utils/types.py

from typing import Callable, Union, Literal
import pandas as pd

Period = int
IsLogReturn = bool
FeatureExtractor = Callable[[pd.DataFrame, Period, IsLogReturn], Union[pd.DataFrame, pd.Series]]
Name = str
FeatureExtractorConfig = tuple[Name, FeatureExtractor, list[Period]]
Path = str
FileName = str
DataSource = list[tuple[Path, FileName]]
DataCollection = list[DataSource]

ScalerTypes = Literal['normalize', 'minmax', 'standardize', 'none']
feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105 ) 2022-01-04 11:44:35 +01:00			`from typing import Callable, Union, Literal`
feat(Data): added feature extractors, and feature extractor presets, removed a bunch of custom arguments from load_data (#42 ) 2021-12-19 12:14:59 +01:00			`import pandas as pd`
fix(WalkForward): major bug where we passed in "window of windows of data" is resolved, refactored walk_forward_train_test() and load_data() 2021-12-14 21:07:03 +01:00
feat(Data): added feature extractors, and feature extractor presets, removed a bunch of custom arguments from load_data (#42 ) 2021-12-19 12:14:59 +01:00			`Period = int`
			`IsLogReturn = bool`
feat(Config): feature extractors are enabled one-by-one with a bool, added previous model to model.fit() (#77 ) 2021-12-23 10:35:20 +01:00			`FeatureExtractor = Callable[[pd.DataFrame, Period, IsLogReturn], Union[pd.DataFrame, pd.Series]]`
			`Name = str`
feat(Data): create DataSource, DataCollection, added hourly crypto data (#96 ) 2021-12-31 19:04:27 +01:00			`FeatureExtractorConfig = tuple[Name, FeatureExtractor, list[Period]]`
			`Path = str`
			`FileName = str`
			`DataSource = list[tuple[Path, FileName]]`
feat(DataLoader): caching MVP, added ability to use standard scaling for exogenous data, scaling is now also done before feature selection (#105 ) 2022-01-04 11:44:35 +01:00			`DataCollection = list[DataSource]`

			`ScalerTypes = Literal['normalize', 'minmax', 'standardize', 'none']`