I am somewhat of an OG. I first joined in 2016 and started staking in 2018/2019. I am incredibly grateful to Numerai and its employees, founders, investors, and advisors. I have learned a great deal from Richard Craib, Marco Lopez De Prado and Michael Oliver.
I have been a long-time believer in Numerai. I bought a decent amount of NMR around $2-5 at a time when there was not that much NMR being staked and there was a lot of negativity surrounding the tournament and crypto in general. My models have been up and down the leaderboard all the way into the top 20 positions. I got started with data science on Kaggle. I suggest people try out Kaggle simply because there is a lot of good information that can be applied to Numerai. Most of my pipeline is inspired from what I learned from the community there.
Recently, I have been playing around with Signals and it has been a lot of fun. I have many ideas but not enough time to implement them because of my day job. If I had it my way, I would just work on Numerai stuff.
At little about myself… I work for a big tech company. I am obsessed with AI and its implications for humanity. I recommend everyone read the book Life 3.0 by Max Tegmark. Also, I have been a longtime believer in TSLA (my stake is up over 1000%). I invest in companies that are innovators in AI. Besides investing and tech stuff I like powerlifting, photography and hiking.
Here is some random advice I have for participants… Run Monte Carlo simulations on payouts to get an idea of how volatility and compounding affects your returns. Focus on the long term, the short term is noisy. Read all of Michael Oliver’s posts and everything in rocket chat. Read everything by Marco Lopez De Prado. For Signals, read everything by SurajP, JRAI and JRB. Do not train on validation or select models based off validation scores. Focus on feature neutrality and experiment with it on your different models. Build a pipeline to train, cross validate, save/load models, ensemble and hyper parameter tune. Ideally, your pipeline should be dataset independent so that it can be used for different projects. If you do not have money or compute resources use Google Colab for free.
Books I recommend: Advances in Financial Machine Learning, Life 3.0, Superintelligence: Paths, Dangers, Strategies, The Black Swan, Hedge Fund Market Wizards, The Man Who Solved the Market, A Man for All Markets, Dune.