
Algorithms take control of Wall Street - shawndumas
http://arstechnica.com/tech-policy/news/2011/01/algorithms-take-control-of-wall-street.ars
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chrisaycock
This article is very poorly researched. For example, it refers to high-
frequency traders as "flash traders". I have never heard that term before. The
cited Wikipedia article discusses flash _orders_ , which was a quoting
mechanism that DirectEdge experimented with in 2009. Flash orders are not some
most automated market markers even care about.

Also, the article mentions Harold Bradley as "among the first traders to
explore the power of algorithms in the late '90s", completely omitting much
earlier pioneers such as Peter Muller and David E. Shaw.

And of course it goes into depth about the Flash Crash without once mentioning
Waddell & Reed, a discretionary (non-quant) fund whose errant trade started
the crash to begin with.

Reading this was like watching the 11-o'clock news discuss a medical
discovery.

~~~
w1ntermute
Nothing unusual then. Pretty much every tech article in the WSJ is of this
caliber.

~~~
atuladhar
Except that I see no indication this is a Wall Street Journal article. The
site is Ars Technica; the by-line says "By Felix Salmon (wired.com), Jon
Stokes" and the end of the article says "Felix Salmon (felix@felix salmon.com)
is a blogger for Reuters."

~~~
chrisaycock
I think what @w1ntermute means is that we can expect Ars to screw-up finance
stories just as the WSJ screws up tech stories. Kind of an admonition for when
journalists choose to venture beyond their expertise.

~~~
w1ntermute
Yeah, sorry I didn't make that clearer. I made the comment while this[0]
poorly researched/written WSJ tech article was still on my mind.

0: <http://news.ycombinator.com/item?id=2086738>

------
Sniffnoy
> "Automated trading systems will follow their coded logic regardless of
> outcome,"

Of course, so will humans. They won't make the absurd trades referred to, but
a system of trading humans can still, say, get stuck in a positive feedback
loop. Of course, they aren't pure trading algorithm, so they may notice the
problem, but actually putting a stop to it requires coordination and is hard.
Mostly the loop just occurs more slowly.

~~~
chrisaycock
Yep, which is precisely why economic bubbles happen. Algo traders weren't
responsible for housing prices in the last decade.

------
extension
Comforting to know that the economy is being gambled on a roshambo programming
competition.

<http://webdocs.cs.ualberta.ca/~darse/rsbpc.html>

~~~
jrockway
Why do you think this is true? For every trade, there is someone on each side.
The market as a whole does not care if you make bad trading decisions. Someone
else makes up for it.

~~~
extension
I think our money needs to be working towards things that we actually care
about: advancing science and technology, creating art, socializing, and so on.
Putting it all into a big game of craps is not going to improve the human
condition in the long run. I mean, it's an interesting game, but not
interesting enough to justify the entire economy revolving around it. And
people don't play because they find it interesting.

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leogau
"Even if each individual algorithm makes perfect sense, collectively they obey
an emergent logic—artificial intelligence, but not artificial human
intelligence. It is, simply, alien, operating at the natural scale of silicon,
not neurons and synapses. We may be able to slow it down, but we can never
contain, control, or comprehend it"

I can see algorithms being used to automate decision making in almost every
other industry, not just Wall St. It's both scary and exciting to see what
happens with the resulting "emergent intelligence".

~~~
jacques_chester
The systems dynamics folks point out that systems with more rapid feedback
often have more variability.

The classic example is a car dealership deciding to order cars from the
factory. When should they be ordered, and how many should be ordered?

Suppose they decide that every day they place orders based on the 7 day moving
average. Then they get a busy day and over-order for a week. That's an
expensive mistake.

Next they decide to shrink the window to 3 days (more rapid feedback). Now
after another busy day they find stocks dropping precipitously and order big
to make up, follow by drops, followed by big orders. In an attempt to
stabilise the system, they've made it worse.

The modern stock market has extremely short feedback times, on the order of
microseconds. When a feedback loop forms on the market it can spiral out of
control within seconds and minutes -- hence the 'flash crash'.

Two ways to deal with this might be to develop some kind of balancing feedback
loop (for example, higher prices for more trades-per-second, or a progressive
price for trade based on delta with the last trade) or to reduce the feedback
rate. An economics blogger I host, Nicholas Gruen (you may know of him as
Australia's Gov 2.0 inquiry leader), has suggested just that:

[http://clubtroppo.com.au/2010/08/07/a-modest-proposal-to-
rem...](http://clubtroppo.com.au/2010/08/07/a-modest-proposal-to-remove-the-
more-ridiculous-waste-and-some-corruption-from-our-financial-markets/)

[http://clubtroppo.com.au/2010/10/08/a-self-denying-
ordinance...](http://clubtroppo.com.au/2010/10/08/a-self-denying-ordinance-
for-exchanges/)

~~~
yummyfajitas
_The systems dynamics folks point out that systems with more rapid feedback
often have more variability._

You've got systems dynamics folks completely backwards. Smaller, more frequent
controls tend to stabilized systems, not make them unstable. A simple example
we've all seen: take a stable ODE. Now try to discretize it - if you are
unlucky or uncareful, your discrete approximation can easily blow up
exponentially.

The example you provide is different - you are describing two different
control _strategies_ , one of which fails to correct for noise (and note: any
HFT who makes this mistake loses money FAST). If you made orders every day
based on the 7-day moving average, it would be better than making orders once
per _week_ based on the 7 day moving average.

This is exactly what we saw with the flash crash - there was a large exogenous
shock and the system self-corrected within minutes.

~~~
jacques_chester
Good followup, thanks.

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ct
The neural network that Bradley was working on sounds interesting.

~~~
aothman
NB: This article (and several more like it) was in January's wired.

As an AI researcher, I get suspicious when I see anyone talking about Genetic
Algorithms and Neural Nets. These are techniques that current researchers
simply do not use (Neural Nets are used very sparingly, GAs should never be
used at all). They make up for their technical failings by being approachable,
particularly for journalists. In short, these methods intuitively sound like
they should work much better than they actually do.

~~~
istjohn
What techniques are now used in place of GAs?

~~~
aothman
GAs are just a really complex version of a local search algorithm. The problem
with them is that they're just too complicated - you're trying to solve some
non-linear problem, and your first step is to introduce several more non-
linear problems that also need to be solved (picking chromosomes, mixing,
population size, etc.)?

~~~
adovenmuehle
I'm kind of interested if you could point us in the direction of specific
techniques. I always thought neural nets and genetic algorithms were
interesting and I'm curious what other techniques are out there.

~~~
aothman
For solving local search problems, I use tabu search (hill climb with a
"recently visited" list) or beam search (simultaneous hill search). Both are
simple techniques that show remarkable emergent behavior. Many search problems
are better phrased in terms of numerical optimization or what have you - if
your problems maps a continuous space to a continuous space there's probably a
standard numerical technique that solves it better than a local search hack.

For Machine Learning type applications, SVMs are very popular. Briefly, both
sufficiently deep neural nets and sufficiently dimensional SVMs are
arbitrarily expressive, but SVMs give you a better perspective on what is
actually happening with your problem. If you're interested in Machine
Learning, you should check out Andrew Moore's very well-written tutorials:
<http://www.autonlab.org/tutorials/list.html>

