import numpy as np
from keras.models import load_model
from load_data import csv_to_dataset, history_points
import sys
model = load_model(f'model/{sys.argv[1]}_model.h5')
ohlcv_histories, technical_indicators, next_day_open_values, unscaled_y, y_normaliser = csv_to_dataset(
f'data/{sys.argv[1]}.csv')
test_split = 0.9
n = int(ohlcv_histories.shape[0] * test_split)
ohlcv_train = ohlcv_histories[:n]
tech_ind_train = technical_indicators[:n]
y_train = next_day_open_values[:n]
ohlcv_test = ohlcv_histories[n:]
tech_ind_test = technical_indicators[n:]
y_test = next_day_open_values[n:]
unscaled_y_test = unscaled_y[n:]
y_test_predicted = model.predict([ohlcv_test, tech_ind_test])
y_test_predicted = y_normaliser.inverse_transform(y_test_predicted)
buys = []
sells = []
buy_threshold = .01
sell_threshold = .02
# in the data folder
# To usa call: python model.py <stock_symbol>
# ex. python model.py AMZN
import sys
import keras
from keras.models import Model
from keras.layers import Dense, Dropout, LSTM, Input, Activation, concatenate
from keras import optimizers
import numpy as np
np.random.seed(4)
import tensorflow as tf
tf.random.set_seed(4)
from load_data import csv_to_dataset, history_points
ohlcv_histories, technical_indicators, percent_change_values = csv_to_dataset(
'data/%s.csv' % sys.argv[1])
test_split = 0.9
n = int(ohlcv_histories.shape[0] * test_split)
ohlcv_train = ohlcv_histories[:n]
tech_ind_train = technical_indicators[:n]
y_train = percent_change_values[:n]
ohlcv_test = ohlcv_histories[n:]
tech_ind_test = technical_indicators[n:]
y_test = percent_change_values[n:]
percent_change_values_test = percent_change_values[n:]
# model architecture
import keras
import os
from keras.models import Model
from keras.layers import Dense, Dropout, LSTM, Input, Activation, concatenate
from keras import optimizers
import numpy as np
np.random.seed(4)
import tensorflow as tf
tf.random.set_seed(4)
from load_data import csv_to_dataset, history_points
mse_for_symbol = {}
for filename in os.listdir('./data'):
ohlcv_histories, technical_indicators, next_day_open_values, unscaled_y, y_normaliser = csv_to_dataset('data/%s'%filename)
stock_name = os.path.splitext(filename)[0]
test_split = 0.9
n = int(ohlcv_histories.shape[0] * test_split)
ohlcv_train = ohlcv_histories[:n]
tech_ind_train = technical_indicators[:n]
y_train = next_day_open_values[:n]
ohlcv_test = ohlcv_histories[n:]
tech_ind_test = technical_indicators[n:]
y_test = next_day_open_values[n:]
unscaled_y_test = unscaled_y[n:]
# model architecture
