
Money Machines – An Interview with an Anonymous Algorithmic Trader - jonbaer
https://logicmag.io/06-money-machines/
======
osrec
I'm an ex algo trader (currencies and metals) myself, and found this article
interesting and mostly accurate.

A key takeaway is:

"One of the fallacies that people have is the assumption that because the
people who are working at certain firms are smart, they must be successful."

The number of times a new hotshot hedge fund moved in to office building where
I rent, only to be gone without a trace in a month is staggering. Usually,
they'll raise money from an unsuspecting family office or two, employ a techy
and an admin person to do the heavy lifting, whilst the smooth talking CEO
alt-tabs between Bloomberg and reddit all day. Most have terrible risk
management strategies, and the majority are simply trend following (sorry,
machine learning/AI). Some of these firms are also my clients, and I have
often needed to write off money they owe because they've literally gone
bankrupt overnight.

~~~
xiphias2
Why are you ex algo trader? It still seems like a growing market, so it's
interesting that you stopped doing it.

I'm right now working on optimizing my long term investment strategy, but the
hardest thing is that there's nobody to talk to it about it (unlike when I
have other coding problems), and I have no idea when I'm doing something
totally stupid in my code base. Getting the right metrics to optimize is
really hard, as mostly I'm just looking at numbers and graphs.

~~~
osrec
It's a crowded market, but not necessarily a lucrative one.

I worked for a large investment bank, was paid well, but was also rather
unhappy. I'm not a fan of office politics and also hated the fact that what we
were doing was actually quite unsophisticated, yet we dressed it up like the
most complicated thing ever to clients.

We were using wavelets for analysing market micro-structure and using any
generated signals to influence our trades. Wavelets were the topic of my
thesis, so I was a good fit for the team. I know it sounds complicated, but if
you looked at the signals we generated, they looked awfully similar to a
rather basic trend-following style regression model. I believe we found a very
complicated way to compute signals that a simple trend follower would compute
in far fewer CPU cycles.

Anyway, I got a fairly decent bonus one year (because markets were trending
nicely), and decided I want to set up my own tech company. I was always a
nerdy guy, and I thought I could contribute more in the tech space. With
hindsight it was a good move, and financially I believe I am now better off
than most of my trading colleagues.

My advice to new guys joining our desk (maybe it helps you a bit): focus on
your exit, more than your entry, and if you can help it, be really, really,
really lucky :P

~~~
_cs2017_
> what we were doing was actually quite unsophisticated, yet we dressed it up
> like the most complicated thing ever to clients.

Doesn't this describe nearly all of the financial industry? Do you happen to
know of any exceptions?

~~~
osrec
Yep, and nope.

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lordnacho
As an algo trading guy myself I see the recent ML -> trading stuff as a bit of
a bubble. There's a lot of job ads out at the moment looking for ML people in
trading. Quant firms have not done great recently, and they all want to find
new things. The question is whether there's anything out there to be found
with ML techniques.

Also a lot of the well known quant firms that hire loads of phds don't
actually do anything particularly interesting or profitable. They are simply
benefitting from institutional momentum: there are still pension funds and
family offices with a mandate to stick money in trend followers, so the big
trend following names hold on to their assets.

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nodesocket
I've been playing with the trading platform Alpaca[1] and wrote some code that
essentially pulls trade information (each tick) for a handful of tech
companies I am interested in. It's a lot of data, but once I have the data
stored, I'm now thinking... Ok now what? I have volume, price, time, and a lot
of other metrics.

Not sure where to go from here honestly. Should the code evaluate the data in
real-time for each trade, or wait to the end/start of the trading day and do
some evaluations then make a decision?

Obviously, I am not going to be able to compete with the big boys using really
sophisticated code (written in a low level language like C), executing in
colocation right next to the exchanges, running Apache Hadoop and Spark.
Still, I am interested in just tinkering with simple algorithms with the data.

