About

Monolith Quant

A systematic trading firm built by a mechanical engineering student at ETH Zurich, applying control theory and quantitative modelling to futures and derivatives markets.

ETH
Zurich — mech. eng. student
F&D
Futures & derivatives focus
100%
Systematic execution
In-house
Proprietary infrastructure
Monolith

Monolith Quant was founded by a mechanical engineering student at ETH Zurich — one of the world's foremost institutions for applied mathematics and engineering science. That formation shapes everything: the standard of proof demanded before deployment, the architecture of systems designed to operate under uncertainty, and the discipline applied to risk.

Engineering education of this kind does not produce market narratives. It produces models — constrained, falsifiable, and grounded in observable data. That distinction is foundational to how we operate.

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.