Market Making on Prediction Markets: Beginner's Tutorial

# Market Making on Prediction Markets: Beginner's Tutorial **Market making on prediction markets** means simultaneously posting buy and sell orders on a question's outcome, earning the **bid-ask spread** as your profit every time both sides fill. It's one of the most reliable ways to generate consistent, edge-based income on platforms like Polymarket and Kalshi — even without having a strong opinion on who wins the election or whether Bitcoin hits $100K. This tutorial walks you through the mechanics, real examples, and a practical step-by-step framework to get started safely. --- ## What Is Market Making (And Why Prediction Markets Are Perfect for It)? In traditional finance, **market makers** are institutions — Goldman Sachs, Citadel Securities — that quote two-sided prices on stocks and bonds. They profit not from predicting the future, but from capturing the spread between what buyers pay and what sellers receive. Prediction markets work identically, except the "assets" are binary outcome contracts that settle at $1.00 (YES) or $0.00 (NO) upon resolution. **Example:** On Polymarket, a contract asks: "Will the Fed cut rates in July 2025?" Current market shows: - Best BID (buy YES): 42¢ - Best ASK (sell YES): 46¢ A market maker quotes **both sides simultaneously**: - Posts a buy order at 42¢ - Posts a sell order at 46¢ If both fill, the maker earns **4¢ per share** regardless of what the Fed actually does. At 1,000 shares, that's $40 gross profit from a single round-trip trade. Prediction markets are particularly attractive for market making because: 1. Contracts are **binary** (bounded between 0 and 1), eliminating the infinite downside of stock market making 2. Many markets are **illiquid enough** that spreads are wide — more edge for smaller operators 3. Resolution events are **clearly defined**, so you know exactly when your inventory risk expires --- ## Understanding the Core Math: Spread, Inventory, and Expected Value Before placing a single order, you need to internalize three numbers. ### The Spread Your **gross edge per trade** = ASK price − BID price. But your **net edge** must account for platform fees. | Platform | Taker Fee | Maker Fee | Net Spread Needed to Break Even | |---|---|---|---| | Polymarket | 0% | 0% | Any positive spread | | Kalshi | ~1.5% of premium | ~0.5% of premium | ~2% spread minimum | | Manifold Markets | 0% (play money) | 0% | N/A — good for practice | | PredictIt | 10% of profit | 5% withdrawal | ~15% effective cost | This table reveals something critical: **Polymarket is the most favorable venue for beginners** because zero maker fees mean any spread is pure profit. Kalshi requires wider spreads to remain profitable. ### Inventory Risk When only one side of your quote fills — say, someone buys your YES at 46¢ — you now **hold a short YES position** (you sold a contract you don't own). If the true probability is actually 60%, you're holding a losing position. **Inventory risk** is the biggest danger in market making. The solution is: - Set **position limits** (never hold more than X contracts on one side) - **Skew your quotes** when inventory builds (explained below) - Choose markets where you have at least a rough probability estimate ### Expected Value Calculation For a market making position to have positive expected value: **EV = (Spread/2) − (Probability Error × Contract Value)** If you think the true probability is 44%, and you're quoting 42/46, your midpoint (44¢) exactly matches your estimate — you're indifferent to which side fills. This is the ideal market making setup. --- ## Step-by-Step: How to Place Your First Market Making Trades Here's a concrete workflow for beginners to follow on their first week: 1. **Choose a liquid market with a wide spread.** Look for markets where the best bid and best ask are at least 4–6¢ apart on Polymarket, or 8–10¢ apart on Kalshi (to cover fees). 2. **Form your own probability estimate.** Check external sources: polling aggregators, Metaculus community estimates, relevant news. Your personal estimate is your **anchor price**. 3. **Place a buy (bid) order 2–3¢ below your estimate.** If you think the true probability is 50%, bid at 47¢. 4. **Place a sell (ask) order 2–3¢ above your estimate.** Using the same 50% estimate, ask at 53¢. Your quoted spread is now 6¢. 5. **Set a maximum inventory limit.** For a $500 starting account, cap your exposure to any single market at $100 notional. If you end up holding more than 200 YES shares (valued at ~$100), stop quoting the ask side. 6. **Monitor fill rate.