Isard Labs
Algorithmic trading research · Investor briefing
An Alpha Factory for systematic markets.
Search like a lab.·Reject like a fund.
Confidential — prepared for prospective investors · Not an offer to sell securities
The thesis
A frontier AI lab’s method, run with a quant fund’s discipline.
Search like a lab
The same compute-scaled, empirical method that lets AI labs predict the next token — pointed at the next price move. Hypotheses in, evidence out, at scale.
Reject like a fund
The heritage that made systematic funds compound: reject almost everything. Only signal that is provably non-random earns capital.
The two meet at one gate — and we don’t sell subscriptions, we plug what survives into capital.
Why systematic
If you can’t prove yourself wrong, you can’t know if you’re right.
Discretionary trading runs on hardwired human biases — and can never answer the only question that matters: was it skill, or was it luck?
Systematic is the only path that can be measured, repeated, and disproved. You can’t trade on intuition forever.
Why this is hard
The odds are brutal — and even a flawless backtest can be a lie.
Overfitting
The strategy memorised the past. It cannot predict the future.
Look-ahead bias
Tomorrow’s information leaked into yesterday’s decision. Invisible. Fatal.
Selection bias
Test a hundred, report the best. That’s not an edge — it’s a lottery winner.
Test enough combinations and you are guaranteed to find a winner in pure noise.3 The only defence: search millions — then try to kill each one.
1 — Chague, De-Losso & Giovannetti, Day Trading for a Living? (2020). 2 — ESMA review of retail CFD accounts (2018). 3 — Bailey, Borwein, López de Prado & Zhu, The Probability of Backtest Overfitting (2014).The wedge
No AI agents. No black boxes. No vibes.
The market ships autonomous “AI agents” trading capital nobody should have trusted them with. Isard is the deliberate opposite.
- Deterministic. Same input, same output — rules-based, version-controlled, frozen before testing.
- Reproducible & auditable. Every result byte-for-byte; every spec signed; every change re-validated against the whole prior corpus.
- ML earns its place — never as the trader. It proposes; rules decide. If we can’t explain the trade, we don’t take it.
- Not high-frequency. We don’t race on microseconds, co-location, or latency. Our edge is research, not speed.
The gate
Before an idea touches money, it has to survive five checks.
Does it actually work?
We test the idea hard, across many different market conditions — not just the ones that flatter it.
Can we break it?
A separate team tries to tear it apart. If there’s a flaw, their job is to find it first.
Is it repeatable?
Every step is documented and locked. Nothing depends on one person remembering how it worked.
Is now the moment?
Even a good idea has to meet the market at the right time, not just any time.
Did it behave?
Once it’s live, we compare every real trade to what we expected. Surprises get caught fast.
What we keep private
The exact methods stay ours. The discipline of running all five is the product.
What survives
We search wide and reject hard. Real capital is the final reviewer.
The space of possible strategies runs to the trillions. Five disciplines kill all but a few. What survives has earned the right to manage money.
Depth of research
Five years of research — an archive most funds never build.
Most of it was rejected — and that is the point. The archive of what didn’t work is the map of where the edge isn’t.
The moat — our origin
Two careers on one timeline — academia and the markets — converging into Isard Labs.
Academia and the trading floor, in parallel since 2007 — meeting at the award-winning Liverpool AI master’s that became Isard Labs. Hard to hire. Harder to replicate.
Who built this
The rare profile where deep research and front-line trading live in one person.
- A decade in global banking (2007–2018) — market-risk analyst → senior quantitative developer → Quantitative Analytics Manager, including 7 years at a top-five global investment bank (HSBC), owning the firm-wide modelling platform quant teams used to build and ship models.
- Since 2018 — applied AI beyond finance — machine learning and quantitative methods across industry (Devoteam, OVHcloud, Jolibrain · France), broadening the toolkit that now powers the lab.
- Academic depth — dual bachelor’s (Maths; Computer Science), dual master’s (Big Data; AI — Dissertation of the Year), PhD in progress in Applied Mathematics & Quantitative Finance; peer-reviewed publications in maths & ML journals.
- At the state of the art — builds on the newest quantitative methods and modern compute, not legacy tooling — the same frontier discipline that AI labs run.
