We propose a novel investment decision strategy based on deep learning. Many conventional algorithmic strategies are based on raw time-series analysis of historical prices. In contrast many human traders make decisions based on visually observing candlestick charts of prices. Our key idea is to endow an algorithmic strategy with the ability to make decisions with a similar kind of visual cues used by human traders. To this end we apply Convolutional AutoEncoder (CAE) to learn an asset representation based on visual inspection of the asset's trading history. Based on this representation we propose a novel portfolio construction strategy by: (i) using the deep learned representation and modularity optimisation to cluster stocks and identify diverse sectors, (ii) picking stocks within each cluster according to their Sharpe ratio. Overall this strategy provides low-risk high-return portfolios. We use the Financial Times Stock Exchange 100 Index (FTSE 100) data for evaluation. Results show our portfolio outperforms FTSE 100 index and many well known funds in terms of total return in 2000 trading days.
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