Accelerating Stock Pairs Trading with Graph Search and Simulated Bifurcation

Summary

This paper describes a stock pairs-trading system that searches across an N-stock universe instead of monitoring only a preselected pair. It represents stocks as nodes in a directed graph, with edge weights based on instantaneous price differences and statistical correlation factors. A combinatorial optimization accelerator using the quantum-inspired simulated bifurcation algorithm searches for promising paths, while tabu search helps avoid detecting the same opportunity repeatedly.

The system was demonstrated on the Tokyo Stock Exchange using an FPGA-based implementation. The reported latency is 33 microseconds for a universe of 15 stocks, corresponding to 210 possible pairs. This demonstrates a low-latency opportunity-search implementation, but the excerpt does not report trading returns, transaction costs, risk controls, or comparisons against simpler pair-selection methods. The claimed execution speed therefore provides evidence about computation and system latency, not by itself about strategy profitability.

Key ideas

• The system searches for pairs-trading opportunities across a stock universe using a directed graph.
• Edge weights combine instantaneous price differences with statistical correlation factors.
• Simulated bifurcation accelerates the graph path optimization search.
• Tabu search is used to reduce duplicate opportunity detections.
• The Tokyo Stock Exchange demonstration reports 33-microsecond latency for 15 stocks, but does not establish profitability.

Tags

# 2307.05923


# Pairs-trading System using Quantum-inspired Combinatorial Optimization Accelerator for Optimal Path Search in Market Graphs









Pairs-trading is a trading strategy that involves matching a long position with a short position in two stocks aiming at market-neutral profits. While a typical pairs-trading system monitors the prices of two statistically correlated stocks for detecting a temporary divergence, monitoring and analyzing the prices of more stocks would potentially lead to finding more trading opportunities. Here we report a stock pairs-trading system that finds trading opportunities for any two stocks in an $N$-stock universe using a combinatorial optimization accelerator based on a quantum-inspired algorithm called simulated bifurcation. The trading opportunities are detected through solving an optimal path search problem in an $N$-node directed graph with edge weights corresponding to the products of instantaneous price differences and statistical correlation factors between two stocks. The accelerator is one of Ising machines and operates consecutively to find multiple opportunities in a market situation with avoiding duplicate detections by a tabu search technique. It has been demonstrated in the Tokyo Stock Exchange that the FPGA (field-programmable gate array)-based trading system has a sufficiently low latency (33 $μ$s for $N$=15 or 210 pairs) to execute the pairs-trading strategy based on optimal path search in market graphs.

Shown in full with attribution under the source's licence. Licence: abstract CC0

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.