If you work in the finance industry, especially as a trader, then I bet you can’t live a day without Excel spreadsheets. Excel is one of the most important tools for traders and investors. However, with time, the nature of financial data has become quite complex. The traders need to deal with much larger amounts of data and need to carry our complex analysis in order to gain a competitive edge in the financial markets. This makes Excel a bit outdated or primitive tool of choice. The new tools of choice for traders are R and Python.

Spreadsheets are great. They can help you with crunching numbers quickly. You can easily load data, build a quick model, crunch numbers and get a solution.  However, they are cumbersome. The first problem is that you can’t load large data sets in Excel. As the dataset becomes slightly large, your spreadsheets will start hanging or will take enormous time to refresh. Second, it is difficult to build complex relations between different data sets. Third, since spreadsheets get passed around, it’s difficult to keep them updated and to maintain the sanctity of your solution. You can always write advance macros using Excel VBA, but sometimes they may also not be enough. This is where programming languages like R and Python come into play. Almost any trader can find a vast improvement in their analysis and overall productivity when they shift their work from Excel to R or Python. In our opinion, both R and Python are very competitive, and a trader can benefit from learning either or both. In this article, we are talking from the viewpoint of using R, but the same applies to Python as well.

R is considered to be the best programming tool for conducting statistical analysis using large data sets. It is popular among the financial community, is open-source, and has lots of libraries/packages that can be used to perform almost any kind of analysis you need. It is also a tool of choice for data analysts and data scientists in large financial institutions, so knowledge of R programming also opens new career doors for you.

Almost everything you could do in Excel can be done in R plus much more. You can load large amounts of data from various sources (Including from Excel as a data source), apply statistical models to your data for your research, build financial models, create charts, as well as easily export and share your findings. R is also considered suitable for resource-intensive tasks such as running simulations, which is difficult to do in Excel. Since it has a vibrant development community, there are specific packages that are designed specifically for finance professionals. For example, the 'quantstrat' package provides you the features and functionality to build quantitative trading strategies. Similarly, there are packages for conducting time-series analysis, risk management, forecasting, predictive modeling, and portfolio management, among others. 

Overall, learning R and Python can help you turn yourself into a better trader and also opens doors to careers in data science. This doesn’t mean that you should give up using spreadsheets completely. You can continue to use Excel for basic number crunching and building simple financial models. However, it’s time to start thinking about incorporating R in your everyday work more seriously.