** If you're getting fills on both sides within hours, your spread might be too narrow. If nobody fills for days, your spread may be too wide relative to volume. 7. **Rebalance daily.** Check your inventory, adjust quotes toward the side you're over-exposed to, and re-anchor your probability estimate using fresh information. 8. **Track every trade.** Record entry price, exit price, gross PnL, and fees. This becomes your edge measurement over time. Also consider reviewing [tax considerations for Kalshi trading using AI agents](/blog/tax-considerations-for-kalshi-trading-using-ai-agents) before scaling up — the reporting requirements for market makers are specific. --- ## Real Examples: What Market Making Looks Like in Practice ### Example 1: The Fed Rate Decision Market (Polymarket, 2024) In November 2024, ahead of the FOMC meeting, the "Fed cuts 25bps?" contract on Polymarket showed a persistent 4–6¢ spread for several days before the event. A market maker quoting: - BID: 85¢ / ASK: 90¢ ...could expect roughly 2–4 fills per hour on each side given the market's $800K daily volume. Assuming 50 round trips at 5¢ spread and 100 shares each: **Gross profit = 50 × 5¢ × 100 = $250** over 3 days of active quoting, with near-zero directional risk (since both sides were filling roughly equally). ### Example 2: A Low-Volume Science Market (Kalshi) A "Will CERN announce a major discovery in Q3?" contract sat at BID 12¢ / ASK 22¢ for weeks. A market maker capturing this 10¢ spread: - Buys 200 YES at 12¢ = $24 cost - Sells 200 YES at 22¢ = $44 revenue - Gross profit: $20, minus Kalshi fees (~$0.80) = **~$19.20 net** The risk here was **low volume** — it might take weeks for both sides to fill. The strategy for low-volume markets is patience and tight position sizing. For deeper strategy on science and tech markets, this [Science & Tech Prediction Markets $10K portfolio guide](/blog/science-tech-prediction-markets-10k-portfolio-guide) covers how to size positions when volumes are thin. --- ## Managing Inventory Risk: The Market Maker's Core Skill Inventory management separates profitable market makers from those who bleed slowly. Here are the three main techniques: ### Quote Skewing If you hold 500 YES shares (you're long), you **tighten your ask and widen your bid** to encourage selling and discourage buying. Instead of quoting 47/53, you might quote 44/51. This naturally reduces your inventory without closing positions at a loss. ### Time-to-Resolution Decay Unlike stocks, prediction market contracts approach **certainty** as their resolution date nears. A contract at 50¢ with 30 days left is safer to hold than one with 3 days left. Be more aggressive about clearing inventory as resolution approaches. ### Correlation Hedging If you're long YES on "Biden approval above 40%?" you might want to be short YES on "Democrats win 2026 midterms?" These are correlated markets — being short one partially hedges the other. This is conceptually similar to the strategies covered in [algorithmic hedging with backtested results](/blog/algorithmic-hedging-with-predictions-backtested-results). --- ## Common Beginner Mistakes (And How to Avoid Them) **Mistake 1: Quoting without a probability anchor.** Random quotes around the current midpoint are dangerous — they assume the market is perfectly efficient. Form your own estimate first. **Mistake 2: Ignoring resolution timing.** A contract resolving in 48 hours should have very wide quotes (high gamma risk) or none at all for beginners. **Mistake 3: Over-concentrating in one market.** Diversify across 5–10 markets to smooth out inventory shocks. The [real-world prediction market arbitrage case study](/blog/real-world-prediction-market-arbitrage-small-portfolio-case-study) shows how even a small portfolio benefits from diversification. **Mistake 4: Forgetting that big news kills spreads.** When a major announcement hits, informed traders flood in and spreads collapse. Your quotes may get run over. Cancel open orders during high-uncertainty news windows. **Mistake 5: Underestimating fees on PredictIt.