Research
A continuous pipeline that turns raw market and alternative data into tested, capital-ready signals. Hypothesis, evidence, decision — repeated relentlessly.
Systematic · Quantitative · Proprietary
We trade our own capital. Nothing else.
Shadowell is a systematic quantitative trading group. We research, engineer and run fully automated strategies across global markets — funded entirely by our own balance sheet.
- No external capital
- No client mandates
- One book, one team
The model
Principal risk. Total alignment.
Zero distraction.
We are not an asset manager. There is no fundraising, no investor reporting cycle, no benchmark to hug. Every decision is measured against a single question: does it make the book better?
01
Our capital, our conviction
The firm's own balance sheet is the only capital at risk. That lets us hold positions through noise, size with discipline, and kill strategies the moment the edge decays.
02
Evidence over instinct
Every position begins as a falsifiable hypothesis. It reaches production only after it survives out-of-sample testing, cost modelling and adversarial review by the team.
03
Small team, deep stack
Researchers and engineers sit together and own their work end to end — from raw tick data to the order on the wire. No silos, no handoffs, no pods competing for scraps.
What we are
- A principal trading firm risking only its own balance sheet
- A single book, run by one team on one platform
- An engineering organisation that happens to trade
- Fully systematic — no discretionary overrides
What we are not
- A fund, an asset manager or a broker
- A funded-trader or evaluation programme
- A signal, course or copy-trading service
- A place where research is separated from production
Three disciplines
Research. Engineering.
Risk.
They are not departments — they are the three forces every idea at Shadowell has to pass through.
Engineering
One in-house platform: ingestion, simulation, deployment and execution. The same code path that backtests a strategy is the one that trades it.
Risk
Limits are code, not culture. Exposure, drawdown, correlation and liquidity are enforced automatically — pre-trade, in-flight and at the book level.
The pipeline
From raw tick to live risk.
Five stages. Each one measured, versioned and reproducible — so that a result from three years ago can be rebuilt today, bit for bit.
Stage 01
Data, captured properly
Full-depth market data, reference data and alternative sources are normalised into a single time-correct store. Everything is point-in-time, so a model never sees the future.
- Nanosecond-stamped, exchange-native capture
- Point-in-time corporate actions and reference data
- Automated quality gates on gaps, outliers and drift
Stage 02
Signals, honestly tested
Ideas are expressed as forecasts and judged out of sample, net of realistic costs. Most die here — and that is the point of the process.
- Statistical and machine-learned forecasts across horizons
- Cost, slippage and capacity modelled from day one
- Adversarial review before a single dollar is allocated
Stage 03
Portfolio, built for the whole book
Forecasts become positions through an optimiser that balances expected return against risk, turnover and the correlation of everything already on.
- Multi-horizon signal blending and decay control
- Factor, sector and liquidity constraints
- Turnover-aware sizing against live capacity
Stage 04
Execution, engineered
Target positions are traded by our own smart order routing and scheduling logic, colocated at the venues that matter, and continuously scored against benchmarks.
- In-house routing, scheduling and microstructure models
- Colocated, kernel-bypass networking on critical paths
- Every fill measured — execution alpha is real alpha
Stage 05
Risk, enforced in code
Limits sit in the critical path, not in a policy document. Independent monitoring runs beside the trading system with the authority to halt it instantly.
- Pre-trade limit checks and automated kill switches
- Real-time exposure, drawdown and concentration control
- Daily attribution back to the originating hypothesis
0+
Execution venues and exchanges connected
0+
Instruments continuously modelled
0M
Orders and quotes per trading day
<0µs
Median tick-to-trade on latency-critical paths
Tick-to-trade
— µs0 µsp99 —60 µs
Inbound messages
— /sLast 60sNormalised
Venue sessions
45 / 45All sessions healthyHeartbeat 1s
Illustrative visualisation — synthetic telemetry, not live production data
Strategy lab
Why most edges
never survive.
