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I love R. You could do it R. But a lot of the derivations and Math Finance stuff you can and should be able to do in C/C++. R packages mostly depend on those as well for heavy duty calcs. So, if I wanted to dabble I'd easily use R and if I was in the quant developer world I'd be doing C/C++
I work with a trading team that manages $1B, exclusively with R.
You do any consulting on non-adjacent areas.
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I work with a trading team that manages $1B, exclusively with R.
Second this, seen lots of funds use whatever language their lead QR/QT feels comfortable with. At the end of the day, if you aren't running a strategy that requires colocation on the exchange, whatever speed improvement you get from the language will usually disappear from the network latency. Something like intraday momentum/sector rotations can easily be done entirely in Python/R, from what I've seen.
i'm an old R user, now migrated fully to python. For those of you who us R still what is your use case? We found R has a really hard time integrating into data pipelines and was best used as a standalone tool by individuals, which doesn't really work in our particular professional setup where everyone works collaboratively together. What we found was that R had alot of packages but most haven't been touched in years…
Earlier quoted context omitted.
Second this, seen lots of funds use whatever language their lead QR/QT feels comfortable with. At the end of the day, if you aren't running a strategy that requires colocation on the exchange, whatever speed improvement you get from the language will usually disappear from the network latency. Something like intraday momentum/sector rotations can easily be done entirely in Python/R, from what I've seen.
Likewise interested if a pro has any consulting hours to spare :)
In your other comment, you said you are looking to price "weird derivatives". How weird are we talking? If its OTC I won't be able to help anyway, if its standard then I can at least try to point you in the right direction. The fact you mention Black Scholes makes me think it might be something closer to "vanilla" than the other way around.
If you have to use R, use the tidyverse.
I like R and use it often as it find it more concise to work with than Python for simple statistical purposes. I forced myself to use R instead of spreadsheets and don't regret it.
This is one the reasons why (thanks, Zed Shaw) https://web.archive.org/web/20110702162929/https://zedshaw.c...
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Plotnine is a pretty rocking ggplot clone in Python. Just import star and you're golden.
We (Posit) have hired Hassan (the maintainer of plotnine) so this is great to hear :)
I dipped my feet into R a few years back, but eventually stopped it because of the way it handles integers. At the time it treated all integers internally as signed 32-bit and if the number is too large for that it converted it to a float. I don't know what R does now, but this was a deal breaker for me at the time because I was dealing with really large integers that regularly broke this limit.
A lot of the stuff above was complaining about issues where Python is a lot worse than R, about non-issues or with a fundamental misunderstanding of the language. I'd given up hope of seeing a real weakness named as such :)
There is bit64 and doubles being used as 53bit pseudo-integers - but if I needed 64bit integers, R wouldn't be my first choice, definitely.