
Rich Formula: Quant Trading (2015) - luu
https://www.forbes.com/sites/nathanvardi/2015/09/29/rich-formula-math-and-computer-wizards-now-billionaires-thanks-to-quant-trading-secrets/amp/
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chollida1
Two interesting inside baseball tidbits here that I thik are worth pointing
out:

> From inception Two Sigma’s early funds, like Eclipse and Spectrum, focused
> on trading stocks globally. Eclipse was faster, changing positions within
> weeks, while Spectrum had a longer-term horizon closer to one month.

> The duo eventually used their algorithms to create programs that operate
> outside the global stock markets, like the trend-following Compass funds
> that bet on futures markets. In 2014 another important fund, Horizon, was
> folded into Spectrum, which had diversified its offering beyond stocks. One
> of Two Sigma’s least visible funds is its Partners Fund, an internal fund of
> funds, fueled mostly by capital from the founders.

Most, all?, of the largest hedge funds are actually partnerships that
encompass multiple funds. From an internal perspective this allows each fund
to focus on a few core competencies, like trend following vs market making vs
global macro. Each of these strategies will do well at certain times and do
poorly during others.

From a firms's perspective this allows for diversification, which is almost
always good. From an employee's perspective it allows them to get paid for
their work and be insulated a little bit from the performance of their peers
in other funds.

And in some cases like Steve Cohen's SAC it allows the good ideas to
retroactively be put into the blessed fudn while the bad ideas are shuffled to
the lesser feeder funds. Yes, this is illegal, yes it happens.

From an outsider's perspective its usually means that there is one well
performing fund that is closed for new money while there are several lesser
performing funds that are open to outside money. Even RenTech has funds that
are open to outside money and they don't perform anywhere near as well as
their master fund that is for employee's only.

> Two Sigma researchers spend time testing existing models, and each
> researcher is expected to come up with two or three new models per year.
> These are presented to Overdeck in a white paper that is typically less than
> ten pages long. Since Two Sigma’s trading models can change its forecast in
> seconds, lots of back-testing goes into each model. It’s not unlike the way
> Amazon exhaustively tests various Web-page changes in real time to ensure
> optimal clicks and purchases. At Two Sigma headquarters the model builders,
> who need to write code, sit with the engineers and collaborate with them all
> the time.

From the strategy development side, often idea's have a half life of anywhere
from months in the HFT space to years in the global macro space. For idea's
I've since come to believe that idea generation is equal parts people, ie
brain power, and platform, ie the ability to iterate.

RenTech is a perfect example of this. They had two people leave who were very
high up and go to another fund. They had 2-3 years of poor performance away
from the huge backtesting platform that RenTech had built. it's not like these
Phd's suddenly forgot everything. Its the ability to iterate quickly on idea's
that is the key once the bar has been met for math and intellect.

As they say, its not the algorithm you use but the features that produce your
alpha. If you want to make money in the markets focus all your time on feature
engineering.

Fun reading:

[https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-
me...](https://medium.com/@63ey5f4uw3k42v1exp7/chronology-mercer-medallion-
fund-9aa719ceeb4f)

~~~
twic
> idea generation is equal parts people, ie brain power, and platform, ie the
> ability to iterate

To be clear, when you talk about the platform, you mean the research platform
used to develop the idea, rather than the execution platform used to apply the
idea, right?

What is there to such a platform? What does it actually do? Is it just a
question of pumping historical data into a model and measuring its
performance?

~~~
jacques_chester
Based on very little more than other journalism and recruiters trying to chat
me up, here is my understanding.

The major hedge funds build in-house big data platforms that allow their
quantitative traders come up with an idea (what if the price of IBM is 22%
predicted by soybean futures in Tokyo on Tuesdays?) and then essentially
replay history and see the result.

Say you have the soybean-IBM theory. You feed it into the platform. It applies
your proposed strategy across billions or trillions of data points. It
probably does a lot more than an SQL query -- possibly checking for
interactions with other current strategies in use at the firm, running
sensitivity analyses, blasting various parts of it with Monte Carlo
simulations and so on. A lot of the same tech is also used to perform
forecasts of the coming hour, day, week, month or whathaveyou.

It comes back with the simulated return. If it's good, and if you think you
can disguise your strategy while trading, then and only then can you code it
up and deploy it to the trading platform. In this article it's claimed that
the models are reviewed by a company founder as well.

