# Trading via Image Classification

## TL;DR

This paper explores transforming financial time-series data into images for classification, demonstrating that vision-based models can effectively recover complex trading signals and strategies.

## Contribution

It introduces a novel approach of converting time-series data into images for trading strategy classification, leveraging image recognition techniques.

## Key findings

- Image-based models accurately classify trading strategies
- Visual representation captures multiscale signals effectively
- Transforming time-series to images aids in technical analysis

## Abstract

The art of systematic financial trading evolved with an array of approaches, ranging from simple strategies to complex algorithms all relying, primary, on aspects of time-series analysis. Recently, after visiting the trading floor of a leading financial institution, we noticed that traders always execute their trade orders while observing images of financial time-series on their screens. In this work, we built upon the success in image recognition and examine the value in transforming the traditional time-series analysis to that of image classification. We create a large sample of financial time-series images encoded as candlestick (Box and Whisker) charts and label the samples following three algebraically-defined binary trade strategies. Using the images, we train over a dozen machine-learning classification models and find that the algorithms are very efficient in recovering the complicated, multiscale label-generating rules when the data is represented visually. We suggest that the transformation of continuous numeric time-series classification problem to a vision problem is useful for recovering signals typical of technical analysis.