  The introduction of electronic trading platforms effectively changed the
organisation of traditional systemic trading from quote-driven markets into
order-driven markets. Its convenience led to an exponentially increasing amount
of financial data, which is however hard to use for the prediction of future
prices, due to the low signal-to-noise ratio and the non-stationarity of
financial time series. Simpler classification tasks -- where the goal is to
predict the directions of future price movement -- via supervised learning
algorithms, need sufficiently reliable labels to generalise well. Labelling
financial data is however less well defined than other domains: did the price
go up because of noise or because of signal? The existing labelling methods
have limited countermeasures against noise and limited effects in improving
learning algorithms. This work takes inspiration from image classification in
trading and success in self-supervised learning. We investigate the idea of
applying computer vision techniques to financial time-series to reduce the
noise exposure and hence generate correct labels. We look at the label
generation as the pretext task of a self-supervised learning approach and
compare the naive (and noisy) labels, commonly used in the literature, with the
labels generated by a denoising autoencoder for the same downstream
classification task. Our results show that our denoised labels improve the
performances of the downstream learning algorithm, for both small and large
datasets. We further show that the signals we obtain can be used to effectively
trade with binary strategies. We suggest that with proposed techniques,
self-supervised learning constitutes a powerful framework for generating
"better" financial labels that are useful for studying the underlying patterns
of the market.