By now, going on Google's publications about Millwheel and Dataflow, the
hedges will already be advanced enough to provide model testing that can
converge to a number quickly enough that you can abort the test if it's not
shaping up well, thus saving platform time for other uses.

Building your own platforms is a mixed blessing. If you the first to do it you
can get an enormous advantage because you are the only company with that
capability. Later, as technology continues the march of commoditisation,
others gain the capability "for free". Now you are at a slowly increasing
disadvantage -- again, because you are the only company with that platform.

------
downandout
Could somebody explain why so much effort is being put into quant strategies,
when it seems that real-world information gathering would be a much easier way
to gain an edge over others? Let’s say you pay to place a camera on a building
next to a given company’s factory, and use analysis software to count the
number of trucks coming and going from the factory to predict their order flow
and earnings. This kind of thing is harder to scale up, but also gives an edge
because not everyone else is doing it.

In an age when all hedge funds have the resources to hire the best and
brightest engineers and buy the fastest processing hardware, it seems that
none of them will have an edge if they are all starting with the same publicly
available data.

~~~
dsacco
_> Could somebody explain why so much effort is being put into quant
strategies, when it seems that real-world information gathering would be a
much easier way to gain an edge over others?_

I used to be part of a research group that sold the so-called "alternative
data" you're describing to 30 or so hedge funds in the NYC area, including
several of the largest. The example I like to give is that we knew well ahead
of time that Tesla would miss on the Model 3 because we knew every vehicle
they were selling by model, year, configuration, date and price with <99%
accuracy. I still occasionally sell forecasts like this and the methodology is
straightforward enough that even a solo investor can consistently beat the
market if they know how to source the data. But I've mostly lost faith in this
technique as the sole differentiator of a fund's alpha.

Some funds, like Two Sigma, have large divisions with a very sophisticated
pipeline for this kind of analysis. They do exactly what you describe. For the
most part it works, but there are several obstacles that keep this from being
the holy grail of successful trading:

1\. First and foremost, this analysis is fundamentally incomplete. You are not
forecasting market movements, you're forecasting singular features of market
movements. What I mean by that is that you aren't predicting the future state
of a _price_ ; if the price of a security is a vector representing many
dimensions of inputs, you're predicting one dimension. As a simple example, if
I know precisely how many vehicles Tesla has sold, I don't know how the market
will react to this information, which means I have some nontrivial amount of
error to account for.

2\. This analysis doesn't generalize well. If I have a bunch of information
about the number of cars in Walmart parking lots, the number of vehicles sold
by Tesla (with configurations), the number of online orders sold by Chipotle,
etc. how should I design a data ingestion and processing pipeline to deal with
all of this in a unified way? In other words, my analysis is dependent upon
the kind of data I'm looking at, and I'll be doing a lot of different munging
to get what I need. Each new hypothesis will require a lot of manual effort.
This is fundamentally antagonistic to classification, automation and risk
management.

3\. It's slow. Under this paradigm you're coming up with hypotheses and
seeking out unique and exclusive data to test those hypotheses. That means
you're missing a lot of unknown unknowns and increasing the likelihood of
finding things that other funds will also be able to find pretty easily. You
are only likely to develop strategies which can have somewhat straightforward
and intuitive explanations for their relationship with the data.

This is not to say the system doesn't work - it very clearly works. But it's
also easy to hit relatively low capacity constraints, and it's imperfect for
the reasons I've outlined. You might _think_ exclusive data gives you an edge,
but for the most part it does not (except for relatively short horizons). It's
actually extremely difficult to have data which no other market participant
has, and information diffusion happens very quickly. Ironically, in one of the
very few times my colleagues and I had truly exclusive data (Tesla), the
market did not react in a way that could be predicted by our analysis.

The most successful quantitative hedge funds focus on the _math_ , because
most data has a relatively short half-life for secrecy. They don't rely on the
exclusivity of the data, they rely on superior methods for efficiently
classifying and processing truly staggering amounts of it. They hire people
who are extraordinarily talented at the _fundamentals_ of mathematics and
computer science because they mostly don't need or want people to come up with
unique hypotheses for new trading strategies. They look to hire people who can
scale up their research infrastructure even more, so that hypothesis testing
and generation is automated almost entirely.

This is why I've said before that the easiest way to be hired by RenTech, DE
Shaw, etc. is to be on the verge of re-discovering and publishing one of their
trade secrets. People like Simons never really cared about how unique or
informative any particular dataset is. They cared about how many diverse sets
of data they could get and how efficiently they could find useful correlations
between them. The more seemingly disconnected and inexplicable, the better.

Now with all of that said, I would still wholeheartedly recommend this
paradigm for anyone with technical ability who wants to beat the market on $10
million or less (as a solo investor). A single creative and competent software
engineer can reproduce much of this strategy for equities with only one or two
revenue streams. You can pour into earnings positions for which your forecast
predicts an outcome significantly at odds with the analyst consensus. You can
also use your data to forecast volatility on a per-equity basis and sell
options on those which do not indicate much volatility in the near term. Both
of these are competitive for holding times ranging from days to months and,
with the exception of some very real risk management complexity, do not
require a large investment in research infrastructure.

