Everyone can try to do it, but you need to spend the time and educate yourself like in any other profession or hobby and the first step is to go to eBay and search for historical stock market data, you can buy 20 years of data for less than $100 and you can test all trading ideas for free and without losing a single penny...the barrier for entry is very low, some Python knowledge + Linux machine and the data and off…
If you are going to use machine learning on the data, though, make sure you know what you are doing unless you are just using someone else's complete package. It's really easy to screw up machine learning. I recall an example given in a class I took. (I may be misremembering the details, though). Some people were trying to apply machine learning to currency trading. They had a bunch of data. They normalized the data…
You cannot normalize the entire dataset at once; the normalization contains some parity effectively allowing the algorithm to cheat and see the future... but in real life we can't see the future.
The simple example that eliminates some of the "black magic" is that normalizing against the entire set lets the algorithm know what the highest and lowest points across the entire data set are - and knowing the lowest and highest lets the algorithm buy low/sell high for all of the known data.