Quantitative research
Alpha under non-stationary market dynamics, treated as a signal-and-noise problem.
Independent practice · London · October 2025 – August 2026
An independent research programme in futures, derivatives and equities. The organising question was simple to state and hard to answer: how do you detect a regime shift without overfitting to the last one?
Lines of work.
- Non-stationarity. Most published edges are stationary-regime artefacts. I was interested in signals whose derivation survives a change in the data-generating process, not signals that happened to survive a backtest.
- Structural constraints. Market impact, inventory, liquidity and latency are not frictions to be assumed away; they are boundary conditions. A strategy specified without them has not been specified.
- Signal and noise. Physics-inspired separation methods — the same problem as pulling a faint astrophysical signal out of an instrument’s systematics, with an adversary added.
- Stochastic control. HJB formulations for execution and inventory, Monte Carlo for path-dependent problems, and Bayesian methods for parameter uncertainty.
Background. Securities Education Certificate (Distinction, Imperial College Investment Society); Finance Accelerator, London; member of Imperial’s Algorithmic Trading and Investment societies.
A note on the dual track. I do not treat physics and quant as a hedge against each other. They are the same discipline — build a model of a process you cannot fully observe, quantify what you do not know, and act on the result — applied to data that pays differently.