Dynamic Financial Portfolio Optimization Using Temporal Convolutional Networks and Real-Time Data Analysis

This paper presents an integrated framework for AI-driven portfolio optimization combining temporal convolutional networks (TCNs) with conditional value-at-risk (CVaR) minimization. Our system processes real-time market data through an automated pipeline implementing volatility-adjusted feature engineering and walk-forward validation. The architec- ture employs dilated causal convolutions for temporal pattern extraction combined with Ledoit-Wolf shrinkage covariance es- timation for robust portfolio optimization. Experimental results demonstrate an 18.7% annualized return with 22.3% volatility, outperforming traditional mean-variance optimization by 14.2% in risk-adjusted returns. The implementation addresses key challenges in numerical stability and computational efficiency through eigenvalue clamping and gradient checkpointing.