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May 12, 2026
Bacaan 5 minit

Prediction Market Arbitrage: Hidden Costs, Fees, and the Real Math

#prediction markets
#arbitrage
#Polymarket
#fees
#DeFi
#risks
#cross-chain

Prediction market arbitrage

The idea sounds beautiful: buy "Yes" on one platform for $0.40, buy "No" on another for $0.55, and collect $1.00 for a total outlay of $0.95. Five percent risk-free.

In practice, three layers of costs, several non-obvious risks, and different resolution rules across platforms stand between you and those 5%.

Three Layers of Costs

The iceberg of hidden costs

1. Platform Trading Fees

The key trap: many DEX platforms use dynamic fees that depend on event probability.

Platform Taker Fee Maker Fee Payout Fee Notes
Polymarket 0–1.8% (peaks at 50/50) 0% + rebate 0% Geopolitics markets: 0%
Limitless 0.03–3% 0–1.5% Lower fee for likelier outcomes
Predict.fun 1–2% (standard) Often zero-fee promos + cashback
Opinion 1–2% Fee goes to LP pool
Kalshi Fixed Regulated exchange, USD, not Web3. US residents only

2. Network Gas Fees

Network Platforms Gas per TX Native Token
Polygon Polymarket Fractions of a cent MATIC / POL
Base Limitless, Opinion $0.01–0.05 ETH
BNB Chain Predict.fun $0.03–0.10 BNB

3. Cross-Chain Bridges

Cross-chain infrastructure

Bridge fees: $1–5 per transfer + percentage. Time: 1–15 minutes (spread may close while you wait).

Three Critical Risks

1. Slippage

AMM worsens your price on large orders due to thin liquidity.

2. Resolution Risk — Where 90% of Arbitrageurs Burn

Same event, different platforms, different resolution rules and data sources. Your positions can lose on both sides simultaneously.

3. Timing Lag

No atomic cross-chain execution. Second leg may execute after the spread closes.

Minimum Spread for Break-Even

Cost Item Approximate
Platform A fee (taker) 1.0–1.8%
Platform B fee (taker) 1.0–2.0%
Gas (4–6 transactions) $0.10–0.50
Slippage (both legs) 0.5–2.0%
Total 3–6%

Profitable arbitrage realistically starts at 7–10% spreads.

Premarket.me — Aggregator for Arbitrage

Manually monitoring spreads across five platforms is impractical. Premarket.me solves this: it's a prediction market aggregator with a unified API that combines data from Polymarket, Limitless, Predict.fun and other platforms.

The key endpoint — /api/arbitrage — returns pre-calculated pairs: where to buy "Yes", where to buy "No", total cost, profit %, APR adjusted for time to expiry, and liquidity depth in USD.

Open-Source Arbitrage Bot

A ready-made Go arbitrage bot built on the Premarket API: suenot/premarket-arbitrage-bot.

Scanner (Premarket API) → Filter (profit/APR/depth) → Display → Executor (Polymarket CLOB)

The bot polls /api/arbitrage, filters pairs by configurable thresholds (MIN_PROFIT_PCT, MIN_APR, MIN_DEPTH_USD), and optionally executes trades via Polymarket CLOB with EIP-712 signing. Default mode: DRY_RUN=true (monitoring only).

Links

Conclusion

Prediction market arbitrage is not "free money." It's systematic work with three layers of costs, cross-chain logistics, and resolution risk that can wipe out both legs of a trade. Minimum spread for break-even is 5%; for profit — 7–10%.

But the key insight: mechanical arbitrage alone is not enough. The real edge comes from building AI agent analytics around each specific bet: real-time news monitoring, tracking social signals, analyzing on-chain activity of large players, and correlating with macro events. The spread between platforms is a symptom; the cause is information asymmetry. The winner is the one who understands that event probability has shifted — not the one who clicks the button fastest.

Pre-funding wallets, maker orders, and API automation are table stakes. The real edge is in the analytical layer on top of market data.

Penafian: Maklumat yang disediakan dalam artikel ini adalah untuk tujuan pendidikan dan maklumat sahaja dan bukan merupakan nasihat kewangan, pelaburan, atau dagangan. Dagangan mata wang kripto melibatkan risiko kerugian yang ketara.

Pengarang

Eugen Soloviov
Eugen Soloviov

Trading-systems engineer

Trading-systems engineer building bots since 2017: cross-exchange arbitrage (connected up to 30 venues), cointegration-based pairs arbitrage across spot and futures, scalping, news and sentiment-driven strategies, trend algorithms, and portfolio management and balancing algorithms. Also builds sub-millisecond order execution, big-data warehouses, backtesting engines, AI agents, and trading interfaces (incl. open-source profitmaker.cc). Stack: JS/TS, Python, Rust/Zig/Go, DevOps, backend, frontend, architecture.

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