Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization

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 $N$-stock universe according to an objective function involving maximizing instantaneous expected returns defined as deviations from volume-weighted average prices and minimizing the summation of statistical correlation factors (for diversification). It has been demonstrated in the Tokyo Stock Exchange that the trading strategy based on NP-hard portfolio optimization for $N$=128 is executable with the FPGA (field-programmable gate array)-based trading system with a response latency of 164 $\mu$s.

arXiv comment12 pages, 5 figures. arXiv admin note: text overlap with arXiv:2307.05923

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Cited by in corpus (8)

• Correlation-diversified portfolio construction by finding maximum independent set in large-scale market graph
• Efficient and Scalable Architecture for Multiple-chip Implementation of Simulated Bifurcation Machines
• Highly Versatile FPGA-Implemented Cyber Coherent Ising Machine
• Towards Arbitrary QUBO Optimization: Analysis of Classical and Quantum-Activated Feedforward Neural Networks
• Metrics for spin-based computing
• Evaluating the solution performance of the augmented Lagrangian function on Ising machines
• Machine Learning-assisted High-speed Combinatorial Optimization with Ising Machines for Dynamically Changing Problems
• Large-scale portfolio optimization with variational neural annealing