Earlier in the month (December 2017) I gave an invited talk at the inaugural ML Conference in Berlin, titled, "Algorithmic Architecture, Real-time AI and the Search for Alpha". The talk looked at the general problem of extracting information from social media and some of the challenges arising from delivering a real-time system to trade from.
This talk was a slight update to a previous talk given at JAX Finance and once again as a backstory to the session I used the Yedup story to place things into context. Yedup are a leading company in this space who have shown it is possible to detect and disseminate real, actionable alpha from alternative data sources.
This talk back-ended Tuesday as the last session so it was a long day! Here are the slides:
As with most talks these days there is a video which will make the slides a lot easier to follow!
Lastly here is the talk abstract from the conference programme as a summary:
“Building any AI system is hard but building a real-time AI system brings its own challenges. At Yedup and Clearpool we have been building a real-time AI system to trade against hundreds of securities simultaneously by extracting fundamental signals from real-time social media feeds. We've gone back to first principals to build out a technology stack from scratch to meet our specific needs. This talk looks at how we've exploited algorithmic architecture to build a real-time AI system that delivers market leading alpha. That's interesting in itself, but what's more interesting is looking at some of the challenges that we've had to overcome in order to deliver a system that can not only trade hundreds of instruments simultaneously, but that can also correlate the relationships across industries and sectors to extract yet more alpha. These are challenges that would be applicable to any similar class of problems (such as real-time AI) and this talk explores a couple of the key ones, such as maintaining and recovering with huge amounts of in-flight state to deliver fast, scalable and robust AI systems.”