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.