We adopt Deep Reinforcement Learning algorithms to design trading strategies for
continuous futures contracts. Both discrete and continuous action spaces are considered …

We adopt deep learning models to directly optimise the portfolio Sharpe ratio. The
framework we present circumvents the requirements for forecasting expected returns and …

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 …

The authors provide practical insights for investors seeking exposure to the growing
cryptocurrency space. Today, crypto is much more than just bitcoin, which historically …

Momentum strategies are an important part of alternative investments and are at the heart of
commodity trading advisors (CTAs). These strategies have, however, been found to have …

We introduce Spatio-Temporal Momentum strategies, a class of models that unify both time-
series and cross-sectional momentum strategies by trading assets based on their cross …

In this article, the authors construct a pipeline to benchmark hierarchical risk parity (HRP)
relative to equal risk contribution (ERC) as examples of diversification strategies allocating …

We introduce the Momentum Transformer, an attention-based deep-learning architecture,
which outperforms benchmark time-series momentum and mean-reversion trading …

We introduce Deep Inception Networks (DINs), a family of Deep Learning models that
provide a general framework for end-to-end systematic trading strategies. DINs extract time …

This paper compares the performance of three enhanced momentum strategies proposed in
the literature: constant volatility-scaled momentum, constant semi-volatility-scaled …