{"id":78417,"date":"2021-03-04T11:35:44","date_gmt":"2021-03-04T16:35:44","guid":{"rendered":"https:\/\/ibkrcampus.com\/?p=78417"},"modified":"2022-11-21T09:47:11","modified_gmt":"2022-11-21T14:47:11","slug":"technical-analysis-with-python","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/technical-analysis-with-python\/","title":{"rendered":"Technical Analysis with Python"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">In this post, we will introduce how to do technical analysis with Python. Python has several libraries for performing technical analysis of investments. We\u2019re going to compare three libraries \u2013&nbsp;<strong>ta<\/strong>,&nbsp;<strong>pandas_ta<\/strong>, and&nbsp;<strong>bta-lib<\/strong>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-the-ta-library-for-technical-analysis\"><strong>The ta library for technical analysis<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">One of the nicest features of the\u00a0<strong>ta<\/strong>\u00a0package is that it allows you to add dozens of technical indicators all at once. To get started, install the\u00a0<strong>ta<\/strong>\u00a0library using pip:<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">\t\n\t\npip install ta\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Next, let\u2019s import the packages we need. We\u2019ll be using&nbsp;<a href=\"https:\/\/theautomatic.net\/yahoo_fin-documentation\/\">yahoo_fin<\/a>&nbsp;to pull in stock price data. Now,&nbsp;<strong>data<\/strong>&nbsp;contains the historical prices for AAPL.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">\t\n# load packages<br>\nimport yahoo_fin.stock_info as si<br>\nimport pandas as pd<br>\nfrom ta import add_all_ta_features<br><br>\n \n# pull data from Yahoo Finance<br>\ndata = si.get_data(&#8220;aapl&#8221;)\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Next, let\u2019s use&nbsp;<strong>ta<\/strong>&nbsp;to add in a collection of technical features. Below, we just need to specify what fields correspond to the open, high, low, close, and volume. This single call automatically adds in over 80 technical indicators, including RSI, stochastics, moving averages, MACD, ADX, and more.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">\t\n# add technical analysis features<br>\ndata = add_all_ta_features(<br>\n    data, open=&#8221;open&#8221;, high=&#8221;high&#8221;, low=&#8221;low&#8221;, close=&#8221;adjclose&#8221;, volume=&#8221;volume&#8221;)\n<\/p><\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"640\" height=\"477\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-1.png\" alt=\"\" class=\"wp-image-78437 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-1.png 640w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-1-300x224.png 300w\" data-sizes=\"(max-width: 640px) 100vw, 640px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 640px; aspect-ratio: 640\/477;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">For example, here\u2019s the&nbsp;<a href=\"https:\/\/en.wikipedia.org\/wiki\/Relative_strength_index\">RSI<\/a>&nbsp;values (using the standard 14-day calculation):<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img decoding=\"async\" width=\"376\" height=\"182\" data-src=\"\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-2.png\" alt=\"\" class=\"wp-image-78439 lazyload\" data-srcset=\"https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-2.png 376w, https:\/\/ibkrcampus.com\/campus\/wp-content\/uploads\/sites\/2\/2021\/03\/python-technical-indicators-theautomatic-next-2-300x145.png 300w\" data-sizes=\"(max-width: 376px) 100vw, 376px\" src=\"data:image\/svg+xml;base64,PHN2ZyB3aWR0aD0iMSIgaGVpZ2h0PSIxIiB4bWxucz0iaHR0cDovL3d3dy53My5vcmcvMjAwMC9zdmciPjwvc3ZnPg==\" style=\"--smush-placeholder-width: 376px; aspect-ratio: 376\/182;\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>ta<\/strong>&nbsp;also has several modules that can calculate individual indicators rather than pulling them all in at once. These modules allow you to get more nuanced variations of the indicators. For example, if you want to calculate the 21-day RSI, rather than the default 14-day calculation, you can use the&nbsp;<strong>momentum<\/strong>&nbsp;module.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">\t\nfrom ta.momentum import RSIIndicator<br><br>\n \nrsi_21 = RSIIndicator(close = data.adjclose, window = 21)<br><br>\n \ndata[&#8220;rsi_21&#8221;] = rsi_21.rsi()\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Similarly, we could use the&nbsp;<strong>trend<\/strong>&nbsp;module to calculate&nbsp;<a href=\"https:\/\/en.wikipedia.org\/wiki\/MACD\">MACD<\/a>.<\/p>\n\n\n\n<p style=\"background-color:#fcfcdb;font-size:11px\" class=\"has-background\">\t\nfrom ta.trend import macd<br><br>\n \ndata[&#8220;macd&#8221;] = macd(data.adjclose, window_slow = 26, window_fast = 12)\n<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">To learn more about&nbsp;<strong>ta<\/strong>&nbsp;<a href=\"https:\/\/technical-analysis-library-in-python.readthedocs.io\/en\/latest\/index.html\">check out its documentation here<\/a>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><em>Visit <a href=\"https:\/\/TheAutomatic.net\">TheAutomatic.net<\/a><\/em> <em>for a tutorial on the pandas_ta library and read the rest of the article: <a href=\"https:\/\/theautomatic.net\/2021\/02\/02\/technical-analysis-with-python\/\">https:\/\/theautomatic.net\/2021\/02\/02\/technical-analysis-with-python\/<\/a><\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This tutorial will showcase how to use Python&#8217;s ta library for technical analysis. Ready-to-use code is available for download.<\/p>\n","protected":false},"author":388,"featured_media":29845,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[339,343,349,338,341,352,344],"tags":[9322,865,187,1224,9321,595,9323,9320,1291,9325,9324],"contributors-categories":[13695],"class_list":["post-78417","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-development","category-quant-north-america","category-quant-regions","tag-bta-lib","tag-github","tag-macd","tag-pandas","tag-pandas_ta","tag-python","tag-ta-library","tag-ta-package","tag-technical-analysis","tag-trend-module","tag-yahoo_fin-stock_info","contributors-categories-theautomatic-net"],"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 v28.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Technical Analysis with Python | IBKR Quant<\/title>\n<meta name=\"description\" content=\"This tutorial will showcase how to use Python&#039;s ta library for technical analysis. 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