Technical Indicator Networks (TINs): An Interpretable Neural Architecture Modernizing Classic al Technical Analysis for Adaptive Algorithmic Trading

ArXiv ID: 2507.20202 “View on arXiv”

Authors: Longfei Lu

Abstract

Deep neural networks (DNNs) have transformed fields such as computer vision and natural language processing by employing architectures aligned with domain-specific structural patterns. In algorithmic trading, however, there remains a lack of architectures that directly incorporate the logic of traditional technical indicators. This study introduces Technical Indicator Networks (TINs), a structured neural design that reformulates rule-based financial heuristics into trainable and interpretable modules. The architecture preserves the core mathematical definitions of conventional indicators while extending them to multidimensional data and supporting optimization through diverse learning paradigms, including reinforcement learning. Analytical transformations such as averaging, clipping, and ratio computation are expressed as vectorized layer operators, enabling transparent network construction and principled initialization. This formulation retains the clarity and interpretability of classical strategies while allowing adaptive adjustment and data-driven refinement. As a proof of concept, the framework is validated on the Dow Jones Industrial Average constituents using a Moving Average Convergence Divergence (MACD) TIN. Empirical results demonstrate improved risk-adjusted performance relative to traditional indicator-based strategies. Overall, the findings suggest that TINs provide a generalizable foundation for interpretable, adaptive, and extensible learning architectures in structured decision-making domains and indicate substantial commercial potential for upgrading trading platforms with cross-market visibility and enhanced decision-support capabilities.

Keywords: Technical Indicator Networks (TINs), Deep Neural Networks (DNNs), Moving Average Convergence Divergence (MACD), Reinforcement Learning, Algorithmic Trading, Equities

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 4.5/10
• Quadrant: Lab Rats
• Why: The paper introduces a novel neural architecture with moderate mathematical complexity involving vectorized operators and topological mapping, but the empirical validation is limited to a single-market backtest on DJIA constituents without detailed performance metrics or implementation details, leaning towards theoretical development over rigorous backtesting.

  flowchart TD
    A["Research Goal: Create an interpretable, adaptive neural architecture for algorithmic trading that modernizes classic technical indicators"] --> B["Methodology: Technical Indicator Networks (TINs)"]
    
    B --> C["Core Design: Reformulate rule-based indicator logic (e.g., MACD) into trainable neural modules with vectorized operators"]
    
    C --> D["Data/Inputs: Multi-dimensional market data from Dow Jones Industrial Average constituents"]
    
    D --> E["Computational Process: Apply analytical transformations (averaging, clipping, ratio computation) via modular layers, enable optimization through reinforcement learning"]
    
    E --> F["Key Findings: Improved risk-adjusted performance vs. traditional strategies, retains interpretability, demonstrates extensibility for adaptive trading systems"]
    
    F --> G["Outcomes: TINs provide a generalizable foundation for interpretable learning in structured decision-making domains with commercial potential for modernizing trading platforms"]