One area where D has the edge

[Laeeth Isharc via Digitalmars-d]

> Out of curiosity, what is lacking in the current commercial 
> offerings for hedge fund management? Why not use an existing 
> engine?

In the general sense, lots is lacking across the board.  I 
started a macro fund in 2012 with a former colleague from Citadel 
in partnership with another company, with the idea that they 
would provide infrastructure as they had experience in this 
domain.  I should not say more, but let's say that I was not so 
happy with my choice of corporate partner.  This experience made 
me think more carefully about the extent to which one needs to 
understand and control technology in my business.

One of the things that was striking was the very limited set of 
choices available for a portfolio management system.  Macro 
involves trading potentially any liquid product in any developed 
(and sometimes less developed) market, so it doesn't fit well 
with product offerings that have a product silo mentality.  One 
uses a portfolio management system very intensively, so user 
interface matters.  But very few of the offerings available 
seemed even to be passable.  We ended up going with these guys 
who have a decent system because it was spun out of a hedge fund 
but if you asked me about passable alternatives, I do not know if 
there are any.  http://www.tfgsystems.com/

There are of course specific challenges for macro and for startup 
funds that may not be generally true of the domain - it is a big 
area and what people need may be different.  Larger funds use a 
combination of third party technologies and their own bits, but I 
am not sure that everyone is perfectly happy with what they have. 
  I formerly jointly ran fixed income in London for Citadel, a big 
US fund, so have some background in the area.  Things changed a 
lot since then, and I certainly wouldn't want to speak about 
Citadel.

It's a funny domain, because the numbers are more like a large 
business, but there are not all that many people involved.  
People on the investment side don't necessarily have a technology 
background, or have the time and attention to spare to hone their 
specification of exactly how they want things to work.  So one 
can have a strange experience of on paper being in a situation 
where one ought to have one's pick of systems, but in practice 
feeling starved of resources and control.  This is one of the 
reasons I decided to spend time refreshing my technology skills, 
even though by conventional wisdom the basic tenets of 
opportunity cost and division of labour would suggest there is no 
point.  Things have changed a lot in the past twenty years, and 
the only way to keep up is to get one's hands dirty now and then.

Again on the resources front - given what happened in 2008, there 
has been an understandable focus on reporting, compliance, and 
the like.  It's a surprisingly brittle business because your 
costs are fixed, whereas revenues depend on performance and 
assets and investment strategies tend to intrinsically experience 
an ebb and flow whilst it is human nature to extrapolate 
performance and investors, being human, tend to chase returns.  
So it's not today necessarily the fashion to have a large group 
of people to develop ideas and tools that might pay off, but 
where it is hard to demonstrate that they will beforehand.  There 
has been a cultural change in the industry accompany its 
institutionalisation, so it's today much more 'corporate' in 
mindset than it once was, and this shift has not only positive 
aspects.

In many cases, you can kind of do what you want in theory using 
Bloomberg.  The problem is that it is closed, and with a 
restrictive API, so if you want to refine your analysis, that 
becomes limiting.  But because you can do a lot that way (and it 
is presented very attractively) it's not so easy to justify 
rebuilding some functionality from scratch in order to have 
control.

To take am almost trivial example, Bloomberg offers the ability 
to receive an alert by email when market hit various price 
conditions (or certain very basic technical analysis indicators 
are triggered).  That's valuable, but not enough for various 
reasons: one needs to maintain the alerts by hand (last I 
checked); I don't trust email for delivery of something 
important; and I want to be able to consider more complex 
conditions.  One could do this in a spreadsheet, but that's not 
in my opinion the way to run a business.  Python is fine for this 
kind of thing, but I would rather engineer the whole thing in a 
better way, since the analytics are shared between functions.

Or to take another example, charting and data management 
platforms for institutional traders remain unsatisfactory.  It's 
not easy to pull data in to Bloomberg, and to do so in an 
automated way where your data series are organized.  One wants to 
have all the data in one place and be able to run analyses on 
them, and I am not aware of a satisfactory platform available for 
this.  Quite honestly, the retail solutions are much more 
impressive - it's just that they don't cover what one needs as a 
professional.  By building it oneself, one has control and can 
work towards excellence.  The combination of incremental 
improvements, small in themselves, is underestimated in our world 
today as a contribution to success.

> Also, why D? Why not use a language or platform designed for 
> scalability and distributed computing like 
> http://chapel.cray.com/ ?

Pragmatically, I am an old C programmer, and there is a limit to 
how much I can learn in the time available.  It seems to me I can 
do everything I need in D in a way that is scalable for practical 
purposes.  Some of what I want to do is totally straightforward 
scripting, and some is more ambitious.  It is nice to be able to 
use a single language, if it's the right tool for the job (and if 
not, then interoperability matters).  If sociomantic (and that 
advertising company linked to in the blog post from a while back 
about using D for big data) can do what they do, I can't imagine 
it will be limiting for me for a while.  I will check it out, but 
there is a beauty to starting with the smallest useful version, 
and knowing that you can scale if you need to.

I recognize this reply is meandering a bit - since the major 
topic is use of D for big data in finance, whereas I am touching 
on a whole host of applications where I see it being rather 
useful.



Laeeth.