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andro
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Python and Numpy, for code overfilled with "if"s Cython. And just to be precise I'm talking about code "vectorization" and "parallelization". But the same applies to CUDA.
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Unless you are executing the same process for many tickers ;-) From simple view of the world and our actions to it - yes it is not really possible to do stuff in parallel. But when you move a bit into what is really going on - you'll find a lot of things for parallel computing Anyway back to topic - CUDA is usually about heavy C programs. If your are able to do that - than try first to write backtest for your data on your CPU in C... maybe you will find the performance sufficient.... / like me, I was planning to the same. Then I found that by using optimized libraries and 4 CPU cores my speed up was :haha: 250x