Systematic Liquid Alternatives

Systematic research in established liquid markets

Applying the same research and governance discipline used in digital assets to foreign exchange and commodity markets — where the data history is longer, the participants differ, and the return drivers are not the same.

Capability overview

Systematic FX & Commodities

Foreign exchange and commodity markets are deep, long-established and driven by participants with objectives that are frequently not return-seeking — central banks, corporate hedgers and producers. That mix creates behaviour a rules-based process can be designed to study.

For an allocator already exposed to digital assets, the relevance of this capability is that its return drivers are structurally different. Diversification of that kind is only meaningful if the underlying drivers genuinely differ, which is why we assess it explicitly rather than assume it.

Opportunity set

Participants who are not return-seeking

A significant share of activity in these markets comes from participants hedging exposure or implementing policy, whose behaviour is not driven by expected return.

Long data history

Compared with digital assets, these markets offer decades of data across multiple economic cycles, allowing model behaviour to be examined across regimes that digital-asset history does not yet contain.

Different sensitivities

Foreign exchange and commodity markets respond to macroeconomic, policy and physical supply factors that bear little relation to the drivers of digital-asset prices.

Our approach

Shared oversight discipline

Signal research, validation and monitoring are assessed under the same governance framework we apply across the systematic strategies we oversee, adapted to the characteristics of these markets.

Regime testing across cycles

The longer history available in these markets permits examination of model behaviour through interest-rate cycles, inflationary periods and commodity shocks.

Diversification assessed, not assumed

We measure the relationship between this capability and digital-asset exposure rather than presuming independence, and we monitor whether that relationship is stable.

Machine learning within defined governance

As with the other systematic strategies we oversee, where machine-learning techniques support signal research and validation they operate under human oversight. They are a research method, not a performance claim.

Research framework

What we study when assessing this area.

Signal behaviour across multiple economic and policy regimes
Robustness to changes in volatility and liquidity conditions
Sensitivity to transaction costs, financing and roll dynamics
Correlation with digital-asset exposure, and the stability of that correlation
Drawdown characteristics through historical stress episodes
Out-of-sample and walk-forward validation
Indicators of model decay and defined review thresholds

Model governance

Model validation

Defined pre-deployment criteria, with particular attention to whether historical fit reflects a persistent effect or an artefact of a specific period.

Ongoing model monitoring

Continuous comparison of live behaviour against research expectations, with divergence investigated rather than tolerated.

Human oversight

Defined oversight of model behaviour, exposure and the conditions under which a model is reduced or withdrawn.

Implementation principles

Conceptual principles. Product-specific portfolio information is restricted.

Rules-based position sizing

Exposure follows predefined rules rather than discretionary judgement.

Defined exposure limits

Leverage, concentration and aggregate exposure operate within limits set in advance.

Liquidity-first market selection

Research concentrates on markets deep enough to support rules-based implementation at reasonable cost.

Risk considerations

Capital is at risk. Investors may lose part or all of the amount invested.
Strategies in this capability may be implemented through Contracts for Difference (CFDs). CFDs are leveraged derivative instruments and carry significant risk, including losses that may exceed the initial margin.
Implementation through derivatives does not involve direct ownership of the underlying instruments or commodities.
Foreign exchange and commodity markets can be materially affected by macroeconomic, policy, geopolitical and physical supply developments.
Systematic models may degrade or fail to adapt to a change in market regime.
Diversification between capabilities does not ensure a profit or protect against loss, and correlations may change.
Past performance is not a reliable indicator of future results.

Institutional considerations

What an allocator should understand when evaluating this type of strategy.

Test the diversification claim

Ask how the relationship with digital-asset exposure is measured, over what period, and whether it has held during stress.

Distinguish the market from the manager

Long data history is a property of these markets, not evidence about a particular manager's process. Both should be assessed separately.

Understand the instrument

Where leveraged derivatives are used, financing, roll and margin mechanics are a material part of the return and risk profile.

Consider the role in a portfolio

This capability is most usefully evaluated for what it contributes alongside existing exposures rather than in isolation.

Related research

Product-specific materials

Product-specific materials are available to eligible investors through controlled investor access.

Eligible Investor Access