Introducing: The State of LATAM Crypto Markets

Deep Dive: EthCC Edition





Welcome to Deep Dive!

Today we have a special edition of our research dedicated to EthCC, which in our view is one of the most qualitative conferences in the industry (and not just because it takes place in Paris).

Over the course of the week, there were hundreds of technical talks exploring DeFi, privacy, security, gaming, rollups, token engineering, and so much more. While Vitalik’s talk on “Account Abstraction” was the star of the show, the Kaiko team came a close second with three technical presentations exploring the below market concepts. Let’s dive in!

  • Why Liquidity Data is Crucial to DeFi Stability

  • The Black Box of Centralized Exchanges

  • Unlocking LP Profitability in Uniswap V3

Why Liquidity Data is Crucial to DeFi Stability

Presented by Anastasia Melachrinos

Where does liquidity reside in crypto markets? This question is extremely critical when considering DeFi protocols, which often leverage price feeds sourced from centralized exchanges (CEXs). Our data shows that liquidity is not evenly distributed across CEXs and decentralized exchanges (DEXs), which has created many opportunities for exploit.

Taking the two largest stablecoins as examples, USDC is primarily used in DeFi and thus can be considered to have greater liquidity on DEXs compared to CEXs. Meanwhile, USDT is primarily used on CEXs, although it still has relatively large volumes on DEXs.

However, this analysis specifically focuses on CRV, the native token of the DEX Curve, not only because it has been a hot topic on Crypto Twitter but also because its liquidity is heavily concentrated in just a handful of markets. Additionally, CRV is extensively utilized as collateral by Curve’s founder, Michael Egorov, for his lending position on Aave.

The data shows that CRV is relatively illiquid on CEXs compared with DEXs. Total Value Locked on Curve for the CRV-ETH pool is $50mn while on CEXs, 2% market depth on the bid side aggregated across all exchanges is just $1mn.

The lack of CEX liquidity is an acute risk for all CRV positions, especially on lending protocols, as it means that any large price movement could cause liquidations. This is because price oracles often leverage price feeds sourced directly from CEXs, thus making these markets a target for manipulation.

In fact, notorious DeFi exploiter Avi Eisenburg attempted to exploit CRV’s illiquidity on CEXs, and almost succeeded. Eisenburg attempted to crash the price of CRV on CEXs which would have caused liquidations on Aave. Although the attempt failed, today there remains 6X more CRV deposited as collateral on Aave as there is liquidity on CEXs and DEXs, thus risk still remains.

Ultimately, centralized and decentralized markets are tightly linked, and due to the illiquidity of many altcoins, there are many possible ways to exploit these markets.

However, by having access to liquidity data for both CEXs and DEXs, token holders, lending protocols, oracles, and money market protocols can build better barriers against manipulation. For example, Pyth in partnership with Kaiko has built the first liquidity oracle for DeFi, which aggregates CEX liquidity for a token.

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The Black Box of Centralized Exchanges

Presented by Clara Medalie

Paradoxically, CEXs have become some of the last true havens of anonymity for crypto traders. This is due to the rise of on-chain investigators, both individual and enterprise, who use increasingly complex analyses to trace blockchain addresses to real-world entities. While tokens can still be traced into and out of an exchange, what happens within is often a mystery. This is because all CEX market data is anonymous.

However, just because the data is anonymous, does not mean that it is impossible to gain powerful insights. This presentation explores three examples of how CEX data can be used to combat bad behavior, focusing on: wash trading, U.S. trading activity, and oracle exploits.

Wash trading is enabled by exchanges that have lax market monitoring and trading surveillance. In fact, the latest SEC lawsuit against Binance.US alleges that the exchange had no trade surveillance until February of 2022. Thus, this type of manipulation is still a problem in crypto markets.

However, using market data, we can try and identify suspect exchanges. The volume to market depth ratio assumes that exchanges with high trade volume should also have very deep order books, so any outlier exchanges could be enabling wash trading.

We took this ratio for 20 exchanges, and found two big outliers: Bitforex and Binance. Binance is an outlier because they offered several zero-fee trading pairs at the time of the analysis, which can instantly cause volumes to surge. This investigation thus mostly focuses on Bitforex.

If we dive deeper into Bitforex’s tick-level transactions, we instantly notice suspicious activity. Where we see an overlap between red crosses and green circles, this means a buy and sell trade happened at the exact same moment in time at the exact same price. We did not notice this trend on exchanges like Coinbase and Kraken.

This is a very obvious example of wash trading, but some bad actors could be using more complex methods. Ultimately, it is the exchange’s responsibility to ensure that this type of behavior does not take place.

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Unlocking LP Profitability in Uniswap V3

Presented by Naomie Halioua

Uniswap V3 is by far the most liquid decentralized exchange in crypto markets, possessing ~90% of Ethereum-based trade volume. At times, Uniswap’s trade volume has even surpassed Coinbase’s. Yet, Uniswap’s market data can be quite complicated for traders to understand, especially due to the DEX’s unique liquidity model.

This presentation explores how traders can maximize alpha by leveraging Uniswap V3 liquidity data, which includes mints, burns, and liquidity pool snapshots. Liquidity providers (LPs) can place liquidity within certain price ranges, but this often needs to be monitored and adjusted to avoid experiencing impermanent loss. There are also multiple fee tiers to choose from, adding to the complexity of trading strategies.

Liquidity providers seek to maximize profits by optimizing the fee structure and liquidity provision within a specified range, considering their price predictions and their share of liquidity compared to the total available in the pool. And this needs to be done all while avoiding impermanent loss (IL). On Uniswap V3, liquidity providers suffer IL only when the current price of the asset crosses their LP price range. The example below looks at various LP positions around the current price for a token, and how IL (VP-VH) can be avoided.

The full presentation explores an example of how to use Kaiko’s Uniswap V3 data to build a profitable LP strategy.

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Data Used In This Analysis

  • DeFi Liquidity Data

    Mints, burns and liquidity pool snapshots.

  • Order Book Data

    Bids and asks for an asset across all exchanges.

  • Trade Data

    Tick-level transaction data from 100+ exchanges

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