基于NP难组合优化选择潜在盈利、不相关且平衡股票的实时交易系统
Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization
AI总结：
本文提出基于模拟分叉Ising机器与FPGA的实时股票交易系统，通过NP难组合优化选择delta中性、低相关且高预期收益的股票组合，并在东京证券交易所实现N=128、164 μs延迟的多空交易。
摘要：
金融投资组合构建问题通常被表述为二次且离散（组合）优化，在计算复杂性理论中属于非确定性多项式时间（NP）难类别。Ising机器是依据量子力学/量子启发原理工作的硬件设备，可快速求解NP难优化问题，从而有可能在高速交易策略的时间约束内，基于NP难优化做出交易决策。本文提出一种实时股票交易系统，通过NP难投资组合优化确定多头（买入）/空头（卖出）头寸，以提高夏普比率；该系统使用嵌入式Ising机器，其基础是一种称为模拟分叉的量子启发算法。Ising机器根据目标函数，从包含N只股票的股票池中选择一组平衡（delta中性）股票；该目标函数包括最大化定义为相对成交量加权平均价格偏差的瞬时预期收益，以及最小化统计相关因子之和（用于分散化）。在东京证券交易所的演示表明，基于FPGA（现场可编程门阵列）的交易系统可执行N=128的NP难投资组合优化交易策略，响应延迟为164 μs。
英文摘要：
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 $μ$s.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
