{"id":21743,"date":"2019-10-15T09:39:10","date_gmt":"2019-10-15T13:39:10","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=21743"},"modified":"2022-11-21T09:44:24","modified_gmt":"2022-11-21T14:44:24","slug":"k-means-clustering-algo-python-part-iv","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/k-means-clustering-algo-python-part-iv\/","title":{"rendered":"K-Means Clustering Algorithm For Pair Selection In Python – Part IV"},"content":{"rendered":"\n

In the previous <\/em>installment <\/em><\/a>of this series, Lamarcus demonstrated how to build a heatmap.<\/em><\/p>\n\n\n\n

Earlier we used Matplotlibs scatter plot method. So now we’ll introduce Seaborn’s scatter plot method. Note that Seaborn is built on top of Matplotlib and thus Matplotlibs functionality can be applied to Seaborn.<\/p>\n\n\n\n

#Creating a scatter plot using Seaborn
\nplt.figure(figsize=(15,10))
\nsns.jointplot(newDF[‘WMT’],newDF[‘TGT’])
\nplt.legend(loc=0)
\nplt.show()<\/p>\n\n\n\n

\"K-Means<\/figure>\n\n\n\n

One feature that I like about using Seaborn’s scatter plot is that it provides the Correlation Coefficient and P-Value. From looking at this pearsonr value, we can see that WMT and TGT were not positively correlated over the period. Now that we have a better understanding of our two stocks, let’s check to see if a tradable relationship exists.<\/p>\n\n\n\n

We’ll use the Augmented Dickey Fuller Test<\/a> to determine of our stocks can be traded within a Statistical Arbitrage Strategy.<\/p>\n\n\n\n

Recall that we imported the adfuller test from the statsmodels.tsa.api package earlier.<\/p>\n\n\n\n

To perform the ADF test, we must first create the spread of our stocks. We add this to our existing newDF dataframe.<\/p>\n\n\n\n

#adding the spread column to the nemDF dataframe
\nnewDF[‘Spread’]=newDF[‘WMT’]-newDF[‘TGT’]
\n#instantiating the adfuller test
\nadf=adfuller(newDF[‘Spread’])<\/p>\n\n\n\n

We have now performed the ADF test on our spread and need to determine whether or not our stocks are cointegrated. Let’s write some logic to determine the results of our test.<\/p>\n\n\n\n

#Logic that states if our test statistic is less than
\n#a specific critical value, then the pair is cointegrated at that
\n#level, else the pair is not cointegrated
\nif adf[0] < adf[4][‘1%’]:
\nprint(‘Spread is Cointegrated at 1% Significance Level’)
\nelif adf[0] < adf[4][‘5%’]:
\nprint(‘Spread is Cointegrated at 5% Significance Level’)
\nelif adf[0] < adf[4][‘10%’]:
\nprint(‘Spread is Cointegrated at 10% Significance Level’)
\nelse:
\nprint(‘Spread is not Cointegrated’)<\/p>\n\n\n\n

Any trading symbols displayed are for illustrative purposes only and are not intended to portray recommendations.<\/em><\/p>\n\n\n\n

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":"

Lamarcus introduces Seaborn’s scatter plot method. Note that Seaborn is built on top of Matplotlib and thus matplotlibs functionality can be applied to Seaborn.<\/p>\n","protected":false},"author":261,"featured_media":21725,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[343,349,338,350,341,344],"tags":[851,4658,4656,806,4582,4581,4124,852,4659,1225,4657,1224,595,4580,2536],"contributors-categories":[13654],"class_list":{"0":"post-21743","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-programing-languages","8":"category-python-development","9":"category-ibkr-quant-news","10":"category-quant-asia-pacific","11":"category-quant-development","12":"category-quant-regions","13":"tag-algo-trading","14":"tag-augmented-dickey-fuller-test","15":"tag-correlation-coefficient","16":"tag-data-science","17":"tag-dataframe","18":"tag-heatmap","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":"\nK-Means Clustering Algorithm For Pair Selection In Python – Part IV<\/title>\n<meta name=\"description\" content=\"Lamarcus introduces Seaborn's scatter plot method. 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