For trading energy, alternative data provider RavenPack utilizes five well-known machine learning algorithms to predict next day returns across a basket of energy commodities.
The underlying signals of trading energy futures are driven by news events detected across thousands of sources. Each model is evaluated individually and as part of an ensemble strategy.
RavenPack’s research shows how combining all models using machine-learning techniques produce solid risk-adjusted returns with lower average bias and without the need to select one particular model.
The ensemble portfolio provides an Information Ratio of 0.65 reducing the risk associated to model selection.
By incorporating regimes which limit trading during high-volatility, it can improve on the out-of-sample return irrespective of whether one looks at annualized, risk-adjusted, or per-trade return.
IR climbs from 0.65 to 1.27 and annualized returns from 9.8% to 21.3% for the high-volatility strategy, despite the reduction in the number of trades. Moreover, a more than 2x increase in per-trade returns from 3.88bp to 8.82bp is observed.