IP Reservoir, LLC

Exegy Incorporated

Marathon Oil Company

David E Taylor

Scott Parsons

David Vincent Schuehler

Roger Dean Chamberlain

Mark Allen Franklin

Ronald S Indeck

Jeremy Walter Whatley

Naveen Singla

Kwame Gyang

Andy Young Lee

Timothy Gorham

Richard Bradley

Michael DeWulf

Jason R White

Benjamin Curry Brodie

Ron Kaplan Cytron

David Mark Indeck

Brandon Parks Thurmon

Ryan L Eder

Nathaniel Sutton McVicar

Justin Ryan Thiel

Todd Alan Strader

Nathaniel S Mcvicar

Benjamin C Brodie

Justin R Thiel

Various techniques are disclosed for offloading the processing of data packets that contain financial market data. For example, data packets that contain financial market data can be processed through an offload processor deployed in a network element of a data distribution network to link the data packets with appropriate parsing metadata for the data packets. The network element can be an intelligent switch, such as an intelligent switch upstream or downstream from an electronic trading platform.

Offload processing of data packets containing financial market data

Systems and methods are disclosed herein that compute trading signals with low latency and high throughput using highly parallelized compute resources such as integrated circuits, reconfigurable logic devices, graphics processor units (GPUs), multi-core general purpose processors, and/or chip multi-processors (CMPs). The trading signals can be summarized over time durations to create derived summaries of the trading signals. These derived summaries can be communicated with one or more data consumers.

Methods and systems for generating derived summaries of trading signals

Disclosed herein are automated trading engine embodiments that operate on market data and re-engineer trading logic to operate on computational resources that are capable of providing highly parallelized and pipelined processing operations to improve tick to trade latency. As an example, logic resources for the automated trading engine can compute updated theoretical fair prices for derivatives at low latency. The automated trading engine can then use such real-time derivative pricing to better drive decision-making by trading strategies implemented by the automated trading engine, such as market making strategies and aggressing strategies.

Methods and systems for pricing derivatives at low latency

Systems and methods are disclosed herein that compute trading signals with low latency and high throughput using highly parallelized compute resources such as integrated circuits, reconfigurable logic devices, graphics processor units (GPUs), multi-core general purpose processors, and/or chip multi-processors (CMPs). For example, an estimation that estimates a quote price duration for a quote on a financial instrument can be generated from streaming financial market data.

Methods and systems for low latency generation and distribution of quote price duration estimates

Systems and methods are disclosed herein that compute trading signals with low latency and high throughput using highly parallelized compute resources such as integrated circuits, reconfigurable logic devices, graphics processor units (GPUs), multi-core general purpose processors, and/or chip multi-processors (CMPs). For example, a liquidity estimation that estimates an amount of hidden liquidity for a financial instrument can be generated from streaming financial market data.

Methods and systems for low latency generation and distribution of hidden liquidity size estimates

Automated trading engine embodiments operate on market data and re-engineer trading logic to operate on computational resources that are capable of providing highly parallelized and pipelined processing operations to improve tick to trade latency. As an example, logic resources for the automated trading engine can perform multiple types of trading strategies such as (1) market making strategies that post and cancel quotes on markets, (2) aggressing strategies that place aggressing orders on markets when defined conditions are met, (3) hedging strategies that place hedging orders on markets to hedge against aggressing orders, and/or (4) canceling strategies to cancel quotes and/or orders on markets when defined conditions are met. Further still, one or more of the trading strategies can be driven by low latency derivative pricing.

Methods and systems for low latency automated trading

Disclosed herein are automated trading engine embodiments that operate on market data and re-engineer trading logic to operate on computational resources that are capable of providing highly parallelized and pipelined processing operations to improve tick to trade latency. As an example, logic resources for the automated trading engine can implement market making strategies that post and cancel quotes on markets. These logic resources can include logic for scheduling the transmissions of quotes to the markets according to various criteria.

Methods and systems for mass quoting at low latency

A variety of embodiments for hardware-accelerating the processing of financial market depth data are disclosed. A coprocessor, which may be resident in a ticker plant, can be configured to update order books and price books based on financial market depth data at extremely low latency. Such a coprocessor can also be configured to enrich a stream of limit order events pertaining to financial instruments with data from a plurality of updated order and price books.

Method and apparatus for high-speed processing of financial market depth data

Disclosed herein are automated trading engine embodiments that operate on market data and re-engineer trading logic to operate on computational resources that are capable of providing highly parallelized processing operations to improve tick to trade latency. As an example, logic resources for the automated trading engine can implement aggressing strategies to place aggressing orders on markets when defined conditions are met. Further still, the aggressing strategies can be driven by low latency derivative pricing.

Various techniques are disclosed for offloading the processing of data packets. For example, the offloaded processing can be resident in an intelligent switch, such as an intelligent switch upstream or downstream from an electronic trading platform. The data packets processed through the intelligent switch can be unreliable datagram protocol (UDP) data packets from multiple feeds of financial market data, and the offloaded processing can include line arbitration, gap detection, and/or gap mitigation of redundant copies of UDP data packets from one or more financial market data feeds.

Intelligent packet switch

Systems and method are disclosed for distributed data processing across multiple compute resources. For example, the system may comprise a ticker plant configured to receive streaming financial market data events from a plurality of feeds, the events comprising financial market data pertaining to financial instruments. The ticker plant may comprise a processor, an FPGA, and a shared memory, wherein the shared memory is shared between the processor and the FPGA, wherein the processor is configured to execute software to manage a flow of financial market data derived from the streaming events between the processor and the FPGA via DMA transfers of the financial market data between the processor and the FPGA via the shared memory, and wherein the FPGA comprises reconfigurable hardware that is configured to perform processing operations on the financial market data to generate processed financial market data for delivery to data consumers.

System and method for distributed data processing across multiple compute resources

An integrated order management engine is disclosed that reduces the latency associated with managing multiple orders to buy or sell a plurality of financial instruments. Also disclosed is an integrated trading platform that provides low latency communications between various platform components. Such an integrated trading platform may include a trading strategy offload engine.

Method and apparatus for managing orders in financial markets

Disclosed herein are automated trading engine embodiments that operate on market data and re-engineer trading logic to operate on computational resources that are capable of providing highly parallelized and pipelined processing operations to improve tick to trade latency. As an example, logic resources for the automated trading engine can implement canceling strategies to cancel quotes and/or orders on markets when defined conditions are met. For example, if new trades indicate that existing quotes and/or orders are no longer advantageous, the logic resources can act quickly to cancel those quotes and/or orders.

Methods and systems for low latency automated trading using a canceling strategy

Systems and methods are disclosed herein that compute trading signals with low latency and high throughput using highly parallelized compute resources such as integrated circuits, reconfigurable logic devices, graphics processor units (GPUs), multi-core general purpose processors, and/or chip multi-processors (CMPs). For example, an estimation that estimates a quote price direction for a quote on a financial instrument can be generated from streaming financial market data.

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St. Louis, MO, United States of America

David E Taylor