# Enhancing Time Series Momentum Strategies Using Deep Neural Networks

## TL;DR

This paper introduces Deep Momentum Networks, a deep learning approach that learns trend estimation and position sizing for time series momentum strategies, significantly improving Sharpe ratios in backtests of futures portfolios.

## Contribution

It presents a novel hybrid deep learning model that optimizes trading rules directly for momentum strategies, outperforming traditional methods in backtests.

## Key findings

- Sharpe ratio more than doubled without transaction costs.
- Outperforms traditional methods even with 2-3 basis points transaction costs.
- Incorporates turnover regularization for illiquid assets.

## Abstract

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.