  Much of modern practice in financial forecasting relies on technicals, an
umbrella term for several heuristics applying visual pattern recognition to
price charts. Despite its ubiquity in financial media, the reliability of its
signals remains a contentious and highly subjective form of 'domain knowledge'.
We investigate the predictive value of patterns in financial time series,
applying machine learning and signal processing techniques to 22 years of US
equity data. By reframing technical analysis as a poorly specified, arbitrarily
preset feature-extractive layer in a deep neural network, we show that better
convolutional filters can be learned directly from the data, and provide visual
representations of the features being identified. We find that an ensemble of
shallow, thresholded CNNs optimised over different resolutions achieves
state-of-the-art performance on this domain, outperforming technical methods
while retaining some of their interpretability.
