{"id":6122,"date":"2022-02-02T13:27:26","date_gmt":"2022-02-02T12:27:26","guid":{"rendered":"https:\/\/joapen.com\/blog\/?p=6122"},"modified":"2022-02-02T15:21:49","modified_gmt":"2022-02-02T14:21:49","slug":"lstm-1-property-1-stock-on-normalized-data","status":"publish","type":"post","link":"https:\/\/joapen.com\/blog\/2022\/02\/02\/lstm-1-property-1-stock-on-normalized-data\/","title":{"rendered":"LSTM, 1 property, 1 stock on normalized data"},"content":{"rendered":"\n<p>This code is the result of some work I have done on LSTM.<\/p>\n\n\n\n<p>Purpose: predict the next day price with the use of a LSTM neural network and just one property.<\/p>\n\n\n\n<p>How:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>I have normalized the data,<\/li><li>I have used just 1 parameter as input (close price),<\/li><li>I have built the code using basic functions to module the basis.<\/li><\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"code-sections\">Code sections<\/h2>\n\n\n\n<p>I have divided the code in Jupyter sections, so it&#8217;s easy to run.<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>1.-&nbsp;Load&nbsp;libraries&nbsp;common&nbsp;data&nbsp;and&nbsp;functions<\/li><li>2.-&nbsp;Preparing&nbsp;the&nbsp;data&nbsp;for&nbsp;LSTM<\/li><li>3.-&nbsp;Split&nbsp;Train\/Test&nbsp;dataset&nbsp;&amp;&nbsp;dimension&nbsp;the&nbsp;data&nbsp;vectors<\/li><li>4.-&nbsp;Creating&nbsp;the&nbsp;LSTM&nbsp;model<\/li><li>5.-&nbsp;Build&nbsp;prediction&nbsp;on&nbsp;test&nbsp;dataset<\/li><li>6.-&nbsp;Predict&nbsp;the&nbsp;stock&nbsp;price&nbsp;for&nbsp;tomorrow<\/li><li>10.-&nbsp;Analysis:&nbsp;Generate&nbsp;and&nbsp;save&nbsp;data&nbsp;to&nbsp;analyze&nbsp;results<\/li><li>20.-&nbsp;Generate&nbsp;Next&nbsp;day&nbsp;result<\/li><\/ul>\n\n\n\n<p>The training of the code takes between 45-60 minutes on Google Collab, so I save the model and I use it to predict the following days for testing purposes (it takes 5 minutes).<\/p>\n\n\n\n<p>Once you have generated the models, to predict the following day you just have to run section #1 and section #6.<\/p>\n\n\n\n<p>The configuration I have used is this one:<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"598\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-13-1024x598.png\" alt=\"\" class=\"wp-image-6124\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-13-1024x598.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-13-300x175.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-13-768x448.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-13.png 1182w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"results\">Results<\/h2>\n\n\n\n<p>Despite the accuracy is over 98%, the results are very poor. The True Positives and True Negatives ratio is low (61.97%) and that makes the manual back-test I did very poor.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"304\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-12-1024x304.png\" alt=\"\" class=\"wp-image-6123\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-12-1024x304.png 1024w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-12-300x89.png 300w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-12-768x228.png 768w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-12.png 1306w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption>Last 5 days forecast for Microsoft (MSFT)<\/figcaption><\/figure>\n\n\n\n<p>To calculate the results, I do it in 2 steps. First, I extract the data (predictions and real data) into a CSV executing the section &#8220;10.-&nbsp;Analysis:&nbsp;Generate&nbsp;and&nbsp;save&nbsp;data&nbsp;to&nbsp;analyze&nbsp;results&#8221;. Then I run another Jupyter notebook that enables me to calculate the results.<\/p>\n\n\n\n<p>In this case on Microsoft the table is:<\/p>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"390\" height=\"360\" src=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-14.png\" alt=\"\" class=\"wp-image-6127\" srcset=\"https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-14.png 390w, https:\/\/joapen.com\/blog\/wp-content\/uploads\/2022\/02\/image-14-300x277.png 300w\" sizes=\"auto, (max-width: 390px) 100vw, 390px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"next-step\">Next step?<\/h2>\n\n\n\n<p>My next step is to turn this code from a single parameter to a multiple parameters, and see how it works.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"the-code\">The code<\/h2>\n\n\n\n<p>You can find the code here:<\/p>\n\n\n\n<ul class=\"wp-block-list\"><li>Generate the LSTM and its results: <a href=\"https:\/\/github.com\/joapen\/ML-Learning-bucket\/blob\/main\/LSTM_on_1_Stock_v1.ipynb\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joapen\/ML-Learning-bucket\/blob\/main\/LSTM_on_1_Stock_v1.ipynb<\/a><\/li><li>Test the accuracy of the results: <a href=\"https:\/\/github.com\/joapen\/ML-Learning-bucket\/blob\/main\/Check_Results_v1.ipynb\" target=\"_blank\" rel=\"noreferrer noopener\">https:\/\/github.com\/joapen\/ML-Learning-bucket\/blob\/main\/Check_Results_v1.ipynb<\/a><\/li><\/ul>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This code is the result of some work I have done on LSTM. Purpose: predict the next day price with the use of a LSTM neural network and just one property. How: I have normalized the data, I have used just 1 parameter as input (close price), I have built the code using basic functions &#8230; <a title=\"LSTM, 1 property, 1 stock on normalized data\" class=\"read-more\" href=\"https:\/\/joapen.com\/blog\/2022\/02\/02\/lstm-1-property-1-stock-on-normalized-data\/\" aria-label=\"Read more about LSTM, 1 property, 1 stock on normalized data\">Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[151],"tags":[],"class_list":["post-6122","post","type-post","status-publish","format-standard","hentry","category-machine-learning"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.4 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>LSTM, 1 property, 1 stock on normalized data -<\/title>\n<meta name=\"description\" content=\"This code is the result of some work I have done on LSTM. 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