I'm going to break down the complete roadmap to become an institutional level Quant for Prediction Markets. I'll also share the exact resources and step by step path that works.
Let's get straight to it.
I recently came across a tweet of Susquehanna International Group.
One of the largest quantitative trading firms in the world. They were hiring a Senior Trader for their prediction markets desk.
Around the same time, I was talking with someone at a large institution who told me something mind blowing: they have more capital available than crypto markets have profitable opportunities to deploy it into and this clears one thing that the demand for quants specifically in the prediction markets space is real and growing fast.
Bookmark This -
I’m Roan, a backend developer working on system design, HFT-style execution, and quantitative trading systems. My work focuses on how prediction markets actually behave under load. For any suggestions, thoughtful collaborations, partnerships DMs are open.
When I was 16, I had zero understanding of how probability and mathematics actually worked in real markets. Today I lead systematic trading strategies in prediction markets at an institutional level. This happened because I followed a structured path from complete beginner to understanding the mathematical frameworks, technical execution and market microstructure that institutions use to extract edge systematically.
By the end of this article, you'll understand what quantitative trading actually means in prediction markets, the complete mathematical and technical foundation you need, how to think about market structure and execution and the realistic path from knowing nothing to building the skills that make you valuable. Even if you don't know what quantitative analysis means right now or have zero background in mathematics, this roadmap will take you from ground zero to having a complete framework for systematic trading in prediction markets.
Note: If you're looking for quick wins, this isn't for you. This requires dedicated time and serious level focus over an extended period. Only if you're ready to commit to the full process you should read till the end.
What is a Quant and Why Prediction Markets Need Them ?
Before understanding how to become a quant, you need to understand what a quant actually is.
A quant uses mathematics, statistics, and programming to find systematic edge in markets. Not intuition. Not gut feel. Mathematical frameworks that identify mispricing, calculate optimal position sizes, and execute strategies that generate returns through systematic processes.
Quants build systems that analyze thousands of markets simultaneously, size positions using probability theory, and execute trades automatically based on predefined mathematical criteria.
Prediction markets need quants because the markets are systematically inefficient in ways that mathematical analysis can identify and exploit.
TradFi compressed most inefficiencies decades ago through quantitative arbitrage. Prediction markets are young. The mathematical infrastructure doesn't exist yet. This creates opportunity for systematic approaches.
Institutions and hedge funds are building dedicated desks because the edge is real, the mathematical frameworks work and systematic execution at scale generates consistent returns. What's missing is people who understand both the theory and the practical implementation.
Phase 0: Mental Model Reset
Before math. Before code. Before anything.
You need to stop thinking about prediction markets the way 99% of people think about them.
Most people see Polymarket and think: betting platform. Place a bet. Hope you are right. Collect.
That mental model will keep you broke.
Here is what prediction markets actually are -
They are continuous Bayesian updating machines. Every trade is a piece of information. Every price change is the market revising its collective belief about the probability of an outcome based on new evidence arriving in real time.
They are orderbook microstructure systems. There is a bid side and an ask side. Liquidity providers and liquidity takers. Informed flow and noise flow. The same mechanics that govern equities and futures govern every single contract on Polymarket.
They are probability calibration datasets. Every market that resolves gives you a ground truth data point. At $0.30, did that event actually happen 30% of the time? That is a testable, measurable question with a real answer across millions of historical trades.
They are sentiment compression layers. Political events, sports outcomes, macro developments. Polymarket compresses all the noise of public information into a single number between 0 and 1.
Take Polymarket as the specific example.
Price = crowd posterior probability
Orderbook = liquidity supply curve
Resolution = Bernoulli outcome (exactly 0 or exactly 1, nothing in between)
Read this phase again until those four definitions feel completely obvious.
That is the foundation everything else is built on.
Phase 1: Build the Mathematical Foundation
This is where most people make the mistake of skipping ahead. They want to jump straight to coding a bot. That is how you build something that loses money systematically instead of making it.
The mathematics here is not complicated. But it needs to be understood deeply, not memorized.
Probability from first principles
A prediction market is a probability machine. Every price you see is the market's best estimate of the likelihood that an event resolves YES. If a contract trades at $0.35, the market believes there is a 35% chance the event happens.
Your job as a quant is to determine whether that estimate is correct. If the market says 35% and your model says 50%, that is edge. That gap between market probability and true probability is where all the money comes from. Not luck. Not timing. That gap.
