Stock Chart Pattern recognition with Deep Learning Marc Velay and Fabrice Daniel Artificial Intelligence Department of Lusis, Paris, France
[email protected] http://www.lusis.fr June 2018 ABSTRACT the number of patterns recognized implies having a human examining candlestick charts in order to deduce signal char- This study evaluates the performances of CNN and LSTM acteristics before implementing the detection using condi- for recognizing common charts patterns in a stock historical tions specific to that pattern. Adding different parameters data. It presents two common patterns, the method used to to modulate ratios allows us to tweak the patterns’ charac- build the training set, the neural networks architectures and teristics. This technique does not have any generalization the accuracies obtained. potential. If the pattern is slightly outside of the defined Keywords: Deep Learning, CNN, LSTM, Pattern recogni- bounds, it will not be detected, even if a human would have tion, Technical Analysis classified it otherwise. Another solution is DTW3 which consists in computing 1 INTRODUCTION the distance between two time series. DTW allows us to Patterns are recurring sequences found in OHLC1 candle- recognize a pattern that could vary in size and length. To stick charts which traders have historically used as buy and use this algorithm, we must use reference time series, which sell signals. Several studies, notably by Bulkowski2, have have to be selected by a human. The references must gener- found some correlation between patterns and future trends, alize well when compared with signals similar to the pattern although to a limited extent. The correlations were found to in order to capture the whole range.