We’re excited to announce the alphavantager package, a lightweight R interface to the Alpha Vantage API! Alpha Vantage is a FREE API for retreiving real-time and historical financial data. It’s very easy to use, and, with the recent glitch with the Yahoo Finance API, Alpha Vantage is a solid alternative for retrieving financial data for FREE! It’s definitely worth checking out if you are interested in financial analysis. We’ll go through the alphavantager R interface in this post to show you how easy it is to get real-time and historical financial data. In the near future, we have plans to incorporate the alphavantager into tidyquant to enable scaling from one equity to many.

Tidy Time Series Analysis, investigate lags and autocorrelation to understand seasonality and form the basis for autoregressive forecast models.

In the third part in a series on Tidy Time Series Analysis, we’ll use the runCor function from TTR to investigate rolling (dynamic) correlations. We’ll again use tidyquant to investigate CRAN downloads. This time we’ll also get some help from the corrr package to investigate correlations over specific timespans, and the cowplot package for multi-plot visualizations. We’ll end by reviewing the changes in rolling correlations to show how to detect events and shifts in trend. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance. If interested, contact us.

We have several announcements regarding Business Science R packages. First, as of this week the R package formerly known as timekit has changed to timetk for time series tool kit. There are a few “breaking” changes because of the name change, and this is discussed further below. Second, the sweep and tidyquant packages have several improvements, which are discussed in detail below. Finally, don’t miss a beat on future news, events and information by following us on social media.

In the second part in a series on Tidy Time Series Analysis, we’ll again use tidyquant to investigate CRAN downloads this time focusing on Rolling Functions. If you haven’t checked out the previous post on period apply functions, you may want to review it to get up to speed. Both zoo and TTR have a number of “roll” and “run” functions, respectively, that are integrated with tidyquant. In this post, we’ll focus on the rollapply function from zoo because of its flexibility with applying custom functions across rolling windows. If you like what you read, please follow us on social media to stay up on the latest Business Science news, events and information! As always, we are interested in both expanding our network of data scientists and seeking new clients interested in applying data science to business and finance.

We’re pleased to introduce a new package, sweep, now on CRAN! Think of it like broom for the forecast package. The forecast package is the most popular package for forecasting, and for good reason: it has a number of sophisticated forecast modeling functions. There’s one problem: forecast is based on the ts system, which makes it difficult work within the tidyverse. This is where sweep fits in! The sweep package has tidiers that convert the output from forecast modeling and forecasting functions to “tidy” data frames. We’ll go through a quick introduction to show how the tidiers can be used, and then show a fun example of forecasting GDP trends of US states. If you’re familiar with broom it will feel like second nature. If you like what you read, don’t forget to follow us on social media to stay up on the latest Business Science news, events and information!

In the first part in a series on Tidy Time Series Analysis, we’ll use tidyquant to investigate CRAN downloads. You’re probably thinking, “Why tidyquant?” Most people think of tidyquant as purely a financial package and rightfully so. However, because of its integration with xts, zoo and TTR, it’s naturally suited for “tidy” time series analysis. In this post, we’ll discuss the the “period apply” functions from the xts package, which make it easy to apply functions to time intervals in a “tidy” way using tq_transmute()!

The EARL SF 2017 conference was just held June 5 - 7 in San Francisco, CA. There were some amazing presentations illustrating how R is truly being embraced in enterprises. We gave a three-part presentation on tidyquant for financial data science at scale, timekit for time series machine learning, and Business Science enterprise applications. We’ve uploaded the EARL presentation to YouTube. Check out the presentation, and don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information!

The R/Finance 2017 conference was just held at the UIC in Chicago, and the event was a huge success. There were a ton of high quality presentations really showcasing innovation in finance. We gave a presentation on tidyquant illustrating the key benefits related to financial analysis in the tidyverse. We’ve uploaded the tidyquant presentation to YouTube. Check out the presentation. Don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information!

We’ve just released timekit v0.3.0 to CRAN. The package updates include changes that help with making an accurate future time series with tk_make_future_timeseries() and we’ve added a few features to tk_get_timeseries_signature(). Most important are the new vignettes that cover both the making of future time series task and forecasting using the timekit package. If you saw our last timekit post, you were probably surprised to learn that you can use machine learning to forecast using the time series signature as an engineered feature space. Now we are expanding on that concept by providing two new vignettes that teach you how to use ML and data mining for time series predictions. We’re really excited about the prospects of ML applications with time series. If you are too, I strongly encourage you to explore the timekit package important links below. Don’t forget to check out our announcements and to follow us on social media to stay up on the latest Business Science news, events and information! Here’s a summary of the updates.

In advance of upcoming Business Science talks on tidyquant at R/Finance and EARL San Francisco, we are releasing a technical paper entitled “New Tools For Performing Financial Analysis within the ‘Tidy’ Ecosystem”. The technical paper covers an overview of the current R financial package landscape, the independent development of the “tidyverse” data science tools, and the tidyquant package that bridges the gap between the two underlying systems. Several usage cases are discussed. We encourage anyone interested in financial analysis and financial data science to check out the technical paper. We will be giving talks related to the paper at R/Finance on May 19th in Chicago and EARL on June 7th in San Francisco. If you can’t make it, I encourage you to read the technical paper and to follow us on social media to stay up on the latest Business Science news, events and information.

The timekit package contains a collection of tools for working with time series in R. There’s a number of benefits. One of the biggest is the ability to use a time series signature to predict future values (forecast) through data mining techniques. While this post is geared toward exposing the user to the timekit package, there are examples showing the power of data mining a time series as well as how to work with time series in general. A number of timekit functions will be discussed and implemented in the post. The first group of functions works with the time series index, and these include functions tk_index(), tk_get_timeseries_signature(), tk_augment_timeseries_signature() and tk_get_timeseries_summary(). We’ll spend the bulk of this post introducing you to these. The next function deals with creating a future time series from an existing index, tk_make_future_timeseries(). The last set of functions deal with coercion to and from the major time series classes in R, tk_tbl(), tk_xts(), tk_zoo() (and tk_zooreg()), and tk_ts().