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Mark Aron SzulyovszkyandGitHub
d3d7184ea4
* feat(Models): added StaticAverageModel for average ensembling * feat(Models): made sure we only pipe in predictions to StaticAverageModel, added StaticNaiveModel as potential baseline * chore(Models): removed unnecessary commented out code
27 lines
753 B
Python
27 lines
753 B
Python
from models.base import Model
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import numpy as np
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class StaticMomentumModel(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_format = 'dataframe'
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data_scaling = 'unscaled'
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only_column = 'mom'
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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, y):
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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):
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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 clone(self):
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return self |