I set out on a journey to learn quantitative trading based on signals. The first thing I did was study linear algebra, calculus, statistics, probability, and deep learning. Four months in, I’m on chapter 11 of a deep-learning time-series forecasting book, working through the problems when the author mentions that, to date (it has changed since), there hasn’t been a published paper proving deep learning works better than traditional statistical analysis—i.e., everything in scikit-learn. That sucked.

So I moved on to the next step. There’s an industry that generates hundreds of millions of dollars in ad revenue from blogs and videos on how to trade. For several months, I tested each of these strategies using a backtesting library, vectorbt. People had blogs and videos about using XGBoost and LSTM with other deep-learning libraries—every single one failed.

There’s so much BS in the industry, and I got sucked into the rabbit hole. At least I’m honest enough not to take advantage of other people. Maybe I’ll start a YouTube channel where I backtest every strategy to show everyone that they all fail—and explain why.

Anything that is related to HFT or day trading is almost impossible in my experience as well. The big funds can do it but I dont think retail has a chance with these approaches.

Have you researched trend or stage analysis? They're slower strategies and dont offer the get rich quick hype, but they do have far better success when done well.