{"id":184771,"date":"2023-02-09T15:07:00","date_gmt":"2023-02-09T20:07:00","guid":{"rendered":"https:\/\/ibkrcampus.com\/traders-insight\/popular-python-libraries-for-algorithmic-trading-part-i\/"},"modified":"2023-02-13T15:16:36","modified_gmt":"2023-02-13T20:16:36","slug":"popular-python-libraries-for-algorithmic-trading-part-i","status":"publish","type":"post","link":"https:\/\/www.interactivebrokers.com\/campus\/ibkr-quant-news\/popular-python-libraries-for-algorithmic-trading-part-i\/","title":{"rendered":"Popular Python Libraries for Algorithmic Trading &#8211; Part I"},"content":{"rendered":"\n<p>Python libraries are the most useful part of the Python programming language. These libraries make the work of a programmer easy and quick. Each Python library is essential since each consists of a code that can be readily used for a particular purpose.<\/p>\n\n\n\n<p>With this article on Python Libraries, we would be covering the most popular and widely used Python libraries for quantitative trading beginning with a basic introduction.<\/p>\n\n\n\n<p>This blog covers:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>What are Python libraries?<\/li>\n\n\n\n<li>How are Python libraries useful for algorithmic trading?<\/li>\n\n\n\n<li>Popular libraries with categories for different functions<\/li>\n\n\n\n<li>Data fetching<\/li>\n\n\n\n<li>Data collection<\/li>\n\n\n\n<li>Data manipulation<\/li>\n\n\n\n<li>Technical analysis<\/li>\n\n\n\n<li>Machine learning<\/li>\n\n\n\n<li>Backtesting<\/li>\n\n\n\n<li>Plotting structures<\/li>\n\n\n\n<li>Open source Python trading platforms<\/li>\n<\/ul>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"what-are-python-libraries\">What are Python libraries?<\/h2>\n\n\n\n<p>Python is a free open-source and cross-platform language which has a rich library for almost every task imaginable and also has a specialised research environment.&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/python-trading\/\">Python<\/a>&nbsp;is an excellent choice for automated trading in case of low\/medium trading frequency, i.e. for trades which last more than a few seconds.<\/p>\n\n\n\n<p>It has multiple APIs\/Libraries that can be linked to make it optimal and allow greater exploratory development of multiple trade ideas. For example, we can get the historical market data through the&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/historical-market-data-python-api\/\">Python Stock API<\/a>.<\/p>\n\n\n\n<p>A&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/python-trading-top-blogs\/\">Python library<\/a>&nbsp;is a collection of related modules. The libraries contain bundles of code that can be used repeatedly in different codes. The libraries make&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/python-programming\/\">Python programming<\/a>&nbsp;simpler and more convenient for the programmer as we don\u2019t need to write the same code again and again for different programs. Python libraries play a very vital role in the fields of Machine Learning,&nbsp;<a href=\"https:\/\/quantra.quantinsti.com\/course\/introduction-to-data-science\" target=\"_blank\" rel=\"noreferrer noopener\">Data Science<\/a>, Data Visualization, etc.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"how-are-python-libraries-useful-for-algorithmic-trading\">How are Python libraries useful for algorithmic trading?<\/h2>\n\n\n\n<p>A library, usually, is a collection of books or a room or place where many books are stored to be used later.<\/p>\n\n\n\n<p>Similarly, in the programming world, a library is a collection of precompiled codes that can be used later on in a code for some specific well-defined operations. Other than pre-compiled codes, a library may contain documentation, configuration data, message templates, classes, values, etc.<\/p>\n\n\n\n<p>When we trade algorithmically, Python libraries can be used while coding for different trade-related functions.<\/p>\n\n\n\n<p>For example, Yahoo Finance allows data access from any time series data CSV.<\/p>\n\n\n\n<p>Most of the libraries can be installed with the following syntax:<\/p>\n\n\n\n<p><strong>pip install Python library name<\/strong><\/p>\n\n\n\n<p><strong>or<\/strong><\/p>\n\n\n\n<p><strong>!pip install Python library name<\/strong><\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"popular-libraries-with-categories-for-different-functions\">Popular libraries with categories for different functions<\/h2>\n\n\n\n<p>Now, we will discuss different popular Python libraries belonging to each category. So, let us begin!<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"python-libraries-for-data-fetching\">Python libraries for data fetching<\/h3>\n\n\n\n<p><strong>yFinance<\/strong><\/p>\n\n\n\n<p>yFinance is a library for fetching historical prices\u2019 data of securities and their fundamental information from&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/download-futures-data-yahoo-finance-library-python\/\">Yahoo Finance<\/a>. In 2017, \u201cYahoo Finance\u201d decommissioned its official data API. Ever since then, yFinance has become an alternative method to acquire financial data.<\/p>\n\n\n\n<p>The yFinance usually fetches the OHLC data from Yahoo Finance and returns it in a data frame format.