Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization
Kosuke Tatsumura, Ryo Hidaka, Jun Nakayama, Tomoya Kashimata, and, Masaya Yamasaki

TL;DR
This paper presents a real-time stock trading system that uses an Ising machine based on quantum-inspired algorithms to solve NP-hard portfolio optimization problems, selecting uncorrelated and balanced stocks to improve trading performance.
Contribution
It introduces a novel FPGA-based trading system utilizing an Ising machine with simulated bifurcation for high-speed NP-hard portfolio optimization in real-time trading.
Findings
Successfully implemented on Tokyo Stock Exchange with 128 stocks
Achieved response latency of 164 microseconds
Demonstrated improved Sharpe ratio through optimized stock selection
Abstract
Financial portfolio construction problems are often formulated as quadratic and discrete (combinatorial) optimization that belong to the nondeterministic polynomial time (NP)-hard class in computational complexity theory. Ising machines are hardware devices that work in quantum-mechanical/quantum-inspired principles for quickly solving NP-hard optimization problems, which potentially enable making trading decisions based on NP-hard optimization in the time constraints for high-speed trading strategies. Here we report a real-time stock trading system that determines long(buying)/short(selling) positions through NP-hard portfolio optimization for improving the Sharpe ratio using an embedded Ising machine based on a quantum-inspired algorithm called simulated bifurcation. The Ising machine selects a balanced (delta-neutral) group of stocks from an -stock universe according to an…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Stock Market Forecasting Methods · Computability, Logic, AI Algorithms
