Paper: arXiv 2508.02356

Authors: Wěi Zhāng

Abstract

This paper explores neural network-based approaches for algorithmic trading in cryptocurrency markets. Our approach combines multi-timeframe trend analysis with high-frequency direction prediction networks, achieving positive risk-adjusted returns through statistical modeling and systematic market exploitation. The system integrates diverse data sources including market data, on-chain metrics, and orderbook dynamics, translating these into unified buy/sell pressure signals. We demonstrate how machine learning models can effectively capture cross-timeframe relationships, enabling sub-second trading decisions with statistical confidence.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced neural network architectures like multi-head CNNs with attention mechanisms and statistical modeling, showing high mathematical density. It details real-world implementation challenges, data processing, and claims consistent performance metrics, indicating strong empirical rigor and backtest readiness.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Neural Network-Based Trading<br>in Crypto Markets"] --> B["Data Collection & Integration<br>Market Data, On-Chain, Orderbook"]
  B --> C["Computational Process<br>Multi-Timeframe Analysis<br>High-Frequency Prediction"]
  C --> D["Statistical Modeling<br>Systematic Market Exploitation"]
  D --> E["Key Findings:<br>Positive Risk-Adjusted Returns<br>Sub-Second Decisions<br>Statistical Confidence"]
  
  classDef goal fill:#e1f5fe,stroke:#01579b,stroke-width:2px;
  classDef data fill:#fff3e0,stroke:#f57c00,stroke-width:2px;
  classDef process fill:#f3e5f5,stroke:#7b1fa2,stroke-width:2px;
  classDef outcome fill:#e8f5e9,stroke:#2e7d32,stroke-width:2px;
  
  class A goal;
  class B data;
  class C,D process;
  class E outcome;