How to Build a Prediction Market Trading Desk in 2027

Strategy, capital, technology and the challenge of finding traders with a genuine edge.

Over the past year, we've been speaking with a growing number of firms interested in establishing prediction market trading operations.

Some are established proprietary trading businesses with substantial capital, sophisticated infrastructure and experienced quantitative teams. Others are smaller funds or independent investors trying to work out where to start.

The conversations are quite different, but a familiar problem keeps coming up: finding people who have actually demonstrated that they can make money trading prediction markets, and understanding whether their strategies can work with substantially more capital.

We've encountered independent traders generating hundreds of thousands of dollars in PnL with surprisingly small amounts of active capital. At the other end of the market, proprietary trading firms are prepared to offer exceptional compensation packages and capital allocations in the tens of millions to traders with proven, scalable alpha.

There's clearly an opportunity here. But there's also a considerable gap between hiring a profitable trader and building a sustainable trading business.

For firms considering entering prediction markets in 2027, here are the decisions we'd focus on.

1. Why Established Trading Firms Are Paying Attention

Prediction markets have changed considerably from the relatively niche ecosystem they were a few years ago.

Polymarket and Kalshi have helped bring event contracts into the mainstream, while established financial institutions have begun developing trading, liquidity and market-access capabilities around the sector.

Susquehanna, Jump Trading, Galaxy Digital and Cantor Fitzgerald are among the established names associated with institutional prediction market activity.

Their involvement is significant because these firms already understand quantitative trading, market making, execution and risk management. They aren't approaching prediction markets as an entirely new discipline. They're applying existing capabilities to a different set of instruments.

There are similarities with the early institutionalization of cryptocurrency trading, although the differences between the two markets are substantial.

In crypto, many of the first successful trading businesses exploited fragmented liquidity, inefficient pricing and relatively unsophisticated competition. As more professional firms entered, some of those opportunities disappeared, while new ones emerged.

Prediction markets may follow a similar path.

The difference is that event contracts introduce their own complications. Settlement rules matter enormously. Liquidity can be highly concentrated around individual events. And prices may react abruptly to information that is difficult to hedge.

For an established trading firm, the opportunity is worth investigating. But entering the market successfully requires more than adapting an existing trading engine.

2. Start With the Strategy, Not the Hiring Plan

One of the first questions we ask firms considering a prediction market desk is what kind of trading they actually intend to do.

It sounds obvious, but the answer isn't always clear.

Some want to recruit successful independent traders and give them more capital. Others are interested in systematic strategies, market making or cross-venue arbitrage. Occasionally, a firm wants to explore several approaches before deciding where to concentrate.

These strategies require different people and infrastructure.

Strategy	Where the potential edge comes from	Core expertise
Discretionary event trading	Better probability assessments and event analysis	Specialist traders, event-driven research
Systematic trading	Statistical models and repeatable pricing inefficiencies	Quant research, automated execution
Market making	Spreads, liquidity provision and inventory management	Market makers, quant developers
Cross-venue arbitrage	Pricing discrepancies across related contracts	Arbitrage trading, execution engineering
Information-driven trading	Faster analysis of lawful public information	Research, data science, trading

Discretionary Trading

Many successful independent prediction market traders fall into this category.

They specialize in particular events or markets, develop their own probability assessments and trade when they believe prices are wrong.

Some have built impressive track records without formal financial-industry backgrounds.

Their strength is often a combination of specialist knowledge, experience and an intuitive understanding of how particular markets behave.

The difficulty for an institutional employer is establishing how repeatable that performance is.

A trader may have an exceptional understanding of US politics, for example, but that doesn't mean their strategy will generate similar opportunities every month. Nor does it mean the same approach will work in sports, economics or cryptocurrency events.

Systematic Trading

Systematic strategies are a natural area of interest for quantitative firms.

They can involve statistical pricing models, automated analysis of public information, relationships between contracts and other repeatable signals.

