  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.
