stock_period = StockPeriod(symbol, settings.stock_period_start, today, data_source = settings.data_source)
first_learners = map(lambda fs: EnsembleLearner.machine_learner_for_features_string(symbol, fs, stock_period),
map(lambda x: x.feature_string, found_ensemble.found_forests))
prediction_performances = []
val_prediction_performances = []
another_domain_pp = []
use_another_domain = (settings.predict_cache_end_time == dt.datetime(2013,2,1))
for found_ensemble in gse.found_ensembles:
all_predictions = map(lambda ff: ff.test_predictions, found_ensemble.found_forests)
#all_predictions = [found_ensemble.found_forests[0].test_predictions]
joined_cached_predicitons = EnsembleLearner.process_predictions(all_predictions, 0.1)
test_period_predicitons = joined_cached_predicitons[settings.test_start_time:settings.test_end_time]
#test_period_predicitons = joined_cached_predicitons[settings.val_end_time:(settings.val_end_time + relativedelta(months=1) )]
pp = PredictionPerformance(test_period_predicitons, first_learners[0].feats.relative_data['close'][symbol], 2)
prediction_performances.append(pp)
val_period_predicitons = joined_cached_predicitons[settings.val_start_time:settings.val_end_time]
val_pp = PredictionPerformance(val_period_predicitons, first_learners[0].feats.relative_data['close'][symbol], 2)
val_prediction_performances.append(val_pp)
if use_another_domain:
another_domain_predictions = joined_cached_predicitons[settings.test_end_time:(settings.test_end_time + relativedelta(months=1))]
#another_domain_predictions = joined_cached_predicitons[(settings.val_end_time + relativedelta(months=1)):(settings.val_end_time + relativedelta(months=2) )]
another_pp = PredictionPerformance(another_domain_predictions, first_learners[0].feats.relative_data['close'][symbol], 2)
another_domain_pp.append(another_pp)
first = True
first_predictions = []
part = -1
for predictions, fforest in iter.izip(all_predictions, found_ensemble.found_forests):
print "score", fforest.score
new_part = find_partition(found_ensemble.found_forests, fforest.score)
print "partition", new_part
color = colors[new_part % 7]
print "color", color
pp = PredictionPerformance(predictions, ensemble.learners[0].feats.relative_data['close'][symbol], 2)
if new_part > part:
part = new_part
pp.predictions['tot_return'].plot(style=color)
first_predictions.append(predictions)
else:
pp.predictions['tot_return'].plot()
joined_cached_predicitons = ensemble.process_predictions(all_predictions, 0.1)
pp2 = PredictionPerformance(joined_cached_predicitons, ensemble.learners[0].feats.relative_data['close'][symbol], 2)
print "tot return:", pp2.tot_return()
print "mean return:", pp2.return_mean()
print "std return:", pp2.return_std()
print "sharpe's ratio:", pp2.sharpe_ratio()
pp2.predictions['tot_return'].plot(style='r')
plt.show()