The research challenge is interesting because prediction market data is often less standardized than conventional financial-market data.

Historical prices don't necessarily tell you what could actually have been traded. Liquidity varies substantially. Contracts can have unusual settlement conditions, and comparable historical events may be limited.

A model can look excellent in a backtest and perform poorly in live trading.

For that reason, we'd put considerable weight on execution experience when assessing researchers for a new systematic desk.

Market Making

Market making is another natural entry point for established proprietary trading firms.

They may already have the infrastructure required for automated quoting, order management, inventory control and real-time risk monitoring.

What they may lack is experience with the particular behavior of prediction market contracts.

A market maker can find itself holding significant exposure just as important information becomes available. Hedging that exposure may be difficult, especially in thinner markets.

The best setup may therefore combine existing institutional technology with people who understand prediction-market-specific pricing and risk.

Cross-Venue Arbitrage

Price differences between related contracts on different platforms can create trading opportunities.

But apparent arbitrage isn't always genuine arbitrage.

Two contracts referencing the same event may differ in settlement rules, deadlines, fees or collateral requirements.

Those differences can become important precisely when the trade is supposed to settle.

A desk pursuing this strategy needs both strong execution capabilities and a detailed understanding of contract specifications.

3. How Much Capital Does a Prediction Market Desk Need?

This is usually one of the first questions prospective entrants ask.

Unfortunately, there isn't a particularly useful universal answer.

A small discretionary operation and an institutional market-making business have very different requirements.

We'd separate the discussion into two parts: the cost of operating the desk and the capital available for trading.

The Operating Budget

Consider a hypothetical three-person team consisting of a Head of Trading, a Quant Researcher and a Quant Developer.

Expense	Illustrative annual cost
Head of Trading	$250,000
Quant Researcher	$180,000
Quant Developer	$170,000
Employer costs and benefits	$120,000
Data and infrastructure	$60,000
Legal, compliance and professional services	$75,000
Other operating expenses	$45,000
Total	$900,000

These are planning assumptions rather than verified industry averages. Actual costs will depend heavily on location, seniority, regulatory requirements and the firm's existing infrastructure.

The figures also exclude performance bonuses, recruitment fees, trading losses and certain potentially significant legal expenses.

A firm with an established quantitative trading operation may already have most of the technology and operational support it needs. A new business starting from scratch will have to build or acquire those capabilities.

Trading Capital Is a Different Question

Having $10 million available doesn't mean a desk should immediately deploy $10 million.

The amount of capital that can be used profitably depends on the strategy and the liquidity of the markets being traded.

This becomes particularly relevant when assessing independent traders.

Someone who has generated extraordinary returns with a relatively small account may be exceptionally skilled. But if their strategy depends on exploiting small pricing discrepancies, allocating substantially more capital could reduce the returns dramatically.

Before discussing large allocations, we'd want to understand the strategy's actual capacity.

How much capital was employed? How frequently was it recycled? How much of the PnL came from the largest few trades? What happens to expected returns when order sizes increase?

Those answers are often more informative than the headline return.

4. Who Should You Hire First?

There's no shortage of people interested in prediction markets.

Finding someone with a credible live track record is much more difficult.

The relevant talent pool is also broader than it might appear.

We've seen interesting candidates coming from independent prediction market trading, quantitative hedge funds, crypto market making, sports trading and traditional proprietary trading firms.

The backgrounds are different, and each has advantages.

An experienced traditional quant may bring excellent statistical research and risk-management skills but limited knowledge of event-contract settlement or prediction market liquidity.

An independent Polymarket trader may understand those markets exceptionally well but have little experience with institutional systems, team management or large capital allocations.

Neither background should be dismissed.

The question is what the firm already has and what it genuinely needs.

Building Around a Head of Trading

For a new fund without internal trading expertise, recruiting an experienced Head of Trading may be the most sensible approach.

