The book, “An Introduction to Algorithmic Trading” by Edward Leshik and Jane Cralle contains a very interesting remark on markets. It says, “In the order of complexity the markets rank a good fourth after the Cosmos, Human Brain, and Human Immune System.”
It is indeed true that markets can be complex, but I feel that this complexity should not deter anyone from not participating in the markets and not trading them. Markets are very exciting and many a passionate traders have made a fortune for themselves by trading in the markets.
Thanks to the ever-evolving technological advances in trading, traders of the 21st century have access to feature-rich trading platforms, high-speed connectivity, algorithmic trading, and have multiple resources available to learn the nitty-gritties of trading. All these vital factors, I feel, have reduced the order of complexity for the trading community at large and especially for the newbie traders.
You must have noticed the word, “algorithmic trading” in the previous paragraph. Since its advent, thousands of traders have taken to algorithmic trading and thousands more are looking to switch from discretionary trading to algorithmic trading. Towards this end, Interactive Brokers offers and supports multiple API solutions in different programming languages along with its feature-rich trading platform which enables algorithmic trading for its clients.
Algorithmic Trading using R programming
Traders can use R programming for algorithmic trading on the Interactive Brokers platform. Here’s how R makes it easy to trade markets algorithmically:
Free and open-source software – R is a programming language used for scientific computing and graphics, which has gained wide popularity in recent years and ranks among the top programming languages for data science. R also refers to the software environment used to run programs written in R. The R environment is free and open-source software and one can use an integrated development environment (IDE) like RStudio to develop trading strategies in R.
Thousands of contributed packages on CRAN – The Comprehensive R Archive Network (CRAN) offers a wide variety of packages for R users on many different topics including statistical modeling, time series analysis, classification, clustering, and visualization (see the complete list of available packages here). R also offers the ability to create your own packages.
Strong developer community – The popularity of R can be gauged by its strong developer community. There are many forums and sites like Stackoverflow, R-bloggers, Revolutions that serve as a great help to R programmers.
Packages for Quantitative Trading – Traders can make use of packages like Quantstrat, QuantTools and PerformanceAnalytics for backtesting and analyzing their trading strategies. You can also find numerous R packages on Machine Learning and Sentiment Analysis to create trading strategies.
Quick prototyping of back-tested strategies – Since R-based strategies can be used for both backtesting and in live markets, algorithmic traders can quickly prototype their backtested strategies and implement it live by making minimal code changes. See the example of the pairs trading strategy in R.
Given all these factors, traders who want to learn R but are little skeptical can easily overcome their fear of programming and trade with trading strategies based in R.
Algorithmic Trading using IBrokers R API
Given the simplicity and the benefits of R, it’s the ideal environment to develop trading strategies. The IBrokers API authored by Jeffrey Ryan and maintained by Joshua Ulrich offers traders the necessary support to implement their R-based strategies in live markets.
The IBrokers API connects the user’s R application to Interactive Brokers Trader Workstation (TWS) and helps execute the R-based trading strategies. Some of the key features of the IBrokers package include:
Access to account information – The user connects his R application to TWS using the twsConnect function and can retrieve the real-time account information using the reqAccountUpdates function.
Retrieval of historical and real-time data from TWS – The IBrokers package offers functions like the reqHistoricalData, reqMktData, reqMktDepth, and reqRealTimeBars to retrieve data from TWS. The duration of the data can be in seconds, days, weeks, months, and years. Similarly, the user can set the bar size with these functions. The retrieved data can also be stored in files of the desired format for later use.
Customization of the data functions – The market data functions can also be customized via their eventWrapper and CALLBACK arguments to obtained data in the desired format and desired fields.
Construct contracts for different instruments – The IBrokers package offers functions like the twsEquity, twsOption, twsFuture, twsCurrency, and the twsIndex to construct contracts for different instruments.
Execute orders programmatically – Once the contracts are constructed and the signals generated from the trading strategy, users can submit orders to the TWS. The orders can be of different types (market, limit, stoploss, trailing etc.) and these can also be modified or cancelled per the strategy requirements.
These are some of the key features of the IBrokers API. Traders can build a variety of trading strategies using the IBrokers API and also test them with the paper trading simulation offered by Interactive Brokers before implementing them live in the markets.
To lean more about automated trading using the IBrokers API, you can check our latest course, “Trading Using R” offered on IB Trader’s Academy. It is an exciting course that covers the key functions from the IBrokers package with illustration of the code execution in RStudio. It also includes relevant examples and an R sample trading strategy at the end. We are sure that you will enjoy the course and influence you to explore trading with Interactive Brokers using R.
Start the course from here: https://gdcdyn.interactivebrokers.com/en/index.php?f=25243#course5
Milind Paradkar holds an MBA in Finance from the University of Mumbai and a Bachelor’s degree in Physics from St. Xavier’s College, Mumbai. At QuantInsti®, Milind is involved in creating technical content on Algorithmic & Quantitative trading. Prior to QuantInsti®, Milind had worked at Deutsche Bank as a Senior Analyst where he was involved in the cash flow modeling of structured finance deals covering Asset-backed Securities (ABS) and Collateralized Debt Obligations (CDOs).
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