About

Monolith Quant

A Zurich-based quantitative firm. We compute options positioning for futures and index markets faster than anyone, model what dealers do about it, and turn that into systematic strategies with automated execution.

Zurich
Switzerland-based
F&D
Futures & derivatives focus
100%
Systematic execution
In-house
Proprietary infrastructure
Monolith

Monolith Quant was founded in Zurich on an engineering premise: markets are dynamic systems and should be modelled as such. Options dealers hedging their book are the clearest feedback loop in modern markets: measurable, forecastable, and tradeable. That premise shapes everything we do: the standard of proof demanded before deployment, the architecture of systems designed to operate under uncertainty, and the discipline applied to risk.

We do not produce market narratives. We produce models: constrained, falsifiable, and grounded in observable data. That distinction is foundational to how the firm operates.

Our approach draws on three disciplines, applied in combination rather than isolation.

Quantitative Modelling
Probabilistic signal construction, statistical inference, and model validation with explicit out-of-sample discipline.
Control Theory
Dynamic systems mathematics applied to position sizing, regime adaptation, and execution under stochastic conditions.
Software Engineering
Fully proprietary stack built for auditability, latency determinism, and zero dependency on third-party strategy frameworks.

We operate exclusively in listed futures and derivatives, instruments that offer deep liquidity, transparent pricing, and a structural efficiency that rewards systematic approaches. These markets reward discipline and punish noise. That alignment is intentional.

Our strategies are macro-agnostic and driven by defined statistical criteria. Position management is governed by explicit risk parameters; no discretionary override enters the execution chain once a strategy is deployed.

Auditability
Every model, every backtest, every live position is fully documented and reproducible. No black-box outputs enter the decision process.
Constraint-first design
Risk limits are defined before strategy deployment and enforced at the infrastructure level, not the discretionary level.
Execution rigour
Signal quality does not survive poor execution. Our infrastructure is engineered to close the gap between modelled and realised performance.
Concentrated conviction
We maintain a small number of high-conviction strategies rather than optimising for breadth. Depth of process over breadth of coverage.