<\/p>\n\n\n\n<p><strong>Alpha Vantage<\/strong><\/p>\n\n\n\n<p>Similar to the yFinance, Alpha Vantage is another Python library that helps obtain the historical prices data as well as the fundamental data through the Alpha Vantage API.<\/p>\n\n\n\n<p>One additional bonus of Alpha Vantage is that it also offers technical indicator data such as SMA, EMA, MACD, Bollinger Bands, etc.<\/p>\n\n\n\n<p><strong>Pandas-DataReader<\/strong><\/p>\n\n\n\n<p>In addition to the stock OLHC and fundamental data, the Pandas-DataReader allows to extract other alternative financial data such as the Federal Reserve Economic Data, Fama\/French Data, World Bank Development Indicators, etc.<\/p>\n\n\n\n<p><strong>Summary:&nbsp;<\/strong>For fetching the historical OHLC data of stocks, yFinance, Alpha Vantage and Pandas-DataReader work the best. yFinance is simply a library for returning data, whereas, Alpha Vantage helps with the technical indicator data as well.<\/p>\n\n\n\n<p>Going forward, Pandas-DataReader is usedful for the data needed from Federal Reserve Economic Data, Fama\/French Data, World Bank Development Indicators, etc.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"python-libraries-for-data-collection\">Python libraries for data collection<\/h3>\n\n\n\n<p><strong>Trading on Interactive Brokers using Python<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/www.interactivebrokers.com\/en\/home.php\" target=\"_blank\" rel=\"noreferrer noopener\">Interactive Brokers<\/a>&nbsp;is an electronic broker which provides a trading platform for connecting to live markets using various programming languages including Python. It provides access to over 100 market destinations worldwide for a wide variety of electronically traded products including stocks, options, futures, forex, bonds, CFDs and funds. IB not only has very competitive commission and margin rates but also has a very simple and user-friendly interface.<\/p>\n\n\n\n<p>There are a couple of interesting Python libraries which can be used for&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/ibridgepy-webinar-14-november-2019\/\">connecting to live markets using IB<\/a>. You need to first have an account with IB to be able to utilise these libraries to trade with real money.<\/p>\n\n\n\n<p><strong>IBridgePy<\/strong><\/p>\n\n\n\n<p><a href=\"https:\/\/quantra.quantinsti.com\/course\/Automated-Trading-IBridgePY-Interactive-Brokers-Platform\" target=\"_blank\" rel=\"noreferrer noopener\">IBridgePy library<\/a>&nbsp;is an easy to use and flexible python library which can be used to trade with&nbsp;<a href=\"https:\/\/www.youtube.com\/watch?v=Cg3gejGX3Xk\" target=\"_blank\" rel=\"noreferrer noopener\">Interactive Brokers<\/a>. It is a wrapper around&nbsp;<a href=\"https:\/\/blog.quantinsti.com\/implement-python-in-interactive-brokers-api\/\">IBridgePy\u2019s API<\/a>&nbsp;which provides a very simple to use solution while hiding IB\u2019s complexities.<\/p>\n\n\n\n<p><strong>Summary:&nbsp;<\/strong>Interactive Brokers provides access to over 100 market destinations worldwide. It provides access to products such as stocks, options, futures, forex, bonds, CFDs and funds.<\/p>\n\n\n\n<p><a href=\"https:\/\/quantra.quantinsti.com\/course\/Automated-Trading-IBridgePY-Interactive-Brokers-Platform?_gl=1%2a11iklbr%2a_ga%2aNzg5ODg3OTY4LjE1ODQ0MzIwNzc.%2a_ga_SXP1W7WL9G%2aMTY3NTEzNDE5MS43ODUuMS4xNjc1MTM2MjAxLjAuMC4w\" target=\"_blank\" rel=\"noreferrer noopener\">IBridgePy<\/a>&nbsp;is used for trading with Interactive Brokers. It does not re-implement Interactive brokers\u2019 API. Instead, it helps Python to call IB\u2019s C++ API directly as it acts as a wrapper. Since IBridgePy calls on Interactive Broker\u2019s C++ API directly, therefore, we can expect fewer errors and exceptions in the program.<\/p>\n\n\n\n<p><em>Stay tuned for the next installment in this series to learn about Python libraries used for data manipulation.<\/em><\/p>\n\n\n\n<p><em>For additional insight on this topic and to read the article originally posted on QuantInsti, visit <a href=\"https:\/\/blog.quantinsti.com\/python-trading-library\/\">https:\/\/blog.quantinsti.com\/python-trading-library\/<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Python libraries are the most useful part of the Python programming language. These libraries make the work of a programmer easy and quick.<\/p>\n","protected":false},"author":368,"featured_media":184772,"comment_status":"closed","ping_status":"open","sticky":true,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[339,343,349,338,350,341,9563,344],"tags":[851,575,14687,14688,595,14689,6674],"contributors-categories":[13654],"class_list":{"0":"post-184771","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-options-quant","14":"category-quant-regions","15":"tag-algo-trading","16":"tag-ibkr-api","17":"tag-ibridgepy","18":"tag-pandas-datareader","19":"tag-python","20":"tag-python-libraries-for-algorithmic-trading","21":"tag-yfinance","22":"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 v27.3) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Popular Python Libraries for Algorithmic Trading &#8211; Part I<\/title>\n<meta name=\"description\" content=\"Python libraries are the most useful part of the Python programming language. 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