Start with conditional probability.
The probability of A given B has already happened. Written as:
P(A|B)
This matters most in prediction markets because outcomes are never independent. If Trump wins Pennsylvania, what does that tell you about Wisconsin? These correlations create arbitrage that only conditional probability thinking can find.
Most retail traders ignore this entirely. That is exactly why they lose.
Bayes' Theorem
This is the single most important concept for a prediction market quant.
In plain English: you start with a prior belief about something. You observe new evidence. You update your belief rationally and mathematically.
In practice this means: a new poll drops. An injury is announced. A key political figure makes a statement. Your model does not just react to this emotionally. It updates the probability estimate in a precise, calculated, systematic way based on exactly how much that new information should shift the prior belief.
This is how you build models that do not panic and do not overreact. Pure Bayesian updating, every single time.
Expected Value
Every trade decision reduces to one number.
EV = (Probability of WIN × Profit) − (Probability of LOSS × Loss)
If a YES contract pays $1 on a 40% probability event and costs $0.30 to buy:
EV = (0.40 × $0.70) − (0.60 × $0.30) = $0.28 − $0.18 = +$0.10
Positive expected value means trade. Negative means do not.
The entire game is estimating that probability more accurately than the market. Everything else is secondary.
The Kelly Criterion
Kelly tells you exactly how much capital to put on any given trade for maximum long-run growth without risking ruin.
f* = (p × b − q) / b
Where f* is the optimal fraction of capital, p is your win probability, b is the net odds received, and q is the probability of losing which is simply 1 − p.
A 10% edge means bet 10% of capital for maximum long-run growth. Bet more and you will eventually go broke even with a genuine edge. Bet less and you leave compounding returns on the table. Kelly is not a suggestion. It is a mathematical theorem about optimal capital allocation.
The institutional application goes much further than this textbook formula. You never bet full Kelly in practice. You bet fractional Kelly because your probability estimates carry uncertainty. More on this in Phase 3.
Game Theory
Most people building prediction market systems never study game theory. That is a massive mistake
Prediction markets are not just you versus an objective market. They are you versus other participants who are also actively trying to extract edge. Informed traders. Noise traders. Other market makers. Arbitrageurs. Everyone is playing a strategic game simultaneously.
The Nash Equilibrium concept is foundational. In any market, a state exists where no participant can improve their outcome by changing their strategy unilaterally. Understanding where that equilibrium sits tells you where the exploitable edges are. They are always found away from equilibrium.
The Prisoner's Dilemma framework helps you understand why market makers sometimes widen spreads simultaneously. Why arbitrageurs sometimes delay trading. Why informed traders disguise their order flow patterns. These are all game theoretic behaviors. Recognizing them in live order flow is a skill you build by studying the theory first.
Naive Bayes Classifier
The model:
P(outcome | features) ∝ P(outcome) × ∏ P(feature_i | outcome)
In practice this means: given a set of observable signals like poll data, historical patterns, market momentum, news sentiment, what is the probability the event resolves YES? Naive Bayes gives you a fast and transparent baseline probability estimate that you refine with more sophisticated models over time. It is where most quantitative probability models begin before getting more complex. Do not skip it because it seems too simple. Its simplicity is its advantage at the early model-building stage.
Information Theory and Entropy
Shannon entropy measures uncertainty directly.
H = −∑ p_i × log(p_i)
In prediction markets, entropy is a direct measure of how much edge is available in a given market. A contract near $0.50 has maximum entropy. Maximum uncertainty. Maximum room for your model to add value if it is better calibrated than the crowd. A contract near $0.95 has near-zero entropy. The outcome is almost certain. The remaining edge is small and the adverse selection risk from informed traders is extreme.
Understanding entropy tells you which markets are worth making markets in and which ones to avoid entirely as resolution approaches.
Phase 2: Understand Market Microstructure
Math alone does not make you money. Understanding the machine you are operating inside is what separates people who backtest well from people who actually profit.
In Phase 3 you will build the models that sit on top of everything you learn here. But models built without microstructure understanding fail in live markets. Every time.
I have personally read the Polymarket documentation more than 50 times. Each reading reveals something invisible in the previous one. That is how deep this goes.
