A Modern Paradigm for Algorithmic Trading

TL;DR

This paper presents a new paradigm for algorithmic trading that incorporates complex systems theory, self-organization, and event-based time to develop fully-automated trading algorithms, exemplified by the Delta Engine.

Contribution

It introduces a novel framework that shifts from traditional analytical methods to complex systems concepts for developing trading algorithms.

Findings

Proposes the Delta Engine algorithm as an example implementation.

Demonstrates the integration of self-organization and emergence in trading models.

Highlights the benefits of event-based time framing in algorithmic trading.

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.