A toy version of the decision we make every week. Move the sliders: costs, turnover and latency will happily eat an edge that looked beautiful on paper.
Predicted return per trade, before any cost.
What the venue and the touch charge you to get in and out.
More trades means more edge — and more market impact.
Slower means more adverse selection on every fill.
Scales the book. Changes the size of the swings, not the quality of the edge.
SEED —
·
Net Sharpe
—
Return / yr
—
Max drawdown
—
Net edge
—
Net of cost (one simulated path)
Gross, cost-free fantasy
Drawdown
Net edge —
Spread & fees —
Impact —
Latency —
—
Adjust the controls to see how an edge behaves once reality is priced in.
Illustrative toy model with a fixed random seed — not a representation of Shadowell's strategies, positions or performance
Technology
Built here.
End to end.
We buy connectivity and compute. Everything that touches a decision — simulation, portfolio, execution, risk — is ours.
One research platform
A single, deterministic simulator drives research and production. If it happened in the backtest, it can happen on the wire — same code, same clock, same assumptions.
Core languagesC++20 · Rust · Python
Research computeGPU cluster · distributed sim
DataColumnar tick store · petabyte scale
ReproducibilityVersioned data + code + seeds
Low-latency core
Lock-free, cache-aware C++ on tuned kernels with bypass networking and hardware timestamping. Latency is budgeted per hop and regression-tested like any other feature.
Simulation at scale
Thousands of parallel scenarios per idea — with queue position, latency and impact modelled, not assumed. Cheap experiments make expensive mistakes rare.
Risk in the path
Independent limit engine with hard pre-trade checks and automated halts.
Global footprint
Colocated presence across major cash, derivative and digital-asset venues.
Total observability
Every order, signal and fill traceable to the decision that produced it.
Coverage
Where we trade,
and how.
Diversified across asset classes and horizons — because a book that depends on one regime is not a book, it's a bet.
Asset classes
Equities & ETFsCASH · DELTA-1
Futures & index productsGLOBAL · 24/5
Listed options & volatilitySURFACE · RV
Foreign exchangeSPOT · FWD
Digital assetsSPOT · PERP · OPT
Strategy families
Statistical arbitrageINTRADAY → DAYS
Market microstructureSUB-SECOND
Volatility relative valueDAYS → WEEKS
Systematic macro & trendWEEKS → MONTHS
Execution alphaCONTINUOUS
Points of presence
LD4Slough, LondonEQ · FX · FUT
FR2FrankfurtEQ · FUT
NY4Secaucus, New JerseyEQ · FX · OPT
CH2Chicago / AuroraFUT · OPT
TY3TokyoEQ · FUT
SG1SingaporeFX · DIGITAL
HK1Hong KongEQ · DIGITAL
SY4SydneyFUT · FX
Indicative infrastructure footprint — drag the globe to rotate
How we work
Five things we
refuse to bend on.
- 01
Truth beats comfort
The fastest way to lose money is to fall in love with your own backtest. We attack our best ideas hardest, and we celebrate the ones we kill.
- 02
One book, one team
No pods, no internal competition for capital, no hoarding of signals. Everything is allocated against the same risk budget, in the open.
- 03
Limits are code
If a constraint isn't enforced in the critical path, it isn't a constraint — it's a hope. Risk sits in front of every order, automatically.
- 04
Reproducible or it didn't happen
Data, code and seeds are versioned together. A result from three years ago rebuilds today, bit for bit, or we don't trust it.
- 05
Small, senior, unblocked
We would rather stay deliberately small and give everyone the compute, the data and the authority to finish what they start.
Milestones
A short history.
Founded
Two researchers, one venue, one strategy — and a rule that everything must be reproducible.
Cross-asset
Futures and FX added to the book. First colocation racks in LD4 and NY4.
Simulator rebuilt
Vendor backtesting replaced by our own deterministic simulator. Research and production converge.
Options & vol
Surface modelling goes live, opening a relative-value book alongside stat arb.
