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feat(Project): use SKLearn models directly, removed custom ensembling, use 5 minute data, batch inference, numba cusum filter (#192)
* 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
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@@ -17,7 +17,7 @@ def walk_forward_process_transformations(
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from_index: Optional[pd.Timestamp],
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transformations: list[Transformation],
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) -> TransformationsOverTime:
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transformations_over_time = [pd.Series(index=y.index).rename(t.get_name()) for t in transformations]
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transformations_over_time = [pd.Series(index=y.index, dtype='object').rename(t.get_name()) for t in transformations]
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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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