Research rule-based, systematic and quantitative trading strategies with an AI team working for you.
You bring a trading idea, a rule-based strategy, or a research hypothesis. The desk investigates it through an institutional process and comes back with either a validated strategy or an honest refutation. The process is the same across the whole range, from straightforward rule-based systematic strategies to advanced quantitative and machine-learning research. A refutation is a result, not a failure.
01 · You
State the idea and the constraints: a trading rule you want stress-tested, a parameter you want researched, or a hypothesis you want investigated. You do not need a finished strategy, only a question worth answering. Your Chief of Staff turns it into a brief and puts it on the backlog.
02 · Your team
The Head of Research dispatches researchers. They explore the data, extract signals, test, build, and write every experiment to the desk history. Then the Risk Analyst signs off independently, or sends it back.
03 · You
You get a defended result: what held up, what did not, and the evidence behind both. The allocation call is yours, and it stays yours.
Your team
Every plan ships the whole roster. The only thing that changes between plans is how many researchers you hire. The process itself is encoded in the harness, so the desk behaves like a real research function rather than a prompt wrapped around a model.
The role boundaries are the ones real desks enforce: researchers do not size or deploy, validation never originates, nothing goes live without independent sign-off, nobody reviews their own work, and information barriers are enforced at the platform level.
Directing the lab
Your Chief of Staff is the surface you work through. The team runs while you are away, and the measure of a good week is decisions made, not hours logged. This is research orchestration, not advanced prompting.
01 · Your morning
What finished overnight, what the Risk Analyst flagged, what is blocked waiting on you, what the team suggests next, and which experiments are starting to overlap. You open it to decide, not to operate.
02 · The backlog
Untested, in progress, defended, refuted, promoted to strategy. This is the object you actually manage, and it is where a belief you had on a Tuesday goes to be settled, with the experiment history attached.
03 · Your attention
The team works without asking. You are brought in to approve what is irreversible, and left alone for everything else.
The builder stays yours throughout. Direct access at all times, and manual work and team work write to the same state, so the two views never diverge. Multiple-testing risk is managed by the process, not left to memory.
The builder stays yours. Work by hand on the canvas or hand it to the team, and both write to the same state.
What your researchers arrive with
A researcher is only as good as the desk they sit at. Ours arrive at one that is already built: data already licensed and aligned, an engine that returns the same answer every time, validation they cannot skip, and the surrounding process a real quant desk needs to function.
Stocks, futures, forex, options, prediction markets, and crypto, plus news and fundamentals: contracts paid for, timestamps aligned, survivorship handled, datasets normalized. The layer researchers at real firms spend most of their time fighting is the layer we bring.
The same strategy, the same data, the same result, every time. What the team hands you is deterministic code executed on that engine, not a model’s narration of what it might do, so every simulated trade can be reproduced line by line. Reinforcement learning is the one exception, and it is labelled where it is used. Determinism is what makes a result checkable, and checkable work is what lets a desk scale.
The Risk Analyst is independent of whoever built the strategy, and it signs off before anything reaches you. The sign-off is a deterministic robustness run, never model judgment. It is part of every plan, never an add-on.
Signal extraction, statistical analysis, walk-forward, Monte Carlo, meta-labeling, reinforcement learning, regime testing, leakage checks, parameter stability, and execution testing. Every method is on every plan, because methods are the researcher’s training and every hire arrives fully trained.
Equities and ETFs.
Index, energy, metals, agriculture.
Major venues, 24/7.
Major and minor pairs.
Equity and index options.
Event-driven contracts.
The independent check
The Risk Analyst is independent of whoever built the strategy and never originates work of its own. Its sign-off is a deterministic robustness run on a platform-enforced configuration, not a second opinion. Before a result is put in front of you, it has to survive four questions, with the rules enforced by the desk rather than improvised by the researcher.
Strategies are re-fit on rolling windows and judged on the windows that follow. A backtest that holds up on a single split tells you what worked once. Walk-forward tells you whether it keeps working, and by how much it degrades when it stops. The partitioning is enforced at the platform level, not the strategy level, so no one on the team can peek at data they were not given.
A result that only works in one volatility regime, one rate environment, or one direction of trend is a result about that period, not about the market. The check separates the two and says which you have.
Edges that live inside the spread are not edges. Fees, slippage, and fill assumptions are stressed, and how much room the strategy has before the cost assumptions matter is reported rather than assumed.
Capacity is part of the verdict. A strategy that works at one size and breaks at ten is reported as exactly that, so the allocation call you make is made with the size in front of you.
Every strategy the Risk Analyst passes is deployed to a paper account and builds a real, forward, out-of-sample record. Walk-forward simulates out-of-sample performance; paper trading measures it. The same standard applies to us: the Trader runs paper before live, and going live is always your call.
Starting with Claude or GPT is the easy part. The desk around the model, data contracts, aligned pipelines, the backtesting and execution engine, robustness runs, experiment history, controls, and validation independent enough to be worth having, is years of work before it is a single tested idea.
