[HN Gopher] Multi-Horizon Forecasting for Limit Order Books
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       Multi-Horizon Forecasting for Limit Order Books
        
       Author : ArtWomb
       Score  : 21 points
       Date   : 2021-07-02 13:21 UTC (9 hours ago)
        
 (HTM) web link (arxiv.org)
 (TXT) w3m dump (arxiv.org)
        
       | dcolkitt wrote:
       | The problem with using neural networks in market microstructure
       | is the latency at inference time. Market makers and HFTs need to
       | compute decisions on the order of microseconds. That's not
       | feasible with large, deep networks.
       | 
       | With specialized hardware you can get close. But you're still
       | talking about a mid-single digit number of microseconds on
       | inference alone. The competitor using linear models can get down
       | to hundreds of nanoseconds. If you're in FPGA world, that kind of
       | latency advantage is worth way more than a 30% accuracy
       | improvement from using a complex ML model.
        
         | tomas789 wrote:
         | This describes one extreme of the spectra. That is go fast but
         | be dumb. As far as I know this works well for many people.
         | There are other grous of people going a bit slower but making
         | more informed decision. I think of it as a scatter plot of time
         | on one axis and smartness on the other one. As long as you are
         | siting at Pareto front, you can make money.
        
         | clipradiowallet wrote:
         | Furthermore... the HFT market participants are not using CPU-
         | intensive calculations to win consistently. They are using
         | simple calculations(eg 6-period SMA) and _extremely_ low
         | latency to win. They are competing with other HFT participants
         | to get their order on the inside bid /ask before everyone else.
         | 
         | At it's core, macro-level algorithmic trading is answering a
         | question with only 2 possible answers, at any point in
         | time...the question is, will the next tick be either "up" or
         | "down".
        
         | jstrong wrote:
         | what is the special hardware/setup that achieves mid-single
         | digit number of microseconds latency for deep learning
         | inference you referred to?
        
           | dcolkitt wrote:
           | My understanding is that O(5 uS) is achievable on optimized
           | FPGAs with reasonably large networks. Because of the
           | parallelization, large networks don't add that much more
           | latency as long as you have enough gates. But I have little
           | experience on FPGA stacks, so can't say for sure.
           | 
           | Even in software, I've been able to hit O(15 uS) using
           | optimized FANN libraries. But the nets are far smaller than
           | deep, and pretty ruthlessly pruned and compressed. Another
           | trick that helps is pre-differentiating across all the
           | variables you don't expect to change on a latency critical
           | event. E.g. if you're running a liquidity take strategy, you
           | can pre-differentiate assuming the opposite touch size and
           | deep book stays constant, because you're only gonna act
           | following on an aggressor trade at the touch.
        
         | echelon wrote:
         | What about use over longer time horizons? The paper seems to be
         | geared for longer predictions.
        
           | [deleted]
        
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