The Big Picture

Q69. Let’s look at borrows on Folks.Finance a borrowing and lending protocol built on Algorand. We want to get an understanding of how what assets users most want to borrow, how often borrows are happening, and the average collateral ratio between the collateral offered and the asset that is borrows. Ultimately, we want to get a better understanding of what people are borrowing, and what the collateral ration looks like.

Let's Answer the following questions :

• Chart number of borrow actions by day. Additionally chart the number of borrow actions by day for each asset.
• Chart the volume that is borrowed for each asset by day(USDC, USDt, goBTC, goETH, ALGO), are there any notable borrowing days? What might have caused this?
• Note the average collateral debt position of ALGO(fALGO) to the borrowed asset for each asset. Then create a scatter plot for each asset plotting the collateral ratio for each borrow(x-axis is date) and highlight which ones were above the average collateral ratio and which ones were below. If you have a better way to visualize showing the borrows each day that were above and below the average collateral ratio for the asset show us!

Tips :

• Try borrowing on Folks Finance here: https://app.folks.finance/borrow

• The borrow process is as follows: a wallet will first commit an escrow transaction converting ALGO to fALGO. Then the wallet will place the fAlgo up as collateral and borrow the asset in a separate tx group. We are only concerned with the borrowing action

• Borrow Examples:

  • https://algoexplorer.io/tx/group/AKAaPlaaXqX3A%2FXJjvRPb5rUJ6u%2FXqIQEuGRvXhNTwQ%3D

  • https://algoexplorer.io/tx/group/9rtUuXDx0CMwB6LWsAGONovmhG3PDHRzmYoCb4fOPCU%3D

• Borrow Documentation: https://docs.folks.finance/protocol-architecture/borrow

• The borrowing app_id’s will be the assets poolAppId https://docs.folks.finance/developer/contracts

• To decode and filter borrow application call transactions: TRY_BASE64_DECODE_STRING(tx_message :txn :apaa [0] :: STRING) = ‘b’

Analysis Methodology

In this analysis, based on the guidelines performed, ‌we are looking for transactions in the ‍flipside_prod_db.algorand.application_call_transaction table whose first apaa argument is of type b.

Among these types of transactions, we filter the required tokens based on the APP_ID codes in the Algorand documentation.

Also, due to the fact that the ALGO token had a different transaction structure in the lending section, we need small adjustments to obtain the amount of loans received.

The chart below, which is the result of these filters, shows the number of loans taken out on the Folks Finance platform:

Key Findings

• The popularity of this platform in its early days was higher than the average daily transactions of the last two months.
• On May 19, we see a huge jump in the number of borrowers from Folks Finance, which further shows that this increase in loan demand ‌ has been spent on borrowing USDC.

In the second step, we examine the distribution of borrowed tokens.

• USDC has been the most popular asset demanded by users for borrowing.
• The third most popular is the USDt, which indicates the popularity of stablecoin among users.

In the third step of the analysis, let's look at how much the demand for tokens in Folks Finance has grown over time.

• The popularity of the two stable currencies USDC and USDT has been expanding over time.

Finally, considering the price of tokens in the last two months, we examine the popularity of borrowed tokens in terms of the value of borrowed assets.

• The two USDC and ALGO tokens are ranked first (approximately equal in value) in terms of the value of the borrowed asset.
• Ethereum and Bitcoin have not been much needed by users

The Final Key Point :

Users tend to pledge their assets to borrow stablecoins so that they can invest in or participate in various blockchains and projects.