I think thats all you need. ;)

In realiity, trading is hard because it requires 2 points where you have to get the timing right.

First you have buy the position at a time when it is favorable. Then you have to exit the position when you get profit. Sounds easy, but the hard part is that this profit also has to cover any past losses. where you failed timing the entry into the position.

Its easy when the stock goes up 800% and then falls 50% to 400% your original position, that you should have sold when it was at its "peak". But along the way to 800% you had so many times to sell for 500%, 600%, 700%, etc. and along the way the stock had fluctuations with many peaks.

If you sell to early you can't get enough profit to cover past and future losses. If you sell to late same story. So you have to nail the exit position also and that is where most models that rely on past data fail. People just walk through the parameters until the entry and exit positions on their test data line up to make a profit, but then can't replicate when going "live".

Another way to look at investing is everyday you are in the market, it is almost the same logically as selling and choosing to reinvest every day. So if you hold AAPL for 5 years, that is about 1000 days where the algo is choosing to invest (i.e. keep invested) in AAPL. Its pretty tough to have that many decisions points because even 0.1% noise would cause you to sell.