Finance-Grounded Optimization For Algorithmic Trading
This work addresses the challenge of interpretable AI in finance for specialists, though it appears incremental by adapting existing metrics to a specific domain.
The paper tackled the problem of applying deep learning to algorithmic trading by introducing financially grounded loss functions and turnover regularization, resulting in improved performance over traditional mean squared error loss in return prediction tasks.
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.