def do_prediction_test(ticker, hidden_layers, min_back, epochs, granularity, datasetinputs,
learningrate, bias, momentum, recurrent, weightdecay,
timedelta_back_in_granularity_increments):
try:
predict_v2(ticker,
hidden_layers=hidden_layers,
NUM_MINUTES_BACK=min_back,
NUM_EPOCHS=epochs,
granularity_minutes=granularity,
datasetinputs=datasetinputs,
learningrate=learningrate,
bias=bias,
momentum=momentum,
recurrent=recurrent,
weightdecay=weightdecay,
timedelta_back_in_granularity_increments=timedelta_back_in_granularity_increments)
except Exception as e:
print_and_log("(p)" + str(e))
def do_prediction_test(ticker, hidden_layers, min_back, epochs, granularity,
datasetinputs, learningrate, bias, momentum, recurrent,
weightdecay, timedelta_back_in_granularity_increments):
try:
predict_v2(ticker,
hidden_layers=hidden_layers,
NUM_MINUTES_BACK=min_back,
NUM_EPOCHS=epochs,
granularity_minutes=granularity,
datasetinputs=datasetinputs,
learningrate=learningrate,
bias=bias,
momentum=momentum,
recurrent=recurrent,
weightdecay=weightdecay,
timedelta_back_in_granularity_increments=
timedelta_back_in_granularity_increments)
except Exception as e:
print_and_log("(p)" + str(e))
def rerun(self,keep_new_obj = False):
from history.predict import predict_v2
try:
pk = predict_v2(self.symbol,
hidden_layers=self.hiddenneurons,
NUM_MINUTES_BACK=self.minutes_back,
NUM_EPOCHS=self.epochs,
granularity_minutes=self.granularity,
datasetinputs=self.datasetinputs,
learningrate=self.learningrate,
bias=self.bias,
momentum=self.momentum,
weightdecay=self.weightdecay,
recurrent=self.recurrent)
pt = PredictionTest.objects.get(pk=pk)
if not keep_new_obj:
pt.delete()
else:
return pt.pk
except Exception as e:
print(e)
def rerun(self, keep_new_obj=False):
from history.predict import predict_v2
try:
pk = predict_v2(self.symbol,
hidden_layers=self.hiddenneurons,
NUM_MINUTES_BACK=self.minutes_back,
NUM_EPOCHS=self.epochs,
granularity_minutes=self.granularity,
datasetinputs=self.datasetinputs,
learningrate=self.learningrate,
bias=self.bias,
momentum=self.momentum,
weightdecay=self.weightdecay,
recurrent=self.recurrent)
pt = PredictionTest.objects.get(pk=pk)
if not keep_new_obj:
pt.delete()
else:
return pt.pk
except Exception as e:
print(e)
def handle(self, *args, **options):
ticker_options = ['BTC_ETH', 'USDT_BTC']
hidden_layer_options = [1, 5, 15, 40]
# 2/23 -- removed 15, it was barely edged out by 1,5.
# 2/25 -- added 15, 40 in because of recent bugs
min_back_options = [100, 1000, 24 * 60, 24 * 60 * 2]
# 2/22 - eliminated 10000
# 2/25 -- added 24*50, 24*60*2 because "maybe because NN only contains last 1000 data points (1/3 day). if only selling happened during taht time, nn will bias towards selling. duh!"
