Parallel Derivatives Pricing Engines for Sub-Microsecond Trading
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Solution Overview
Problem
Conventional automated trading systems for derivatives are not fast enough to compute fair values and respond to market changes at the necessary latency required for profitable and risk-minimized trading, particularly in volatile markets, due to high computational latency in software-based approaches and slow price computation methods like Black-Scholes and CRR models.
Innovation Solution
Implementing trading logic on parallelized and pipelined computational resources such as FPGAs or ASICs, combined with feed handlers and market gateways, to perform highly parallelized operations, reducing tick-to-trade latency to less than 1 microsecond and enabling fast computation of theoretical fair prices through extrapolation techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If software-based approaches with conventional pricing models (Black-Scholes, CRR) are used, then ease of operation and adaptability are maintained, but computational latency increases beyond acceptable thresholds for profitable trading
Solution Approach 1:
The patent replaces software-based computational systems with hardware-based systems (FPGAs, ASICs, GPUs) to perform pricing calculations. This substitution of mechanical/computational implementation fundamentally changes the speed characteristics from software-execution-limited to hardware-parallelism-limited, achieving microsecond and sub-microsecond latencies while maintaining the same pricing model logic
Solution Approach 2:
The patent segments the pricing computation into independent parallel operations that can be executed simultaneously across multiple hardware units. By dividing the computational workload into discrete, parallelizable tasks (e.g., separate calculation paths for different option types, strike prices, or expiration dates), the system achieves linear or near-linear speedup with the number of hardware resources deployed
2Loss of time
If conventional software-based trading systems are used, then device complexity remains manageable, but tick-to-trade latency exceeds the sub-microsecond threshold required for competitive derivatives trading
Solution Approach 1:
The patent implements preliminary action by pre-computing and caching pricing parameters, volatility surfaces, and other lookup tables in high-speed memory before trading events occur. When market data arrives, the system performs rapid interpolation or lookup operations rather than full recalculation, dramatically reducing the critical path latency for trade execution
Solution Approach 2:
The patent transitions from sequential software execution to parallel hardware execution, adding the dimension of spatial parallelism. Multiple pricing calculations that would sequentially follow each other in software are instead executed simultaneously across thousands of hardware logic elements, effectively transforming time-based computation into space-based computation
3Productivity
If fast hardware-based computation is implemented, then trading speed and responsiveness improve, but system complexity and manufacturing difficulty increase
Solution Approach 1:
The patent designs universal hardware architectures (such as reconfigurable FPGA-based pricing engines) that can handle multiple derivatives pricing models and instrument types through configuration rather than physical redesign. This multi-functionality allows a single hardware platform to serve various trading strategies and market conditions, reducing the need for multiple specialized systems and simplifying deployment
Data Source
AI summary
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.