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

## TL;DR

This paper introduces a HyperNEAT-based framework for discovering neural network-based chart patterns in stock markets, demonstrating its effectiveness and potential over existing search methods.

## Contribution

It formulates a general search problem for neural network chart patterns and applies a novel HyperNEAT framework to improve pattern discovery in financial data.

## Key findings

- Successfully identified attractive patterns in the Korean stock market
- The proposed method outperforms other search schemes in robustness and speed
- Demonstrates potential for cross-representational quantitative comparison

## Abstract

A neural network-based chart pattern represents adaptive parametric features, including non-linear transformations, and a template that can be applied in the feature space. The search of neural network-based chart patterns has been unexplored 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 attractive patterns on the Korean stock market. We compared newly found patterns with those found by different search schemes, showing the proposed approach has potential.