[1] [https://alpaca.markets/](https://alpaca.markets/)

~~~
khold_stare
I'd say play around and see if you can predict the price - treat it like any
machine learning/optimization problem - if you want to learn and have fun. If
your goal is to make money, unfortunately it'll take a lot more. I've worked
at a high frequency trading company, and now work at an algorithmic hedge
fund, and you really need to scale horizontally to make money overall.

There's a lot I can talk about but here are some points:

\- Most of the time you don't trade instruments in isolation. They are all
correlated (even if inversely) one way or another , so movements in one will
affect others. Having a myopic view of just one instrument will have too much
unexplainable randomness.

\- The other way to scale is having more data inform your pricing - i.e. don't
just look at the prices of one or more instruments. Look at twitter, look at
weather reports, news etc. It kind of suggested that in the article. The
meaningful movements in the market are most of the time due to truths outside
of the price itself.

\- You have to decide whether you want to trade directionally and long term
(like a hedge fund) and take on positions over time, or trade in and out of
positions quickly (like high frequency trading firms) to minimize risk. You
can be anywhere along that spectrum. The faster you are, the more myopic you
can be and just react to current trading activity of other participants. The
more long-term you are, the more you have to look at the big picture, more
data, more instruments, making sure you have the right balance in your
portfolio (whatever that means to you).

I'd be happy to expand more :)

~~~
nodesocket
Thanks for the great reply. Indeed I am interested in you expanding more. I've
been a long-term "valueish" investor, following the preachings of Warren
Buffett for over 15 years. My long-term portfolio is pretty well balanced,
Apple, to banks, to Berkshire Hathaway, Amazon, Ford, AMD, and SPY.

Recently, when the market got way overdone and oversold, I'd even call some of
it panic selling with the bottoming happening Christmas Eve. I am curious how
I can be more analytical, and data driven to make decisions during these
events, rather than just gut feeling of panicking, negative news, and oversold
conditions. Well I did add to my Apple position in this late December, I
should have pulled the trigger more. I was somewhat conservative, missing the
huge runup last month in January of most all stocks.

~~~
khold_stare
I can give some advice, but you should take it with a grain of salt :) I have
been more of an infrastructure guy, and building the platforms with
quants/traders, and not really trading myself. I am also more familiar with
HFT trades overall.

I think you're on the right track regarding value investment, if you're
thinking about it long-term, and it's for personal investments. Passive index
funds like SPY are the best bet for most people. Finding companies or
following trends you in particular have some insight on can also help. Things
like "having worked in industry X, and having read their whitepapers, this new
company is clearly overbought and is all marketing hype".

If you want to get more analytical, look at portfolio theory, different
hedging strategies, and try and find "alpha" \- roughly meaning the extra
factors explaining the price of an instrument that gives you an edge over
others. Check out Quantopian - they have some nice articles too. The key is
coming up with a model that accurately captures the risk of your assets which
would allow you to properly allocate your money amongst them.

------
ggm
Exploiting microtime moments in information flows is (in my view) parasitical
on the real element of information-flow in markets. I don't think profiting in
10us of speed advantage seeing a thing, and acting on a thing, is itself
market-informing and so it has to be (kind of definitionally) market-
distorting.

algorithmic trading is market distorting. its not actually helping "us" with
real value for our futures, funds, company value. Its not informed trade on
real conditions, its trade on the mechanistic moments in trading message
flows.

its a really bad feedback of second-order differential.

~~~
floatrock
Things you can do with that 10us advantage like frontrunning are parasitical,
but why is a speed advantage itself a bad thing?

Surely waiting a day to act on the information isn't helpful. How about a
minute? A few hundred ms, about the speed of a conscious awareness? Getting
down to 10us seems like the logical conclusion of all this.

I think the more nuanced view was talked 3/4 of the way down: with algo
trading, prices are based on the models not the fundamentals. And then you add
in models of what the other guys are doing. And pretty soon you don't care
where the price _should_ be, you just care about computing in 10us where
everyone else thinks it's going to be in 15us.

> Another fallacy in the lead-up to the financial crisis was the assumption
> that financial markets were so efficient that participants didn’t need to do
> the underlying work to figure out what the securities were actually worth.
> Because you could rely on the market to efficiently incorporate all
> available information about the bond. All you need to think about is the
> price that someone else is willing to buy it from you at or sell it to you
> at.... if they assume that the price of an instrument already reflects all
> of the information and analysis that you could possibly do—then they are
> vulnerable to that assumption being false.

That's now flash-crash territory.

~~~
ggm
I used to believe the only socialised goal of a market I appreciated was
setting price. I now realize a significantly higher percentage of trade is not
aimed at that outcome but solely at extraction of profit in the movement of
price. At the point nobody cares what the price _is_ but only cares about it's
Delta and velocity, I'm out. Why are we doing this?

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bdibs
In Ray Dalio's book Principles, he attributes his success to modeling the
markets with computers, but never relying solely on them.

All trades would be looked at by an actual human, making sure that everything
made sense and lined up.

I have my own doubts about a purely algorithmic approach (for now at least).
Computers are great for many tasks, but for something as inherently irrational
as the markets I think that should be at least partially moderated by a person
with a deep understanding of the markets.

