用金融指标优化交易模型，提升真实投资表现。
Finance-Grounded Optimization For Algorithmic Trading
- 基于夏普比率等金融指标设计损失函数
- 结合换手率正则化，控制交易频率
- 在真实交易指标上优于传统误差方法
深度学习快速发展并融入多个领域，但金融领域仍具挑战性，尤其在可解释AI方面。尽管经典方法在自然语言处理、计算机视觉和预测任务中表现优异，但在金融领域却面临评估标准不一致的问题。本文首次提出基于关键量化金融指标（如夏普比率、盈亏（PnL）、最大回撤）的金融根基损失函数，并引入换手率正则化，以在预设范围内约束生成仓位的交易频率。实验表明，这些损失函数与换手率正则化结合，在返回预测任务中，使用算法交易指标评估时显著优于传统的均方误差损失。研究证实，金融根基指标能有效提升交易策略与组合优化的预测性能。
原文摘要 · Abstract (English)
Deep Learning is evolving fast and integrates into various domains. Finance is a challenging field for deep learning, especially in the case of interpretable artificial intelligence (AI). Although classical approaches perform very well with natural language processing, computer vision, and forecasting, they are not perfect for the financial world, in which specialists use different metrics to evaluate model performance. We first introduce financially grounded loss functions derived from key quantitative finance metrics, including the Sharpe ratio, Profit-and-Loss (PnL), and Maximum Draw down. Additionally, we propose turnover regularization, a method that inherently constrains the turnover of generated positions within predefined limits. Our findings demonstrate that the proposed loss functions, in conjunction with turnover regularization, outperform the traditional mean squared error loss for return prediction tasks when evaluated using algorithmic trading metrics. The study shows that financially grounded metrics enhance predictive performance in trading strategies and portfolio optimization.
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