Programming skills are math skills. For example, how would you prove that your code is the fastest possible for a given task? A hunch? A stopwatch? Would it make a difference if you used a different language or machine? What if you could objectively compare the algorithm performance itself without conflating factors? What if you could prove it to others without having to simply trust that you are right? This is math. This is why you can't solve the halting problem or lossless compress past the Shannon limit no matter how hard you try. But it's also why a simple fractal contains an infinite universe of complexity. Don't be afraid of math, embrace it! Find some good teachers/books and maybe you'll find that math isn't some arcane priesthood setup by mathematicans, but that it's the language of nature, the most practical and precise way we have of explaining and understanding these things.
I had to laugh when I saw my response next to @getsat's. Both are valid viewpoints. In other words, not everyone who uses tools has to know how to make tools.
Tool makers have more insight into the assumptions behind the tools they create -- they might know how a tool was meant to be used and what its limits are. That being said, there are times when using a screwdriver as a hammer is expedient and does no harm. Of course, there are other times where using Black-Scholes to model a high-volatility market outside of the 'smoothly differentiable' market assumptions it was built on can crash a large part of the economy. It can be a dangerous game to use someone else's tools without knowing their assumptions.
2) Read this essay "A Mathematician's Lament" which points out the math learned in school is likely not really mathematics and that surprisingly math is not practical but aesthetic -- mathematics is closer to art than we are taught:
http://www.maa.org/external_archive/devlin/LockhartsLament.p...
I'm no algo trader, but I used to think the same way you do about math. I found that studying physics really gave me that first appreciation for how awesome math really is, and how it can be used to model so much of our world.
The same could be said by mathematicians about code. They look at a hash map and might think it is cool that you have O(1) lookups, but have no idea how it matters.
With experience and practice with theoretical math, you also learn how to apply it.
Pardo's book is ballin'. Walk Forward Analysis is the key to a successful trading strategy. There isn't really any math in it.
I haven't really had much use for math in algo trading so far. I get ideas from looking at historical chart data, code up a base algorithm which implements an idea, and use genetic algorithms to find optimal values for range-bound variables (e.g., a float between 0.995 and 0.997) in the "optimization" step of WFA. I then run WFA across my defined in and out of sample periods on historical tick data directly from my broker (a few gb per year per symbol).
The most complex math I've done in trading so far has been writing some R scripts to generate pretty graphs. It's a stretch to call that "math", though.
WFA completely changed the game for me. I was aware of curve fitting and tried to avoid it before, but after reading about WFA it was like the wool covering was removed from my eyes...
