Micro-arbitrage happens when prices briefly drift out of alignment due to noise, latency, or small imbalances in trading. Instead of waiting for obvious errors, traders look for subtle gaps—often just a few percentage points—and act quickly before the market corrects. These opportunities appear and disappear fast.
In prediction markets, micro-arbitrage is common in active markets on platforms like Polymarket, Kalshi, Myriad, and Manifold. Small probability mismatches may appear after minor news updates, orderbook shifts, or AMM price adjustments. In prediction markets data, micro-arbitrage shows up as short-lived deviations followed by rapid convergence once traders step in.
While each opportunity is small, repeated execution can add up over time. Micro-arbitrage rewards speed, discipline, and careful risk control rather than bold directional bets.
Micro-arbitrage helps smooth prices and reduce noise. It improves prediction markets data by correcting small inefficiencies before they grow larger.
They exist because markets are never perfectly synchronized. Minor delays, liquidity shifts, or emotional trades create tiny pricing gaps. These gaps are visible in prediction markets data for short windows before the market self-corrects.
By acting on small inconsistencies, micro-arbitrageurs tighten spreads and improve calibration. Their activity reduces friction and keeps probabilities aligned with available information. This leads to cleaner, more stable prediction markets data.
Analysts can identify which markets are most active, how quickly prices adjust, and where latency or structural frictions exist. Frequent micro-arbitrage activity often signals healthy liquidity and rapid information processing within prediction markets data.
In a highly active Polymarket market, a small probability gap appears after a minor news update. A trader buys and sells within minutes as the price corrects, capturing a narrow spread before the market stabilizes again.
Micro-arbitrage analysis requires high-frequency, time-stamped data. FinFeed's Prediction Markets API provides structured prediction markets data— probability updates, historical curves, and OHLCV —that developers can use to detect small inefficiencies and study rapid price convergence.
