Zeng Zeliang, Qin Panke, Zhang Yue, Tang Yongli, Cheng Shenjie, Tu Sensen, Ding Yongjie, Gao Zhenlun, Liu Yaxing
School of Software, Henan Polytechnic University, Jiaozuo, Henan, China.
Hebi National Optoelectronic Technology Co, Ltd, Hebi, Henan, China.
PeerJ Comput Sci. 2024 Aug 9;10:e2215. doi: 10.7717/peerj-cs.2215. eCollection 2024.
Arbitrage spread prediction can provide valuable insights into the identification of arbitrage signals and assessing associated risks in algorithmic trading. However, achieving precise forecasts by increasing model complexity remains a challenging task. Moreover, uncertainty in the development and maintenance of model often results in extremely unstable returns. To address these challenges, we propose a K-fold cross-search algorithm-optimized LSTM (KCS-LSTM) network for arbitrage spread prediction. The KCS heuristic algorithm incorporates an iterative updating mechanism of the search space with intervals as the basic unit into the traditional ant colony optimization. It optimized the hyperparameters of the LSTM model with a modified fitness function to automatically adapt to various data sets, thereby simplified and enhanced the efficiency of model development. The KCS-LSTM network was validated using real spread data of rebar and hot-rolled coil from the past three years. The results demonstrate that the proposed model outperforms several common models on sMAPE by improving up to 12.6% to 72.4%. The KCS-LSTM network is shown to be competitive in predicting arbitrage spreads compared to complex neural network models.
套利价差预测可以为识别算法交易中的套利信号和评估相关风险提供有价值的见解。然而，通过增加模型复杂度来实现精确预测仍然是一项具有挑战性的任务。此外，模型开发和维护中的不确定性通常会导致回报极其不稳定。为应对这些挑战，我们提出了一种用于套利价差预测的K折交叉搜索算法优化的长短期记忆网络（KCS-LSTM）。KCS启发式算法将以区间为基本单元的搜索空间迭代更新机制纳入传统蚁群优化算法中。它使用改进的适应度函数优化LSTM模型的超参数，以自动适应各种数据集，从而简化并提高了模型开发的效率。使用过去三年螺纹钢和热轧卷板的实际价差数据对KCS-LSTM网络进行了验证。结果表明，所提出的模型在对称平均绝对百分比误差（sMAPE）方面优于几个常见模型，提高幅度高达12.6%至72.4%。与复杂的神经网络模型相比，KCS-LSTM网络在预测套利价差方面具有竞争力。
