1_result_graphic_generation.py

# Execute model for the whole history range
copy_df = init_df.copy()
y_predicted = model.predict(x_train)
y_predicted_transformed = np.squeeze(scaler.inverse_transform(y_predicted))
first_seq = scaler.inverse_transform(np.expand_dims(y_train[:6], axis=1))
last_seq = scaler.inverse_transform(np.expand_dims(y_train[-3:], axis=1))
y_predicted_transformed = np.append(first_seq, y_predicted_transformed)
y_predicted_transformed = np.append(y_predicted_transformed, last_seq)
copy_df[f'predicted_close'] = y_predicted_transformed

# Add predicted results to the table
date_now = dt.date.today()
date_tomorrow = dt.date.today() + dt.timedelta(days=1)
date_after_tomorrow = dt.date.today() + dt.timedelta(days=2)

copy_df.loc[date_now] = [predictions[0], f'{date_now}', 0, 0]
copy_df.loc[date_tomorrow] = [predictions[1], f'{date_tomorrow}', 0, 0]
copy_df.loc[date_after_tomorrow] = [predictions[2], f'{date_after_tomorrow}', 0, 0]

# Result chart
plt.style.use(style='ggplot')
plt.figure(figsize=(16,10))
plt.plot(copy_df['close'][-150:].head(147))
plt.plot(copy_df['predicted_close'][-150:].head(147), linewidth=1, linestyle='dashed')
plt.plot(copy_df['close'][-150:].tail(4))
plt.xlabel('days')
plt.ylabel('price')
plt.legend([f'Actual price for {STOCK}', 
            f'Predicted price for {STOCK}',
            f'Predicted price for future 3 days'])
plt.show()