Trading via Image Classification
This work addresses the challenge of improving technical analysis in financial trading for traders and institutions by applying image classification methods, though it is incremental as it adapts existing techniques to a new domain.
The authors tackled the problem of financial trading by transforming time-series data into candlestick chart images and training machine learning models to classify them according to predefined trade strategies, finding that the algorithms efficiently recovered complex, multiscale rules from visual representations.
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.