This Guy Made $3.5M in 33 days from $25K: Mevcollector

This interview was published in In The Loop course.

An interview with @mevcollector, he turned 11 ETH into 862 ETH in 33 days back in Sep '21 flipping NFTs using a data platform called Nansen, here is some advice from him 2 years later that is still applicable now 👇

I asked him some questions about he did it:

1) What were the main data points you looked at to figure out what to buy/sell?

"Early on simply following "Smart Money" wallets on Nansen was optimal, but soon everyone had the "Smart Money NFT" tag because everyone was minting everything, so it wasn't as good as a metric anymore.

From then it shifted to tracking demand through mint volume and sweep volume, using icytools and nftnerds to see what to mint and buy. Deciding when to sell depends more on volume and holder count; there is a limited amount of participants rotating through these projects, so most projects end up stalling out when they reach a max point of attention/inflow (~3-4e floor for most degen stuff, ~10-15e if exceptional).

Buying/selling still was heavily influenced though by who or what group was minting or discussing a project and trying to front-run that demand and exit into the liquidity (e.g. if punks/kongz etc. were buying). Outside of these metrics, looking at the current driven narrative and trying to front-run what could be a possible narrative or fork was another important factor (e.g. the loot/text saga and its forks, cc0 meta, gremplin meta, dao/treasury meta)."

2) How much was your trading driven by data versus gut feeling?

"Majority of trading was definitely data-driven. Early on as the market was more about flipping and just rotating to the next project (e.g. during the art blocks phase, free mint phase after loot), the aforementioned data metrics were vital. Later on, trades were still data-driven, but I would say that the narratives mattered more and the data was more of a supplement to decide when to sell."

3) What is one piece of advice you'd give to people on how to use data platforms correctly?

"Many data platforms have come out since Nansen for both NFTs and on-chain analytics in general, there is an abundance of data and noise that makes misinterpreting data very likely. Even commonly believed metrics may be false (e.g. recently Solana TVL) and this is especially true in NFTs as wash trading and faking activity has grown more significant.

I would say the best use of these platforms is keeping it simple and using them to track activity (wallets/hot contracts). Things always start onchain before threads pop up on Twitter because people have to look onchain to see what is going on. Using dashboards like Nansen's Hot Contracts or general alerts to see activity, can give a significant edge."

4) Did any previous life events lead you to data-driven trading or what made you make the push?

"I think maybe trading stock/options for a while made me look for data-driven plays. Trading in general whether or not it's NFTs, there is lots of noise and volatility, so data can build your conviction/thesis and help your execution. Data for NFTs and crypto' in general, is also much easier to interpret, like for options you could look at call/put flows or big blocks but you can't really tell if the order is to hedge or maybe some IV strat. In short, I guess I was always looking for a way to find a way to build conviction in the chaos that is trading, and NFTs/crypto data, especially in the bull, worked."

What We Can Learn From This

The Evolution of Metrics

The trader began their journey by relying on "Smart Money" tags on Nansen, but quickly adapted as the metric lost its efficacy. The shift towards analyzing mint and sweep volumes via tools like icytools and nftnerds reveals the trader's ability to pivot with market changes. The trader's emphasis on volume and holder count as sell indicators reflects a nuanced understanding of market saturation points, usually at ~3-4e floor for 'degen' projects and ~10-15e for standout ones.

Front-Running and Narrative-Driven Trades

The trader engages in front-running strategies, particularly when recognizable entities like 'punks' or 'kongz' enter a project. They also pay attention to the market's prevailing narratives, making bets on potential narrative shifts or forks like the loot/text saga and various metas (cc0, gremplin, dao/treasury). This highlights the blend of data and story in decision-making, with the latter gaining prominence in later stages.

Data-Driven Conviction

While gut feeling plays a role, the trader leans heavily towards data-driven decisions. Prior experience in stocks and options trading seems to have instilled a belief in the power of data to cut through market noise and volatility. In crypto and NFT trading, the trader finds the data more straightforward to interpret, aiding in decision-making and conviction building.

Navigating Data Platforms

As for advice on using data platforms, the trader warns against the dangers of data misinterpretation given the abundance of metrics and the rise of wash trading. They advocate for simplicity, suggesting that platforms like Nansen should primarily be used for tracking activity via hot contracts and wallet metrics. This, they argue, provides a significant edge as it surfaces on-chain activity before it becomes a Twitter trend.

Actionable Insights

1. Adapt Metrics Over Time: Don't stick to a single metric; be ready to adapt your data points as market dynamics change.
2. Blend Data and Narrative: Use data to validate your trade, but also consider the larger market narrative. Look to front-run trends and narratives, not just prices.
3. Understand Sell Indicators: Know the limits of market saturation and use data like volume and holder count to time your exit.
4. Simplicity in Data Platforms: Use data platforms for actionable insights like tracking wallet activity and hot contracts, but be wary of data noise and manipulation.
5. Build Data-Driven Conviction: Use data to build your trading thesis, especially in volatile markets where noise can cloud judgment.

In summary, this trader exemplifies a balanced, yet data-intensive approach to NFT trading, underscored by a willingness to adapt and a keen understanding of both numerical and narrative market dynamics.