Conference:March 6-8, 2017
Workshops:March 9-10, 2017
Presentation: The Move to AI: From HFT to Laplace Demon
Location:
- Churchill, G flr.
Duration
Day of week:
- Tuesday
Level:
- Advanced
Persona:
- Data Scientist
Key Takeaways
- Learn the principles and awareness needed to leverage AI in trading.
- Hear about the time/data balance for choosing an algorithm for your next AI project
- Understand the power of being able to keep feeding an algorithm with more and more sources of data.
Abstract
The race for low latency data continues. 10 years ago, Flashboys were helping HFT make money with low-latency infrastructures. Today, hedge funds build AI brains pumping hundreds of sources of data in real-time, seeking ubiquity to build Laplace Demons.
Interview
This talk is based on my experience working with hedge funds in places like New York, 10 years ago providing infrastructure to HFTs and Exchanges, and now streaming exotic data to them
Hedge funds over the past ten years have evolved from going after the time dimension (HFT) to working in the data dimension (a Laplace Demon). A Laplace Demon can predict anything as it knows everything instantly and understand their causality links. After HFT opportunities diminished, HFT turned into a Formula 1 race (nice to watch, but expensive and exclusive). One of these funds have over the past few years started to consume enormous amounts of real time data and make market predictions and bets out of it.
To get to the point of working with real time data, I discuss in the talk the history of AI with hedge funds starting with Renaissance Technologies. To describe the options to build our Laplace demon, I will cover some fairly recent AI evolutions -GPU, backprop- as applied to trading, and then discuss how I made my specific choice. With Albert, we will demo one of the frameworks. This is a non-commercial open source framework called MOA.
No. For hedge funds to consume lots of sources of data simultaneously and then to be able to make correlation and find causation, the time horizon (second/minute) we are talking about is much slower than High Frequency Trading (HFT: micro/millisecond). HFT is a way to be faster than others, AI is a way to be smarter than others.
In HFT, the longest timeframe you have is 10 milliseconds and that’s very long. You typically fight for nanoseconds (you are happy when you can shave off a microsecond). 15 years ago it was estimated that one millisecond for a broker was worth several million dollars. So this talk is about using AI to make better decision, not necessarily faster ones.
The end game is for you to be able to predict the next tick and make less mistakes than others. So what hedge funds are starting to do is, in addition to consuming market data (which they’ve been doing forever), to consume social data from sentiment APIs like Vetr or Estimize, and social/social trading networks like Twitter, Facebook and Stocktwits.. We are even starting to see them consume IoT data sources. It’s not theoretical anymore that a guy trading meat in Chicago can make an educated decision on when and for how much he should buy based on API calls he makes to IoT devices on cows Argentina. Things that were just not possible until recently are all of a sudden possible. The race to train your own Laplace Demon has just started, a great time to join.
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