- Real capital, real consequences — production systematic strategies, reconciled bar by bar. 18 years across Brazil, the UK and France · Mensa · fluent EN/ES/PT/FR.
Why now
The compute era rewards disciplined search — and punishes hype.
The Isard — the Pyrenean chamois — is sure-footed where others slip. As capital floods toward unauditable “AI agents,” the durable edge belongs to whoever can search at scale and refuse to fool themselves about what they find.
Search like a lab.·Reject like a fund.·The moment compute makes that decisive is now.
The universe
What we point the search at — today crypto, expanding across asset classes.
Today we cover the top-20 crypto assets by market cap. The raise widens the same disciplined search into the world’s most liquid futures and ETFs:
Top-20 by market cap
BTC, ETH, SOL, XRP, and the rest of the large-cap set — where the research began.
The benchmark indices
ES (S&P 500), NQ (Nasdaq-100), RTY (Russell 2000) — the deepest, most-traded equity markets.
Energy & metals
CL (crude oil), GC (gold) — classic trend and macro markets.
Government bonds
ZN (10-yr Treasury note), ZB (30-yr Treasury bond) — the core of the interest-rate complex.
Major currencies
6E (euro / USD), 6J (Japanese yen / USD) — the most liquid currency futures.
One-click exposure
SPY (S&P 500), QQQ (Nasdaq-100), IWM (Russell 2000), TLT (long Treasuries), GLD (gold), USO (oil) + sector ETFs.
The business model
We don’t run the fund. We license the blueprint — and earn on every dollar it manages.
Isard Labs delivers a validated, ready-to-build strategy methodology. A fund — new or existing — builds and operates it. Our service contract: 1% per year on AUM + 10% of profits. Annual revenue across fund size and performance, as clients scale (10% · 15% · 20% net/yr):
| AUM / fund | Net | 1 fund | 5 funds | 10 funds |
|---|---|---|---|---|
| US$100M | 10% | US$2M | US$10M | US$20M |
| 15% | US$2.5M | US$12.5M | US$25M | |
| 20% | US$3M | US$15M | US$30M | |
| US$500M | 10% | US$10M | US$50M | US$100M |
| 15% | US$12.5M | US$62.5M | US$125M | |
| 20% | US$15M | US$75M | US$150M | |
| US$1B | 10% | US$20M | US$100M | US$200M |
| 15% | US$25M | US$125M | US$250M | |
| 20% | US$30M | US$150M | US$300M |
Isard Labs annual revenue. We carry no market risk and no fund operations — we are paid to provide the edge.
Illustrative — 1%/yr AUM + 10% of profit; conservative base = 10% net/yr. Fee terms indicative, pending contracts & legal review.The opportunity
Managing US$1B is a rounding error in markets this size.
Against this, a US$1B fund is a fraction of a fraction. The ceiling on this business isn’t market capacity — it’s how much validated edge we can manufacture. Compute lifts that ceiling.
1 — SIFMA / Visual Capitalist, global equity market cap (2024). 2 — SIFMA, global bond market (2024). 3 — BIS Triennial Survey, daily FX turnover (Apr 2022). 4 — HFR, hedge-fund industry AUM (2024).The field
The quant funds we benchmark against — their scale, and the returns they run on.
| Fund (flagship) | AUM ≈ | Net return |
|---|---|---|
| Man Group · AHL | ~US$168B | +3.2% |
| D.E. Shaw · Composite | ~US$120B | +18% |
| AQR · Apex | ~US$100B | +15.1% |
| Millennium | ~US$70B | +15% |
| Citadel · Wellington | ~US$65B | +15.1% |
| Two Sigma · Spectrum | ~US$60B | +10.9% |
| Renaissance · Medallion | ~US$60B | ~39%* |
| Point72 | ~US$42B | +19% |
Tens to hundreds of billions each, on reported 2024 net returns mostly +15–19% (trend-following lagged at +3%; Medallion is the ~39% long-run outlier). A US$1B client fund is a rounding error — and our conservative 10% base sits below the field.
