def do_classifier_test(name, ticker, data_set_inputs, granularity, min_back,
timedelta_back):
try:
ct = ClassifierTest(
name=name,
type='mock',
symbol=ticker,
datasetinputs=data_set_inputs,
granularity=granularity,
minutes_back=min_back,
timedelta_back_in_granularity_increments=timedelta_back)
ct.get_classifier()
ct.save()
return_data = "(ct) {} {} {} {} {} {} returned {}% correct ".format(
name, ticker, data_set_inputs, granularity, min_back,
timedelta_back, ct.percent_correct)
print_and_log(return_data)
# Hack to only graph successful charts, until we figure out this warning
# http://bits.owocki.com/010Z1M3d170p/Image%202016-03-02%20at%208.30.17%20AM.png
if ct.percent_correct > 60 or not settings.MAKE_TRADES:
ct.graph(ct.graph_url())
return return_data
except Exception as e:
return "Exception in {} {} {} {} {} {}: {}".format(
name, ticker, data_set_inputs, granularity, min_back,
timedelta_back, str(e))
def do_classifier_test(name, ticker, data_set_inputs, granularity, min_back, timedelta_back):
try:
ct = ClassifierTest(name=name,
type='mock',
symbol=ticker,
datasetinputs=data_set_inputs,
granularity=granularity,
minutes_back=min_back,
timedelta_back_in_granularity_increments=timedelta_back)
ct.get_classifier()
ct.save()
return_data = "(ct) {} {} {} {} {} {} returned {}% correct ".format(name, ticker, data_set_inputs,
granularity,
min_back,
timedelta_back,
ct.percent_correct)
print_and_log(return_data)
# Hack to only graph successful charts, until we figure out this warning
# http://bits.owocki.com/010Z1M3d170p/Image%202016-03-02%20at%208.30.17%20AM.png
if ct.percent_correct > 60 or not settings.MAKE_TRADES:
ct.graph(ct.graph_url())
return return_data
except Exception as e:
return "Exception in {} {} {} {} {} {}: {}".format(name, ticker, data_set_inputs,
granularity,
min_back,
timedelta_back,
str(e))
def get_traders(self):
predictors = {}
self.confidence = {}
self.trs = {}
self.predictor_configs.reverse()
for i in range(0, len(self.predictor_configs)):
config = self.predictor_configs[i]
if config['type'] == 'nn':
pt = PredictionTest()
pt.type = 'real'
pt.symbol = config['symbol']
pt.datasetinputs = config['datasetinputs']
pt.hiddenneurons = 5
pt.minutes_back = 100
pt.epochs = 1000
pt.momentum = 0.1
pt.granularity = config['granularity']
pt.bias = True
pt.learningrate = 0.05
pt.weightdecay = 0.0
pt.recurrent = True
pt.timedelta_back_in_granularity_increments = 0
pt.save()
predict_runtime = pt.predict_runtime()
predict_confidence = pt.confidence()
print_and_log(
"(t)predicted trainingtime for nn #{} {}: {}s, predicted confidence: {}%"
.format(i, config['name'], round(predict_runtime, 1),
int(predict_confidence)))
pt.get_nn(train=settings.MAKE_TRADES)
print_and_log("(t)done training")
predictors[i] = pt
self.confidence[i] = predict_confidence
else:
ct = ClassifierTest(name=config['name'],
type='real',
symbol=config['symbol'],
datasetinputs=config['datasetinputs'],
granularity=config['granularity'],
minutes_back=config['minutes_back'],
timedelta_back_in_granularity_increments=0)
predict_runtime = ct.predict_runtime()
predict_confidence = ct.confidence()
print_and_log(
"(t)predicted trainingtime for nn #{} {}: {}s, predicted confidence: {}%"
.format(i, config['name'], round(predict_runtime, 1),
int(predict_confidence)))
ct.get_classifier(test=False)
print_and_log("(t)done training")
predictors[i] = ct
self.confidence[i] = predict_confidence
ct.save()
self.predictors = predictors
return self.predictors
def handle(self, *args, **options):
ticker_options = ['BTC_ETH', 'USDT_BTC']
min_back_options = [100, 1000, 24 * 60, 24 * 60 * 2]
granularity_options = [10, 15, 20, 30, 40, 50, 60, 120, 240]
if not settings.MAKE_TRADES:
granularity_options = [1]
datasetinput_options = [2]
#TODO: enable more than just 1 type
timedelta_back_in_granularity_increments_options = [
10, 30, 60, 100, 1000
] #sets how far apart (in granularity increments) the datasets are
name_options = [
"Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "AdaBoost", "Naive Bayes",
"Linear Discriminant Analysis", "Quadratic Discriminant Analysis"
