I will use this post as repository of all the publicly available shiny dashboards I have developed through time. DTCC Swap Data Dashboard All the investment banks in the world must submit the information about their trades to a trade repository called DTCC. We report the anonymysed version of such data as published by DTCC here (https://pddata.dtcc.com/gtr/cftc/dashboard.do) in a dashboard that tracks the evolution through time of the global investments in a specific trade called “interest rate swaps”.

The EU is set to become the largest supranational bond issuer in the world thanks to its €90bn billion instrument for temporary Support to mitigate Unemployment Risks in an Emergency (SURE) and €750bn Next Generation EU (NGEU) recovery fund. If we look at the distribution of bond notional issued by the European Union outstanding as at the end of June and we divide it between SURE, NGEU and legacy programmes (mainly for loans to Ireland and Portugal during the last debt crisis) we can see that the SURE programme (now concluded) represents almost 50% of total with NGEU already representing 20%.

I have released a new version of the package SwapPricer on GitHub here. As we are now at version 1.0.1 the toolbox is able to price using just a one-curve framework but is able to price multiple currencies (ie. CHF, EUR, GBP, JPY and USD) and any convention in terms of coupon frequency, day count convention. We are working to introduce OIS Discounting in the next releases SwapPricer: instructions for use To run a multi-currency swap portfolio valuation you need the following three “ingredients”:

In the previous posts we have seen how easy it is to price interest rate swaps using R. I am honoured to announce that I have decided to put all the functions I have described together into a package that is called…SwapPricer! Ok, the name is not super original, but it should at least be easy to remember. You can install it as follows:

In this post we will close the trilogy on (old style) swap pricing. In particular, we will look at how downloading the data for the variable rate needed to calculate the variable leg accrual. Part 1 gave the general idea behind tidy pricing interest rate swaps using a 7 lines pipe Part 2 went much more into detail and priced some real world contract comparing the results obtained vs Bloomberg and showing significantly good results.

In this post I will show how easy is to price a portfolio of swaps leveraging the purrr package and given the swap pricing functions that we introduced in a previous post. I will do this in a “real world” environment hence using real market data as per the last 14th of April. Import the discount factors from Bloomberg Let’s start the pricing of the swap portfolio with purrr by loading from an external source the EUR discount factor curve.

Introduction I am a big passionate of the tidyverse packages: I think they make the code very clean and clear. In particular, I like the lubridate packages for managing and making operations with dates but its major drawback is that it doesn’t manage financial holidays, which are key when projecting financial cashflows linked to instruments like interest rte swaps. In this case, I prefer to use the RQuantLib package.

This week's #TidyTuesday is about federal spending. I was interested in understanding if spending had shifted towards clean energy sub-agencies in the last 20 years. Spoiler alert: unfortunately no! 😰#Rstats #tidyverse cc @thomas_mock @R4DScommunity pic.twitter.com/AbQzheSagU — Davide Magno (@DavideMagno) February 12, 2019

Here's my #TidyTuesday submission.I studied the total increase in the housing index for each US State compared to the US federal index. No surprise in California house price has increased much more than the rest of US.The #mapdata library was 👍🏼@thomas_mock @R4DScommunity #rstats pic.twitter.com/sYI1zKhIpC — Davide Magno (@DavideMagno) February 10, 2019

Finally my first #TidyTuesday submission! 🎊🎉I am starting from week 1 2019 but I will catch up soon😉When do people submit their visualisations?Mostly on Tuesday and Wednesday afternoon, but also on Monday before the new set is released #rstats #ggplot @rstats4ds @thomas_mock pic.twitter.com/pKZAyL8vp1 — Davide Magno (@DavideMagno) February 3, 2019