  While time series momentum is a well-studied phenomenon in finance, common
strategies require the explicit definition of both a trend estimator and a
position sizing rule. In this paper, we introduce Deep Momentum Networks -- a
hybrid approach which injects deep learning based trading rules into the
volatility scaling framework of time series momentum. The model also
simultaneously learns both trend estimation and position sizing in a
data-driven manner, with networks directly trained by optimising the Sharpe
ratio of the signal. Backtesting on a portfolio of 88 continuous futures
contracts, we demonstrate that the Sharpe-optimised LSTM improved traditional
methods by more than two times in the absence of transactions costs, and
continue outperforming when considering transaction costs up to 2-3 basis
points. To account for more illiquid assets, we also propose a turnover
regularisation term which trains the network to factor in costs at run-time.
