Paper: arXiv 2501.06032

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.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 2.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: 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).

Research Flowchart

  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