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.
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.
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.
Quants have worked in finance for ages. What's so unusual about Two Sigma? Headcount?
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.
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.
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.
Quant trading is easy with small accounts < 10 millions, the more money you trade, the harder is to find an edge...
Article is from October, 2015.
python pandas is a digital blood diamond after what they did to Gao