_gsgc's comments

I spent 1 year learning about AI/ML with books and moocs. Then 1 year building a prototype. Then 1 year + 2 months as a team.

Agreed. Good question. Regarding our current approach, my hope is that someday our alpha goes away entirely and we can publish our strategy in detail. But going forward, the plan is to have a trading division that keeps research secret, and a fundamental AI research division (unrelated to trading) that publishes publicly.

We have a hard time believing it ourselves too. I did leave out that around 25M of it is profits reinvested over time and it did do well because we are in a bull market. Binance forces you to hold 11k BNB to be vip9 to get the best fees, we started buying it at around $15, it's worth now around $300 per BNB, and we also hold other cryptos as a side investment. The rest is algotrading profits.

The initial investment was 40k, no other funding rounds or money laundering.

I do not see your email in your profile. (Not OP, but really interested to talk about starting a research institute)

Most of it comes down do conviction and sticking to my not-so-promissing setup for 1 year, while every single of my peers were either thinking WTF are you doing, or telling me why my thing would fail. The rest is luck where we could scale way past what we though was possible.

I stayed focused by creating a penalty system where if I worked for less than 7 hours that day, I'd burn 1$ for every missing hour. I still maintain that spreadsheet with the self-debt over time (that I have yet to pay, but I will by burning crypto). Also my screen saver message is "get back to work"

I think it's both easy and hard, depending how you look at it. Hard because it's a tough ML problem where you have to combat overfitting and the messiness of the real world; easy because crypto is clearly one of the easiest markets to trade and there are opportunities all over the place (compared to equities for example). On top of my head: stat arb, yield arb, exchange arb, funding arb, market making / liquidity providing (CEX/DEX), doing/preventing sandwich attacks, good old yield farming...

There are idealists out there who get all worked up with 'watch the beauty of it'. Yeah in practice it will devolve into blood, sweet and tears, but it matters that you have the right spirit and can appreciate the fleeting moments of beauty

Hi. I went through this journey myself. I have a CS master degree, and always wanted to transition into AI research (focus on probabilistic programming, deep learning and self-play, but also simulation, learning-to-learn, bayesian inference, genetic algorithms). The initial plan was to self-learn machine learning, deep learning and statistics. I did that for 1 year, with the hope of joining on of the big AI labs afterwards. As it's very competitive, I couldn't join one of those labs, and wasn't willing to compromise on that. So I turned to plan B: salvage my ML skills by applying them to crypto trading, and if it works, then I'll have funding to start my own lab.

Plan B worked: in 1 year and 2 months, we turned 40k of our own funds into [redacted]. Today we are a team of 5 and we do $5B of daily volume, around 3% of the global crypto markets. Now the challenge is to start the research division, operate the trading and research in tandem, and create the right incentives: the research converts money into ideas, the trading ideas into money. The research would have two sub-divisions: fundamental and applied to trading.

My motivation for the research is mostly curiosity: I want to de-mystify intelligence (natural or artificial) and reverse-engineer it. When you talk about your lab, I resonate a lot with "watch[ing] the beauty of it". The way I would describe my research agenda is the following: put a bunch of phds in a room, see what happens.

If that sounds interesting, email me at [redacted]