zygomega, I'm one of the zipline maintainers and we'd love to collaborate with you. There are a few people doing HFT research with zipline, and there's a lot of work to do. At quantopian (my day job), we focus on longer hold periods, so there is room in the zipline ecosystem for you to do HFT.

The main benefit we've found with python as the algo language is that it allows for stat programming with pandas, but also OO or functional programming for the algo logic. This smoothes the transition from research to production, just as you're describing with R -> haskell, but you can stay in one language.

I think one of the biggest potential wins with parallelization is if you can assume all positions are closed overnight, most often true for HFT. That way, you can simulate all the trading days in a test range in parallel. This is quite similar to the parallel processing we do to handle the large number of concurrent backtests running at quantopian. We did all of that with python, but I'd be fascinated to see it done with haskell.