~~~
Bromskloss
> The example I like to give is that we knew well ahead of time that Tesla
> would miss on the Model 3 because we knew every vehicle they were selling by
> model, year, configuration, date and price with <99% accuracy.

Is the way in which you got that information something you can divulge? I
mean, was it talking to an employee or was it something exciting and far
fetched? By the way, I presume you meant ">99%" or something similar.

> A single creative and competent software engineer can reproduce much of this
> strategy

By "this strategy", do you mean prediction based on a source of "alternative
data"?

Interesting comment, in any case.

------
tntn
I'll just leave this here:

"Our definition of success has become narrow, boring, and limited. If we want
young people to be creative and innovative, we need to reward them for it."

from "Skip The Hedge Fund: We Need Young People To Take Risks And Build
Inspiring Things" at [https://www.fastcompany.com/3026586/skip-the-hedge-fund-
we-n...](https://www.fastcompany.com/3026586/skip-the-hedge-fund-we-need-
young-people-to-take-risks-and-build-inspiring-things).

------
module0000
Quant trading is harder than people assume - really, really, hard. Nearly
everything you can think of has already been done, and is being done within
latency limits you are priced out of as a retail guy.

So you have to get creative, and if you want it to keep working, you can't
tell anyone about it. Ever.

~~~
FundThrowaway
Let me let you in on a little secret of the quant world, it is easier than you
assume at least in terms of idea generation. Every fund I've ever been with
the vast majority of strategies are sourced from academic papers, you don't
need to think up new and novel ideas.

~~~
pakitan
If every fund is getting the majority of their strategies from the same
academic papers, that must do wonders for the profit :)

~~~
FundThrowaway
The edge is found usually in the details of the implementation of a strategy.

------
polskibus
Quants have worked in finance for ages. What's so unusual about Two Sigma?
Headcount?

~~~
tomp
Marketing strategy.

------
anonu
Tldr for this 2015 article: 2 sigma uses tons of data and processing power to
extract any alpha from the markets. They've done it consistently since 2004.
People leave 2 sigma and sometimes email themselves code. Bad idea as the
company will come down hard on you with lawsuits.

~~~
amerine
Email themselves code? That’s an astonishingly stupid way to try stealing
something. What did these people expect?

~~~
anonu
Most financial firms have decent lockdown... Generally firewalls prevent file
sharing sites, web mail, connecting to any external port that's not http or
https. I've seen some places prevent direct IP connections (need a hostname to
connect). USB and SD card slots are disabled... Not to say that there still
aren't dozens of ways to get around all this if you really wanted to.

------
cheez
It seems possible that mathematical breakthroughs are no longer being
published, as they are now trade secrets/matters of national security. I
wasn't surprised when Tao was beaten by a hedge funder.

~~~
dsacco
This has been the case for a long time in applied mathematics and computer
science (not so much pure mathematics). There are hedge funds using work that
is not only unpublished, but also unknown to research labs like FAIR and
Google Brain. The easiest way to be scouted by one of those funds is to
publish research that looks like you’re on the verge of re-discovering their
work.

~~~
joshuamorton
Do you have any proof of this, or is this or is it just your opinion that the
comparatively smaller groups of researchers at hedge funds are well ahead of
academia and the rest of industry?

~~~
dsacco
1\. I don't have proof I can share publicly,

2\. It's not just my opinion, and

3\. I didn't say they're "well ahead" unilaterally.

This isn't unique to finance; industry labs in tech also often have novel
results in applied mathematics and computer science that are ahead of academia
and other industry labs. You don't have to believe me but it's not exactly a
controversial topic. Not everything is published or patented.

