US 12,340,414 B2
Methods and systems for low latency generation and distribution of hidden liquidity size estimates
David Edward Taylor, St. Louis, MO (US); Andy Young Lee, Ballwin, MO (US); and David Vincent Schuehler, St. Louis, MO (US)
Assigned to Exegy Incorporated, St. Louis, MO (US)
Filed by Exegy Incorporated, St. Louis, MO (US)
Filed on Feb. 28, 2022, as Appl. No. 17/682,079.
Application 17/682,079 is a continuation of application No. 16/874,474, filed on May 14, 2020, granted, now 11,263,695.
Claims priority of provisional application 62/847,641, filed on May 14, 2019.
Prior Publication US 2022/0180441 A1, Jun. 9, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 40/00 (2023.01); G06F 17/18 (2006.01); G06N 20/00 (2019.01); G06Q 30/0201 (2023.01); G06Q 40/04 (2012.01)
CPC G06Q 40/04 (2013.01) [G06F 17/18 (2013.01); G06N 20/00 (2019.01); G06Q 30/0201 (2013.01)] 26 Claims
OG exemplary drawing
 
1. A field programmable gate array (FPGA) or graphics processor unit (GPU) for accelerated processing of streaming financial market data that pertains to a plurality of financial instruments to derive trading signals at low latency, the FPGA or GPU comprising:
a plurality of feature compute stage circuits arranged in parallel to define a plurality of parallel paths within the FPGA or GPU, wherein the parallel feature compute stage circuits comprise parallelized hardware logic and state memory that are configured to compute a plurality of features of the streaming financial market data in parallel; and
a combine stage circuit connected to the feature compute stage circuits, wherein the combine stage circuit comprises parallelized hardware logic configured to compute a hidden liquidity size estimation based on a weighted combination of the computed features, wherein the hidden liquidity size estimation represents an estimated size of a hidden order for a financial instrument, wherein each computed feature has a corresponding weight for the weighted combination;
wherein the computed features comprise features that are correlated to and predictive of the estimated size for the hidden order; and
wherein a selection of which features to use for the correlated and predictive features and what values to use for the corresponding weights are derived from a supervised machine learning model.