]
for ticker in ticker_options:
for min_back in min_back_options:
for granularity in granularity_options:
for datasetinputs in datasetinput_options:
for timedelta_back_in_granularity_increments in timedelta_back_in_granularity_increments_options:
for name in name_options:
try:
ct = ClassifierTest(
name=name,
type='mock',
symbol=ticker,
datasetinputs=datasetinputs,
granularity=granularity,
minutes_back=min_back,
timedelta_back_in_granularity_increments
=timedelta_back_in_granularity_increments
)
ct.get_classifier()
ct.save()
if ct.percent_correct > 60 or not settings.MAKE_TRADES: #hack to only graph successful charts, until we figure out this warning http://bits.owocki.com/010Z1M3d170p/Image%202016-03-02%20at%208.30.17%20AM.png
ct.graph(ct.graph_url())
except Exception as e:
print("exception:" + str(e))
def get_traders(self):
predictors = {}
self.confidence = {}
self.trs = {}
self.predictor_configs.reverse()
for i in range(0, len(self.predictor_configs)):
config = self.predictor_configs[i]
if config['type'] == 'nn':
pt = PredictionTest()
pt.type = 'real'
pt.symbol = config['symbol']
pt.datasetinputs = config['datasetinputs']
pt.hiddenneurons = 5
pt.minutes_back = 100
pt.epochs = 1000
pt.momentum = 0.1
pt.granularity = config['granularity']
pt.bias = True
pt.learningrate = 0.05
pt.weightdecay = 0.0
pt.recurrent = True
pt.timedelta_back_in_granularity_increments = 0
pt.save()
predict_runtime = pt.predict_runtime()
predict_confidence = pt.confidence()
print_and_log("(t)predicted trainingtime for nn #{} {}: {}s, predicted confidence: {}%".
format(i, config['name'], round(predict_runtime, 1), int(predict_confidence)))
pt.get_nn(train=settings.MAKE_TRADES)
print_and_log("(t)done training")
predictors[i] = pt
self.confidence[i] = predict_confidence
else:
ct = ClassifierTest(name=config['name'],
type='real',
symbol=config['symbol'],
datasetinputs=config['datasetinputs'],
granularity=config['granularity'],
minutes_back=config['minutes_back'],
timedelta_back_in_granularity_increments=0)
predict_runtime = ct.predict_runtime()
predict_confidence = ct.confidence()
print_and_log("(t)predicted trainingtime for nn #{} {}: {}s, predicted confidence: {}%".
format(i, config['name'], round(predict_runtime, 1), int(predict_confidence)))
ct.get_classifier(test=False)
print_and_log("(t)done training")
predictors[i] = ct
self.confidence[i] = predict_confidence
ct.save()
self.predictors = predictors
return self.predictors
def handle(self, *args, **options):
ticker_options = ['BTC_ETH', 'USDT_BTC']
min_back_options = [100, 1000, 24 * 60, 24 * 60 * 2]
granularity_options = [10, 15, 20, 30, 40, 50, 60, 120, 240]
if not settings.MAKE_TRADES:
granularity_options = [1]
datasetinput_options = [2]
# TODO: enable more than just 1 type
timedelta_back_in_granularity_increments_options = [10, 30, 60, 100, 1000] # sets how far apart (in granularity increments) the datasets are
name_options = ["Nearest Neighbors", "Linear SVM", "RBF SVM", "Decision Tree",
"Random Forest", "AdaBoost", "Naive Bayes", "Linear Discriminant Analysis",
"Quadratic Discriminant Analysis"]
for ticker in ticker_options:
for min_back in min_back_options:
for granularity in granularity_options:
for datasetinputs in datasetinput_options:
for timedelta_back_in_granularity_increments in timedelta_back_in_granularity_increments_options:
for name in name_options:
try:
ct = ClassifierTest(name=name,
type='mock',
symbol=ticker,
datasetinputs=datasetinputs,
granularity=granularity,
minutes_back=min_back,
timedelta_back_in_granularity_increments=timedelta_back_in_granularity_increments)
ct.get_classifier()
ct.save()
print_and_log("(ct) {} {} {} {} {} {} returned {}% corrrect ".format(name, ticker, datasetinputs, granularity, min_back, timedelta_back_in_granularity_increments, ct.percent_correct))
if ct.percent_correct > 60 or not settings.MAKE_TRADES: # hack to only graph successful charts, until we figure out this warning http://bits.owocki.com/010Z1M3d170p/Image%202016-03-02%20at%208.30.17%20AM.png
ct.graph(ct.graph_url())
except Exception as e:
print("exception:" + str(e))