The order book and adverse selection
The bid-ask spread exists for one reason: some people trading against you know more than you do. A market maker quoting $0.60 bid and $0.64 ask is saying the 4 cents of spread compensates me for the times an informed trader already knows the true probability is 80%.
A spread that suddenly widens means someone just received information you do not have. That signal alone is actionable.
Minting and merging
When no one is selling YES tokens, the system mints a new YES and NO pair by locking $1 USDC as collateral. The reverse destroys equal YES and NO tokens and returns $1 USDC. This enforces one invariant at the smart contract level:
P(YES) + P(NO) = $1.00
When this breaks across correlated markets, guaranteed profit exists.
A recent research paper found 41% of all conditions on Polymarket showed exploitable mispricing at some point.
The hybrid architecture
Orders are signed off-chain and matched by Polymarket's centralized operator. Settlement happens on-chain via Polygon in approximately 2-second block times. Gas costs average $0.007 per transaction.
The critical implication: the bottleneck is not placing orders. It is canceling them. Your ability to cancel stale quotes before an informed trader fills them is the single most important latency metric in the entire system.
Fee structure and rewards
Most markets carry 0% fees. Fee-enabled markets use:
fee = baseRate × min(price, 1 − price) × size
Polymarket distributes approximately $12 million annually in liquidity rewards. Two-sided quoting earns roughly 3 times the rewards of single-sided quoting. Rebate income is a meaningful component of total returns when operating across hundreds of markets.
Phase 3: Build Quantitative Models
Once you have the mathematical foundation and the microstructure understanding, something genuinely shifts.
You start to see edges everywhere. A price that looks completely reasonable to a casual observer looks obviously mispriced to you. A spread that looks tight suddenly looks dangerously thin given the current information environment. The way you process a market changes completely. That perspective shift is Phase 3 beginning to work on you.
The Avellaneda-Stoikov Framework
Published in 2008, this became the foundation of modern quantitative market making. Every serious market making desk uses some version of it.
The reservation price formula:
r = s − q × γ × σ² × (T − t)
Where s is the mid-price, q is your current inventory position, γ is your risk aversion parameter, σ² is market variance and (T − t) is time remaining until resolution.
Long inventory means reservation price drops below mid. You want to sell more than you want to buy more. Short inventory means it rises above mid. You want to buy.
The optimal spread:
δ = γσ²(T − t) + (2/γ) × ln(1 + γ/κ)
Two terms. Two sources of edge. The first compensates for inventory risk and scales with volatility and time remaining. The second is pure liquidity provision profit that persists even with zero risk aversion.
For prediction markets the framework requires modification. Prices must stay between 0 and 1. Use the log-odds transformation:
logit(p) = ln(p / (1−p))
This maps the bounded price space to the unbounded real line where standard diffusion models apply correctly. Prices are guaranteed to stay in (0,1) by construction. This is the same sigmoid function at the heart of every neural network. It is not a coincidence.
Empirical Kelly with Monte Carlo
The textbook Kelly formula assumes you know your edge with certainty. That assumption is wrong in practice.
When your model estimates 6% edge, that is a point estimate with a distribution of uncertainty around it. Standard Kelly treats that 6% as ground truth and leads to systematic overbetting and eventual ruin even when a genuine edge exists.
The institutional solution builds an empirical return distribution from historical data and then runs Monte Carlo resampling to generate 10,000 alternative path scenarios by randomly reordering the same historical return sequence.
After simulating 10,000 paths, position size targets the 95th percentile drawdown across all scenarios. Not the median. The bad luck case.
f_empirical = f_kelly × (1 − CV_edge)
Where CV_edge is the coefficient of variation of edge estimates across simulations. High uncertainty means an aggressive haircut to position size. The difference between naive Kelly and empirical Kelly with Monte Carlo is the difference between probable ruin and steady compounding.
VPIN and toxicity detection
VPIN measures imbalance between buy and sell volume over rolling windows:
VPIN = |V_buy − V_sell| / (V_buy + V_sell)
When buy and sell volume are balanced, flow is noise. When they diverge sharply in one direction, someone with private information is trading aggressively. When VPIN rises sharply, widen spreads immediately. When it continues rising, withdraw quotes entirely. The adverse selection cost near resolution dominates every other consideration. There is no spread wide enough to justify quoting into fully informed flow.
The loop that makes this addictive
Get a new strategy. Backtest it on historical data. Measure the results honestly. Find exactly where it breaks. Improve it. Repeat.
