> I have always kept in mind is that feature engineering is almost always the key difference between success and failure I also developed an ML-powered service

> I have always kept in mind is that feature engineering is almost always the key difference between success and failure I also developed an ML-powered service heavily relying on feature engineering https://github.com/asavinov/intelligent-trading-bot https://github.com/asavinov/intelligent-trading-bot Intelligent Trading Bot Its difference from Didact is that this intelligent trading bot is focused on trade signal generation with higher frequency of evaluation. It is more suitable for cryptocurrencies but also works for traditional stocks with daily frequencies so it could be adapted for stock picking. What I find interesting in your work is the general design of such kind of ML systems relying on feature engineering.