def get_best(self):
""" Produce a best instance of this strategy.
Args:
void.
Returns:
self: Euler instance. With the params and model having the
highest score among all combinations.
"""
# Log enter.
logger.info("Euler: Selecting best for %s.", self.instrument)
# Initialize the scores array and strategy parameters.
scores = []
strategy_params = util.get_euler_params()
# TODO: Do more than 1 run.
# Run for all predictive models.
counter = 0
for model in self.all_models:
scores_row = []
model_params = util.get_model_params(model)
# Try different model parameters.
for model_param in model_params:
scores_col = []
model = self.learner.build_model(model, 0.9, **model_param)
pred, _ = self.learner.test_model(model)
# And different strategy parameters, e.g. threshold.
for strategy_param in strategy_params:
self.set_params(**strategy_param)
# Do the dry run.
plot_name = '{0}_{1}.png'.format(self.instrument, counter)
balance = self.dry_run(pred, export_plot=plot_name)
# Determine the quality of the params via score.
scores_col.append(util.get_strategy_score(balance))
counter += 1
scores_row.append(scores_col)
scores.append(scores_row)
# Get the best and set the parameters to the best.
best = np.unravel_index(np.argmax(scores), np.array(scores).shape)
model = self.all_models[best[0]]
model_param = util.get_model_params(model)[best[1]]
model = self.learner.build_model(model, 1, **model_param)
logger.info("Best score is: %s.", str(np.array(scores)[best]))
self.set_params(**strategy_params[best[2]])
self.model = model
return self
def test_strategy_score(self):
""" Make sure the strategy score is calculated properly."""
# Make up some arbitrary balance vector.
balance = [0, 4.5, 8, -19, -8, 4, 5, -1, 4, 5, 33, 2, 0, 0, 3, 3, 3]
balance = np.array(balance)
score = util.get_strategy_score(balance)
# Check the score.
self.assertEqual(score, 11.0/17)
return