Digital assets
24/7 perpetuals and options — new microstructure, same discipline.
Latency rebuild
Kernel-bypass critical path and hardware timestamping across latency-sensitive venues.
One book, five families
A small team, an uncapped research budget and a single question: does it make the book better?
Careers
Bring a hard problem.
We'll bring the data.
We hire for reasoning, not résumés. Prior finance experience is genuinely optional — curiosity, rigour and craft are not.
- Direct P&L attribution
- Flat, non-siloed structure
- Uncapped research compute
- Own your work end to end
- Hybrid friendly
- Relocation & visa support
- Learning budget
- Top-tier hardware
Showing 5 of 5 roles
Own the full lifecycle of a strategy: find the hypothesis, build the evidence, size it, deploy it and live with the results. You'll work with senior researchers and engineers on a shared platform rather than in an isolated pod.
- Strong probability, statistics and time-series foundations
- Fluent in Python; comfortable reading and writing production code
- A track record of rigorous empirical work in any domain
- Healthy scepticism about your own backtests
Build and sharpen the critical path: market data decoders, order gateways, the matching-adjacent simulator and the risk engine that sits in front of every order.
- Expert modern C++ (or Rust) with a feel for what the hardware is doing
- Experience with lock-free structures, NUMA, kernel tuning or bypass networking
- You measure before you optimise, and you write the benchmark first
- Bonus: FPGA, exchange protocols, colocation operations
Apply modern sequence modelling to noisy, non-stationary, adversarial data — where the signal-to-noise ratio is brutal and leakage is the enemy.
- Deep experience training and evaluating models at scale (PyTorch or JAX)
- Instinct for validation design, leakage and regime shift
- Interest in market data as a modelling problem, not a mystery
Own capture, normalisation and the point-in-time guarantees that everything upstream depends on. If the data is wrong, nothing else matters.
- Large-scale pipelines and columnar/time-series storage
- Obsessive about correctness, lineage and reproducibility
- Python plus a systems language; comfortable operating what you build
For exceptional graduates from any quantitative discipline. No finance background required. You'll be given real problems, real data and real mentorship from week one.
- Outstanding mathematical, statistical or engineering ability
- Evidence of building things — research, competitions, open source, side projects
- Applications reviewed on a rolling basis
No roles match that. Try clearing the filters — or apply speculatively.
How hiring works
Deliberately short and deliberately technical. Most candidates go from first contact to decision in two to three weeks.
01
Application review
Every application read by a practitioner.02
Intro conversation
30 minutes. Your work, our work, mutual fit.03
Technical deep dive
Maths, code or systems — depending on the role.04
Research exercise
An open problem, discussed as colleagues.05
Final panel & offer
Meet the team you'd actually sit with.Notes
Things we've written down.
Occasional writing from the team. No market calls, no predictions — just craft.
Questions
Straight answers.
No. Shadowell trades exclusively with its own capital. We do not accept investors, manage client accounts, offer funded-trader programmes or provide investment advice.
From sub-second microstructure strategies through to multi-week systematic macro. Diversification across horizons is deliberate: different regimes reward different clocks.
No. Many of the team came from physics, mathematics, computer science and engineering with no markets background. We care how you think and how fast you learn.
It's a deliberately simple teaching model — a fixed random seed, a linear cost stack and a Gaussian noise process. The intuition is real (costs, impact and latency dominate small edges); the numbers are not ours and are not performance figures of any kind.
Yes, and we'd encourage it. Send your CV and a short note on something you've built or proved to careers@shadowell.com.
Research, portfolio construction, execution and risk are all developed in-house. We license market data and connectivity, and evaluate third-party datasets — never third-party alpha.
London is home, with trading infrastructure colocated at major venues globally. Some roles are open to hybrid or remote arrangements within compatible time zones.
Contact
Let's talk.
Whether you're a researcher with an idea, an engineer who loves hard constraints, or a venue or data partner — we read everything.
Shadowell · London, United Kingdom
Registered office address — replace with yours