Top 5 R Finance Modules Reviewed

In case you’re wondering R is one of the most popular analysis packages for the financial industry. The reason why R is so popular is that it’s easy to use, adaptable with various packages, and offers some really impressive visualization tools that are very beautiful to look at. Aside from its visualization tools, R is also used with something called Shiny which enables it to be run on a server. Are can also be used by financial analysts looking to simplify their jobs and make it easier for them to be able to operate effectively.

The most common tasks that the majority of Quants use R for is in building brand new models, importing data for analysis, porfolio optimization, as well as testing new strategies. R is an essential tool for computational analysis as well as testing and building new financial models.

There are plenty of R packages which you can choose from if you are looking to create your own R script for financial analysis. R is a powerful tool which can allow you to very quickly generate a lot of different types of analysis. Here are some of the best choices which are available to you as a quantitative analyst working in R. The majority of these packages are available as CRAN packages that you can install with a single command. Data analysis can be very time-consuming as well as frustrating, but the right R packages can save you a great deal of time when you are building new models.

Looking for some great choices for building brand new financial models? Here are some of the top 5 most commonly used packages in the financial modeling world. Computational finance analysts use these for risk analysis and building highly robust models that are capable of a wide range of choices. When optimized well, R can quickly operate across huge data sets in order to generate high-quality outputs.

1. Quantlab

Quantlab is a great package because it has a solid object model in place that that it’s very easy to use. Of all R finance packages, Quantlab is designed to be the most comprehensive because its fairly plug and play and features many financial analysis tools for time series applications.

Black-Scholes is a common example of an algorithm that Quantlab is able to successfully Implement. Even though to be a really aged example, quantlab adjust data in and outputs the values of the options prices as text variables. This is a great chance for you to be able to manipulate the output of the Black-Scholes equation that one that produces for you to be able to transform that information into something which is much more useful.

Imagine that you would use the Black-Scholes equation to build a model that you can more readily visualize. Quantlab can easily be installed with the help of a single CRAN command, therefore it’s very robust and user-friendly if you’re installing it for the first time. The best part about this package is users are able to reference values directly from the function outputs that this package generates.

There are plenty of use cases which you can consider using this package for in case you are interested in using some machine learning tools to be able to build innovative and novel financial models using ML methods. You are easily able to access the data which is generated by each of the function outputs from QuantLab and incorporate it directly into your own models.

2. QuantLib

Not to be confused with QuantLab, QuantLib is advertised as being a comprehensive package designed for quantitative analysts that operates across a wide expanse of time series data. Not only is it used for creating an R object that is designed to be portable across many platforms, it can also be exported to other languages such as Python, C++, and more.

This package is aimed at producing cutting-edge pricing models aimed at real-life financial modeling and trading. It’s ideal for both practical applications as well as applications using advanced financial modeling techniques. The strongest use case for QuantLib is in working with time series data, which it does not have any trouble with digesting. This tool is ideally designed to be used for forecasting stock prices, as well as designing new trading strategies.

3. QuantMod

Are you looking to quickly prototype and test new trading model ideas? Quant Mod is designed to be your best choice. It does not, however, replace a lot of the statistical functions which you may come to expect with any R package. If you are looking for statistical functionality, you may want to consider using either some of R’s built in statistical functions, QuantLib or QuantLab.

The Quantmod package is designed to make statistical modeling as well as data management easier and simpler. It has very robust financial charting features, designed to create really impressive graphics which are comparable to what you would see in any trading platform. It also includes many of the common technical indicators such as: MACD, Bollinger bands, among others.

This package is a great tool for creating visualizations and adding indicators to your charts, but the developers emphasize that this tool is not designed to be used for providing statistical functionality and analysis. An ideal solution would be to combine the robust visualizations of the QuantMod package alongside statistical functions from R itself or another finance package.

4. Intrinio

There is a quick caveat here: This R Package is not free, and it costs money. However, what’s most notable is that the reason why it costs money is that this includes real-time financial data feeds with both packages for options and equities. The company is known for providing trustworthy and reliable data feeds that offer a superior choice over loading a data import from a CSV file. Intrinio features choices for both stock price data as well as options price data, meaning that it’s fairly simple for you to be able to customize it to your needs. Paying for this module might be worth it to you, because your trading strategy is as only as good as the data which it is derived from. With that in mind, Intrinio is great for providing a highly accurate and robust dataset.

5. PortfolioAnalytics

Portfolio Analytics is fairly different from the rest of the R packages on the market. This one is aimed for those users whom need to be able to provide customized solutions around a set of clearly defined constraints.

Because it’s aimed at those looking to solve portfolio optimization problems, it’s best used as an estimation tool that can be used in order to generate unique strategies for many of the common optimization methods. It generates random portfolios to test new strategies.

This package is a three-step approach to constructing R portfolios: generate constraints, run the optimization against the constraints, and chart the results (following the generation of random portfolios). It should be considered that this package is designed strictly for generating estimates of portfolios.

R has plenty of great quantitative finance tools aimed at satisfying nearly any objective of both a fund manager as well as a portfolio manager. It’s very customizable and has a quick learning curve. Most importantly, the R analysis software as well as many of the modules which you would use with it are free and open source. Even though it’s free, many large institutions use R as part of their day-to-day work, so the cost is not indicative of the quality of the software.

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