mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-04 14:47:49 +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
30 lines
972 B
Python
30 lines
972 B
Python
from __future__ import annotations
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import numpy as np
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from .base import Model
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from sklearn.base import BaseEstimator, ClassifierMixin
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class StaticMomentumModel(BaseEstimator, ClassifierMixin, Model):
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'''
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Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
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'''
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data_transformation = 'original'
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only_column = 'mom'
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predict_window_size = 'single_timestamp'
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def __init__(self, allow_short: bool) -> None:
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super().__init__()
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self.allow_short = allow_short
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def fit(self, X: np.ndarray, y: np.ndarray) -> None:
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# This is a static model, it can' learn anything
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pass
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def predict(self, X) -> np.ndarray:
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negative_class = -1.0 if self.allow_short == True else 0.0
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prediction = 1.0 if X[-1][0] > 0 else negative_class
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return np.array(prediction)
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def predict_proba(self, X) -> np.ndarray:
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return np.array([])
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