Chain Dominance
This analysis focuses on Sushiswap's dominance across various chains, including Ethereum, Optimism, Arbitrum, Avalanche, Polygon, and BSC. It covers aspects such as the number of users, transaction volumes, and liquidity providers.
In this analysis, you will learn about Sushiswap's dominance in the chains of Sushi supports. I have examined various aspects of Sushiswap, including the number of users, transactions, and trading volumes on popular chains like Ethereum, Optimism, Arbitrum, Avalanche, Polygon, and BSC.
I have also compared Sushiswap's performance on Avalanche, Optimism, and Arbitrum with other popular chains such as TraderJoei, Velodrome, and Camelot.
It's important to note that Uniswap is currently the most popular DEX, so I have also compared Sushiswap and Uniswap on different chains. Lastly, I have looked closely at the liquidity providers operating on these chains.
Sushiswap is a decentralized cryptocurrency exchange (DEX) that operates on the Ethereum blockchain. It was created in 2020 as a fork of Uniswap, another popular DEX. Sushiswap offers a unique "liquidity provision" feature, which allows users to earn rewards for providing liquidity to the exchange's pools. It also has its own native token, SUSHI, which is used for governance and provides incentives for liquidity providers. Sushiswap has since expanded to support blockchains like Polygon, BSC, Avalanche, Arbitrum, and Optimism.
Sushi Dominance: This part was relatively straightforward. Flipside provided tables for Sushiswap on different chains, from which I extracted the relevant parameters. It's worth noting that some swap volumes were missing from the Flipside tables, resulting in some empty days.
Sushi vs. Popular DEXs on different chains: In this section, I compared Sushiswap on Arbitrum with Camelot, Sushiswap on Avalanche with TraderJoe, and Sushiswap on Optimism with Velodrome. All three of these platforms are popular DEXs on their respective chains. I obtained their contract addresses and extracted the swap activities. The most challenging aspect was calculating the swap volume in USD, as no price tables were available for Arbitrum and Avalanche. Therefore, I had to convert the swaps to USDC and extract token prices.
Sushi vs. Uniswap: Similar to the previous section, I extracted swap activities on Uniswap for each chain using the relevant contract addresses.
LPs: I only calculated the number of liquidity providers and their associated transactions due to table limitations. I extracted transaction hashes from the Event table and joined them with the label tables to obtain all LPs that went through Sushiswap and Uniswap.
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