mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-03 22:27:47 +00:00
Mark Aron Szulyovszky
cc7061b456
* fix(Core): correct forward returns calculation, classifiers are now working again, only train from when asset returns are available * feat(Utils): added get_first_valid_return_index() * feat(Ensemble): return models from `run_whole_pipeline` * feat(Ensemble): added ensemble step, fixed walk_forward_train_test predictions index confusion, * chore(Pipeline): remove unnecessary extra ensemble results dataframe * refactor(Core): removed unnecessary ensemble_train_predict, moved run_single_asset_trainig_pipeline to a separate file * feat(Training): added scaling on expanding window (the past) to walk_forward_train_test(), now printing out mean sharpe ratio * feat(CI): added environment.yml file * chore(Environment): update env.yml * feat(CI): added testing workflow * fix(CI): renamed enviroment.yml * fix(Tests): added missing new parameter to walk_forward_train_test()
55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
import pytest
|
|
import numpy as np
|
|
import pandas as pd
|
|
from training.walk_forward import walk_forward_train_test
|
|
from sklearn.base import BaseEstimator
|
|
|
|
no_of_rows = 100
|
|
|
|
def __generate_test_data(no_of_rows):
|
|
no_columns = 6
|
|
X = [[row] * no_columns for row in range(no_of_rows)]
|
|
assert X[0][0] == 0
|
|
assert X[1][0] == 1
|
|
assert X[2][0] == 2
|
|
assert X[3][0] == 3
|
|
X = pd.DataFrame(X)
|
|
|
|
y = [row+1 for row in range(no_of_rows)]
|
|
assert y[0] == 1
|
|
assert y[1] == 2
|
|
assert y[2] == 3
|
|
y = pd.Series(y)
|
|
|
|
return X, y
|
|
|
|
|
|
def test_walk_forward_train_test():
|
|
X, y = __generate_test_data(no_of_rows)
|
|
|
|
window_length = 10
|
|
class StubModel(BaseEstimator):
|
|
|
|
def fit(self, X, y):
|
|
assert len(X) == window_length
|
|
for i in range(len(X)):
|
|
assert X[i][0] + 1 == y[i]
|
|
|
|
def predict(self, X):
|
|
return np.array([X[0][0] + 1])
|
|
|
|
model = StubModel()
|
|
scaler = None
|
|
models, predictions = walk_forward_train_test(
|
|
model_name='test',
|
|
model=model,
|
|
X=X,
|
|
y=y,
|
|
target_returns=y,
|
|
window_size=window_length,
|
|
retrain_every=10,
|
|
scaler=scaler)
|
|
for i in range(window_length, no_of_rows):
|
|
predictions[i] == y[i]
|
|
|