Han Guodong, Li Hecheng
College of Computer, Qinghai Normal University, Xining, China.
Digital Finance Department, Bank of Qinghai, Xining, China.
PeerJ Comput Sci. 2024 Jul 8;10:e2164. doi: 10.7717/peerj-cs.2164. eCollection 2024.
Arbitrage trading is a common quantitative trading strategy that leverages the long-term cointegration relationships between multiple related assets to conduct spread trading for profit. Specifically, when the cointegration relationship between two or more related series holds, it utilizes the stability and mean-reverting characteristics of their cointegration relationship for spread trading. However, in real quantitative trading, determining the cointegration relationship based on the Engle-Granger two-step method imposes stringent conditions for the cointegration to hold, which can easily be disrupted by price fluctuations or trend characteristics presented by the linear combination, leading to the failure of the arbitrage strategy and significant losses. To address this issue, this article proposes an optimized strategy based on long-short-term memory (LSTM), termed Dynamic-LSTM Arb (DLA), which can classify the trend movements of linear combinations between multiple assets. It assists the Engle-Granger two-step method in determining cointegration relationships when clear upward or downward non-stationary trend characteristics emerge, avoiding frequent strategy switches that lead to losses and the invalidation of arbitrage strategies due to obvious trend characteristics. Additionally, in mean-reversion arbitrage trading, to determine the optimal trading boundary, we have designed an optimized algorithm that dynamically updates the trading boundaries. Training results indicate that our proposed optimization model can successfully filter out unprofitable trades. Through trading tests on a backtesting platform, a theoretical return of 23% was achieved over a 10-day futures trading period at a 1-min level, significantly outperforming the benchmark strategy and the returns of the CSI 300 Index during the same period.
套利交易是一种常见的量化交易策略，它利用多个相关资产之间的长期协整关系进行价差交易以获取利润。具体而言，当两个或多个相关序列之间的协整关系成立时，它利用其协整关系的稳定性和均值回归特性进行价差交易。然而，在实际的量化交易中，基于恩格尔 - 格兰杰两步法确定协整关系对协整成立施加了严格条件，这很容易被线性组合呈现的价格波动或趋势特征所干扰，导致套利策略失败并造成重大损失。为了解决这个问题，本文提出了一种基于长短期记忆网络（LSTM）的优化策略，称为动态LSTM套利（DLA），它可以对多个资产之间线性组合的趋势变动进行分类。当出现明显的向上或向下非平稳趋势特征时，它协助恩格尔 - 格兰杰两步法确定协整关系，避免因明显的趋势特征导致频繁的策略切换而造成损失以及套利策略失效。此外，在均值回归套利交易中，为了确定最优交易边界，我们设计了一种动态更新交易边界的优化算法。训练结果表明，我们提出的优化模型能够成功过滤掉无利可图的交易。通过在回测平台上进行交易测试，在1分钟级别下，10天的期货交易期内实现了23%的理论回报率，显著优于基准策略以及同期沪深300指数的回报率。