~~~
joshuamorton
I mean I have little doubt that there are trade secrets that these companies
have. Specific algorithms and models. And yeah, industry labs are often ahead
here.

But I read your claim as saying that there are broad methods and approaches
that they hide. And that's, while possible, more peculiar. Most of the tech
industry labs don't keep their theoretical research secret. Practically
anything that could be published is.

As for 3, the way you described the "rediscovery" made it sound like those
Labs were a number of steps ahead, so I hope you pardon my misunderstanding.

~~~
dsacco
At the highest level there _are_ broad approaches which are kept secret in the
financial industry, but the reason that's peculiar is because their efficacy
is inherently antagonistic to publicity. Tech firms (mostly) don't lose
utility of their trade secrets if they're exposed, they just lose first mover
advantages on those techniques. But if everyone is aware of your techniques in
finance, your techniques cease to have an edge.

Like I said in the original comment: this isn't (to my knowledge at least)
pure mathematics that's being kept secret. But there are absolutely families
of techniques and algorithms whose applications to finance are nontrivial,
non-incremental and very well guarded.

~~~
joshuamorton
I guess my only followup would be are these "techniques" more akin (in
broadness) to ResNet, or to Dropout? (to use an area that I believe we're both
familiar with)

In other words, techniques that are broadly applicable to the field, or
techniques that maybe spawn a family of related techniques, but appear to be
useful only in a specific subdomain.

~~~
dsacco
That's a good comparison. In general, closer to Dropout.

------
madengr
These guys averaged a ~9% return over the last few years. The S&P 500 has
exceeded that. So what’s so special? Seems that passive investing works just
as well.

~~~
dsacco
_> These guys averaged a ~9% return over the last few years._

Who are "these guys"? The funds discussed in the article have average annual
returns well above 9%.

~~~
madengr
Overdeck and Seigel.

“The firm’s biggest fund, Spectrum, has earned an annual average return of
9.4% net of fees since 2004.”

The S&P 500 has averaged 9% for 80 years. Joe Blow can buy index fund and do
just as well as the quant clients. Sure, these guys are averaging 14% before
fees, and it’s a great way to get rich. But after fees the client might as
well just passively invest.

~~~
desu_
You need to consider the following elements: -You compare the S&P and two Six
Sigma funds over different timelines. The index yielded much less than 9% on
2004-2015 (you can halve the performance). -What’s the common feature between:
the S&P, the formal idea of passive indexing, and the emergence of passive
investment funds? None of them are 80 years old. -Retail customers (Joe Blow)
rarely have access to hedge fund products directly (they might still be
exposed through pension funds, sovereign wealth funds, etc. but it makes the
allocation change almost out of their hands). -Stating performance figures
alone is mostly a Bloomberg-reader thing. In practice, there is usually at
least an attempt to incorporate some kind of risk measure when selecting hedge
funds.

I am not even anti-passive funds but you are memeing a bit too hard.

------
SirLJ
Quant trading is easy with small accounts < 10 millions, the more money you
trade, the harder is to find an edge...

~~~
sputknick
Can you elaborate on this? That sounds like the opposite of the way it would
work.

~~~
kevstev
In general, large companies have a lot more data around them, a lot more
coverage from analysts and journalists and the like, and generally have a lot
more eyes on them. These companies are a lot more efficiently priced. You can
do the legwork yourself- getting on management calls, reading industry news,
poring over the financial statements to find strength or weakness that are not
widely known.

So lets say you are a 10 billion dollar fund. You do your research and find
that there is a small $100 million dollar company that doesn't have much debt,
is growing rapidly, and the management seems good and there are good tailwinds
for the industry as whole.

You have 100x the deployable capital than the company's market capitalization.
Even if the stock price doubles or triples in price, your potential upside for
the firm as whole is a relative drop in the bucket. You can't deploy more than
a few million into this company without moving the market. For a large fund,
it isn't worth wasting time chasing these small opportunities. In fact,
"scaling" is a hard problem in funds.

Hence, there is actually a lot of opportunity out there for small scale
investors who are willing to look under rocks that bigger guys don't see
enough potential opportunity in.

------
dogruck
Article is from October, 2015.

~~~
sctb
Thanks! We've updated the headline.

