Machine Learning Stock Prediction with LSTM and Keras – Python Source Code

Python Source Code for Machine Learning Stock Prediction with LSTM and Keras – Python Source Code with LSTM and Keras Below is the code for machine learning stock prediction with LSTM neural network.

# -*- coding: utf-8 -*-

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

data_csv = pd.read_csv (fname)

#how many data we will use 
# (should not be more than dataset length )
data_to_use= 100

# number of training data
# should be less than data_to_use
train_end =70


#most recent data is in the end 
#so need offset
start=total_data - data_to_use

#currently doing prediction only for 1 step ahead
steps_to_predict =1

yt = data_csv.iloc [start:total_data ,4]    #Close price
yt1 = data_csv.iloc [start:total_data ,1]   #Open
yt2 = data_csv.iloc [start:total_data ,2]   #High
yt3 = data_csv.iloc [start:total_data ,3]   #Low
vt = data_csv.iloc [start:total_data ,6]    # volume

print ("yt head :")
print (yt.head())

yt_ = yt.shift (-1)
data = pd.concat ([yt, yt_, vt, yt1, yt2, yt3], axis =1)
data. columns = ['yt', 'yt_', 'vt', 'yt1', 'yt2', 'yt3']
data = data.dropna()
print (data)
# target variable - closed price
# after shifting
y = data ['yt_']

#       closed,  volume,   open,  high,   low    
cols =['yt',    'vt',  'yt1', 'yt2', 'yt3']
x = data [cols]

scaler_x = preprocessing.MinMaxScaler ( feature_range =( -1, 1))
x = np. array (x).reshape ((len( x) ,len(cols)))
x = scaler_x.fit_transform (x)

scaler_y = preprocessing. MinMaxScaler ( feature_range =( -1, 1))
y = np.array (y).reshape ((len( y), 1))
y = scaler_y.fit_transform (y)

x_train = x [0: train_end,]
x_test = x[ train_end +1:len(x),]    
y_train = y [0: train_end] 
y_test = y[ train_end +1:len(y)]  
x_train = x_train.reshape (x_train. shape + (1,)) 
x_test = x_test.reshape (x_test. shape + (1,))


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

seed =2016 
np.random.seed (seed)
fit1 = Sequential ()
fit1.add (LSTM (  1000 , activation = 'tanh', inner_activation = 'hard_sigmoid' , input_shape =(len(cols), 1) ))
fit1.add (Dense (output_dim =1, activation = 'linear'))

fit1.compile (loss ="mean_squared_error" , optimizer = "adam") (x_train, y_train, batch_size =16, nb_epoch =25, shuffle = False)

print (fit1.summary())

score_train = fit1.evaluate (x_train, y_train, batch_size =1)
score_test = fit1.evaluate (x_test, y_test, batch_size =1)
print (" in train MSE = ", round( score_train ,4)) 
print (" in test MSE = ", score_test )

pred1 = fit1.predict (x_test) 
pred1 = scaler_y.inverse_transform (np. array (pred1). reshape ((len( pred1), 1)))

prediction_data = pred1[-1]     

print ("Inputs: {}".format(fit1.input_shape))
print ("Outputs: {}".format(fit1.output_shape))
print ("Actual input: {}".format(x_test.shape))
print ("Actual output: {}".format(y_test.shape))

print ("prediction data:")
print (prediction_data)

print ("actual data")
x_test = scaler_x.inverse_transform (np. array (x_test). reshape ((len( x_test), len(cols))))
print (x_test)

plt.plot(pred1, label="predictions")

y_test = scaler_y.inverse_transform (np. array (y_test). reshape ((len( y_test), 1)))
plt.plot( [row[0] for row in y_test], label="actual")

plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),
          fancybox=True, shadow=True, ncol=2)

import matplotlib.ticker as mtick
fmt = '$%.0f'
tick = mtick.FormatStrFormatter(fmt)

ax = plt.axes()

1. Machine Learning Stock Prediction with LSTM and Keras – Python Source Code with LSTM and Keras

7 thoughts on “Machine Learning Stock Prediction with LSTM and Keras – Python Source Code

  1. Hi owygs156,

    Thanks for writing this, it was very helpful. One quick question, shouldn’t we be plotting pred1 in relation to y_test, not x_test[:, 0]? pred1[0] contains the prediction corresponding to y_test[0], which is the close price from x_test[1, 0].


  2. Hi Alan,
    glad that you find it helpful. You are correct, it should be in relation to y_test. I updated code and graphs. Thanks for pointing out on this.

  3. Very impressive! I see that you have used 1 LSTM layer for each model and it had good results. Is there a way to make these predictions for the future? rather than comparing them to actual past data?

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