1_predict_stock_prices.py

# GET PREDICTIONS
predictions = []

for step in LOOKUP_STEPS:
  df, last_sequence, x_train, y_train = PrepareData(step)
  x_train = x_train[:, :, :len(['close'])].astype(np.float32)

  model = GetTrainedModel(x_train, y_train)

  last_sequence = last_sequence[-N_STEPS:]
  last_sequence = np.expand_dims(last_sequence, axis=0)
  prediction = model.predict(last_sequence)
  predicted_price = scaler.inverse_transform(prediction)[0][0]

  predictions.append(round(float(predicted_price), 2))