Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 indicator that indicates a presence of a hidden order for a financial instrument can be generated based on processing of streaming financial market data that operates to detect the existence of hidden orders within the financial market data.

Abstract: 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 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.

Abstract: 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.

Abstract: 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 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.

Abstract: 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.

Abstract: 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 bettering market making strategies that post and cancel bettering quotes on markets. Further still, the bettering market making strategies can be driven by low latency derivative pricing.

Abstract: 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 joining market making strategies that post and cancel joining quotes on markets. Further still, the joining market making strategies can be driven by low latency derivative pricing.

Abstract: 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. Further still, the market making strategies can be driven by low latency derivative pricing.

Abstract: 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 hedging strategies to place hedging orders on markets when defined conditions are met. Such hedging strategies can be combined with aggressing strategies to hedge against aggressing orders placed by the automated trading engine.

Abstract: 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 multiple market making strategies that post and cancel quotes on markets. Examples of market making strategies that can be implemented by the automated trading engine include basic market making, joining market making, and bettering market making. Further still, the market making strategies can be driven by low latency derivative pricing.

Abstract: 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.

Abstract: 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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 indicator that indicates a presence of a hidden order for a financial instrument can be generated based on processing of streaming financial market data that operates to detect the existence of hidden orders within the financial market data.

Abstract: 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). Examples of trading signals that can be computed in this fashion include a liquidity indicator that indicates a presence of a reserve order for a financial instrument, a liquidity estimation that estimates an amount of hidden liquidity for a financial instrument, a quote price stability estimation that estimates a duration of time for which a price quote for a financial instrument will be valid, and/or a quote price direction estimation that estimates whether the price in a next quote for a financial instrument will be higher or lower than the price for that financial instrument in the current quote.