Price Trackers Inspired by Immune Memory

TL;DR

This paper introduces an immune-inspired algorithm that dynamically evolves price trackers to identify trends in financial time series data, demonstrating effective trend detection in small datasets.

Contribution

The paper proposes a novel immune-inspired algorithm with dynamic tracker evolution and long-term memory for trend detection in price data.

Findings

Algorithm successfully identifies price trends

Long-term memory enhances trend generalization

Efficient trend detection in small datasets

Abstract

In this paper we outline initial concepts for an immune inspired algorithm to evaluate price time series data. The proposed solution evolves a short term pool of trackers dynamically through a process of proliferation and mutation, with each member attempting to map to trends in price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. Tests are performed to examine the algorithm's ability to successfully identify trends in a small data set. The influence of the long term memory pool is then examined. We find the algorithm is able to identify price trends presented successfully and efficiently.