Transaction routing and orchestration More on reddit.com

I’ve personally done a fair amount of ML work in the investing space. One thing that can save you a bunch of time is to focus less on forecasting and more on portfolio construction. (You can build models for both.) The real problem with forecasting, in my humble opinion, is that markets aren’t stationary. Also, unless you’re doing day trading, you have limited signals to train off of (like daily price data). I’m not saying forecasting is impossible, but it’s tricky. Folks I know that have worked in the algo trading space are often building new models every week or so, because that’s about how long they might reasonably expect their model to work, before the market dynamics change. Trading off your algorithm influences the market, so good algos don’t last. So, I’d start with portfolio theory, and learn that, then separately learn ML, and use ML to help with portfolio construction. Adaptive algorithms, like genetic algorithms, can be particularly useful, since markets aren’t stationary. More on reddit.com

Here are some of the Beginner Machine learning Projects to do: Handwriting recognition with neural networks Breast cancer classification House price prediction Stock price prediction Emotion recognition Image recognition More on reddit.com

It’s a mixed bag. Dixon is good, Bilokon lacks a bit but knows how to explain and Halperin is quite a bit too full of himself and just assumes finance and physics are the same. For a brief scan his stuff is okay. If you find the book, read some sections but I don’t think it’s worth buying. More on reddit.com