ApeSwap.finance is a decentralised finance platform that provides a comprehensive set of tools for exploring and engaging with the future of wealth creation. It is governed by the ApeSwap Decentralized Autonomous Organization (DAO). The protocol offers asset swapping, lending & borrowing, and staking services to its users.
Indexing Apeswap’s Smart Contracts
The Apeswap DEX has two types of smart contracts associated with it — ApeFactory and ApeRouter. In order to build the Apeswap DEX Analytics dashboard, the Parser indexes events & function data for both Factory and Router contracts. All historical data right from the deployment of smart contracts are synced by the tool.
ApeFactory is the primary DEX contract which is used to create and track all pairs created on the ApeSwap protocol. When a new pair needs to be created, this contract deploys a completely new pair contract specifically for these tokens and notes the address for future lookup.
ApeRouter is an external, non-value holding, contract which manages different sets of interactions with the ApeFactory related to adding/removing liquidity and swapping tokens. Because this contract holds no value it can be upgraded without needing to redeploy the ApeFactory.
In the ApeFactory contract, we track only the event “PairCreated”.
For ApeRouter contract, functions such as “removeLiquidity”, “addLiquidity”, “swapTokensForExactTokens”, and so on, are tracked. The image below shows all the functions for the ethereum chain. Similarly, we track and index these for Binance Smart Chain and Polygon chain as well.
DEX User Analytics
The Unmarshal Parser tracks Apeswap’s contracts across BNB, Ethereum and Polygon chains. We will now explore some noteworthy observations made with respect to user analytics.
User Acquisition & Retention
- Every week, Apeswap receives around 17,000 to 19,000 new users across all chains. There was a gain of around 6.8% this week.
- When we examine the user interaction in the previous week, we can infer that the user retention rate was 13%, i.e, 13% of users from the preceding week returned to the platform, made a transaction, or contributed to the volume.
- 2.8% of all users made transactions in the last 24hours.
The analysis of user behaviour reveals that 70–75% of users have traded just once over the last week, while the other 20–25% users have made 2–10 trades, the final 2–5% of users have made more than 10 trades during the past week.
User Contributions to Trade Volume
An interesting statistic that we observed is that 75–80% of the overall volume transacted over the past seven days is contributed by only 2–3% of all users.
It’s remarkable to note that a single user has interacted with a maximum of 233 pairs, and that a user has contributed a volume of more than $200k through a single transaction.
Frequency of Transactions
Finally, if we solely look at user transactions on a daily basis, we can see that the graph has a skew, with some users completing more than 200 transactions each day while the rest average between 80 and 100 transactions.
All in all, we note that there is regular interaction between users and the DEX along with a consistent, notable increase in the number of new users. It is clear that Apeswap is a preferred platform for asset exchange and gaining popularity daily.
Do you want to build similar dashboards to track user metrics for your dApp? Visit http://unmarshal.io/parser to start now.
For any queries on using the Parser, reach out to us at firstname.lastname@example.org. We also have a dedicated developer hive on Discord. Just drop us a message in the “Parser Support” channel and we’ll get back to you.
Unmarshal is a Multi-chain Web 3.0 data network aiming to deliver granular, reliable & real-time data to dApps, DeFi protocols, NFTs, Metaverse and GameFi solutions. Unmarshal provides the easiest way to query Blockchain data from Ethereum, Polygon, BNB Chain, Avalanche, Fantom, Celo, Solana, and XDC Network. Unmarshal network consists of data indexers and transforming tools to power Web 3.0 applications on any chain while providing a latent view of transformed data.