R Integration
R Programming Language Integration of InfoReach TMS

The "R" programming language is swiftly emerging as a dominant cutting-edge tool for quantitative finance and optimization. Designed expressly to analyze and display large amounts of data, R is ideal for developing models for data and risk analysis, statistics and visualization. And all standard quant and risk models already are available in R—providing end users with a large, high-quality code library they can easily use and customize. With the integration of R language into its technology, InfoReach enables its Execution Management System (EMS) users to access any bank of R functions directly within the InfoReach interface.


What is R?

R is a full-fledged programming language with a radically different approach to processing large and complex datasets. It is an open-source project that depends on a worldwide development community to grow and evolve. Like Linux, R is maintained and supported by those individuals who use it and contribute to its ongoing development. Mike King, a quantitative analyst at Bank of America, for example, uses R to write programs for capital adequacy modeling, decision systems design, and predictive analytic

R is data analysis software: data scientists, statisticians, analysts, quants, and others who need to make sense of data use R for statistical analysis, data visualization, and predictive modeling.

R is a programming language: you perform data analysis in R by writing scripts and functions in the R programming language. R is a complete, interactive, object-oriented language: designed by statisticians, for statisticians. The language provides objects, operators and functions that make the process of exploring, modeling, and visualizing data a natural one.

R is an environment for statistical analysis: Available in the R language are functions for virtually every data manipulation, statistical model, or chart that the data analyst could ever need. Not only are all the "standard" methods available, but because most cutting-edge research in statistics and predictive modeling is done in R, the latest techniques are usually available first in the R system.

R is an open-source software project. You can download and use R for free, and the source code is open for inspection and modification to anyone who wants to see how the methods and algorithms work under the covers. And R has open interfaces, meaning that it readily integrates with other applications and systems.

R is a community. As a thriving open-source project, R is supported by a community of more than 2 million users and thousands of developers worldwide. Whether you?re using R to optimize portfolios, analyze genomic sequences, or to predict component failure times, experts in every domain have made resources, applications and code available for free, online.

What makes R powerful?

  1. Some of the most advanced statistical analysis capabilities
  2. Facilitates predictive analytics (its core strength)
  3. Mindblowing data visualizations to help you report better
  4. Ability to process and make sense of unstructured data such as text, video, voice and log data

Why use R?

There's an abundance of software available for data analysis today: spreadsheets like Excel, batch-oriented procedure-based systems like SAS; point-and-click GUI-based systems like SPSS; data mining systems, and so on. What makes R different?

R is free. As an open-source project, you can use R free of charge: no worries about subscription fees, license managers, or user limits. But just as importantly, R is open: you can inspect the code and tinker with it. Thousands of experts around the world have done just that, and their contributions benefit the millions of people who use R today.

Graphics and data visualization. One of the design principles of R was that visualization of data through charts and graphs is an essential part of the data analysis process. As a result, it has excellent tools for creating graphics, from staples like bar charts and scatterplots to multi-panel Lattice charts to brand new graphics of your own devising. Graphics based on R appear regularly in venues like the New York Times, the Economist, and the FlowingData blog.

A flexible statistical analysis toolkit. All of the standard data analysis tools are built right into the R language: from accessing data in various formats, to data manipulation (transforms, merges, aggregations, etc.), to traditional and modern statistical models (regression, ANOVA, GLM, tree models, etc). All are included in an object-oriented framework that makes it easy to programatically extract out and combine just the information you need from the results, rather than having to cut-and-paste from a static report.

Access to powerful, cutting-edge analytics. Leading academics and researches from around the world use R to develop the latest methods in statistics, machine learning, and predictive modeling. There are expansive, cutting-edge edge extensions to R in finance, genomics, and dozens of other fields. All standard quant and risk models are already available in R, providing end-users with a large high-quality code library on which to build even more sophisticated models. To date, more than 2000 packages extending the R language in every domain are available for free download, with more added every day.

Unlimited possibilities. With R, you're not restricted to choosing a pre-defined set of routines. You can use code contributed by others in the open-source community, or extend R with your own functions. And R is excellent for "mash-ups" with other applications: combine R with a MySQL database, an Apache web-server, and the Google Maps API and you've got yourself a real-time GIS analysis toolkit, for example.

Why R is well-suited to the financial industry

Every data analysis technique at your fingertips. Instead of limiting your analysis options with paid add-on modules, R includes virtually every data manipulation, statistical model, and chart that the modern data scientist could ever need. And when ?close enough? isn?t good enough for your predictive models, you can easily find, download and use cutting-edge community-reviewed methods in statistics and predictive modeling from leading researchers in data science, free of charge.

The power to create beautiful and unique data visualizations. Representing complex data with charts and graphs is an essential part of the data analysis process, and R goes far beyond the traditional bar chart and line plot. R makes it easy to draw meaning from multidimensional data with multi-panel charts, 3-D surfaces and more.

Better results faster. Instead of using point-and-click menus or inflexible ?black-box? procedures, R is a programming language designed expressly for data analysis. Experienced R programmers create data analyses faster than users of legacy statistical software, with the flexibility to mix-and-match models for the best results. And R scripts are easily automated, promoting both reproducible research and production deployments.

Help with external data. R enthusiasts have created add-on packages to help other users download data into R with a minimum of fuss. For instance, the financial analysis package Quantmod, developed by quantitative software analyst Jeffrey Ryan, makes it easy to not only pull in and analyze stock prices but graph them as well.

There are many other packages with R interfaces to data sources such as twitteR for analyzing Twitter data Quandl and rdatamarket for access to millions of data sets at Quandl and Data Market, respectively; and several for Google Analytics, including rga, RGoogleAnalytics and ganalytics.

Looking for a specific type of data to pull into R but don't know where to find it? You can try searching Quandl and Data Market, where data can be downloaded in R format even without needing to install the site-specific packages mentioned above.

Contributed packages for R span every application in finance, including:

  • Financial Data: Time series (regular and irregular); financial calendars; database query; access to financial data sources Yahoo, Bloomberg, FAME, Interactive Brokers and more.
  • Financial instruments: options, bonds, yield curves, portfolios, technical trading rules.
  • Time Series: Classical time series analysis (ARIMA, Kalman filters); volatility modeling (incl. GARCH); unit root and cointegration tests; dynamic models; wavelet analysis; and more.
  • Risk management: Value at Risk, cVaR, Credit Risk, quantitative risk modeling, portfolio optimization.
  • Econometrics: generalized linear regression, quantile regression, generalized additive models, microeconomic models.
  • Financial analytics: Simulation, backtesting, regression, copulas, random matrix theory, stochastic differential equations, factor analysis, actuarial methods, constrained optimization.
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