For a small or medium-sized prop firm, fund, CTA, family office, or emerging manager, the decision is closer to build and staff a quant research desk versus hire one that is already standing.
Firms engage a contracted desk, starting with a free scoped pilot. Individuals hire the same desk self-serve, with one researcher. Every plan ships the full staff, the built desk, the encoded process, and the independent sign-off before anything reaches you. The researcher count is what changes.
Firm Desk Talk to us | Your Desk $990/mo | |
|---|---|---|
| Talk to us | Join the waitlist | |
| Your team | ||
| Chief of Staff | ✓ | ✓ |
| Head of Research | ✓ | ✓ |
| Researchers | 1 to 5 | 1 |
| Risk Analyst | ✓ | ✓ |
| Trader | ✓ | ✓ |
| Monitor | ✓ | ✓ |
| Mentor | ✓ | ✓ |
| Quant devs & data engineering | ✓ | ✓ |
| Capacity | ||
| Hypotheses in parallel | 1 to 5 | 1 |
| Working capacity | 2.5x to 7.5x | Standard |
| Seats | Multi-seat | 1 |
| Shared workspace, attribution & sign-off record | ✓ | — |
| Weekly status, monthly review, dossiers | ✓ | — |
| The desk, included in every plan | ||
| Licensed, aligned data across six asset classes | ✓ | ✓ |
| Deterministic backtesting engine | ✓ | ✓ |
| Full methods bench | ✓ | ✓ |
| Visual strategy builder | ✓ | ✓ |
| API access (MCP) | ✓ | ✓ |
| Deployment | ||
| Paper deployment & forward track record | ✓ | ✓ |
| Live deployment, on your authorization | ✓ | ✓ |
Starting on Your Desk costs you nothing later: moving up to a Firm Desk carries your hypothesis backlog, your research history and your forward track records across intact.
AlphaLab for firms
One scoped investigation, about thirty days, taken through the full process to a defended strategy or an honest refutation. That is how a firm relationship starts. From there, a standing desk under your direction, or the Lab Setup program if you have the capital and the theses but no quant capability yet. This is built for small and medium-sized prop firms, funds, CTAs, family offices, and emerging managers. We run a small number of these at a time, so ask about the current slot.
You stop using a tool and start directing a desk. OpenAI or Anthropic can provide the intelligence layer; AlphaLab provides the surrounding research function: licensed and aligned data, the deterministic engine, the validation harness, the role boundaries, the experiment history, and the process that moves a hypothesis to a verdict. Nothing replaces your judgment: the allocation call stays yours.
No. AlphaLab supports systematic research across the full range, from straightforward rule-based strategies, entries, exits, filters, and position rules, through statistical and machine-learning approaches. You bring a trading idea worth testing; the desk runs the research process, and the Mentor explains any method a result uses, written to your level. The validation bar is the same everywhere on that range: independent sign-off before anything reaches you.
Parallelism and the desk. Your team investigates several hypotheses at once instead of one, and skips the layer that eats most research time at real firms: data pipelines, normalization, engine work, execution testing, walk-forward configuration, robustness, and validation nobody can accidentally skip. Your researchers keep the judgment, and the capital stays yours.
You can. The question is what it costs you. Starting from Python, Claude, or GPT still leaves you building and maintaining pipelines, normalizing datasets, wiring backtests and execution, managing experiment history, handling multiple-testing risk, and enforcing the research process yourself. AlphaLab removes that tax. If you'd rather build infrastructure than test ideas, stay in Python. If you'd rather spend your time on hypotheses, that's what AlphaLab is for.
No. Nothing reaches a live account without your explicit authorization. Strategies that pass validation are deployed to paper first and build a forward track record; going live is a separate, gated decision you make. A real edge is what you have that others do not, and anything sold to a crowd stops being an edge the moment the crowd buys it.
US stocks, US futures, forex, options, prediction markets, and crypto. Full markets across all six, plus news and fundamentals.
No. Licensed data is part of the offering, but it is not the core differentiation. The product is the desk: infrastructure, methodology, controls, process, validation, and the ability to run institutional-quality research at scale. The data matters because the desk needs it, not because data alone is the product.
A Firm Desk runs from one researcher to five, with a multi-seat workspace, attribution and sign-off records, and a weekly and monthly cadence; it is contracted rather than checked out, so the researcher count and the terms are agreed with you. Firms start with a free scoped pilot rather than a subscription, and firms with capital and theses but no quant capability yet can take the Lab Setup program. Your Desk is $990/month: one researcher, directed by you, on your own book or on one account before terms. It is the only plan with a list price. Moving from Your Desk to a Firm Desk carries your backlog, research history and forward track records across intact, so nothing is lost by starting small. Already subscribed? Your rate is locked in for as long as you stay subscribed, even when list prices change.