None of these are zero sum games. Especially stock market. Stock market is not a zero sum game at all. https://money.stackexchange.com/questions/72945/is-the-stock...
Ofcourse, if you are not trading, or buy a company stock and hope to get dividends than there is no transaction and hence it is not zero sum. But if you are trading it is zero sum.
Trading algorithms are often implemented in Python/Pandas so learning hackerrank problems about stacks/queues can make one better at implementing trading algorithms. Some hackerrank problems involve hashes which are verified empirically which calculus. Studying calculus can lead to better understanding of the black-scholes theorem and brownian motion.
There's even an aesthetic nature to really good trades which can lead down an interesting path on gestalt theory and composition. Drawing charts is an art form too. Check out https://www.tradingview.com/u/tntsunrise/ for examples of minimalism in the stock market.
A previous commentor on this topic wrote that I'm studying tea leaves but what if for fun I wrote an image processing algorithm that processed the position of tea leaves in a cup and traded small amounts based on that?
Even something like moving averages has applications in programming language theory. It's easier to implement a moving average in a stateful rather than functional way. It's fun to figure out faster ways of cranking out new algorithms to trade markets which leads to better productivity. Markets move really fast. Old algorithms quickly become outdated.
As long as one does not make huge risks (that aren't well thought out), one can trade based on whatever their imagination leads them to. Anything can be tried and systematically backtested. Most traders won't think outside the box but it's so much more fun when you do.
There's so much synergy between math, computer science, betting strategies, art, drawing, data science and visual theory. Everything one studies in trading can lead to 10 insights in other areas. It's just a genuinely fun topic that can lead to a lifetime of happiness.
For backtesting to work, wouldn't one need to re-run (i.e., playback and not simulate) all inputs from what the real data was (instrument pricing, the weather, social media sentiment, whatever) at a time resolution finer than what your algorithm operates on? If so, that's a massive amount of data that may even be impossible to get.
Backtesting definitely reveals ways in which trading algorithms can fail though. It can reveal more scenarios than one would initially consider which is super helpful.