The Evolution of Neural Network-based Chart Patterns: A Preliminary Study

Abstract

A neural network-based chart pattern represents adaptive para- metric features, including non-linear transformations, and a tem- plate that can be applied in the feature space. The search of neural network-based chart patterns has been underexposed despite its potential expressiveness. In this paper, we formulate a general chart pattern search problem to enable cross-representational quantitative comparison of various search schemes. We suggest a HyperNEAT framework applying state-of-the-art deep neural network techniques to find attractive neural network-based chart patterns; these techniques enable a fast evaluation and search of robust patterns, as well as bringing a performance gain. The proposed framework successfully found considerable patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.

Myoung Hoon Ha

Postdoctoral Researcher

My research interests include deep learning, reinforcement learning, representation learning, and adversarial robustness.