A Modern Paradigm for Algorithmic Trading

ArXiv ID: 2501.06032 “View on arXiv”

Authors: Unknown

Abstract

We introduce a novel framework for developing fully-automated trading model algorithms. Unlike the traditional approach, which is grounded in analytical complexity favored by most quantitative analysts, we propose a paradigm shift that embraces real-world complexity. This approach leverages key concepts relating to self-organization, emergence, complex systems theory, scaling laws, and utilizes an event-based reframing of time. In closing, we describe an example algorithm that incorporates the outlined elements, called the Delta Engine.

Keywords: Complex Systems Theory, Self-organization, Emergence, Scaling Laws, Event-based Trading, General Financial Markets

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper employs advanced mathematical concepts like scaling laws, intrinsic time, and complex systems theory, but lacks any concrete backtest results, statistical metrics, or implementation details (e.g., code, data, performance tables).

  flowchart TD
    A["Research Goal: Develop automated trading framework embracing real-world complexity"]
    
    B["Key Methodology<br>Complex Systems Theory<br>Self-organization & Emergence"]
    
    C["Data/Inputs<br>High-frequency financial data streams<br>Event-based temporal markers"]
    
    D["Computational Process<br>Event-based reframing of time<br>Delta Engine algorithm"]
    
    E["Key Outcomes<br>Novel paradigm shift from analytical complexity<br>Scalable trading framework"]
    
    A --> B
    B --> C
    C --> D
    D --> E