On Monday, 22 December 2014 at 12:24:52 UTC, Laeeth Isharc wrote:
In case it wasn't obvious from the discussion that followed:
finance is a broad field with many different kinds of creature
within, and there are different kinds of problems faced by
different participants.
High Frequency Trading has peculiar requirements (relating to
latency, amongst other things) that will not necessarily be
representative of other areas. Even within this area there is
a difference between the needs of a Citadel in its option
marketmaking activity versus the activity of a pure delta HFT
player (although they also overlap).
A JP Morgan that needs to be able to price and calculate risk
for large portfolios of convex instruments in its vanilla and
exotic options books has different requirements, again.
You would typically use Monte Carlo (or quasi MC) to price more
complex products for which there is not a good analytical
approximation. (Or to deal with the fact that volatility is
not constant). So that fits very much with the needs of large
banks - and perhaps some hedge funds - but I don't think a
typical HFT guy would be all that interested to know about
this. They are different domains.
Quant/CTA funds also have decent computational requirements,
but these are not necessarily high frequency. Winton Capital,
for example, is one of the larger hedge funds in Europe by
assets, but they have talked publicly about emphasizing
longer-term horizons because even in liquid markets there
simply is not the liquidity to turn over the volume they would
need to to make an impact on their returns. In this case,
whilst execution is always important, the research side of
things is where the value gets created. And its not unusual to
have quant funds where every portfolio manager also programs.
(I will not mention names). One might think that rapid
iteration here could have value.
http://www.efinancialcareers.co.uk/jobs-UK-London-Senior_Data_Scientist_-_Quant_Hedge_Fund.id00654869
Fwiw having spoken to a few people the past few weeks, I am
struck by how hollowed-out front office has become, both within
banks and hedge funds. It's a nice business when things go
well, but there is tremendous operating leverage, and if one
builds up fixed costs then losing assets under management and
having a poor period of performance (which is part of the game,
not necessarily a sign of failure) can quickly mean that you
cannot pay people (more than salaries) - which hurts morale and
means you risk losing your best people.
So people have responded by paring down quant/research support
to producing roles, even when that makes no sense.
(Programmers are not expensive). In that environment, D may
offer attractive productivity without sacrificing performance.
I agree with most of these points.
For some reason, people often relate quant finance / high
frequency trading with one of the two: either ultra-low-latency
execution or option pricing, which is just wrong. In most
likelihood, the execution is performed on FPGA co-located grids,
so that part is out of question; and options trading is just one
of so many things hedge funds do. What takes the most time and
effort is the usual "data science" (which in many cases boil down
to data munging), as in, managing huge amounts of raw
structured/unstructured high-frequency data; extracting the
valuable information and learning strategies; implementing
fast/efficient backtesting frameworks, simulators etc. The need
for "efficiency" here naturally comes from the fact that a
typical task in the pipeline requires dozens/hundreds GB of RAM
and dozens of hours of runtime on a high-grade box (so noone
would really care if that GC is going to stop the world for 0.05
seconds).
In this light, as I see it, D's main advantage is a high
"runtime-efficiency / time-to-deploy" ratio (whereas one of the
main disadvantages for practitioners would be the lack of
standard tools for working with structured multidimensional data
+ linalg, something like numpy or pandas).
Cheers.
