{"id":1798,"date":"2018-01-20T22:25:42","date_gmt":"2018-01-20T22:25:42","guid":{"rendered":"http:\/\/intelligentonlinetools.com\/blog\/?p=1798"},"modified":"2018-03-02T01:13:41","modified_gmt":"2018-03-02T01:13:41","slug":"machine-learning-stock-prediction-lstm-keras-python-source-code","status":"publish","type":"post","link":"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/20\/machine-learning-stock-prediction-lstm-keras-python-source-code\/","title":{"rendered":"Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code"},"content":{"rendered":"<p>Python Source Code for <a href=\"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/19\/machine-learning-stock-prediction-lstm-keras\" target=\"_blank\">Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code with LSTM and Keras<\/a> Below is the code for machine learning stock prediction with LSTM neural network.<\/p>\n<pre class=\"brush: python; title: ; notranslate\" title=\"\">\r\n# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn import preprocessing\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nfname=&quot;C:\\\\Users\\\\stock_data.csv&quot;\r\ndata_csv = pd.read_csv (fname)\r\n\r\n#how many data we will use \r\n# (should not be more than dataset length )\r\ndata_to_use= 100\r\n\r\n# number of training data\r\n# should be less than data_to_use\r\ntrain_end =70\r\n\r\n\r\ntotal_data=len(data_csv)\r\n\r\n#most recent data is in the end \r\n#so need offset\r\nstart=total_data - data_to_use\r\n\r\n\r\n#currently doing prediction only for 1 step ahead\r\nsteps_to_predict =1\r\n\r\n \r\nyt = data_csv.iloc [start:total_data ,4]    #Close price\r\nyt1 = data_csv.iloc [start:total_data ,1]   #Open\r\nyt2 = data_csv.iloc [start:total_data ,2]   #High\r\nyt3 = data_csv.iloc [start:total_data ,3]   #Low\r\nvt = data_csv.iloc [start:total_data ,6]    # volume\r\n\r\n\r\nprint (&quot;yt head :&quot;)\r\nprint (yt.head())\r\n\r\nyt_ = yt.shift (-1)\r\n    \r\ndata = pd.concat ([yt, yt_, vt, yt1, yt2, yt3], axis =1)\r\ndata. columns = ['yt', 'yt_', 'vt', 'yt1', 'yt2', 'yt3']\r\n    \r\ndata = data.dropna()\r\n    \r\nprint (data)\r\n    \r\n# target variable - closed price\r\n# after shifting\r\ny = data ['yt_']\r\n\r\n       \r\n#       closed,  volume,   open,  high,   low    \r\ncols =['yt',    'vt',  'yt1', 'yt2', 'yt3']\r\nx = data [cols]\r\n\r\n  \r\n   \r\nscaler_x = preprocessing.MinMaxScaler ( feature_range =( -1, 1))\r\nx = np. array (x).reshape ((len( x) ,len(cols)))\r\nx = scaler_x.fit_transform (x)\r\n\r\n   \r\nscaler_y = preprocessing. MinMaxScaler ( feature_range =( -1, 1))\r\ny = np.array (y).reshape ((len( y), 1))\r\ny = scaler_y.fit_transform (y)\r\n\r\n    \r\nx_train = x [0: train_end,]\r\nx_test = x[ train_end +1:len(x),]    \r\ny_train = y [0: train_end] \r\ny_test = y[ train_end +1:len(y)]  \r\nx_train = x_train.reshape (x_train. shape + (1,)) \r\nx_test = x_test.reshape (x_test. shape + (1,))\r\n\r\n    \r\n    \r\n\r\nfrom keras.models import Sequential\r\nfrom keras.layers.core import Dense\r\nfrom keras.layers.recurrent import LSTM\r\nfrom keras.layers import  Dropout\r\n\r\n\r\nseed =2016 \r\nnp.random.seed (seed)\r\nfit1 = Sequential ()\r\nfit1.add (LSTM (  1000 , activation = 'tanh', inner_activation = 'hard_sigmoid' , input_shape =(len(cols), 1) ))\r\nfit1.add(Dropout(0.2))\r\nfit1.add (Dense (output_dim =1, activation = 'linear'))\r\n\r\nfit1.compile (loss =&quot;mean_squared_error&quot; , optimizer = &quot;adam&quot;)   \r\nfit1.fit (x_train, y_train, batch_size =16, nb_epoch =25, shuffle = False)\r\n\r\nprint (fit1.summary())\r\n\r\nscore_train = fit1.evaluate (x_train, y_train, batch_size =1)\r\nscore_test = fit1.evaluate (x_test, y_test, batch_size =1)\r\nprint (&quot; in train MSE = &quot;, round( score_train ,4)) \r\nprint (&quot; in test MSE = &quot;, score_test )\r\n\r\n   \r\npred1 = fit1.predict (x_test) \r\npred1 = scaler_y.inverse_transform (np. array (pred1). reshape ((len( pred1), 1)))\r\n    \r\n \r\n\r\n \r\nprediction_data = pred1[-1]     \r\n   \r\n\r\nfit1.summary()\r\nprint (&quot;Inputs: {}&quot;.format(fit1.input_shape))\r\nprint (&quot;Outputs: {}&quot;.format(fit1.output_shape))\r\nprint (&quot;Actual input: {}&quot;.format(x_test.shape))\r\nprint (&quot;Actual output: {}&quot;.format(y_test.shape))\r\n  \r\n\r\nprint (&quot;prediction data:&quot;)\r\nprint (prediction_data)\r\n\r\n\r\nprint (&quot;actual data&quot;)\r\nx_test = scaler_x.inverse_transform (np. array (x_test). reshape ((len( x_test), len(cols))))\r\nprint (x_test)\r\n\r\n\r\nplt.plot(pred1, label=&quot;predictions&quot;)\r\n\r\n\r\ny_test = scaler_y.inverse_transform (np. array (y_test). reshape ((len( y_test), 1)))\r\nplt.plot( [row[0] for row in y_test], label=&quot;actual&quot;)\r\n\r\nplt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05),\r\n          fancybox=True, shadow=True, ncol=2)\r\n\r\nimport matplotlib.ticker as mtick\r\nfmt = '$%.0f'\r\ntick = mtick.FormatStrFormatter(fmt)\r\n\r\nax = plt.axes()\r\nax.yaxis.set_major_formatter(tick)\r\n\r\n\r\nplt.show()\r\n\r\n<\/pre>\n<p><strong>References<\/strong><br \/>\n1. <a href=\"http:\/\/intelligentonlinetools.com\/blog\/2018\/01\/19\/machine-learning-stock-prediction-lstm-keras\" target=\"_blank\">Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code with LSTM and Keras<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python Source Code for Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code with LSTM and Keras Below is the code for machine learning stock prediction with LSTM neural network. References 1. Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code with LSTM and Keras<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"jetpack_publicize_message":"","jetpack_is_tweetstorm":false,"jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":[]},"categories":[9,10,3],"tags":[53,48,51],"jetpack_publicize_connections":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Machine Learning Stock Prediction with LSTM and Keras - Python Source Code - Machine Learning Applications<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/intelligentonlinetools.com\/blog\/2018\/01\/20\/machine-learning-stock-prediction-lstm-keras-python-source-code\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning Stock Prediction with LSTM and Keras - Python Source Code - Machine Learning Applications\" \/>\n<meta property=\"og:description\" content=\"Python Source Code for Machine Learning Stock Prediction with LSTM and Keras &#8211; Python Source Code with LSTM and Keras Below is the code for machine learning stock prediction with LSTM neural network. 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References 1. 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I tried to do first multiple steps ahead with few techniques described in the papers on the web. But I discovered that I need fully understand and test the simplest\u2026","rel":"","context":"In &quot;Machine Learning&quot;","img":{"alt_text":"Forecasting one step ahead LSTM 60","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/01\/Forecasting-one-step-ahead-LSTM-60-3_1_2018.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1974,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/04\/03\/machine-learning-stock-market-prediction-lstm-keras\/","url_meta":{"origin":1798,"position":1},"title":"Machine Learning Stock Market Prediction with LSTM Keras","date":"April 3, 2018","format":false,"excerpt":"In the previous posts [1,2] I created script for machine learning stock market price on next day prediction. But it was pointed by readers that in stock market prediction, it is more important to know the trend: will the stock go up or down. So I updated the script to\u2026","rel":"","context":"In &quot;Machine Learning&quot;","img":{"alt_text":"Stock Data Prices Prediction with LSTM","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/04\/timeseries_differenced_and_inverted_back.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1995,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/04\/17\/lstm-neural-network-training-techniques-tuning-hyperparameters\/","url_meta":{"origin":1798,"position":2},"title":"LSTM Neural Network Training &#8211; Few Useful Techniques for Tuning Hyperparameters and Saving Time","date":"April 17, 2018","format":false,"excerpt":"Neural networks are among the most widely used machine learning techniques.[1] But neural network training and tuning multiple hyper-parameters takes time. I was recently building LSTM neural network for prediction for this post Machine Learning Stock Market Prediction with LSTM Keras and I learned some tricks that can save time.\u2026","rel":"","context":"In &quot;Machine Learning&quot;","img":{"alt_text":"LSTM NN Training Value Loss Charts with High Number and Adjusted","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/04\/LSTM-NN-Training-Value-Loss-with-High-Number-and-adjusted-e1524097124417.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1754,"url":"http:\/\/intelligentonlinetools.com\/blog\/2018\/02\/27\/time-series-prediction-lstm-keras\/","url_meta":{"origin":1798,"position":3},"title":"Time Series Prediction with LSTM and Keras for Multiple Steps Ahead","date":"February 27, 2018","format":false,"excerpt":"In this post I will share experiment with Time Series Prediction with LSTM and Keras. LSTM neural network is used in this experiment for multiple steps ahead for stock prices data. The experiment is based on the paper [1]. The authors of the paper examine independent value prediction approach. With\u2026","rel":"","context":"In &quot;Machine Learning&quot;","img":{"alt_text":"Multiple step prediction with separate neural networks","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2018\/02\/multiple-step-prediction.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1628,"url":"http:\/\/intelligentonlinetools.com\/blog\/2017\/12\/17\/time-series-analysis-python-prophet\/","url_meta":{"origin":1798,"position":4},"title":"Time Series Analysis with Python and Prophet","date":"December 17, 2017","format":false,"excerpt":"Recently Facebook released Prophet - open source software tool for forecasting time series data. Facebook team have implemented in Prophet two trend models that can cover many applications: a saturating growth model, and a piecewise linear model. [4] With growth model Prophet can be used for prediction growth\/decay - for\u2026","rel":"","context":"In &quot;Machine Learning&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/intelligentonlinetools.com\/blog\/wp-content\/uploads\/2017\/12\/time-series-analysis-python-300x180.png?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":1178,"url":"http:\/\/intelligentonlinetools.com\/blog\/2017\/05\/14\/time-series-prediction-with-convolutional-neural-networks\/","url_meta":{"origin":1798,"position":5},"title":"Forecasting Time Series Data with Convolutional Neural Networks","date":"May 14, 2017","format":false,"excerpt":"Convolutional neural networks(CNN) is increasingly important concept in computer science and finds more and more applications in different fields. Many posts on the web are about applying convolutional neural networks for image classification as CNN is very useful type of neural networks for image classification. 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