How Quants Choose Technology

As a former buy-side-ish quant, I think we are terrible customers for builders and sellers of analytical tools. Quants are smart enough that proof-by-intimidation doesn’t work. What’s worse, quants are very particular and very peculiar in their needs. You built a factor attribution system? Well what about rolling up into 5 day or 10 day returns? How about using Newey-West corrections to eliminate the effect of auto-correlation? Hey let’s try taking our risk model, computing an ex-ante covariance matrix for our alpha factors, and then using that derived covariance matrix in a GLS regression. I’ve actually been on both sides of that question so I know how frustrating it can be, but also how important these things are.

On the other hand, quants are also great customers. Because what they need tend to be fairly advanced both in terms of computational and mathematical requirements, there are not a lot of tools out there that meet their main criteria for good software — reliability, flexibility, transparency, usability,  shrink-wrapping, and quality of support. If you are a good technologist who also happen to understand the domain problems, or if you’re a quant who can also code, there is a lot of value that you would be able to add.

Quants tend not to be expert software engineers (there is an important distinction between programmer and software engineer), which means that the NIH (Not-Invented-Here) syndrome is much less among quants. It’s not the architecture and design that rubs quants egos the wrong way, and there is no competitive tension that exists between an internal development team and a software vendor. Instead, what drives quants to want to build software in-house generally tend to be the fact that no software package currently available meet all  of the criteria listed above. Commercial packages like Palantir and ClariFi are inflexible and not transparent (really they’re not even targeted towards quants at all). Traditional analytical software like Matlab is difficult to use as soon as you move away from the core matrix computations. R and other open source software are big steps in the right direction, but R packages are scattered, unsupported, and isn’t nearly as readable (which goes into transparency and usability) as Python.

Out of the criteria listed above, reliability, quality of support, and shrink-wrapping tend to be solved problems. Transparency is also not too difficult and is only complicated by the need to balance between being open source (to customers) versus protecting IP. This tends to be a business problem that can be ameliorated by rapid innovation and consulting relationships to provide more value to the customer than they would obtain by stopping a subscription and building in-house extensions to an older version of your software. Usability is a more difficult problem in that it requires a good deal of domain knowledge. What techniques do quants generally like to use? What are sensible default arguments? What are the most common ways to chain together statistical tools? These are all questions that can only be answered by someone with solid quantative finance experience. And the answers will only be useful for someone with solid skills in software engineering. The final criteria, flexibility, is a fairly complex problem that requires careful consideration. There’s not enough room in a blog to go into details, but two things are important here. One is being able to handle a large number of parameters. What defaults do you set? Do you use a separate configuration object? Do you use variable parameter magic? How do you make sure users who need to do simple tasks aren’t drowning in a sea of parameters that they don’t need to set and shouldn’t have to stress about reading all the documentation? The other issue that is important is how do you structure a flexible framework so that it makes standard tasks very simple, but still leaves room open for the user to leave the framework at any point, add their own customizations, and then come back into the framework?

It may be a bold claim I made that nothing meets the needs of quants, but I think the evidence supports my conclusion very well. This is based on two underlying claims, what exactly quants need in terms of technology, and whether currently available software meets those needs. Are these two claims valid? You be the judge.

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About Chang She

Engineer @ Cloudera. Ex-cofounder/CTO @ DataPad. Builder of data tools. Recovering financial quant.
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