Web3 Startup Analytics

[video] To God be the glory. I’m grateful to have completed my MSc in Data Science. Now it’s time to use what I’ve learned to solve blockchain problems through data.

[image] My friends think this is what I was taught during my Master’s in Data Science in the UK. It wasn’t, survival taught me this. Build something that can solve a real problem. Build something that can at least make me money daily. This used to feel impossible. Until I started

[image] Guess who just followed me ? 👀

[image] A moving man will surely meet his luck. Another day of yapping what I built.

[image] When @AxisRobotics announced the AXIS Points snapshot, I was genuinely excited. After the recent $12M funding announcement, I thought we were about to see another major spike in contributors and trajectory submissions. As someone who enjoys studying on-chain behaviour, it felt

[image] After reaching a milestone with my Telegram alert bot and seeing promising win rates, I became obsessed with a different question: What actually makes a token become a runner? The deeper I went into on-chain data, the more I realized there were many behaviors happening every

[text] One of the new philosophical analyses Aladdin will be testing is IFAGRITHM. I’ve spent this week researching IFA, not from a religious perspective, but from the perspective of information systems. From what I’ve studied, Ifá isn’t interesting because it claims to predict the

[text] Another research hypothesis I’ve been exploring since the weekend: Some wallets consistently take less risk while capturing a good amount of upside asymmetries. So I started finding the humans behind some of these wallets through social engineering. And what did I notice?

[text] I think I might be obsessed with data. Or maybe it’s the upside asymmetry hidden in the trenches. Every day, thousands of tokens launch. Most fail, some don’t. I’ve become less interested in finding the next winner and more interested in this question: Can repeatable

[text] Looking at one wallet is research. Looking at 100,000 wallets is analytics. Building labels others use is infrastructure. Think of a supermarket. 100,000 customers walk into your shop. Some only buy when there’s a discount. Some always buy premium products. Some buy every

[image] The spirit of the addiction provides for the addiction I’m addicted to only one thing. What are you addicted to?

[image] I analyzed 10,797 wallets across 155 migrated tokens on Pumpfun in last 30 days. I wasn’t looking for “smart money.” I was testing a different idea: Can wallet behavior be used to identify repeatable trading strategy ? Using a behavior-based labeling framework, I identified

[text] If you’ve been stuck looking for a job for months, stop looking for more advice. Treat your career like an experiment. Write down at least five competing hypotheses for how opportunities are created in your field. For instance; One meaningful relationship beats 100

[image] One of the best thing we can have in CT right now is if @axisrobotics can have Outlier type of campaign Web3 is for everyone so training physical AI should be for everyone I’ve been tracking contributor activities on chain and I can see how everyone has been performing

[text] I have come to the realization that Web3 experience is geographical, personal, and contextual. Web3 works on a timing system, which makes it impossible for individuals to have the same experience. People don’t experience the same market, even when looking at the same chart.

[image] One thing I’ve learnt from crypto/on-chain is this: If you can track money movement, where it’s coming from, where it’s going, and where it finally lands, you’ll also land there too. The difference is, you got there earlier and with a better entry than 90% of people. And the

[image] The trench looks random until you start filtering behavior instead of narratives. Building datasets across market cap ranges is making one thing obvious: The best runners leave different on-chain signals before the chart explodes.

[text] One thing about crypto: You can do research and fail. You can do research and keep it a secret. You can bring a model that predicts and gives insights to 90% of Web2 companies and still watch it fail in Web3. I’ve had research ideas that looked brilliant at the start. By the

[image] Shoutout to @nansen_ai for the API credits 🤝 I’m currently researching Solana on-chain winners, identifying profitable wallets and studying how they actually make money across the ecosystem, beyond just memecoin trading. The work involves segmenting wallets by behaviour:

[image] Data in Web3 is only useful if it leads to better outcomes. These are a few alerts and their outcomes (2x–3x+). The goal isn’t to find every winner, but to reduce the chances of losing.

[image] Aladdin scanned all migrated tokens in past 24 hours. Detected patterns based on volume that happened before migration. What question would you like to ask @TradeAladdin in today’s market edge?

[image] My previous research reduced uncertainty and consistently found 2x–3x opportunities. But I wasn’t satisfied. I wanted to understand why some tokens stop at 2x while others become 5x, 10x, or much larger. So I went back to the data. Removed assumptions and refined my research.

[text] From my own on-chain analysis, one thing I’ve realized about “smart wallets” is that they’re only considered smart based on the platform labeling them. Different analysts and data scientists are behind each platform, and each team defines “smart” differently. For example, the

[text] not everything needs to be optimized for trading, sometimes convenience matters more, and @SimpleSwap_io fits that for me

[image] One thing I discovered during my wallet research is that knowing a wallet is profitable doesn’t tell you why it’s profitable. Knowing a wallet made money doesn’t tell you how it made money. While researching profitable wallets, I kept running into the same problem. I’d find a

[image] One thing trading taught me is that outcomes are rarely random. Wallets repeat behavior., Tokens repeat behavior, Markets repeat behavior. The goal isn’t predicting the future. The goal is identifying structures that historically produced better outcomes than average. A

[image] GM GM 💛 The mistake people make is thinking good bot predicts winners. Reality: Good bot finds situations where winners occur more often than average. No model predicts the future. The edge comes from identifying repeatable on-chain behaviors that historically lead to