Optimize liquidity provision - identify best liquidity price range given the LP's risk tolerance
For a specified risk tolerance defined as the probability that the liquidity provision position goes out of range, we identify the corresponding price ranges for each pair by fitting a metalog distribution to histric pair prices. These ranges are then used to calculate the range liquidity percentage which is defined as sum of the tick liquidity within the price range divided by the total liquidity of the pair. Range liquidity percentage along with other useful information is presented to the user in the form of a scatterplot and a color-coded table.
How it's made
We leverage the scaffold-eth project utilizing ReactJS with ant-design as a frontend, we use web3.js to connect wallets, and GraphQL to query Uniswap v3 API. We also SIPMath python to simulate chance data and AWS Lambda to calculate and query these numbers across development stacks.