I love Python and cloud storage. Python and cloud storage has helped me get rid of some of my data clutter, and I can now retrieve it on command from anywhere in the world. With Python code, and the APIs of Facebook, Twitter, YouTube, and other fun things like that are making high level concepts easier to develop, right in the cloud, and providing a fertile playground for creative people.
By using some analytical tools that I applied mostly to financial engineering problems, I created interesting algorithms to monitor key parameters of my own body. I aggregated them in an Pandas or R Dataframe, and created a sort of a ‘body control panel’. It was that (almost) real-time monitoring of those parameters which allowed me to transform my body from obese fat to six-pack abs fit in a natural, low volatility and controlled way. As an added bonus, I have increased my life expectancy as measured by several devices. Aside from the health benefits, it feels good to be in the best shape of my life at 45.
My efforts on data collection & analysis had paid off in the past. An early beneficiary was the hedge fund I was working for in 1993.
That year, the first Pentium chips were released, but in limited numbers. The hedge fund where I worked had ordered a few custom PCs with the new chip for the trading floor. They were expensive back then, even for a hedge fund! A friend of mine helped me network the PCs and made sure my algorithms ran smoothly. Now, I could run my pricing models faster, and we could also act on the information faster. The increased processing speed allowed me to spot arbitrage trades early on.
1994 was a good year: a relatively small allocation of capital to a quant trade flagged by my algorithms generated a nice return for our clients. And, on a personal level, it was gratifying that The Washington Post featured a story on that trade, in which the journalist referred to me as a “a young Venezuelan mathematics wizard”.
Over the next few years, my algorithms spotted abnormal volume and price movements in call options of Lotus Corporation happening over the course of one week. On Friday, June 2, 1995 we bought cheap out-of-the-money Lotus calls at around $5, when Lotus stock was trading at $30. Over the weekend, IBM announced a takeover of Lotus, and the stock moved to over $60. The options we had bought at $5 revalued to around $30 the following Monday.
This story illustrates that observing patterns that deviate from the norm and taking action in time can be a very good thing. It also shows that when it came to turning data into results, I was not just talking the talk, I was walking the walk.
Since the advent of the browser, data collection has become easier, faster, and cheaper. And now I’m slowly migrating my most interesting stuff to Google, and collecting and analyzing more than just financial data: tweets, calories, box office numbers, Jai Alai results, my heart rate, the relative popularity of celebrities, my own excess post-exercise oxygen consumption (EPOC) to burn fat and increase muscle growth, bio-trackers of aging, GPS data, frequency and severity of hurricanes, earthquakes, and volcanic activity all over the world, and other things like that. Basically, anything that strikes me as interesting.
Now, amassing data just to have it is not fun – that’s called ‘data hoarding’, and is just plain weird. The fun is in analyzing the data, tweaking it, correlating it with other data, using it to create original research, then taking the results and developing new stuff. That’s the heart of the Quantitative Method. The guys in Silicon Valley call it ‘mashups’. I guess that I have been creating mashups since I was studying engineering back in the ‘80s!
A big chunk of my career has been spent developing private financial mashups for my employers, to basically make money in liquid securities. Probably the most profitable was one I developed for Deutsche Bank in London in 2004, called “Synthetic Swap Funding” – can’t say more than that here. However, the most fun mashups are 100% mine, and were never deployed. I created them right after I left Deutsche Bank in 2005 to start my own firm, and before I joined Lehman Brothers a few years later.
Since the collapse of Lehman, younger Quants, especially in fixed income, are reinventing themselves in other areas, or taking lower paying jobs. But what about the older crowd of Quants like me?
Well, before answering that question, I need to differentiate one Quant from another because not all Quants are equal. You see, the ones you hear about in the press are what I call ‘high-volatility Quants’, the ones that made the news because they made billions for the banks they worked for, or lost billions for the banks they worked for. It doesn’t seem to matter either way: they are personally wealthy guys, like my former colleagues at Deutsche Bank’s ABS/CDO group, Eugene Xu and Boaz Weinstein. You could call them Quant ‘Superstars’. Some of them are still working at those banks, or started hedge funds of their own, or retired to their own personal vineyards in Sonoma. In that group, age is not really a factor.
But most Quants are not Superstars. Non-superstar Quants are or were working as hard or harder than the Superstars, making consistent but measured gains over extended periods of time in their areas of expertise: trading, structuring, risk management, etc. They always kept a low profile. Those I call the ‘low-volatility Quants’. The younger crowd of low-volatility Quants are the lucky ones, since they were relatively cheaper to hire and/or retain, and/or re-train compared to senior low-volatility Quants.
So, what are the senior low-volatility Quants doing? A great number are underemployed, many are doing general advisory work, learning new skills, traveling, unemployed, or some combination of these. And statistically speaking, half are fat or overweight.