mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 06:07:50 +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
55 lines
2.4 KiB
Python
55 lines
2.4 KiB
Python
import pandas as pd
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from models.base import Model
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from training.types import ModelOverTime, TransformationsOverTime
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from utils.helpers import get_first_valid_return_index
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from tqdm import tqdm
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from typing import Optional
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from data_loader.types import ForwardReturnSeries, XDataFrame, ySeries
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def walk_forward_train(
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model: Model,
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X: XDataFrame,
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y: ySeries,
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forward_returns: ForwardReturnSeries,
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expanding_window: bool,
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window_size: int,
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retrain_every: int,
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from_index: Optional[pd.Timestamp],
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transformations_over_time: TransformationsOverTime,
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) -> ModelOverTime:
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models_over_time = pd.Series(index=y.index).rename(model.name)
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first_nonzero_return = max(get_first_valid_return_index(forward_returns), get_first_valid_return_index(X.iloc[:,0]), get_first_valid_return_index(y))
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train_from = first_nonzero_return + window_size + 1 if from_index is None else X.index.to_list().index(from_index)
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train_till = len(y)
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if model.only_column is not None:
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X = X[[column for column in X.columns if model.only_column in column]]
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if model.data_transformation == 'original':
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transformations_over_time = []
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for index in tqdm(range(train_from, train_till, retrain_every)):
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train_window_start = X.index[first_nonzero_return] if expanding_window else X.index[index - window_size - 1]
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train_window_end = X.index[index - 1]
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current_transformations = [transformation_over_time[index] for transformation_over_time in transformations_over_time]
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X_slice = X[train_window_start:train_window_end]
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for transformation in current_transformations:
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X_slice = transformation.transform(X_slice)
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X_slice = X_slice.to_numpy()
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y_slice = y[train_window_start:train_window_end].to_numpy()
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current_model = model.clone()
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current_model.initialize_network(input_dim = len(X_slice[0]), output_dim=1)
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current_model.fit(X_slice, y_slice)
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models_over_time[X.index[index]] = current_model
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for transformation_index, transformation in enumerate(current_transformations):
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transformations_over_time[transformation_index][X.index[index]] = transformation
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return models_over_time |