DeFi is one of the most exciting investment innovations since hedge funds emerged in the 1980s, with their promise of uncorrelated, absolute returns – what became known as “alpha.” The inner workings of hedge funds initially seemed inscrutable to those on the outside, but soon a common data set emerged. Information on the strategies, the AUMs, the fees and the performance became aggregated and comparable, and with that a new asset class was born.

In much the same way, investors putting money into DeFi need to be able to look through four lenses of analysis to properly assess opportunity and risk: protocol, pool, asset and wallet. But for this analysis to have utility, the data on each of these platforms must not only be objective, but must also comparative, so that investors can assess each DeFi platform on a like-for-like basis.

Many DeFi platforms and decentralized exchanges (DEXs) operate under a similar premise: Investors provide pairs of tokens that create liquidity so that lending and trading can occur and profits generated. This is relatively simple in concept, but in practice, it’s more complicated. For investors to get comfortable with this complexity, they need data to make informed decisions – otherwise, they’re just guessing.

Specifically, they need global visibility on all wallet, asset, pool and protocol activity, as well as the interactions between each. They also need reliable data on all the protocol actions and governance, including votes, mints, burns, swaps, syncs, skims, flash swaps and flash loans. They also need information on the protocol-generated tokens and what token pairs can be used in which pool.

Use Cases

Financial institutions need reliable data to offer, or transact in, digital asset products and services, but the data is complex, and the infrastructure required is difficult to build and maintain. Digital data firm Amberdata helps financial institutions enter the digital asset class without the need to undertake a massive, proprietary data infrastructure project.

How does this work in practice for institutions in different use cases? Traders generally want to identify and quantify both the current and historical opportunities and risks using series data from across various timeframes. Before starting, traders would want to backtest their trading strategy, and then programmatically trigger trade openings and closings, taking advantage of arbitrage opportunities between centralized and decentralized exchanges (DEXs), as well as between different DEXs or between liquidity pools, and potentially create DeFi vault strategies.

To do all that on just one DeFi platform (e.g., Uniswap V3), they would need to know which pools and their pairs are available to provide liquidity. Then they need to know the current and historical liquidity prices, and the trades (swaps) per pool, OHLCV data, as well as time-weighted average prices and volume-weighted average prices across different pools.

Analysts looking to assess the investment potential of a DeFi platform may look at a different set of data to identify trends and test assertions for DEX, pool and assets also delivered over a range of time series. These would include the total volume and value locked in the DEX, as well as the total fees generated in the last 24 hours. They would want to know the total number of unique addresses that have interacted with the DEX, in addition to those that are currently active. They would need to see who the top trader has been over various time frames and the top liquidity providers sorted by total liquidity, fees earned, total loss and total profit.

They would also need fundamental data on the pools themselves, including their liquidity, fees, pairs by volume, returns on liquidity and the percentage of traders who also deposit as an LP in the pool. They also need specific data pertaining to the DEX itself, including the LP distribution, range orders and information on who is voting within the Uniswap Ggovernance interface.

All this data is available on-chain, but Amberdata turns that raw data into actionable information, aggregating and normalizing it across chains, and processing the data so it can be presented in the four lenses needed to understand DeFi. This gives financial institutions who are entering the digital asset class the ability to build both trading and investing strategies, as well as the supporting accounting and audit work.

Amberdata’s comprehensive data and insights into blockchain networks, crypto markets and decentralized finance empowers financial institutions with historical and real-time fundamental on-chain data for research, trading, risk, analytics, reporting and compliance. Data drives markets, and Amberdata drives DeFi.

To gain a further understanding of how to use data like this to inform DeFi strategies, sign up for CoinDesk’s “Quantifying Opportunities and Risks in Liquidity Protocols” webinar at 12pm on October 3.

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