Adam: You run a wide gamut of strategies. You have HFT strategies, which many people say are all about the brute force of technology, but you also do sophisticated trading based on science and deep research. How do you marry those two?
Alexei: Even though we trade a variety of different strategies, in reality we are -- probably like many others actually in the systematic trading space -- focused on exploiting two main themes: mean reversion and directional trend-following. This is probably something you would hear from virtually any systematic trader. You may categorise yourself into either one or the other. For historical reasons we have been more focused on mean reversion over the years, and that is marrying well with our focus on shorter average trade holding times. So even though it may look like we use a lot of sub-strategies, if you bracket them, a lot of them will be mean reverting and some of them will be trend-following.
Within these large brackets, we do split into individual ways of doing things. We believe in a certain universality of description of price changes, which allows us to apply similar sub-strategies to a wide range of markets and holding times. This is also similar to how many other systematic traders operate.
Adam: Your firm describes the programs as being high-frequency and contrarian in nature. So is the focus on mean reversion what you mean when you say contrarian, given that mean reversion by definition suggests that a market will revert?
Alexei: In reality, most of the strategies which are not too complex are driven by correlation.
Ultimately it's a question of studying a time series, maybe even building a time series yourself as well, because nobody forces you to use the market-given time series. Maybe you'd like to construct your own synthetic time series out of the individual pieces the market provides you. That is also something we are interested in, which brings us to the notion of market neutrality.
Adam: What do you mean by market neutrality?
Alexei: Normally, conventional systematic futures traders just look at individual futures markets, they try to focus on futures markets like gold, copper or the US 10-year Treasury note. But if you have a more statistical point of view, you can introduce a statistical hypothesis that there is a similar statistical description of price changes across a wide range of short-term observation times -- say, from one minute to a few days in duration -- and also across a wide range of markets. Similarity here is understood, just like it is in statistical physics contexts, that the same algebraic laws are describing the random price change fluctuations, with possibly some features for particular market case constants. Such a hypothesis greatly simplifies the seemingly indefinite complexity of the market description. Instead of trying to find some features for every timescale and market miraculous patterns and rules, we can apply the same description across a wide range of timescales and markets and only focus on fitting a few parameters to individual markets. Obviously, the most difficult part here would be to adequately test such a hypothesis.
Moreover, taking the next step, out of these markets, you can create a synthetic market which will not have a directional exposure to, let's say a benchmark everybody thinks of, either the S&P 500 index or an index of CTAs. You can actually be neutral to one or several of these benchmarks as a byproduct of such a view as well. One of our programs, Systematic Alpha Futures Program, is going along exactly that path. It's both focused on trading mean reversion and it is also market neutral.
Adam: When you talk about using analogies of physics such as fluid dynamics or fluid turbulence, could you give an example of that and how it might work?
Alexei: This subject currently is reasonably well covered in some well-known econophysics books. The random fluctuations which need to be compared are the price changes in time for finance and velocity differences in both space and time for fluid turbulence. In both cases Gaussian distribution function serves as a certain limiting case. Despite that fact, for important situations of strong turbulence and volatile liquid markets there have been observed strong persistent deviations from the Gaussian distribution. In both cases, those deviations lead to much higher probabilities assigned to the tail events, which in the case of finance manifests itself in the abundance of price shocks up or down. In the case of strong turbulence, depending on the space dimension of the problem, those structures lead to strong visible coherent vortices of various shapes. The study of this anomalous behavior in the case of turbulence over at least the last 60 years led to the creation of vast statistical techniques, which can be adapted to finance.