GPU Host System for Real-Time Financial Forecasting
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Solution Overview
Problem
Current single machine systems lack the memory and processor speed to process large volumes of market data in real-time, resulting in significant time lags between data receipt and algorithmic forecast generation, making existing algorithmic trading systems ineffective for real-time financial forecasting.
Innovation Solution
A system comprising a host system and a graphical processing unit (GPU) or co-processor hardware that receives streamed market data, executes financial models, and generates real-time forecasts by structuring data according to timestamps, cloning necessary data, and running engine instances to produce forecast data, enabling low-latency processing of large data volumes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single machine system is used to process market data and generate forecasts, then the system structure is simple, but the processor speed and memory capacity are insufficient, causing substantial time lags
Solution Approach 1:
The system divides the processing workload into two separate machines: a first machine that collects and streams market data, and a second machine that receives the streamed data and generates algorithmic forecasts. This segmentation allows each machine to be optimized for its specific function, with the second machine having enhanced processor speed and memory capacity to handle complex financial models without being bottlenecked by data collection operations.
2Device complexity
If a single machine system is used to process market data and generate forecasts, then the system structure is simple, but the memory capacity is insufficient, causing substantial time lags
Solution Approach 1:
The system divides the processing workload into two separate machines: a first machine that collects and streams market data, and a second machine that receives the streamed data and generates algorithmic forecasts. This segmentation allows each machine to be optimized for its specific function, with the second machine having enhanced processor speed and memory capacity to handle complex financial models without being bottlenecked by data collection operations.
3Loss of time
If the server is located in close physical proximity to the market data source, then the time lag is reduced, but the system loses the ability to process extremely large quantities of data in real time
Solution Approach 1:
The system introduces a data streaming mechanism as an intermediary between the market data source and the processing server. The first machine acts as a buffer that collects data from multiple sources and streams it to the second machine, allowing the second machine to be located optimally for processing large quantities of data while maintaining real-time processing capability through the continuous data stream.
Data Source
AI summary
Forecasting financial market activity includes a host system and a graphical processing unit in data communication with the server. Forecasting financial market activity also includes a computer program product residing on the host system, the computer program product including instructions for causing the host system to send one or more financial models to the graphical processing unit; and a computer program product residing on the graphical processing unit for causing the graphical processing unit to: receive the financial models and a list of types of market data associated with each financial model; generate one or more engine instances; structure the received market data; receive instructions, from the host system, to run an identified financial model; clone at least part of the structured market data; run the engine instances; and generate, in real time, forecast data indicative of an expected market performance.