Paper: arXiv 2507.20202

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.

Complexity vs Empirical Score

  • Math Complexity: 6.0/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: 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.

Research Flowchart

  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"]

Cited References (27)

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  2. Deep learning with long short-term memory networks for financial market predictions (2018) — link
  3. A deep learning framework for financial time series using stacked autoencoders and long short-term memory (2017) — link
  4. Predicting Stock Market Trends Using Machine Learning and Deep Learning: The Case of Tehran Stock Exchange (2020) — link
  5. An ensemble deep learning model for stock price prediction (2021) — link
  6. Feature selection and deep neural networks for stock price prediction (2021) — link
  7. A Deep Learning Approach for Forecasting Cryptocurrencies Using ARIMA-LSTM (2021) — link
  8. DRL-based portfolio management with transaction cost (2022) — link
and 19 more
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  2. Stock price prediction with optimized deep LSTM network (2023) — link
  3. Predictability of stock returns using neural networks (2023) — link
  4. Real-time forecasting of time series in financial markets (2023) — link
  5. Stock price prediction for new energy vehicle companies using deep learning approaches (2024) — link
  6. Interpretable stock price forecasting model using genetic algorithms and machine learning (2023) — link
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  8. An application of deep reinforcement learning to algorithmic trading (2021) — link
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  10. Algorithmic trading using continuous action space deep reinforcement learning (2024) — link
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  13. Multi-scale contrast approach for stock index prediction (2025) — link
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  15. Deep Direct Reinforcement Learning for Financial Signal Representation and Trading (2017) — link
  16. Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning (1992) — link
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0 of 27 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.