That person needs to take responsibility for strategy, risk, capital allocation and building the team.

It's a demanding profile.

A strong trader isn't automatically a strong manager, and someone who has managed a conventional quantitative desk may not have the relevant prediction market experience.

The ideal combination can be difficult to find.

Building Around an Existing Trading Team

An established proprietary trading firm may have a much easier starting point.

It already has engineers, researchers, risk systems and capital.

In that case, hiring one or two people with deep prediction market expertise may be enough to begin testing strategies.

There's little reason to recreate capabilities the firm already possesses.

The Core Roles

Role	Main responsibility
Head of Trading	Strategy, risk, capital allocation and team leadership
Prediction Market Trader	Generating trading PnL
Quant Researcher	Pricing models, signals and research
Market Maker	Quoting, liquidity provision and inventory management
Quant Developer	Trading systems and execution
Data Engineer	Market data and research infrastructure
Risk / Compliance	Risk oversight and regulatory requirements

These aren't necessarily seven separate hires.

The composition of the team should depend on the strategy, existing infrastructure and stage of development.

5. What It Takes to Recruit Proven Traders

This is probably the most interesting part of the market from our perspective.

Prediction market trading is attracting more institutional capital, but the pool of professionals with established, verifiable track records remains relatively small.

And some of the strongest candidates have little reason to accept conventional employment.

$300,000 Base, $300,000 Sign-On and Seven-Figure Upside

We've seen compensation discussions involving traders from top-tier firms, with only a few years of relevant experience, reaching $300,000 in base salary, another $300,000 in signing bonuses and potential seven-figure performance-based upside.

These are exceptional packages, not representative market averages.

But they illustrate the competition for people with demonstrated trading ability.

In this market, years of experience can be a surprisingly poor measure of value.

A trader with three years of strong, independently verifiable PnL may be considerably more attractive than someone who has spent a decade in quantitative finance without direct ownership of trading results.

Of course, the quality of that PnL matters enormously.

Capital Allocations in the Tens of Millions

We're also seeing proprietary trading firms prepared to make tens of millions of dollars in trading capital available to accomplished traders who can demonstrate genuine alpha and disciplined risk management.

That doesn't mean the full amount is necessarily deployed immediately. Allocations are generally subject to strategy capacity, risk limits and performance.

Still, the scale of capital potentially available is striking.

For an independent trader with a scalable strategy, access to that kind of capital can be more attractive than a large fixed salary.

It also explains why recruitment conversations increasingly revolve around economics rather than job titles.

What capital can the firm provide? What infrastructure is available? How much autonomy will the trader retain? How will profits be shared?

A conventional salary negotiation doesn't fully capture what matters to these candidates.

Remarkable PnL With Very Little Capital

One of the more unusual profiles we've encountered was a prediction market maker who had generated over $400,000 in trading PnL, alongside substantial maker rebates, while operating with remarkably little active capital.

The reported results were impressive, particularly relative to the amount of capital being used.

But for a firm considering an institutional allocation, the next questions are critical.

How much of the reported performance came from trading, and how much from incentives? Were rebates already included in the PnL? How much capital was committed at any given time? Could the strategy accommodate substantially larger orders?

There's an enormous difference between a trader who can make $400,000 exploiting small market inefficiencies and someone who can reliably generate several million dollars with a large capital allocation.

Both can be valuable. They simply represent different opportunities.

The Best Candidates May Not Be Looking

Consider a successful independent trader already making several hundred thousand dollars annually.

They control their own schedule, choose their markets and keep their trading profits.

Why would they give that up?

A larger capital allocation might be attractive. So could superior infrastructure, research support or meaningful participation in a larger trading business.

But some traders will prefer to remain independent regardless of the offer.

This is something firms need to understand before beginning a search. Recruiting an accomplished trader can be closer to structuring a commercial partnership than filling a conventional vacancy.

