PropAMMs earn trading edge by active price updates across blockchains
Active Liquidity On Chain: Evidence from PropAMMs Across Chains
Computational Engineering, Finance, and Science
Summary
Most automated trading systems passively wait for trades to update prices, which can lead to losses when market prices change quickly. The authors studied proprietary automated market makers (propAMMs) that actively update their prices on several blockchains without waiting for trades. These active updates give propAMMs a small but consistent profit advantage compared to traditional systems. The study also shows that propAMMs offer better prices to casual traders who do not move the market, improving trading conditions for typical users.
What this means in practice
- •For blockchain protocol developers: Incorporate active pricing logic from propAMMs to improve AMM efficiency and reduce arbitrage losses on decentralized exchanges.
- •For cryptocurrency traders: Use propAMM insights to identify better pricing and lower slippage opportunities across Solana, Base, and Monad blockchains.
- •For financial product designers: Develop advanced on-chain trading tools that integrate active pricing and risk-charging mechanisms demonstrated by propAMMs.$Commercial implications: Enables creation of proprietary trading platforms offering superior pricing and risk management for digital asset markets.
Authors
Ozan Solmaz, Lioba Heimbach, Jason Milionis
Abstract
The liquidity providers of most typical automated market makers (AMMs) are passive and known to suffer from adverse selection. AMMs traditionally rely on trades executed on them to sync the price with external markets (and a notion of fair value thereof). As a result, when an external fair value moves, arbitrageurs trade on AMMs to pick off their stale quotes. A recent approach termed proprietary automated market makers (propAMMs) emerged as a response in 2024: these on-chain programs instead have their singular operator quote from its own inventory, repricing without a trade via efficiently provided price updates. By 2026, propAMMs accounted for more than half of SOL/USDC volume on Solana. We present the first year-long longitudinal measurement of propAMMs on Solana, Base and Monad, and decode the on-chain logic of the dominant propAMM on Base. We find that two seconds after a fill, propAMMs earn 0.37 bps on Solana and 1.19 bps on Base, while AMMs lose 0.22 and 0.62 bps. We empirically quantify and classify their edge as stemming from four factors: propAMMs continuously reprice rather than waiting for a trade, they charge for the source-dependent risk each counterparty might bring, they avoid cross-venue arbitrage from other on-chain markets (such as other AMMs), and they spoof by filling trades at a worse price than they quote. Finally, we show that on our proxy for retail flow (i.e., fills that arrive when the reference price is not moving) propAMMs collect 0.26 bps on Solana where AMMs collect 2.59 bps, indicating that they offer better prices for short-term uninformed flow.