[ryankennedyio]

I don't think any retailer can trade on the scale and speed that would require C/C++ over Python. With lower capital and significantly higher transaction costs, retail traders (mostly) need to have holding periods for at least hours, so speed isn't really an issue. It's more about finding longer term anomalies to deploy maybe at most a few dozen positions, as opposed to reacting to billions of quotes over any trading day.

I think Python is a great tool for finance, particularly with libraries like Pandas, Numpy, etc. Most of the work in those libraries is C/Fortan/etc anyway. Great community. For more 'academic' style work/exploration, I think R might have an edge, but I personally find it more difficult to write and maintain high quality code in R.

I'd like to just throw a quick plug in for an open source project I'm working on [1]. It's an event-driven trading library written in Python. We're pretty close to having live trade execution ready in an alpha state.

The motivation is that you can put together a pretty simply strategy in ~40 lines of code. The codebase is designed to be pretty flexible & modular. Mike from http://quantstart.com has some great resources & many of his recent articles use the same library.

[1]: https://github.com/mhallsmoore/qstrader

[MR4D]

Given the network latency of 43ms between, say, Houston, and NYC, you are completely correct.

Source: https://ipnetwork.bgtmo.ip.att.net/pws/network_delay.html