6. How to Evaluate a Trader's Track Record

Prediction markets have created a culture of public forecasts, leaderboards and PnL screenshots.

Some are impressive. Others tell only part of the story.

For an institutional employer, the assessment needs to go considerably deeper.

We'd concentrate on six areas.

Live trading performance. Ideally, this should be supported by reliable account records, trade histories or independently reviewable data.

Drawdowns. A trader who made $500,000 with a $400,000 drawdown presents a different risk profile from someone who earned the same amount with a $30,000 drawdown.

Capital usage. Reported returns can be misleading when it's unclear whether the denominator represents average capital, peak exposure, account balance or something else.

Repeatability. Did the trader make money consistently, or did most of the PnL come from a few exceptional events?

Execution. How much of the theoretical advantage survived fees, spreads, slippage and market impact?

Scalability. What happens if the firm provides five or ten times more capital?

We'd also spend time understanding how the candidate thinks.

A trader who can explain where their strategy fails, which opportunities they avoid and what changed when a previously profitable approach stopped working often provides more useful information than someone presenting an immaculate equity curve.

Questions We'd Ask

1. What generated most of your PnL over the past 12 months?
2. What was your largest drawdown, and what caused it?
3. What is your edge, and why hasn't the market eliminated it?
4. If we allocated five times your current capital, what would change?
5. Which strategy stopped working for you, and how did you recognize it?
6. How do you account for fees, execution costs and available liquidity?
7. What would another sophisticated trading firm find hardest to replicate about your process?

The answers won't replace proper due diligence, but they help distinguish genuine trading expertise from an impressive presentation.

7. Technology and Infrastructure

A professional prediction market desk needs reliable market data, execution, position monitoring, risk controls and accurate PnL reporting.

Beyond that, the requirements depend on the strategy.

A discretionary trader operating in slower-moving event markets may not need particularly sophisticated automation.

An institutional market maker quoting large numbers of contracts certainly will.

Execution Matters More Than a Beautiful Backtest

Prediction market research has a recurring problem: historical prices aren't necessarily executable prices.

A model might identify a contract worth 60 cents while historical data shows a midpoint of 55 cents.

That looks like a substantial opportunity.

But perhaps the available ask was 59 cents. Perhaps only a small position could have been executed. Perhaps fees consumed most of the remaining advantage.

If the backtest assumes unlimited execution at midpoint prices, the resulting performance may have little relationship to what a live strategy could earn.

We'd want researchers to demonstrate a realistic understanding of order-book depth, trading costs and market impact.

Risk and Settlement

Risk management also needs to account for the particular structure of prediction markets.

Different contracts can depend on the same underlying event. A portfolio spread across several political outcomes, for example, may be considerably more concentrated than it appears.

Settlement criteria introduce another layer of complexity.

Two contracts that seem to reference the same event may have different resolution rules. A trading strategy that ignores those differences can be exposed to risks that don't appear in its pricing model.

Regulatory requirements need to be assessed by qualified advisers before launching, particularly where trading activities cross jurisdictions or involve customer-facing services.

8. Mistakes We'd Avoid

Having spoken with firms at different stages of entering prediction markets, several issues come up repeatedly.

Recruiting before defining the strategy. A mandate to find profitable prediction market traders is too broad unless the firm can explain what those traders will do, what capital they'll manage and how they'll be compensated.

Assuming traditional quantitative experience is sufficient. It can be extremely valuable, but prediction-market-specific knowledge still matters.

Assuming independent trading performance will scale. Extraordinary percentage returns on small capital don't automatically translate into institutional profits.

Overinvesting in technology before validating an edge. Sophisticated infrastructure can improve execution, but the underlying trading opportunity needs to exist first.

Underestimating what successful traders already earn. Some independent candidates have little financial incentive to join an employer unless the opportunity offers substantially more capital or other meaningful advantages.