Net returns — 2024 flagship: Man AHL +3.2%, D.E. Shaw +18%, AQR Apex +15.1%, Millennium +15%, Citadel +15.1%, Two Sigma +10.9%, Point72 +19% (Hedgeweek, Bloomberg, CNBC, Man Group). *Renaissance Medallion ≈39% net annualised 1988–2018, internal capital (Wikipedia). AUM: 2024 rankings (Alternative Fund Insight; firm reports). Figures vary by year/fund; several blend quant with discretionary.The x-ray
Where a US$1B fund and a 10% return actually sit in the industry.
Fund size (AUM) — by number of funds · log scale
Annual net return — across funds · bell with fat tails
By size, ~533 funds over US$1B hold ~85% of all assets — a US$1B client already sits in the top ~5%. By return, the industry clusters near +10%, exactly our conservative base. The edge is moving right.
Universe ~10,000+ funds, ~US$4T (Statista; With Intelligence). >US$1B “Billion-Dollar Club” ≈533 firms ≈82–86% of assets (With Intelligence, 2024). Returns: HFRI Fund-Weighted +10.0% in 2024, bell-shaped with fat tails, top/bottom-decile dispersion ~55pp (HFR; Aurum, 2024). Distribution shapes illustrative, anchored to these reported statistics.The ask
US$50M over two years — to build the compute that scales the discipline.
US$25M/yr × 2 years. The bottleneck between a validated 10% strategy and a 20–30% one is search at scale — this raise turns one machine into a fleet.
1,2 — Public cloud rates (2025): a 192-vCPU node (AWS m7i.metal-48xl) ≈ US$9.68/hr ⇒ ~US$0.05/core-hour ⇒ a ~10-min backtest <1¢; H100 GPUs ≈ US$2–4/GPU-hr; ~US$20M/yr ≈ US$1.7M/mo funds a large generation + validation fleet. 3 — Operations ~US$5M/yr: lean senior quant team ~US$3M (quant comp US$0.3–1M+ each), market data & infrastructure ~US$1M, compliance / legal / audit ~US$1M.Valuation framing
Priced as an applied-AI research lab, not a traditional fund.
Cost to replicate the IP
Replacement cost. AI talent is acqui-hired at ~US$1–2M+ per researcher; senior quants run US$1M+/yr. Five years of a fused academic + trading-floor team ⇒ ~US$20–40M and years to rebuild.1
Recurring licensing revenue
At ~US$1B of client AUM, fees run ~US$20M/yr (conservative 10% net) — about the lab’s run-rate. A second fund makes it self-funding; every further fund is upside, with no market risk to us.
Early AI labs
Priced pre-revenue, on team + thesis: Anthropic raised US$124M (Series A, 2021); OpenAI took US$1B from Microsoft (2019) at ~US$20B.2
Indicative structure: US$50M for 10–15% of the technology company → US$350–500M post-money.
1 — AI acqui-hires ~US$1–2M+/engineer (PitchBook; Founders Forum, 2024–25); quant-researcher comp (eFinancialCareers; CQF, 2024). 2 — Anthropic, “Anthropic raises $124 million” (2021); TechCrunch, “Microsoft invests $1 billion in OpenAI” (2019). · Illustrative & non-binding, pending verification & legal review. Not an offer or solicitation.The close
Five disciplines. One standard.
Real capital is the final reviewer.
We’re raising US$50M to turn an award-winning, academically validated architecture into an institutional-scale Alpha Factory. If that’s a thesis you invest behind — let’s talk.
[ Contact: name · email · LinkedIn — to be inserted ]
Important notice
Confidential & not an offer.
This document is confidential and provided for discussion purposes only. It does not constitute investment advice, a recommendation, or an offer or solicitation to buy or sell any security or financial instrument, nor a prospectus or offering document. All figures — including strategy returns, AUM, breakeven, valuation, and projections — are illustrative, unaudited, and subject to verification, change, and legal review. Research-volume metrics describe development effort, not investment performance. Systematic and quantitative strategies carry risk, including loss of capital; past or simulated performance is not indicative of future results. Any offering of securities would be made only to qualified/eligible investors via definitive documentation and in compliance with applicable law. [ COMPLIANCE: final wording pending securities/legal review — do not distribute until approved. ]