CTA vs CML (CTA with ML Overlay)



  • CTA strategies are popular, but often not suitable for a smaller set of markets, e.g. commodity sector alone.
  • Can we create a ML strategy capturing the main performance attributes of traditional CTA strategies?
  • Using the AiLA approach for trading strategy modelling.


  • Replicate Momentum, Carry and Value CTA signals for ML training, based on public literature [Baz15].
  • Construct a long/short signal prediction model as described in the CTA ML modelling section.
  • Compare performance between traditional CTA signals vs ML signals, for 29 commodity markets typically included in indices like BCOM and GSCI.

[Baz15] J.Baz et al., Dissecting Investment Strategies in the Cross Section and Time Series (December 4, 2015). Available at SSRN:https://ssrn.com/abstract=2695101

Performance Comparison

  • For clear comparison, CTA and CML (CTA-ML) signals are produced in equivalent format and applied to the same market returns.
  • A performance difference therefore directly relates to the signals, and portfolios are compared with same daily risk.
  • A daily P&L correlation of 58% is obtained between the CML and CTA portfolios, indicating a substantial similarity with the performance from traditional CTA strategies.
  • At the same time, the CML portfolio indicate a significantly higher performance than the CTA strategies over the given history and on a risk adjusted basis.
  • It should be noted that in average CML hold positions less often than the CTA strategies and all results are ignoring transaction costs, expected to affect CML results the most.

(* The returns used are based on the front month contract for each market, typically rolled a couple of weeks before the contract expiry)

Performance Breakdown

  • The role of timing is studied by masking out days when CML have non-zero positions for the individual markets.
  • Applying the position mask to CTA signals, indicates an enhancement using CTA signals on days selected by CML.
  • CML performance is broken down into contributions from days with different types of CTA signals, i.e. same/different to CML signal, or CTA signal being classified as zero.
  • Most days with a CML position belong to the same signal category, also indicated by the CML vs CTA correlation.
  • CML performance on days in same or zero CTA signal category indicate a sizable positive CML contribution.
  • CML performance on days in the different CTA signal category indicate that CML is more often right than CTA, however, also a weaker performance when signals disagree.

(* For the comparison of CTA and CML signals, the net CTA signal on a given day is classified by belonging to either a long, short or zero signal group)


  • The AiLA methodology for investment strategy modelling was used to create a long/short prediction model which appears to capture the main performance characteristics of traditional CTA strategies.
  • The CML (CTA-ML) strategy was trained using three CTA signals (Momentum, Carry and Value) from public literature and the performance was compared based on 29 liquid commodity markets, being the typical constituents of indices like BCOM and GSCI.
  • The CML strategy indicates substantial overlap with the CTA strategy performance, as well as a significantly higher risk adjusted return.
  • A break down of the results suggest that the CML excess performance originate both from the signal timing, in terms of the smaller sub-set of days when CML decide to generate non-zero signals, as well as from generating different signal values compared to the CTA strategies.
  • It should be noted that the study was made without considering transaction costs, which are expected to affect the CML results more negatively than the traditional CTA results.