Treating prediction markets as a single homogeneous category. Sports, politics, macroeconomic releases and cryptocurrency events have different participants, information cycles and trading characteristics.

Most of these issues are easier to address before the first hire than after the desk is operational.

9. What the First 90 Days Could Look Like

A professional trading desk may take considerably longer than three months to establish, particularly if regulatory approvals, infrastructure development or senior recruitment are involved.

But the first 90 days should provide a clearer picture of whether the business is worth pursuing.

Period	Focus
Days 1–30	Define the strategy, assess the market opportunity, establish capital and risk assumptions, review regulatory feasibility
Days 31–60	Recruit or identify founding talent, begin research, establish essential infrastructure
Days 61–90	Test execution and strategy assumptions, evaluate limited live deployment where appropriate, decide whether to expand

We'd resist the temptation to build a large team immediately.

A small group with a clearly defined strategy, realistic execution assumptions and appropriate risk controls can establish a great deal before the firm commits to a larger operation.

And if the research doesn't support the original thesis, that's useful information too.

10. Where the Opportunity Goes From Here

Prediction markets are likely to become more competitive as established trading firms increase their involvement.

Some of the easier inefficiencies may disappear. Better infrastructure and deeper liquidity could make certain strategies less profitable while opening opportunities elsewhere.

The market is still developing, and it's difficult to predict which approaches will produce the strongest returns over the next several years.

What seems clearer from the hiring side is that accomplished traders with demonstrable alpha are already attracting serious attention.

Compensation packages can reach levels associated with established quantitative trading businesses. Capital allocations can run into the tens of millions. And firms are increasingly interested in candidates who combine prediction-market-specific expertise with disciplined risk management and the ability to operate at scale.

For new entrants, finding the right people may prove more difficult than raising the capital to employ them.

The sensible approach is to establish a clear strategy, understand the talent available and build the operation around realistic expectations of what those people can achieve.

Frequently Asked Questions

How much does it cost to establish a prediction market trading desk?

Costs vary substantially by strategy and existing infrastructure. A small professional team can require several hundred thousand dollars or more in annual operating expenditure, before performance compensation and trading capital. Market-making operations may require greater investment in technology and liquidity.

Who should a new prediction market desk hire first?

For firms without existing trading expertise, a proven Head of Trading or senior trader is often the logical starting point. Established quantitative firms may instead benefit from recruiting a specialist who complements their existing research and execution capabilities.

How much do professional prediction market traders earn?

Compensation varies widely. We've encountered discussions involving $300,000 base salaries, $300,000 signing bonuses and potential seven-figure performance-based upside for proven traders from top-tier firms. These are exceptional examples, not industry averages.

Can prediction market traders manage tens of millions in capital?

Some proprietary trading firms are prepared to make allocations of that scale available to accomplished traders with demonstrated alpha. Actual deployment depends on liquidity, strategy capacity, risk controls and performance.

How do you verify a prediction market trader's track record?

Review live trading records, realized PnL, drawdowns, capital usage, execution costs and strategy repeatability. Public leaderboards can provide useful information, but rarely capture the complete risk profile.

Can traditional quant traders succeed in prediction markets?

Yes. Statistical research, probability modeling, automated execution and risk management are highly relevant skills. Candidates also need to understand event-contract settlement, market structure and the specific liquidity characteristics of prediction markets.

How long does it take to build a prediction market trading desk?

A lean pilot can potentially be established within several months, while a larger institutional operation may require considerably longer. Recruitment, regulatory considerations, infrastructure and strategy validation usually determine the timeline.

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Building a Prediction Market Trading Desk?

Prediction Talent specializes in executive search and recruitment for prediction markets, quantitative trading and related businesses.

If you’re building a new desk or expanding an existing team, contact our founder, Michael Shlayen, to discuss your hiring plans, compensation expectations and access to proven trading talent.

Contact Michael Shlayen on LinkedIn or get in touch through Prediction Talent.