Financial markets exhibit nonlinear, non-stationary, and multi-scale dynamics that challenge classical econometric models and modern deep learning approaches. Traditional methods offer interpretability but lack flexibility under regime shifts and cross-scale interactions, while deep neural networks improve predictive accuracy at the cost of transparency, raising concerns about governance and systemic risk. This chapter introduces Interpretable Deep Wavelet Learning (IDWL), a framework integrating multi-resolution wavelet decomposition with modular neural architectures. By embedding time-frequency decomposition within the model, IDWL treats temporal scale as an inherent learning dimension. Scale-specific subnetworks capture high-frequency shocks, medium-term cycles, and long-term trends, while constrained cross-scale attention ensures traceability. The framework balances nonlinear modeling with interpretability, supporting applications in portfolio management, risk modeling, and algorithmic trading.