Finance-Grounded Optimization For Algorithmic Trading

ArXiv ID: 2509.04541 “View on arXiv”

Authors: Kasymkhan Khubiev, Mikhail Semenov, Irina Podlipnova

Abstract

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.

Keywords: Interpretable AI, Financially Grounded Loss Functions, Sharpe Ratio, PnL, Turnover Regularization, General Financial Markets (Trading Strategies)

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces custom loss functions (e.g., SharpeLoss, MaxDrawDownLoss) with mathematical formulation and experimental validation on real market data, requiring a balance of advanced concepts and backtest implementation.

  flowchart TD
    A["Research Goal: Develop interpretable AI for algorithmic trading<br>using financially grounded loss functions"] --> B["Methodology: Propose Financially Grounded Loss Functions<br>Sharpe Ratio, PnL, Max Drawdown"]
    A --> C["Methodology: Introduce Turnover Regularization<br>Constrain position turnover within limits"]
    B --> D["Computational Process: Combine loss functions with<br>regularization for training Deep Learning models"]
    C --> D
    D --> E["Key Findings: Financially grounded loss functions<br>outperform traditional MSE loss"]
    D --> F["Key Findings: Enhanced predictive performance<br>in trading strategies & portfolio optimization"]