Word - people don't know shit.

I would agree with this - LLMs would have some "best practice code" that is accessible publicly, but typically institutional implies 1) a myriad of proprietary fine implementation details 2) entire pipelines and systems, that LLMs would have not a clue about. One thing that LLMs can do well is some peripheral juncture that has a feedback/evaluation mechanism without relying on them for core code, such as feature/factor engineering:

Still holiday season vibes in Shanghai as work resumes 🍸✨

Crypto market is not ready for something like this, but here we are on the countdown before new year. #AITrading Merry XMas!

Code review day 👀

Speaking of RL - welcome to check out my whitepapers that utilize RL for feature mining & optimal execution hackworthai.substack.com/api… hackworthai.substack.com/api…

My latest paper (with the help of my team) describes latest methods in adaptive, automated feature construction and evaluation - how to construct features utilizing scalable systems capable of self-optimization and "new alpha" over time. Main approaches described includes genetic algorithmic approach, reinforcement learning-based approach, and LLM-based approach.

Excerpt from the paper: