Proposing New AMM Pricing Formula

TL;DR: MCDEX community has proposed an upgraded pricing formula for AMM which enables lower slippage, better capital efficiency.

For Liquidity Providers:

  • Assuming 20% daily turnover, 20% annualized rate of return
  • Limited risk
  • Better capital efficiency

For Traders:

  • 10x better liquidity than current MCDEX V1 with the same funding size (If Beta = 0.1)
  • Lower slippage

AMM, since invented, has become one of the most successful revolutions in the crypto world. The inception of AMM has shifted many people’s way of swapping cryptocurrencies. Last month, MCDEX launched the first-ever decentralized perpetual contract by introducing AMM to perpetual swap, with a goal to shape the future of trading. But we do realize that the current MCDEX V1 formula has space for improvement and upgrades. Since then, the MCDEX community has been actively researching for better solutions. We’ve finally come up with a proposal of the upgraded AMM pricing formula which will enable:

1. Better capital efficiency

2. Lower Slippage

For the same trading volume, if traders need to maintain the same trading slippage, the required liquidity will be reduced.

EX: If the expected slippage = 0.5%, the liquidity required as per the old formula increases rapidly with the demand for transactions.

If the Liquidity Provider provides the same liquidity, the slippage of the new proposal is significantly reduced.

EX: If y = 5,000 ETH (about $1M), Bob wants to buy 500 ETH (about $100k), then slippage will decrease from 11% to 0.25%.

The only trade-off for the new AMM is that since all inventory capital needs to be concentrated near the index price, the new AMM can only provide a limited market-making scope. For example, when the long position in AMM is exhausted, the trader cannot continue to long with AMM. The price range of services provided by new AMM mainly depends on the liquidity provided.

If you are interested in knowing more, please refer to

This is a proposal for the new AMM pricing formula and we would request researchers/friends to comment. Shoutout to our community contributors — Mingda Lei and Tianchi Zhu, who have created the formula.



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