{"id":2092913,"date":"2018-11-20T18:37:41","date_gmt":"2018-11-20T13:07:41","guid":{"rendered":"http:\/\/stockviz.biz\/index.php\/?p=2092913"},"modified":"2018-11-21T11:46:47","modified_gmt":"2018-11-21T06:16:47","slug":"macro-using-currencies-to-predict-nifty-part-iv","status":"publish","type":"post","link":"https:\/\/stockviz.biz\/index.php\/2018\/11\/20\/macro-using-currencies-to-predict-nifty-part-iv\/","title":{"rendered":"Macro: Using Currencies to Predict NIFTY, Part IV"},"content":{"rendered":"

Please read Part I<\/a> and Part II<\/a> for an introduction. Part III<\/a> extends the treatment to other dollar indices.<\/em><\/small><\/p>\n

In this part of our ongoing series on using SVMs on dollar indices to predict the NIFTY 50, we create an ensemble of two models. We combine one on DTWEXB, using an 8th degree polynomial kernel and another on DTWEXM, using a 4th degree polynomial kernel, to create long-only and long-short portfolios.<\/p>\n

Results<\/h3>\n

Here are the cumulative returns of the standalone models and the ensemble:
\n\"DTWEXB%2BDTWEXM.NIFTY<\/a><\/p>\n

The standalone model over DTWEXM (LS2) seems to outperform all other models, including buy and hold. However, the ensemble model (LS) has lower drawdowns and comes in at second place. Here are the list of drawdowns deeper than 5% over the test dataset:<\/p>\n

\n
\n

Buy and hold drawdowns<\/h4>\n

\"buy<\/a>\n<\/div>\n

\n

Ensemble drawdowns<\/h4>\n

\"DTWEXB+BDTWEXM.NIFTY<\/a>\n<\/div>\n<\/div>\n

In the next (and final) post<\/a>, we will explore if we can add a simple technical signal into to mix to boost returns and reduce drawdowns.<\/p>\n

Code and charts are on github<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"

Please read Part I and Part II for an introduction. Part III extends the treatment to other dollar indices. In this part of our ongoing series on using SVMs on dollar indices to predict the NIFTY 50, we create an ensemble of two models. We combine one on DTWEXB, using an 8th degree polynomial kernel … <\/p>\n","protected":false},"author":2,"featured_media":2077853,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[3471],"tags":[3783,3481,3391,3551],"class_list":["post-2092913","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-investing-insight","tag-machine-learning","tag-macro","tag-nifty","tag-support-vector-machine","entry"],"_links":{"self":[{"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/posts\/2092913","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/comments?post=2092913"}],"version-history":[{"count":0,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/posts\/2092913\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/media\/2077853"}],"wp:attachment":[{"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/media?parent=2092913"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/categories?post=2092913"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/stockviz.biz\/index.php\/wp-json\/wp\/v2\/tags?post=2092913"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}