granularity_options = [10, 15, 20, 30, 40, 50, 60, 120, 240]
# 2/23 notes - results so far: 59 (54% correct) 15 (56% correct) 1 (50% correct) 5 (52% correct). removing 1,5, adding 30 . 2/23 (pt 2) -- added 119, 239
# 2/24 notes -- removed 120,240, added 20, 40, 45
# 2/25 notes -- added 10, 50, removed 45
# 2/25 -- added 120,240 back in to retest in light of recent bugs
datasetinput_options = [1, 2, 3, 4, 5, 6, 15, 10, 20, 40, 100, 200]
# 2/23 -- removed 3,5,15 -- added 20,40,100
# 2/24 -- removed 7,10,20,40,100, added 3,4,5
# 2/25 -- added 3,5,15,10,20,40,100 back in to retest in light of recent bugs
epoch_options = [1000]
# 2/22 -- eliminated 4000, 100
bias_options = [True]
# 2/22 -- Eliminated 'False'
momentum_options = [0.1]
learningrate_options = [0.05]
#2/22 -- elimated 0.005, 0.01, adding 0.03 and 0.1 today. #2/23 - 0.1 (54% correct) 0.05 (55% correct) 0.03 (54% correct) . eliminating everything but 0.05 so i can test more #datasetinput_options
weightdecay_options = [0.0]
# 2/22 -- eliminated 0.1,0.2
recurrent_options = [True]
# 2/23 notes - 0 (52% correct) 1 (55% correct), removed false
timedelta_back_in_granularity_increments_options = [
10, 30, 60, 100, 1000
] #sets how far apart (in granularity increments) the datasets are
for ticker in ticker_options:
for hidden_layers in hidden_layer_options:
for min_back in min_back_options:
for epochs in epoch_options:
for granularity in granularity_options:
for datasetinputs in datasetinput_options:
for bias in bias_options:
for momentum in momentum_options:
for learningrate in learningrate_options:
for weightdecay in weightdecay_options:
for recurrent in recurrent_options:
for timedelta_back_in_granularity_increments in timedelta_back_in_granularity_increments_options:
try:
predict_v2(
ticker,
hidden_layers=
hidden_layers,
NUM_MINUTES_BACK
=min_back,
NUM_EPOCHS=
epochs,
granularity_minutes
=granularity,
datasetinputs=
datasetinputs,
learningrate=
learningrate,
bias=bias,
momentum=
momentum,
recurrent=
recurrent,
weightdecay=
weightdecay,
timedelta_back_in_granularity_increments
=timedelta_back_in_granularity_increments
)
except Exception as e:
print_and_log(
"(p)" + str(e))
def handle(self, *args, **options):
ticker_options = ['BTC_ETH','USDT_BTC']
hidden_layer_options = [1,5, 15, 40]
# 2/23 -- removed 15, it was barely edged out by 1,5.
# 2/25 -- added 15, 40 in because of recent bugs
min_back_options = [100,1000,24*60,24*60*2]
# 2/22 - eliminated 10000
# 2/25 -- added 24*50, 24*60*2 because "maybe because NN only contains last 1000 data points (1/3 day). if only selling happened during taht time, nn will bias towards selling. duh!"
granularity_options = [10, 15, 20, 30, 40, 50, 60, 120,240]
# 2/23 notes - results so far: 59 (54% correct) 15 (56% correct) 1 (50% correct) 5 (52% correct). removing 1,5, adding 30 . 2/23 (pt 2) -- added 119, 239
# 2/24 notes -- removed 120,240, added 20, 40, 45
# 2/25 notes -- added 10, 50, removed 45
# 2/25 -- added 120,240 back in to retest in light of recent bugs
datasetinput_options = [1,2,3,4,5,6, 15,10,20,40,100, 200]
# 2/23 -- removed 3,5,15 -- added 20,40,100
# 2/24 -- removed 7,10,20,40,100, added 3,4,5
# 2/25 -- added 3,5,15,10,20,40,100 back in to retest in light of recent bugs
epoch_options = [1000]
# 2/22 -- eliminated 4000, 100
bias_options = [True]
# 2/22 -- Eliminated 'False'
momentum_options = [0.1]
learningrate_options = [0.05]
#2/22 -- elimated 0.005, 0.01, adding 0.03 and 0.1 today. #2/23 - 0.1 (54% correct) 0.05 (55% correct) 0.03 (54% correct) . eliminating everything but 0.05 so i can test more #datasetinput_options
weightdecay_options = [0.0]
# 2/22 -- eliminated 0.1,0.2
recurrent_options = [True]
# 2/23 notes - 0 (52% correct) 1 (55% correct), removed false
timedelta_back_in_granularity_increments_options = [10,30,60,100,1000] #sets how far apart (in granularity increments) the datasets are
for ticker in ticker_options:
for hidden_layers in hidden_layer_options:
for min_back in min_back_options:
for epochs in epoch_options:
for granularity in granularity_options:
for datasetinputs in datasetinput_options:
for bias in bias_options:
for momentum in momentum_options:
for learningrate in learningrate_options:
for weightdecay in weightdecay_options:
for recurrent in recurrent_options:
for timedelta_back_in_granularity_increments in timedelta_back_in_granularity_increments_options:
try:
predict_v2(ticker,
hidden_layers=hidden_layers,
NUM_MINUTES_BACK=min_back,
NUM_EPOCHS=epochs,
granularity_minutes=granularity,
datasetinputs=datasetinputs,
learningrate=learningrate,
bias=bias,
momentum=momentum,
recurrent=recurrent,
weightdecay=weightdecay,
timedelta_back_in_granularity_increments=timedelta_back_in_granularity_increments)
except Exception as e:
print_and_log("(p)"+str(e))
def handle(self, *args, **options):
ticker = args[0]
print("****** STARTING PREDICTOR " + ticker + " ******* ")
predict_v2(ticker,recurrent=False,datasetinputs=2)