def main(config):
if config.validate:
output_len = config.output_seq_length
l = config.num_layers
loss = config.loss
sl = config.num_stochastic_layers
config = pickle.load( open( 'saved_models/'+config.model_name+'/config.p', "rb" ))
config.validate = True
config.simulate = False
config.output_seq_length = output_len
config.num_layers = l
config.num_stochastic_layers = sl
config.loss = loss
print(config)
t1 = time.time()
data_folder = os.path.abspath(os.path.abspath("../../../../"))+'/data/'
dataset = load_data(data_folder, config)
t2 = time.time()
print('Finished loading the dataset: ' + str(t2-t1) +' sec \n')
model = PricePredictor(config, dataset)
if config.validate:
# model._make_figs(steps = config.output_seq_length, epoch=200)
# model._validate(steps = config.output_seq_length, epoch=160)
model._backtest(epoch=160)
else:
model._train()
def main(config):
if config.validate:
output_len = config.output_seq_length
file_path = config.file_path
seed = config.seed
loss = config.loss
target = config.target
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.validate = True
config.file_path = file_path
config.output_seq_length = output_len
config.seed = seed
config.loss = loss
config.backtest_target = 'close'
config.target = 'NDX'
print(config)
t1 = time.time()
data_folder = os.path.abspath(os.path.abspath("../../../../")) + '/data/'
dataset = load_data(data_folder, config)
t2 = time.time()
print('Finished loading the dataset: ' + str(t2 - t1) + ' sec \n')
model = PricePredictor(config, dataset)
if config.validate:
model._backtest(epoch=150)
# model._validate(steps = config.output_seq_length, epoch=150)
else:
model._train()
def main(config):
if config.validate:
output_len = config.output_seq_length
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.validate = True
config.output_seq_length = output_len
config.num_layers = 6
print(config)
elif config.backtest:
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.backtest = True
print(config)
t1 = time.time()
data_folder = os.path.abspath(os.path.abspath("../../../../")) + '/data/'
dataset = load_data(data_folder, config)
t2 = time.time()
print('Finished loading the dataset: ' + str(t2 - t1) + ' sec \n')
model = PricePredictor(config, dataset)
if config.validate:
model._validate(steps=config.output_seq_length, epoch=40)
# model._make_figs(steps = config.output_seq_length, epoch=40)
if config.backtest:
model._backtest2(epoch=180)
else:
model._train()
def main(config):
if config.validate:
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.validate = True
config.simulate = False
print(config)
t1 = time.time()
data_folder = os.path.abspath(os.path.abspath("../../../")) + '/data/'
dataset = load_data(data_folder, config)
t2 = time.time()
print('Finished loading the dataset: ' + str(t2 - t1) + ' sec \n')
model = PricePredictor(config, dataset)
if config.validate:
# model._validate( epoch=70)
# model._make_figs(epoch=70)
model._make_figs2(epoch=70)
elif config.tsne:
model._tsne(epoch=70)
else:
model._train()
def main(config):
if config.validate:
output_len = config.output_seq_length
file_path = config.file_path
seed = config.seed
loss = config.loss
l = config.num_layers
sl = config.num_stochastic_layers
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.validate = True
config.file_path = file_path
config.output_seq_length = output_len
config.seed = seed
config.loss = loss
config.num_layers = l
config.num_stochastic_layers = sl
config.backtest_target = 'close_btc'
config.target = 'lr_btc'
config.model_name = 'vaegan_mv_hour_'
print(config)
elif config.backtest:
config = pickle.load(
open('saved_models/' + config.model_name + '/config.p', "rb"))
config.backtest = True
config.validate = False
print(config)
t1 = time.time()
data_folder = os.path.abspath(os.path.abspath("../../../../")) + '/data/'
dataset = load_data(data_folder, config)
t2 = time.time()
print('Finished loading the dataset: ' + str(t2 - t1) + ' sec \n')
model = PricePredictor(config, dataset)
if config.validate:
model._validate(steps=config.output_seq_length, epoch=500)
# model._make_figs(steps = config.output_seq_length, epoch=200)
# model._backtest(epoch=500)
else:
model._train()