{"id":25597,"date":"2019-11-19T09:57:54","date_gmt":"2019-11-19T14:57:54","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=25597"},"modified":"2022-11-21T09:44:37","modified_gmt":"2022-11-21T14:44:37","slug":"k-means-clustering-algo-python-part-vii","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-vii\/","title":{"rendered":"K-Means Clustering Algorithm For Pair Selection In Python \u2013 Part VII"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\"><em>See the prior installments in this series&nbsp;<\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-iv\/\"><em>here<\/em><\/a><em>.<\/em>&nbsp;<em>Part I, Part II, Part III,&nbsp;<\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-iv\/\"><em>Part IV<\/em><\/a><em>,&nbsp;<\/em><a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-v\/\"><em>Part V<\/em><\/a><em> and and <\/em><a href=\"\/campus\/contributors\/k-means-clustering-algo-python-part-vi\/\"><em>Part VI<\/em><\/a><em>. <\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Spread is not Cointegrated<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To view the complete print out of the ADF2 test, we can call adf2.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">adf2<br>\n(-1.9620694402101162,<br>\n0.30344784824995258,<br>\n1,<br>\n502,<br>\n{&#8216;1%&#8217;: -3.4434437319767452,<br>\n&#8216;10%&#8217;: -2.5698456884811351,<br>\n&#8216;5%&#8217;: -2.8673146875484368},<br>\n1305.4559226426163)<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">How about we take a breather here and review what we have learned so far. In this section, we began our journey toward understanding the efficacy of K-Means for pair selection and Statistical Arbitrage by attempting to develop a Statistical Arbitrage strategy in a world with no K-Means.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We learned that in a Statistical Arbitrage trading world without K-Means, we are left to our own devices for solving the historic problem of pair selection. We&#8217;ve learned that despite two stocks being related on a fundamental level, this doesn&#8217;t necessarily insinuate that they will provide a tradable relationship.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Understanding K-Means<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Before we start implementing the K-means clustering algorithm for statistical arbitrage, let&#8217;s take a look at how K-Means works.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">We will begin by importing our usual data analysis and manipulation libraries. Sci-kit learn offers built-in datasets that you can play with to get familiar with various algorithms. You can take a look at some of the datasets provided by sklearn&nbsp;<a href=\"https:\/\/scikit-learn.org\/stable\/datasets\/index.html\">here<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To gain an understanding of how K-Means works, we&#8217;re going to create our own toy data and visualize the clusters. Then we will use sklearn&#8217;s K-Means algorithm to assess its ability to identify the clusters that we created. Let\u2019s get started!<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">#importing necessary libraries<br>\n#data analysis and manipulation libraries<br>\nimport numpy as np<br>\nimport pandas as pd<br>\n#visualization libraries<br>\nimport matplotlib.pyplot as plt<br>\nimport seaborn as sns<br>\n#machine learning libraries<br>\n#the below line is far making fake data far illustration purposes<br>\nfrom sklearn.datasets import make_blobs<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Stay tuned -for the next installment in this series. Lamarcus will create the data to begin the analysis<\/em>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Disclaimer: All investments and trading in the stock market involve risk. Any decisions to place trades in the financial markets, including trading in stock or options or other financial instruments is a personal decision that should only be made after thorough research, including a personal risk and financial assessment and the engagement of professional assistance to the extent you believe necessary. The trading strategies or related information mentioned in this article is for informational purposes only.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>To gain an understanding of how K-Means works, we&#8217;re going to create our own toy data and visualize the clusters. Then we will use sklearn&#8217;s K-Means algorithm to assess its ability to identify the clusters that we created. Let\u2019s get started!<\/p>\n","protected":false},"author":261,"featured_media":25709,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,350,341,344],"tags":[851,4658,4656,806,4582,4124,852,4659,1225,4657,1224,595,4580],"contributors-categories":[13654],"class_list":["post-25597","post","type-post","status-publish","format-standard","has-post-thumbnail","category-data-science","category-programing-languages","category-python-development","category-ibkr-quant-news","category-quant-asia-pacific","category-quant-development","category-quant-regions","tag-algo-trading","tag-augmented-dickey-fuller-test","tag-correlation-coefficient","tag-data-science","tag-dataframe","tag-k-means-clustering","tag-machine-learning","tag-matplotlib","tag-numpy","tag-p-value","tag-pandas","tag-python","tag-seaborn","contributors-categories-quantinsti"],"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.9 (Yoast SEO v28.4) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>K-Means Clustering Algorithm For Pair Selection In Python \u2013 Part VII<\/title>\n<meta name=\"description\" content=\"We&#039;re going to create our own toy data and visualize the clusters. 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We will use sklearn&#039;s K-Means algorithm to identify the clusters.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-vii\/\" \/>\n<meta property=\"og:site_name\" content=\"IBKR Campus US\" \/>\n<meta property=\"article:published_time\" content=\"2019-11-19T14:57:54+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2022-11-21T14:44:37+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.interactivebrokers.com\/campus\/wp-content\/uploads\/sites\/2\/2019\/11\/python-charts.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"900\" \/>\n\t<meta property=\"og:image:height\" content=\"540\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"Lamarcus Coleman\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" 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