def test_dry_run(self):
""" Test dry run in euler. Check that it tests a model on historical
data properly.
"""
# Initialize model with fixed random state.
model = tree.DecisionTreeRegressor(random_state=888)
# Initialize learner and force load test data.
learner = Learner("GBP_USD")
learner.data_mat = base.read_features(self.tmp_clean_file)
# Build model and test preliminary results.
learner.build_model(model, 0.78)
pred, _ = learner.test_model(model)
# Initialize Euler and force load test data.
strategy = euler.Euler("GBP_USD")
strategy.set_params(unit_shape=common.UNIT_LINEAR, threshold=85)
strategy.test_data = transformer.read_raw_file(self.tmp_raw_file)
balance = strategy.dry_run(pred)
# Test the results.
self.assertEqual(balance.size, 628)
self.assertEqual(round(balance[0], 4), -0.1965)
self.assertEqual(round(balance[210], 4), 0.3467)
self.assertEqual(round(balance[-143], 4), -19.6579)
self.assertEqual(round(balance[-30], 4), -20.7953)
self.assertEqual(round(sum(balance), 4), -4088.2712)
return
def __init__(self, instrument):
""" Initialize the Learner class.
Args:
instrument: string. The currency pair. e.g. 'EUR_USD'.
Returns:
void.
"""
self.instrument = instrument
self.data_file = util.get_clean_data(instrument)
self.data_mat = base.read_features(self.data_file)
self.sample_index = 0
# Checking input read from file.
assert self.data_mat.shape[1] == 8
return
def test_leaner_tree_regressor(self):
""" Test the learner builds and tests a tree regressor properly."""
# Initialize model with fixed random state.
model = tree.DecisionTreeRegressor(random_state=888)
# Initialize learner and force load test data.
learner = Learner("GBP_USD")
learner.data_mat = base.read_features(self.tmp_file)
# Build model and test preliminary results.
learner.build_model(model, 0.78)
pred, result = learner.test_model(model)
# Test the results.
self.assertEqual(pred.size, 628)
self.assertEqual(pred[0], -160.2)
self.assertEqual(pred[200], -31.0)
self.assertEqual(pred[-30], -53.6)
self.assertEqual(len(result), 2)
self.assertEqual(round(result['ave_diff'], 4), 88.3980)
self.assertEqual(round(result['prop_op'], 4), 0.4341)
return
