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: 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). 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: 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.

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.