A few disorganized remarks that may or may not be any help:
- Different people are good at different things. In particular, the algebra/analysis dichotomy is a pretty standard one and if you're good at analysis and not so good at algebra, it probably matters how good you are at what you're best at.
- It seems like simply not being fast enough could be largely irrelevant (if it's really just a matter of speed; the limiting factor in doing mathematical research is unlikely to be how fast you can do practice-test-level questions) or quite important (if what it really means is that you didn't understand the material well and therefore had to flounder about when someone with a better grasp would have headed straight for the solution). You may or may not be able to judge which.
- Motivation is really really important, perhaps more important than talent once the talent is above a certain level. One piece of advice I've seen (specifically in the context of academic pure mathematics) is that you shouldn't become a mathematician unless you couldn't bear not to. Because mathematics research is really hard, and it will kick your ass, and how successful you are will have a lot to do with how you cope when it does.
(My own background: got the PhD, did a couple of years of postdoc, was quite staggeringly unproductive, got out of academia and into industry, have been reasonably happy there. Probably happier than I'd have been as a struggling academic. Most academics are struggling academics, especially for, say, the first 5-10 years after getting their PhDs.)
Some questions you might want to answer for yourself:
- If you go to grad school, get your PhD, and then don't go into academia, is that a good outcome or a bad one?
- If you don't take the academic path, what will you do instead?
- Whichever way you go, regrettably there's a very good chance that you won't end up revolutionizing the world. If you compare possible academic futures with possible non-academic futures, and make the assumption that you do just OK -- which feels like the better outcome?
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I went a similar path (doing physics but not really excelling at it) and ended up a programmer. I'm pretty happy with programming overall. Note that in real-world applications, most of the effort goes into the engineering-like side of making sure your code is clean and maintainable, rather than the comp-sci-like side of having clever data structures and algorithms. It certainly doesn't feel "too easy" most days, though it can sometimes be frustrating when you end up spending time struggling with tools rather than what you're really trying to do.
Perhaps I should've said, hard in the wrong ways. The long term goal for a good professional programmer seems to be understanding what the client wants. Some math is needed to understand the tools, so you can give some context for options. But I spend most of my creative energy making sure my programs do what I want them to do, and that is really hard when each language has it's own prejudice motivating its design.
I seriously considered looking into real time high risk software applications. But I just decided that instead of learning new languages until I ran out of youth, it'd be more fun learning general relativity, or even measure theory. The ideas in those subjects will probably hold out a lot longer then python.