(or “Discriminated unions for dummies like me”, or “Tagged unions for the rest of us”)
Discriminated unions are one of those things in the lexicon of functional programming that can often sound baffling to “outsiders”; it’s almost up there with monads and currying. But in practice they’re simple and incredibly useful. I thought I’d try and show a concrete example of where they can be used in a way which is more powerful and robust than the equivalent OO approach. Continue reading Beginning F#: Positive Discrimination→
For a while now I’ve been planning to write a blog post about pricing financial instruments using Monte Carlo techniques in F#. As part of this I needed to generate normally distributed random numbers, and while putting together the code to do it I realised it was interesting enough to warrant its own post.
I’m making use of a few F#/.NET idioms to make the process easier. For instance, sequences (.NET’s IEnumerable) are used as the source of uniformly distributed random numbers. Also, to verify that the resulting numbers are actually normally distributed, we can easily use existing WPF/silverlight controls to visualise the values direct from within Visual Studio, in a manner similar to this previous post – but without having to write the plotting code ourselves. Continue reading Generating and plotting random numbers→
I heart spreadsheetsIt’s a well known fact that the financial services industry (where I mean banks, hedge funds, pension providers, fund managers etc) is deeply in love with Excel. But what is it about the Excel ecosystem that makes it so appealing?
Unfortunately, given the ever-increasing focus on accountability and scalability in these domains, Excel is also an increasingly inappropriate platform for delivery of this sort of functionality. How can organisations ease the transition away from this environment to something more easily manageable, while retaining it’s positive benefits?
