Stock prices

    import matplotlib.pyplot as plt
    import numpy as np
    import pandas as pd
    from sklearn.preprocessing import MinMaxScaler

    from keras.layers.core import Dense, Activation, Dropout
    from keras.layers.recurrent import LSTM
    from keras.models import Sequential






    # Data can be downloaded at https://ca.finance.yahoo.com/quote/%5EIXIC/, you can replace it with any other index or stock


    data = pd.read_csv('~/Desktop/LargeFiles/Yahoo/AAPL.csv')
    # Reorder the columns for convenience
    data = data[['Open', 'High', 'Low', 'Volume', 'Close']]
    data.head()

    data.shape




(2015, 5)




    sequence_length = 21 # 20 preceeding inputs
    n_features = len(data.columns)
    val_ratio = 0.1
    n_epochs = 300
    batch_size = 512


    data = data.as_matrix()
    data_processed = []
    for index in range(len(data) - sequence_length):
        data_processed.append(data[index : index + sequence_length])
    data_processed = np.array(data_processed)


    val_split = round((1-val_ratio) * data_processed.shape[0])
    train = data_processed[: int(val_split), :]
    val = data_processed[int(val_split) :, :]

    print('Training data: {}'.format(train.shape))
    print('Validation data: {}'.format(val.shape))

Training data: (1795, 21, 5)
Validation data: (199, 21, 5)



    train_samples, train_nx, train_ny = train.shape
    val_samples, val_nx, val_ny = val.shape

    train = train.reshape((train_samples, train_nx * train_ny))
    val = val.reshape((val_samples, val_nx * val_ny))

    preprocessor = MinMaxScaler().fit(train)
    train = preprocessor.transform(train)
    val = preprocessor.transform(val)

    train = train.reshape((train_samples, train_nx, train_ny))
    val = val.reshape((val_samples, val_nx, val_ny))


    X_train = train[:, : -1]
    y_train = train[:, -1][: ,-1]
    X_val = val[:, : -1]
    y_val = val[:, -1][ : ,-1]

    X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], n_features))
    X_val = np.reshape(X_val, (X_val.shape[0], X_val.shape[1], n_features))


    model = Sequential()
    model.add(LSTM(input_shape=(X_train.shape[1:]), units = 128, return_sequences=True))
    model.add(Dropout(0.5))
    model.add(LSTM(128, return_sequences=False))
    model.add(Dropout(0.25))
    model.add(Dense(units=1))
    model.add(Activation("linear"))

    model.compile(loss="mse", optimizer="adam")


    history = model.fit(
        X_train,
        y_train,
        batch_size=batch_size,
        epochs=n_epochs,
        verbose=1
        )

Epoch 1/300
1795/1795 [==============================] - 2s 1ms/step - loss: 0.1033
Epoch 2/300
1795/1795 [==============================] - 1s 524us/step - loss: 0.0294
...
Epoch 300/300
1795/1795 [==============================] - 1s 582us/step - loss: 0.0011



    preds_val = model.predict(X_val)
    diff = []
    for i in range(len(y_val)):
        pred = preds_val[i][0]
        diff.append(y_val[i] - pred)


    real_min = preprocessor.data_min_[104]
    real_max = preprocessor.data_max_[104]
    print(preprocessor.data_min_[104])
    print(preprocessor.data_max_[104])

    preds_real = preds_val * (real_max - real_min) + real_min
    y_val_real = y_val * (real_max - real_min) + real_min

30.101429
144.770004



    plt.plot(preds_real, label='Predictions')
    plt.plot(y_val_real, label='Actual values')
    plt.xlabel('test')
    plt.legend(loc=0)
    plt.show()