def setUp(self):
self.symbol = 'GOOG'
self.learn_start_date = dt.datetime(2004,9,1)
self.learn_end_date = dt.datetime(2011,1,1)
self.stock_period_start = dt.datetime(2000,1,1)
now = dt.datetime.now()
today = dt.datetime(now.year, now.month, now.day)
# stock_period contains important information like
# stock period start and the market symbol that was used in training and discolvery
self.stock_period = StockPeriod(self.symbol, self.stock_period_start, today)
self.mach_learn = EnsembleLearner.machine_learner_for_features_string(self.symbol,
GOOGF[0],
self.stock_period)
self.mach_learn.learn_period(self.learn_start_date, self.learn_end_date)
self.predictions = self.mach_learn.predict_period(dt.datetime(2012,1,1), dt.datetime(2013,1,1))
self.market_relative_close_data = self.mach_learn.feats.relative_data['close'][self.symbol]
self.perf = PredictionPerformance(self.predictions, self.market_relative_close_data, 2)
from ensemble_learner import EnsembleLearner
from simple_order_gen import SimpleOrderGen
from prediction_performance import PredictionPerformance
import matplotlib.pyplot as plt
symbol = "GOOG"
learn_start_date = dt.datetime(2004, 9, 1)
learn_end_date = dt.datetime(2011, 1, 1)
now = dt.datetime.now()
today = dt.datetime(now.year, now.month, now.day)
stock_period = StockPeriod(symbol, dt.datetime(2000, 1, 1), today)
learners = map(
lambda fs: EnsembleLearner.machine_learner_for_features_string(symbol, fs, stock_period),
ensemble_feats.goog_lower_bound8,
)
ensemble = EnsembleLearner(symbol, learn_start_date, learn_end_date, stock_period, learners)
ensemble.learn()
predictions = ensemble.predict_period(dt.datetime(2012, 1, 1), dt.datetime(2013, 1, 1), 0.1)
pp = PredictionPerformance(predictions, ensemble.learners[0].feats.relative_data["close"][symbol], 2)
print "tot return:", pp.tot_return()
print "mean return:", pp.return_mean()
print "std return:", pp.return_std()
print "sharpe's ratio:", pp.sharpe_ratio()
pp.predictions["tot_return"].plot()
plt.show()
gse = pickle.load(open(filename, 'r'))
print gse
found_ensemble = gse.found_ensembles[0]
settings = found_ensemble.ensemble_settings
symbol = settings.symbol
learn_start_date = settings.learn_start_time
learn_end_date = settings.learn_end_time
now = dt.datetime.now()
today = dt.datetime(now.year, now.month, now.day)
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)
symbol = sys.argv[1]
else:
symbol = "GOOG"
symbol = "GOOG"
learn_start_date = dt.datetime(2004, 9, 1)
learn_end_date = dt.datetime(2011, 1, 1)
now = dt.datetime.now()
today = dt.datetime(now.year, now.month, now.day)
stock_period = StockPeriod(symbol, dt.datetime(2000, 1, 1), today)
learners = map(
lambda fs: EnsembleLearner.machine_learner_for_features_string(symbol, fs, stock_period),
ensemble_feats.goog_lower_bound8,
)
ensemble = EnsembleLearner(symbol, learn_start_date, learn_end_date, stock_period, learners)
ensemble.learn()
monitor_start = dt.datetime(2012, 1, 1)
predictions = ensemble.predict_period(monitor_start, today, 0.1)
relative_data = ensemble.learners[0].feats.relative_data["close"]["GOOG"]
pp = PredictionPerformance(predictions, relative_data, 2)
history_filename = "buy_google_today_history.pkl"
found_ensemble = gse.found_ensembles[int(generation) - 1]
print found_ensemble
settings = found_ensemble.ensemble_settings
symbol = settings.symbol
learn_start_date = settings.learn_start_time
learn_end_date = settings.learn_end_time
now = dt.datetime.now()
today = dt.datetime(now.year, now.month, now.day)
stock_period = StockPeriod(symbol, settings.stock_period_start, today, data_source = settings.data_source)
learners = map(lambda fs: EnsembleLearner.machine_learner_for_features_string(symbol, fs, stock_period),
map(lambda x: x.feature_string, found_ensemble.found_forests))
ensemble = EnsembleLearner(symbol,
learn_start_date,
learn_end_date,
stock_period,
learners)
ensemble.learn()
#all_predictions = map(lambda ff: ff.test_predictions[settings.test_start_time:settings.test_end_time], found_ensemble.found_forests)
all_predictions = ensemble._period_predictions(settings.test_start_time, settings.test_end_time)
joined_cached_predicitons = ensemble.process_predictions(all_predictions, 0.1)
