{"id":52,"date":"2020-08-23T01:41:44","date_gmt":"2020-08-23T01:41:44","guid":{"rendered":"https:\/\/jeremywhittaker.com\/?p=52"},"modified":"2022-01-28T14:47:48","modified_gmt":"2022-01-28T21:47:48","slug":"normalizing-stock-data-for-machine-learning","status":"publish","type":"post","link":"https:\/\/new.jeremywhittaker.com\/index.php\/2020\/08\/23\/normalizing-stock-data-for-machine-learning\/","title":{"rendered":"Normalizing Stock data for Machine Learning"},"content":{"rendered":"\n<p>When analyzing historical time frame data in machine learning it needs to be normalized. In this code example, I will show how to get S&amp;P data then convert it to a percent of daily increase\/decrease as well as a logarithmic daily increase\/decrease.<\/p>\n\n\n\n<p>The first part of this code will use yfinance as our datasource. <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#we're going to use yfinance as our data source\n!pip install yfinance --upgrade --no-cache-dir\n\nimport pandas as pd\nimport numpy as np\nimport yfinance as yf\n<\/code><\/pre>\n\n\n\n<p>Next, we&#8217;re going to create a dataframe called df and download SPY data from 2000 to it. <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#Here we're creating a dataframe for spy data from 2000-current\ndf = yf.download('spy',\n  start='2000-01-01',\n  end='2020-08-21',\n  progress=True)\n  #dauto_adjust=True,\n  #actions='inline',) #adjust for stock splits and dividends\n#print the dataframe to see what lives in it\nprint(df)<\/code><\/pre>\n\n\n\n<p>Finally, we&#8217;ll print the result of df so you can get an idea of what is inside of it.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"597\" height=\"279\" data-src=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-7.png\" alt=\"\" class=\"wp-image-53 lazyload\" data-srcset=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-7.png 597w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-7-300x140.png 300w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-7-500x234.png 500w\" data-sizes=\"(max-width: 597px) 100vw, 597px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 597px; --smush-placeholder-aspect-ratio: 597\/279;\" \/><\/figure>\n\n\n\n<p>We&#8217;re going to drop all the columns except Adj Close. Then we&#8217;ll rename it adj_close. Next, we&#8217;ll create a column labeled simple_rtn. This is the daily simple return or percent increase\/decrease. The next line of code creates a logarithmic increase\/decrease. Logarithmic gives equal bearing to the Y-axis and can be defined as follows, <em>&#8220;A\u00a0<strong>logarithmic<\/strong>\u00a0price scale uses the percentage of change to plot data points, so, the scale prices are not positioned equidistantly. A\u00a0<strong>linear<\/strong>\u00a0price scale uses an equal value between price scales providing an equal distance between values.&#8221; <\/em><\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#only keep adj close\ndf = df.loc&#91;:, &#91;'Adj Close']]\ndf.rename(columns={'Adj Close':'adj_close'}, inplace=True)\n#create column simple return\ndf&#91;'simple_rtn'] = df.adj_close.pct_change()\n#create column logrithmic returns\ndf&#91;'log_rtn'] = np.log(df.adj_close\/df.adj_close.shift(1))\nprint(df)<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"453\" height=\"280\" data-src=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-8.png\" alt=\"\" class=\"wp-image-54 lazyload\" data-srcset=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-8.png 453w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-8-300x185.png 300w\" data-sizes=\"(max-width: 453px) 100vw, 453px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 453px; --smush-placeholder-aspect-ratio: 453\/280;\" \/><\/figure>\n\n\n\n<p>This next command just analyzes the data so you can spot-check what you&#8217;ve created. <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#here we can analyze our data\ndf.info()\n<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"457\" height=\"179\" data-src=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-9.png\" alt=\"\" class=\"wp-image-55 lazyload\" data-srcset=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-9.png 457w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-9-300x118.png 300w\" data-sizes=\"(max-width: 457px) 100vw, 457px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 457px; --smush-placeholder-aspect-ratio: 457\/179;\" \/><\/figure>\n\n\n\n<p>This next section describes what the daily increase\/decrease of the SPY looks like. You can see statistically relevant information about S&amp;P here. <\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#get statistical data on the data frame\ndf.describe()<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"423\" height=\"288\" data-src=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-10.png\" alt=\"\" class=\"wp-image-56 lazyload\" data-srcset=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-10.png 423w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-10-300x204.png 300w\" data-sizes=\"(max-width: 423px) 100vw, 423px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 423px; --smush-placeholder-aspect-ratio: 423\/288;\" \/><\/figure>\n\n\n\n<p>Next, we can see a distribution of adjustable close, logarithmic return, and simple return.<\/p>\n\n\n\n<pre class=\"wp-block-code\"><code>#view chart of data to get an overview of what lives in the data\nimport matplotlib.pyplot as plt\ndf.hist(bins=50, figsize=(20,15))\nplt.show()<\/code><\/pre>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"1024\" height=\"724\" data-src=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11-1024x724.png\" alt=\"\" class=\"wp-image-57 lazyload\" data-srcset=\"https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11-1024x724.png 1024w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11-300x212.png 300w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11-768x543.png 768w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11-424x300.png 424w, https:\/\/new.jeremywhittaker.com\/wp-content\/uploads\/2020\/08\/image-11.png 1260w\" data-sizes=\"(max-width: 1024px) 100vw, 1024px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 1024px; --smush-placeholder-aspect-ratio: 1024\/724;\" \/><\/figure>\n\n\n\n<p>This is all for data normalization. You can now apply different algorithmic analyses to the data. <\/p>\n","protected":false},"excerpt":{"rendered":"<p>When analyzing historical time frame data in machine learning it needs to be normalized. In this code example, I will show how to get S&amp;P data then convert&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-52","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.2 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Normalizing Stock data for Machine Learning - Jeremy Whittaker<\/title>\n<meta name=\"robots\" content=\"noindex, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Normalizing Stock data for Machine Learning - Jeremy Whittaker\" \/>\n<meta property=\"og:description\" content=\"When analyzing historical time frame data in machine learning it needs to be normalized. 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