Risk Charts, Analytics

The charts below are all created using a combination of python and matplotlib. Actual excel reports are created using the excellent win32all library. Most market data comes from Bloomberg. Position & risk data come from various trading systems of record (there are many of them), and I have blurred out anything important and kept everything out of date.

My favorite chart. It shows two dimensions of risk -- by date (as in, time series) and by maturity buckets. Allows you to see the overall risk profile of a book at the same time as I see how it has changed over the past 8 (or so) days.

This is a daily dashboard created using publically available market data. It's fairly simple, but the 'sparkchart'-type graphs allow you to get a quick overview about how different segments of the markets have been moving. Also useful for leaving on your desk or bringing to meetings in order to intimidate people.

A simpler version of the first chart, this one shows "bucketed" risks by maturity. A secondary red line shows the diff from the previous day, allowing you to spot changes in the book quickly.

Another favorite, and one of the earlier automated charts I created. Shows multiple dimensions of risk due to a certain type of non-vanilla option: 1) where option expiration is relative to the current market (time and strike) and 2) size of the potential payout (bubble size and color).

Stress testing is a critical part of risk management. This report gives the user a snapshot for a subset of stress tests, their averages, and a time series of results over time, allowing significant results to jump out.

Impact on a book of non-parallel shocks in the 3M LIBOR/Swap curve. Shocks are scaled across maturities by standard deviation.

One of the longest running daily charts/reports. Shows a sparkline-type chart for a particular stress test for each sub-portfolio in a book. Shows highest 45-day and 10-day points (red dot, red text column) compared to today's current (blue) result.