: float
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no_of_classes: Literal["two", "three-balanced", "three-imbalanced"]
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save_models: bool
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mode: Literal["training", "inference"]
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directional_model: Model
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meta_model: Model
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transformations: list[Transformation]
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@validator("directional_model", "meta_model")
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def check_model(cls, v):
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assert isinstance(v, BaseEstimator)
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return v
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@validator("event_filter")
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def check_event_filter(cls, v):
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assert isinstance(v, EventFilter)
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return v
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@validator("labeling")
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def check_labeling(cls, v):
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assert isinstance(v, EventLabeller)
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return v
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# class Config:
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# arbitrary_types_allowed = True
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