This loop is genuinely addictive to me. Every single iteration teaches you something new about how the market actually behaves versus how you thought it behaved. The gap between those two things is where all the edge lives. That gap never fully closes. Which means the loop never fully ends.
I keep posting the institutional-level math and quant strategies here. Follow along. Each article in this series goes deeper into the frameworks that actually extract systematic edge from prediction markets at scale.
Phase 4: Build Technical Infrastructure
The mathematics means nothing if the infrastructure cannot execute it in real time. This is where theory becomes a production system.
In Phase 5 you will deploy this system into live markets and start the real measurement loop. But infrastructure built poorly makes every other phase irrelevant.
Start with Python. Then go further.
The large institutional players run Rust and Go. That is the right answer when you are running hundreds of millions in capital against 20-person quant desks.
But that is not where you start.
Start with Python. Build your models. Prove your edge exists in backtests. Understand the system deeply before you optimize it. Once your model proves edge, once you understand exactly what your system needs to do at every step, then you pivot to Go or Rust. At that point you are translating something you already understand deeply into a faster runtime.
That sequence makes you genuinely unstoppable. The person who learns Rust first but does not understand microstructure builds a very fast system that loses money very quickly.
Real-time data architecture
Production market making requires WebSocket connections, not polling. In a market where arbitrage windows have compressed from 12 seconds in 2024 to 30ms in Q1 2026, a 500ms REST polling interval is not a disadvantage. It is elimination from the game entirely.
Track sequence numbers on every message to detect gaps. A single missed update means your local order book diverges from reality. You start quoting stale prices. An informed trader fills you. The loss is locked in before you detect the error.
Kill switch architecture
GTD orders auto-expiring before known high-impact events are your passive protection. The active kill switch calls cancelAll() immediately on VPIN spikes, position limit breaches or any error condition. This is the difference between a bad day and a catastrophic loss event that wipes months of returns.
Latency and server infrastructure
Test different server regions. Measure actual latency to Polygon's RPC endpoints from each location. The differences are real. Use AWS KMS for private key management. Never store private keys locally in a live system. The cost of a single compromised key is orders of magnitude larger than any latency gain from insecure handling.
In the next few days this market becomes purely bot versus bot at millisecond level. The competitive frontier is already moving from "can you run a bot" to "can your bot cancel stale quotes faster than incoming informed orders." Building for that reality now is not premature optimization. It is reading where the market is heading.
Phase 5: Deploy, Measure and Compete
Deploy with minimal capital first. Prove the system works in live conditions before scaling anything. Track execution success rate, fill quality versus theoretical fair value and P&L broken down by spread capture versus adverse selection losses separately. If adverse selection losses are growing relative to spread capture, your VPIN detection is failing. Fix that before adding a single dollar more.
Get new strategy. Backtest it. Find where it breaks. Improve it. Repeat. This loop never ends and honestly it should not. The market evolves, competitors improve, edges compress. The people who win long term are not the ones who find one edge. They are the ones who build the system that keeps finding new ones.
Resources
Phase 1: Mathematics
Probability Theory: The Logic of Science by E.T. Jaynes
Kelly Criterion original paper by J.L. Kelly Jr. (1956)
Thinking in Bets by Annie Duke
Phase 2: Microstructure
Polymarket CLOB documentation
Glosten and Milgrom, Bid Ask and Transaction Prices (1985)
Gnosis Conditional Token Framework documentation
Phase 3: Quantitative Models
Avellaneda and Stoikov, High-Frequency Trading in a Limit Order Book (2008)
Easley, López de Prado and O'Hara, Flow Toxicity and Liquidity (2012)
Jon Becker's 400 million trade Polymarket dataset
Phase 4: Infrastructure
Polymarket CLOB client on GitHub
AWS KMS documentation
Polygon network RPC documentation
We are at 9,811 followers.
The people who read articles like this and actually do the work are rare. Most will bookmark this, feel motivated for 48 hours and move on.
A small number will open the Jaynes book, pull the Becker dataset, read the Polymarket docs until the microstructure clicks and start building.
That small number is who I write for.
If you want to be in that group, follow. Every article in this series goes deeper. The math gets harder. The edges get more specific. And the people still reading by the end of each one are exactly the people who will extract real money from these markets while everyone else is still guessing.
The window is open. Not forever.
Math Series continues...
