Systematic Liquid Alternatives

Systematic research in established liquid markets

Independent algorithmic programmes trade foreign exchange, gold and commodity markets, each inside its own risk limits. How much each one contributes is decided by optimisation across their returns and the correlations between them — not by conviction about any single model.

Capability overview

Systematic FX & Commodities

Foreign exchange and commodity markets are deep, long-established and driven in large part by participants whose objectives are not return-seeking — central banks implementing policy, corporates hedging exposure, producers managing physical risk. That mix creates behaviour a rules-based process can be designed to study.

What distinguishes this capability is less any individual model than how several are combined. Each programme is admitted on its own evidence and runs within its own limits; the allocation between them is then set by optimisation over their returns and their covariance, with a floor and a cap on every programme so that none can dominate the portfolio and none can quietly disappear from it.

For an allocator already exposed to digital assets, the relevance is that these return drivers are structurally different. Diversification of that kind is only meaningful if the drivers genuinely differ, which is why the relationship is measured rather than assumed.

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.

Macro and policy dispersion

Monetary policy has stopped moving in step across developed and emerging economies. That divergence produces persistent dispersion in interest rates and exchange rates, which is the raw material a systematic currency programme works on.

Inflation and real-asset linkage

Commodities and gold carry sensitivity to unexpected inflation and to supply-side disruption that equity and fixed-income exposure does not, giving them a distinct role when the price level moves unexpectedly.

Gold as a driver of its own

Gold responds to real-rate volatility, central-bank reserve diversification and geopolitical risk on a rhythm that is not the commodity complex's. That is why it is treated as a separate exposure rather than folded into commodities.

Depth and capacity

These are among the deepest and most continuously traded markets in the world, which matters to a systematic process in a specific way: a strategy can be sized without its own trading moving the price against it.

Our approach

Diversification of return drivers

The programmes respond differently to volatility, trend and macro regime. Combining them is intended to reduce dependence on any one of those conditions continuing to hold.

Admitted on evidence, not on narrative

A programme enters the set only where its edge is quantitatively demonstrable and its record is auditable. A plausible account of why something ought to work is not the test.

Allocation set by optimisation

Weights come from mean-variance optimisation over annualised returns and the covariance between programmes, maximising risk-adjusted return subject to no short positions and to a floor and a cap on each programme.

Correlation-aware sizing

Programmes that tend to move together are sized in the knowledge that they do, rather than counted as separate bets — so the aggregate variance reflects how they behave in combination.

Rules-based execution

Positioning follows the systems' own rules with no discretionary override. That is a constraint as much as a feature: it removes judgement from execution and places the whole weight on design, validation and monitoring.

Resilience over a single thesis

The combination is constructed to hold up across several plausible macro paths rather than to depend on one of them arriving.

Research framework

What we study when assessing this area.

Annualised returns and the covariance between programmes, estimated over a defined window
Signal behaviour across multiple economic and policy regimes
Robustness to changes in volatility and liquidity conditions
Sensitivity to transaction costs, financing and roll dynamics
Concentration limits and correlation-cluster controls at portfolio level
Stress and scenario testing of the combined allocation, not only of its parts
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.

Allocation reviewed on a cycle, not on impulse

Once sufficient overlapping history exists, weights are re-examined on a fixed quarterly cycle using recent data, with statistical smoothing so that a single unusual month cannot swing the allocation. Between reviews the weights stand.

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 programme 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, at the level of each programme and of the portfolio as a whole.

Rebalanced to target weights

The portfolio is returned to its target weights on a monthly cycle, so that drift between reviews is corrected rather than allowed to accumulate into an allocation nobody chose.

Monitored at daily resolution

Risk monitoring uses daily data where it is available. Month-end figures cannot show what happened inside the month, and a control that only looks at month-ends would not see it.

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.
An allocation derived by optimising over historical returns and correlations reflects the period it was estimated on. Those estimates move, and weights that were optimal for one window need not be optimal for the next.
Diversification between programmes or 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.

Separate the components from the combination

A set of programmes may each have a live record while the way they are combined has been derived afterwards from that same history. Ask which of the two a presented track record describes, and over what window the combination was fitted.

Ask what a Sharpe ratio is measured against

A Sharpe ratio computed against a zero risk-free rate is higher than one computed against cash, and across a period of positive short rates the difference is not small. Ask which convention is in use before comparing one figure with another.

Ask at what resolution drawdown was measured

A maximum drawdown computed from month-end figures cannot show a fall that began and ended inside a month. Daily data answers a different and harder question, and the two numbers are not interchangeable.

Test the diversification claim

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

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