Q66. From May 4th to May 10 we saw over a million wallets being created. What are these new wallets doing on Algorand?

Q74. Track stablecoin activity on Solana since April 1st (not limited to but look into USDC, USDT, UST, USDH, UXD, PAI). What have been the flows between coins and what was the result of the UST depegging in the Solana ecosystem? What types of wallets hold a USDH, UXD, or PAI compared to a USDC and what are users doing with those coins?

Q73. Catalina Whales are a popular NFT collection on Solana with an experienced team from the entertainment industry. Chart their sales history and unique holder trends over time.

Q78. What kind of pools do user usually withdraw or deposit into for the last 90 days? is there any pattern/trend we can see from it?

Q1. How does the daily volume of DAI transactions compare to other stablecoins? What is DAI most used for?

Q2. How does the volume on MakerDAO (in terms of DAI minting) change as more collateral tokens are added to the platform? Which tokens have impacted volume the most?

Q89. Which pools have paid out the most IL protection in the last week? Month?

Q88. With highly volatile prices recently, more IL protection has been paid out on THORChain. How do recent days or weeks compare to historical amounts?

Q79. Find the top 5 Kashi pairs on Ethereum based on TVL. Choose one that you like and describe the following information:

• How many people are borrowing on this pair at the moment ?
• How much collateral has each user added to the pair?
• How many users have already partially paid their loans?

Q80. Find the top 5 Kashi pairs on Ethereum based on the number of loans they have given out so far. Choose one that you like, and describe the following information:

• How many people are lending on this pair at the moment ?
• How much each user is lending at the moment?

Q67. This week we saw the depeg of UST(a stablecoin built on Terra) and it sent shockwaves through all blockchain ecosystems causing the crypto market to crash. STBL is a 100% fully collateralized stablecoin created by the AlgoFi protocol. Let’s look at how the circulating supply of STBL was impacted by the events from May 8th to May 14th.

Q68. Folks Finance enables borrowing and lending on the Algraond Blockchain. We are going to look at the deposits and redemption of the assets on the protocol. We want to get a better understanding of how wallets are depositing and withdrawing assets over time and how Folks Finance is growing.

Q75. Track the flows of MNDE being deposited into liquidity pools across the Solana ecosystem. What protocols have seen the most MNDE deposits over time in liquidity pools (hint look into Orca, Rayidum, Serum, anything else?).

Q76. How many total new NFT projects have appeared on Magic Eden per day since the start of the new year (as in new projects that have minted that day)? Compare that to the total number of new NFT projects that were minted/created on Ethereum?

Q4. How impactful is the stability fee (the interest rate) on DAI loans in terms of the impact on volume and dollar amount of loans taken out? How does activity with DAI tokens change when the stability fee is different?

Q3. Analyze large shifts in the amount of DAI on the market over the past year. What might have caused these shifts? Are these events related to Maker directly? Are they related to the crypto ecosystem as a whole? Provide metrics to support your analysis.

Since Sushiswap introduced polygon, many have adopted polygon to either swap or use other services.

With this in mind, what is the contribution of Sushi to the growth of Polygon user base?

Q73. Visualize the realized APY of LP-ers that have removed liquidity from THORChain. What was their actual APY vs HODL (accounting for impermanent loss) when you pro-rate it for how long they were LP-ing for? Are certain pools out-performing others?

Q86. Create a way of identifying bots within THORChain (could be something like a certain number of swaps / day made). What % of the swaps and swap volume are made by bots? Are there certain strategies you can see them employing (most of them are likely arbitrage bots)? Are there many bots or mainly just a few that conduct a high % of bot activity?

Q87. A few weeks ago we did a question on standardized TVL. Now we’d like to see a similar analysis looking at swap volume in a way that neutralized the impact of price changes. Feel free to look at this any way you’d like, but one idea is to fix asset prices at their current value and apply that price historically to all the tokens that were swapped. Show this metric by week or month (or both!)

Q79. For each pool, show the swap volume and the liquidity after its first week going live (you can use the first swap date to determine when a pool went live)

Q2. The past week has been an eventful time for stablecoins on Osmosis. Proposal #218 passed on May 8th, which signals community support for adding OSMO rewards to the OSMO/USDC (#678) pool. In addition, a massive sell-off of UST has caused it to depeg from its price of $1. How have these events affected the liquidity and volume in the USDC and UST pools on Osmosis? How many unique wallets have transferred liquidity from the UST pool to the USDC pool, and what other impacts have been felt on Osmosis liquidity pools as the effects of UST’s depegging continue to reach into the wider crypto markets?

Q1. In the aftermath of UST’s depeg from $1 and Luna’s price descent, economic security for the IBC network has become a concern. The liquidity on the UST/OSMO pool on Osmosis (#560), for example, could be at risk of theft if an entity were to acquire a controlling share of Luna voting power. What kind of risks exist for any tokens paired with UST or Luna on Osmosis? How much LUNA/UST is required to execute such a heist in the current market, and are there any wallets in position to pull it off?

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.