{"id":24670,"date":"2019-11-12T09:52:12","date_gmt":"2019-11-12T14:52:12","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=24670"},"modified":"2022-11-21T09:44:35","modified_gmt":"2022-11-21T14:44:35","slug":"k-means-clustering-algo-python-part-vi","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-vi\/","title":{"rendered":"K-Means Clustering Algorithm For Pair Selection In Python &#8211; Part VI"},"content":{"rendered":"\n<p><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> Part I, Part II, Part III, <a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-iv\/\">Part IV<\/a> and <a href=\"\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-v\/\">Part V<\/a>.<\/p>\n\n\n\n<p>Now that we&#8217;ve gotten our data, let&#8217;s add these stocks to our <code>newDF<\/code> and create their spread.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">#adding dltr and dg to our newDF dataframe<br>\nnewDF[&#8216;DLTR&#8217;]=dltr[&#8216;Close&#8217;]<br>\nnewDF[&#8216;DG&#8217;]=dg[&#8216;Close&#8217;]<br>\n#creating the dltr and dg spread as a column in our newDF dataframe<br>\nnewDF[&#8216;Spread_2&#8217;]=newDF[&#8216;DLTR&#8217;]-newDF[&#8216;DG&#8217;]<\/pre>\n\n\n\n<p>We&#8217;ve now added the DLTR and DG stocks as well as their spread to our newDF dataframe. Let&#8217;s take a quick look at our dataframe.<\/p>\n\n\n\n<pre class=\"wp-block-preformatted\">newDF.head()<\/pre>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" data-src=\"https:\/\/d1rwhvwstyk9gu.cloudfront.net\/2019\/09\/table1.jpg\" alt=\"\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" class=\"lazyload\" \/><\/figure>\n\n\n\n<p>Now that we have Spread_2 or the spread of DLTR and DG, we can create ADF2 or a second ADF test for these two stocks.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">#Creating another adfuller instance<br>\nadf2=adfuller(newDF[&#8216;Spread_2&#8217;])<\/p>\n\n\n\n<p>We&#8217;ve just run the ADF test on our DLTR and DG spread. We can now repeat our earlier logic to determine if the spread yields a tradable relationship.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">if adf2[0] &lt; adf2[4][&#8216;1%&#8217;]:<br>\nprint(&#8216;Spread is Cointegrated at 1% Significance Level&#8217;)<br>\nelif adf2[0] &lt; adf2[4][&#8216;5%&#8217;]:<br>\nprint(&#8216;Spread is Cointegrated at 5% Significance Level&#8217;)<br>\nelif adf2[0] &lt; adf2[4][&#8216;10%&#8217;]:<br>\nprint(&#8216;Spread is Cointegrated at 10% Significance Level&#8217;)<br>\nelse:<br>\nprint(&#8216;Spread is not Cointegrated&#8217;)<\/p>\n\n\n\n<p><em>Stay tuned -for the next installment in this series. Lamarcus will demonstrate how to <\/em><\/p>\n\n\n\n<p><em>Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n\n\n\n<p><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>Now that we\u2019ve gotten our data, let\u2019s add these stocks to our newDF and create their spread. #adding dltr and dg to our newDF dataframe newDF[\u2018DLTR\u2019]=dltr[\u2018Close\u2019] newDF[\u2018DG\u2019]=dg[\u2018Close\u2019] #creating the dltr and dg spread<\/p>\n","protected":false},"author":261,"featured_media":24675,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,344],"tags":[851,4658,4656,806,4582,4124,852,4659,1225,4657,1224,595,4580,2536],"contributors-categories":[13654],"class_list":{"0":"post-24670","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-data-science","8":"category-programing-languages","9":"category-python-development","10":"category-ibkr-quant-news","11":"category-quant-asia-pacific","12":"category-quant-development","13":"category-quant-regions","14":"tag-algo-trading","15":"tag-augmented-dickey-fuller-test","16":"tag-correlation-coefficient","17":"tag-data-science","18":"tag-dataframe","19":"tag-k-means-clustering","20":"tag-machine-learning","21":"tag-matplotlib","22":"tag-numpy","23":"tag-p-value","24":"tag-pandas","25":"tag-python","26":"tag-seaborn","27":"tag-visualization","28":"contributors-categories-quantinsti"},"pp_statuses_selecting_workflow":false,"pp_workflow_action":"current","pp_status_selection":"publish","acf":[],"yoast_head":"<!-- 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