~~~
pc86
What trades are you referring to when you say they "would be looked at" by a
person? The interview states quite the opposite:

> _The level of human oversight varies. Among sophisticated quantitative
> investors, the process is fairly automatic. The models are being researched
> and refined almost constantly, but you would rarely intervene in the trading
> decisions of a live model. A number of hedge funds, mutual funds, and
> exchange-traded funds (ETFs) run on auto-pilot._

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JoblessWonder
Is there an article somewhere that shows what a basic or advanced trading
algorithm looks like and explains how they work? I'd be curious to see one
walked through (even if it wasn't effective at making money and was just a
proof of concept/example.)

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JackPoach
The biggest problem with algo trading is that it all works fine and dandy,
until it doesn't. You can make money several years in a row and then lose it
all. Nassim Taleb makes this point exceedingly clear in his works.

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smileypete
Good topic, would also recommend reading this guys stuff - Robert Carver, ex
AHL:

[https://qoppac.blogspot.com/](https://qoppac.blogspot.com/)
[https://twitter.com/investingidiocy](https://twitter.com/investingidiocy)
[https://www.systematicmoney.org/](https://www.systematicmoney.org/)

Also has a few videos scattered around that are worth a look too.

------
organicdude
This was a really good read. Thank you.

~~~
TheOtherHobbes
Agreed. Very thoughtful and interesting.

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cryptosteve
But isn’t there a strong financial incentive to try to understand why you’re
doing what you’re doing, whether it’s an algorithm or a human executing the
trades? Otherwise it seems very easy to lose a lot of money. I can't find the
article, however a guy once drop a million in bitcoin using a trading bot on a
short sale.

~~~
thibautx
There's typically multiple layers (at different points between tick to an
order hitting an exchange) of risk-management/circuit-breakers that prevent
these types of things from happening at most shops that know what they're
doing. No one wants a repeat of Knight Capital's 2012 meltdown.

I would imagine it may be easy for an unsophisticated/hobby "algo-trader" to
make this type of mistake but with over a million in capital, you should
probably be a bit more prudent with risk management.

------
planktons
The key bit here is where he says that a lot of people will lose their jobs
soon. There are tons of jobs that exist only because of momentum. The
financial incentive to pay all these people will dry up and it’s going to
hurt. And we won’t opt for UBI until many people have suffered a great deal.
People do not seem to appreciate the gravity of what is coming.

Basically every time I bring up jobs, ai and society someone comes out of the
woodwork to insult my character or sarcastically dismiss me. Never any
substantive counter-arguments. I’m really looking forward to what you guys
will come up with this time. Always such a pleasure.

~~~
scottlocklin
OK, here you go: what large group of jobs has been eliminated by "AI" so far?
No time limit answering, unless you die first, in which case "time's up!" My
assertion, made in public and under my real name is that barring some giant
breakthrough, nothing like this is going to happen any time soon. As far as I
can tell, as an active worker in the field (erstwhile finance FWIIW), "AI" is
a force multiplier for statisticians, and basically that's it.

Jobs have been eliminated in America because of poor industrial and trade
policy. Nothing to do with "AI." I get extremely irate when people make the
assertion that "oh, well, the jobs are going away soon anyway" -using this
complete and utter falsehood as an excuse to continue the looting of the
country.

The guy interviewed here is a real mixed bag. Some of what he says is
nonsense, some of it is accurate. FIRE has basically been a vampire squid on
the economy without adding much value. Most of it could be "automated" away by
using a magic 8-ball; it doesn't actually provide any value. Except the
politicians rice bowls depend on the status quo.

~~~
b_tterc_p
Wrong question. You should be asking, what groups have been granted large
productivity gains due to information systems and now run substantially
leaner.

E.g. secretaries, call center employees, accountants, etc.

~~~
scottlocklin
Statisticians. That's it! And it's one of the hottest job fields right now
with the fancy name "data scientist." All the recent improvements in machine
learning have done is make certain kinds of statistical thinking possible, and
that's it. It's not eliminating anyone's job; it's actually growing the market
for statisticians.

Anyway parent poster said "a lot of people will lose their jobs soon" because
of "AI." I deny this has or will happen, and people's misunderstanding of it
is downright criminal.

~~~
b_tterc_p
Not just statistics jobs. Also bad jobs. More bad jobs in fact.

Information systems have created the ease of the gig economy. It allows people
to provide more granular, on demand jobs.

The net effect is more jobs. This may be a bad thinf

