mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-13 19:08:06 +00:00
Mark Aron SzulyovszkyandGitHub
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
33 lines
1.0 KiB
Python
33 lines
1.0 KiB
Python
from __future__ import annotations
|
|
from models.base import Model
|
|
import numpy as np
|
|
|
|
class StaticMomentumModel(Model):
|
|
'''
|
|
Model that uses only one feature: momentum. It's positive if momentum is greater than 0, otherwise it's negative.
|
|
'''
|
|
|
|
method = 'classification'
|
|
data_transformation = 'original'
|
|
only_column = 'mom'
|
|
model_type = 'static'
|
|
predict_window_size = 'single_timestamp'
|
|
|
|
def __init__(self, allow_short: bool) -> None:
|
|
super().__init__()
|
|
self.allow_short = allow_short
|
|
|
|
def fit(self, X: np.ndarray, y: np.ndarray) -> None:
|
|
# This is a static model, it can' learn anything
|
|
pass
|
|
|
|
def predict(self, X) -> tuple[float, np.ndarray]:
|
|
negative_class = -1.0 if self.allow_short == True else 0.0
|
|
prediction = 1.0 if X[-1][0] > 0 else negative_class
|
|
return (prediction, np.array([]))
|
|
|
|
def clone(self) -> StaticMomentumModel:
|
|
return self
|
|
|
|
def initialize_network(self, input_dim:int, output_dim:int):
|
|
pass |