Prediction markets and outcome trading.

Implied Probability from Token Prices

In a binary prediction market, a Yes token trading at 0.65 implies the market believes there is a 65 percent chance the event will occur. This price is not set by an authority. It emerges from the trading activity of participants who disagree about the probability. The AMM or order book translates that disagreement into a continuous price signal. When the event resolves, winning tokens are redeemable for one unit of collateral and losing tokens become worthless.

For multi outcome markets, each outcome token has a price, and the prices of all outcome tokens in a market must sum to one (minus fees). If they drift apart, arbitrageurs can profit by minting a complete set of outcome tokens for one unit of collateral and selling the overpriced ones, or buying underpriced tokens and redeeming a complete set. This arbitrage mechanism keeps prices honest.

Position Management

Traders buy outcome tokens to express a view. A trader who believes an event is more likely than the market price implies buys Yes tokens. A trader who disagrees buys No tokens. Positions can be closed before resolution by selling tokens back to the AMM or on a secondary market. The profit or loss depends on the difference between the entry price and exit price, not on whether the event ultimately occurs.

Position tracking must handle partial fills, cost basis averaging across multiple trades, and unrealized profit and loss calculations that update as the market price moves. For leveraged prediction positions, the system also needs to track margin requirements, maintenance margins, and liquidation thresholds in real time.

Market Creation and Parameter Setting

Creating a new prediction market requires defining the question, the possible outcomes, the resolution source, the resolution date, the initial liquidity, and the fee structure. Each parameter has downstream consequences. A poorly worded question leads to disputed resolution. Insufficient initial liquidity leads to wide spreads that discourage early trading. A resolution date set too far in the future ties up capital and reduces participation.

We build market creation systems that enforce guardrails. Minimum liquidity requirements, question templates for common market types, mandatory resolution source specification, and preview flows that show creators how their market will behave under different trading scenarios before it goes live.

Resolution Conditions and Edge Cases

Resolution seems simple until it is not. What happens if a sporting event is postponed? What if an election is contested? What if the oracle source becomes unavailable? What if the outcome is technically ambiguous? Every market needs clearly defined resolution rules that cover the expected outcome and the unexpected ones. We design resolution conditions that specify the primary data source, fallback sources, the dispute window, and the procedure for outcomes that do not fit cleanly into the predefined categories.

Dispute Resolution Mechanisms

When a market resolves and someone disagrees with the outcome, there needs to be a structured process for challenging it. We design dispute mechanisms with escalating cost and authority. An initial dispute might require a bond and trigger a review period. If the dispute is upheld, the bond is returned and the market resolution is corrected. If the dispute fails, the bond is forfeited. For high stakes disputes, escalation to a governance vote or arbitration committee provides a final backstop.

The goal is not to eliminate disputes but to make them expensive enough to discourage frivolous challenges while remaining accessible enough that legitimate errors get corrected. The incentive design matters as much as the technical implementation.

Logarithmic Market Scoring Rules

LMSR, originally proposed by Robin Hanson, is the most theoretically grounded pricing mechanism for prediction markets. It provides bounded loss for the market maker, meaning the maximum subsidy required to operate the market is known in advance. It produces smooth price curves that respond proportionally to trade size. And it naturally handles multi outcome markets by pricing a complete set of outcomes simultaneously.

The tradeoff is parameter sensitivity. The liquidity parameter (often called b) controls how responsive prices are to trades. Set it too low and small trades move prices dramatically. Set it too high and the market requires enormous volume to reflect new information. We tune this parameter through simulation against expected trading patterns and adjust it dynamically as market conditions change.

Constant Product Adaptations

For platforms that prefer the familiarity of Uniswap style mechanics, we build adapted constant product AMMs that account for prediction market constraints. The key modification is handling the fact that outcome token prices must sum to one (or the collateral price). Standard x*y=k curves do not enforce this constraint, so we add rebalancing logic that maintains price consistency across outcome tokens while preserving the constant product invariant within each trading pair.

We also implement modifications for markets approaching certainty. When one outcome token trades near zero or one, standard curves produce extreme slippage. We use piecewise curves or virtual liquidity to maintain reasonable spreads even at the extremes of the probability range.

Liquidity Bootstrapping and Incentives

New prediction markets face a cold start problem. Without liquidity, spreads are wide and traders stay away. Without traders, there is no reason to provide liquidity. We design bootstrapping mechanisms that seed initial liquidity through the market creator, incentivize early liquidity providers through fee sharing and token rewards, and gradually transition to self sustaining liquidity as trading volume grows.

For liquidity providers, we design incentive structures that account for the unique risks of prediction market LPing. Unlike standard AMM pools where impermanent loss is temporary, prediction market LPs face permanent loss when the market resolves because one side of the pool becomes worthless. Our incentive designs compensate for this risk through higher fee shares, early exit mechanisms, and resolution insurance pools.

Slippage Management and Depth Optimization

We build slippage protection into every trade execution path. Maximum slippage parameters, partial fill logic, and price impact previews give traders confidence that large orders will not move the market more than expected. For operators, we provide depth visualization tools that show how much liquidity exists at each price level and alert when depth falls below configured thresholds. We think about what happens when a whale shows up five minutes before resolution, and we build the safeguards that keep the market orderly when that happens.