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Rich Formula: Quant Trading (2015) (forbes.com)
108 points by luu 10 months ago | hide | past | web | favorite | 60 comments

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:


My analogy (from a guy who grew up in restaurant and later ran hedge funds) is that it's not just the recipes that make a restaurant.

Any given recipe has a history behind it, an environment (ingredients available, staff expertise, equipment, fashion) in which it was popular, and a future that may or may not be good for it.

There's also all sorts of non-recipe things that matter. How is the restaurant decorated? Is it in the right location, serving clientele that like that kind of thing?

And so on. Like many other businesses, there isn't one central piece of business knowledge that determines success.

That reminds me of an episode of This American Life. The Vienna Sausage Company had to relocate their hot dog plant, so they modeled the previous plant exactly and all their production procedures. And yet, the hot dogs at the new plant didn't taste as good and weren't the right color, and they couldn't figure out why.

I won't spoil it for anyone who wants to listen to it (it's only 3 minutes long), but sometimes your secret sauce is not even what you think it is, but a completely unexpected & undocumented interaction:


Well that made my day. A friend of mine told me this anecdote but had forgotten the relevant names for me to google. Wow.

It was also motivated by the same discussion and recipe analogy.

This is how I feel about "agile" software development ;)

> 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?

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.

Nobody will tell you about the platform itself because those are closely guarded secrets.

A more specific description of the problem might help though. There's a huge amount of data in many forms, a huge amount of unpredictable major events, a lot of noise, a lot of hidden data (take hidden orders on exchanges as an example), and any action you take is going to affect the market. If you do something dumb, like place a bad order , nobody would take it in simulation but they would in real life. This makes it extremely difficult to understand why your model performed the way it did, what needs to be fixed, what sort of risk it entails, etc.

Research platforms help answer these questions without quants having to do do complex bespoke analysis over and over again.

This is precisely the kind of question for which you won’t find any meaningful, public answer. I’d be thoroughly shocked if you could find someone in the know to give you an answer even anonymously.

>If you want to make money in the markets focus all your time on feature engineering.

Focus on gearing and slippage.

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.

This is exactly what some quant funds like RenTech and DE Shaw have done—they look for data sets that show signals before they hit the market.

You can view a traditional market as a process where information from the real world flows in, and people can either make money by predicting how others will react to that information, or reacting slightly faster (HFT), or understanding the patterns the information tells you (e.g. stat arb).

But even better if you can get the information before they hit the markets, hence the search for more and more data sets that have predictive value.

This changes the actual market (the exchange) from the place where prices changes reflect different sets of participants' views being hashed out (the market is performing information processing mediated by price signals), to one where the actual predictive signals are monopolized by private parties before they hit the market.

But note that this is exactly the path that the large tech platforms' ad exchanges have gone down as well. The price per click/conversion of a user and the outcomes of real-time bidding are hidden from everyone except the particular participants of the transactions; we're moving from a world where public markets (no matter their limitations) reflect the sum of public information in real time, to one where the thinking and computation are moved into hidden platforms.

At some point, of course, some bright person is going to create an exchange/marketplace for data signals, and then the financialization/reification of another abstraction will take place.

Their category "alpha capture" probably incorporates this sort of thing. I think the article gave the example of proprietary survey data being in that category. I think that's probably what they call anything that doesn't come from publicly available data.

> 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.

> 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.

Wow! Thanks for the detailed answer. You introduced a lot of issues I hadn’t thought of, and your last paragraph gave me some ideas.

Also...generally speaking, what does this type of information sell to hedge funds for? For something like the Tesla information for example? I would assume it's probably not millions, but somewhere in the 5-6 figures?

Good example: Tesla had a miss on Model 3 production for Q1, yet the stock rose significantly. And the miss was predicted by both the fan vin tracker and Bloomberg's vin tracker.

I used to work for D. E. Shaw & Co., now I work in Silicon Valley and invest my money in index funds. Much better that way.

Any tips/starting point for the uninitiated?

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....

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.

I think it is important to understand that low latency = HFT. Because HFT is data driven it is part of Quant trading. But not all Quant Trading requires or is being done at low latency limits.

A great point. When I worked in the industry, the trading (of exotic products) was actually done over the phone and via e-mail. Very much quant-driven, but about as low-frequency as it gets!

Agreed. And it's not just latency that puts the average guy at a disadvantage. It's also access to the market and access to data which are hard to obtain.

The beauty in all this is that 1000s of people keep trying... But only a select few, less than 1/100 of 1 percent of market participants succeed in making reasonable profits

> Nearly everything you can think of has already been done

I find this hard to believe. There's entire fields of research based around AI now and an endless number of ideas that have yet to be tried/implemented. There simply aren't enough people working at hedge funds to cover them all.

A more accurate statement will be "Nearly everything you can think of has already been done or will be done soon and since this is a zero-sum game, the expected profit in the long term from quant trading is approaching zero"

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.

Do you have an example of such an idea – maybe one that is no longer of use?

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

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

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

Marketing strategy.

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.

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

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.

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.

There are always better people around who want neither fame nor money.

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.

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?

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.

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.

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.

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.

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

maybe tao lost his edge

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.

> 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%.

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.

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.

At what risk though? Returns should always be considered by the risk taken to achieve them.

I’ll assume they have a higher risk to generate those higher net returns.

After recently reading A Random Walk Down a Wall Street, my conclusions are: you are not going to time the market, the managed funds don’t do any better than index funds. 90% percent of the effort goes into squeezing out an additional 10%. So may as well spend 10% of the effort and settle with 90%. Of course fortunes are made on that 10%.

Joe Blow slinging garbage cans can retire a multimillionaire by steady, passive investing.

Perhaps read about market neutral strategies, most quant funds have much higher sharpe ratios than index funds.

Ha ha, I’m an EE just passively investing extra income. Nothing more than a side interest. I assume you are talking about using higher volatility stocks with negative correlations; he talks about that in the aforementioned book.

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

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

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.

Typically it means you can invest in niche markets. These market would be out of reach for larger players as they would struggle to find liquidity for their portfolio size. On the other end, smaller players would not be able to adjust their positions sufficiently granularly due to minimum lot/clip sizes specified on the market - similar to a quantisation effect if you don't sample at a sufficiently high frequency.

Many examples: faster in and out of the market, better fills, will not end up owning a company by mistake (e.g. bought all shares), etc... The bigger you get (in billions) the closer the returns will be to the market average...

Article is from October, 2015.

Thanks! We've updated the headline.


What do you mean by that? I can't find any connection between him and pandas

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