print(datetime.datetime.today())
print(f'pickle number: {str(pknum)}')
print(f'delay: {delay_minutes} min')
print(f'model_ui: {model_env["model_ui"]}')
print(f'tasks: {str(tasks)}')
time.sleep(delay_minutes * 60)
portal = DataPortal()
loop = 0
while loop != end_int:
if 'new' in tasks:
gen = portal.iter_get_uids('daily_prices', 'default', model_env['tickers'])
net = ESN(1, 1, n_reservoir=model_env['n_res_list'][0], sparsity=model_env['sparsity_list'][0], noise=0)
for i in range(1, len(model_env['n_res_list'])):
temp_net = ESN(1, 1, n_reservoir=model_env['n_res_list'][i], sparsity=model_env['sparsity_list'][i],
noise=0)
net.merge(temp_net)
x_train, x_test = np.zeros((0, sum(model_env['n_res_list']) + 1)), np.zeros(
(0, sum(model_env['n_res_list']) + 1))
y_train, y_test, y_cv, y_tcv = [], [], [], []
w_train, w_test = [], []
prep = Pipeline([('detrend', LinDetrend()), ('scaler', StandardScaler())])
for ticker in gen:
log_prices = np.log10(np.array(ticker['adjusted_close']).reshape((len(ticker), 1)))
if len(log_prices) > model_env['train_len']:
prep.fit(log_prices[:model_env['train_len']])
log_prices = prep.transform(log_prices)
if model_env['vol']:
log_vol = np.log10(np.array(ticker['volume'] + 1).reshape((len(ticker), 1)))
prep.fit(log_vol[:model_env['train_len']])
log_vol = prep.transform(log_vol)
else:
