Stock prices – Orbifold Consulting

    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()

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()

	Open	High	Low	Volume	Close
0	27.881428	28.600000	27.774286	153832000	28.461428
1	28.104286	28.338572	27.367144	189413000	27.435715
2	27.518572	28.000000	27.264286	212576700	27.922857
3	27.955715	28.268572	27.714285	119567700	27.731428
4	28.059999	28.214285	27.821428	158221700	28.027143

    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()

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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()

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