** That platform's 10% profit fee plus 5% withdrawal fee can eliminate market making edge entirely. Stick to Polymarket or Kalshi when starting out. --- ## Tools and Automation for Scaling Up Manual market making works at small scale but becomes unwieldy above 10–15 simultaneous positions. This is where automation matters. **Spreadsheet tracking:** At minimum, use a Google Sheet to log every order: market name, order price, fill time, inventory balance, and daily PnL. **APIs:** Both Polymarket and Kalshi offer public APIs. Polymarket uses the CLOB (Central Limit Order Book) API, which lets you programmatically place and cancel orders. A basic Python bot can monitor your inventory and reprice quotes every few minutes. **Platform tools:** [PredictEngine](/) provides an integrated environment for tracking positions across markets, analyzing spread opportunities, and monitoring your market making performance in real time — without needing to build your own infrastructure from scratch. For an introduction to automated approaches, the [Polymarket bot overview](/polymarket-bot) covers the technical setup in accessible detail. And if you want to layer arbitrage strategies on top of your market making, the [Polymarket arbitrage guide](/polymarket-arbitrage) explains how to capture cross-platform mispricings. --- ## Frequently Asked Questions ## How much money do I need to start market making on prediction markets? You can begin with as little as **$100–$200** on Polymarket since there are no maker fees eating into small profits. A more practical starting amount is $500–$1,000, which lets you spread across 5–10 markets without over-concentrating risk. Scale up only after you've demonstrated consistent positive PnL across at least 30–50 completed round trips. ## What's the difference between market making and arbitrage on prediction markets? **Market making** earns the spread by quoting both sides of a single market — your edge comes from transaction flow, not price mispricings. **Arbitrage** exploits price differences for the same outcome across different platforms — for example, buying YES at 44¢ on Polymarket and selling YES at 48¢ on Kalshi simultaneously. Both strategies are non-directional, but arbitrage requires cross-platform accounts and faster execution. ## Is prediction market market making legal in the United States? It depends on the platform. Polymarket is **not legally available to U.S. residents** due to CFTC regulations. Kalshi is a CFTC-regulated designated contract market, making it fully legal for U.S. traders. PredictIt operates under a no-action letter with restrictions. Always verify your jurisdiction's rules before depositing funds, and consult the [crypto prediction markets tax guide](/blog/crypto-prediction-markets-tax-considerations-guide-2025) for U.S.-specific reporting obligations. ## How do I know if a market has enough volume for market making? Target markets with at least **$10,000 in daily volume** for reliable fill rates. On Polymarket, sort by volume and look for the top 20–30 active contracts. Lower-volume markets can still work but require patience — expect fills to take days rather than hours, and size positions accordingly small. ## Can I market make on political or sports prediction markets? Absolutely — these are often the **most liquid** prediction markets, with political elections routinely reaching $1M+ in daily volume. The tradeoff is higher information risk: a breaking news event can move prices 20–30% instantly, running over your quotes. For political markets specifically, the [Senate race predictions with limit orders guide](/blog/senate-race-predictions-risk-analysis-with-limit-orders) covers how to structure orders to protect against adverse fills during volatile periods. ## How do I measure whether my market making is actually profitable? Track **realized spread** (the average spread you actually captured per round trip, after fees) and compare it to your **inventory loss** (losses from positions that moved against you before closing). Your strategy is profitable when realized spread consistently exceeds inventory loss over a rolling 30-day window. Most beginners take 4–6 weeks of data before this signal becomes statistically meaningful. --- ## Start Market Making With the Right Tools Market making on prediction markets is genuinely accessible to individual traders — you don't need an institutional desk or millions in capital to earn consistent, spread-based returns. The keys are starting small, anchoring every quote to an honest probability estimate, managing inventory before it manages you, and tracking every trade with discipline. As you grow your operation from manual quoting to semi-automated strategies, having the right platform makes all the difference. [PredictEngine](/) is built specifically for active prediction market traders — providing real-time spread analytics, multi-market position tracking, and tools that scale from your first $100 test to a fully systematic market making book. Sign up today and put your first market making quotes to work.