This is a meditation on the essence of what makes for good quantitative trading. From a purely intellectual viewpoint this has attracted attention and has led to questions about what is at the heart of good quantitative models.
The Search For Structure
Whether a quant modeler is able to articulate it or not, eventually good algorithmic trading is about a search for structure in the noisy data of markets. It is about finding patterns, regularity or pockets of predictability. Here is a simple example of what is meant by structure. Let’s say that we observe that whenever the market goes up two days in a row, it usually goes up the third day. If this happens quite often, we have found the pattern or regularity we were looking for. The trading strategy immediately follows. If the market goes up two days in a row then buy at the close of the second day and sell it at the third day’s close. If only!
It is easy to get fooled by randomness and see patterns that in hindsight seem nonsensical at best. Technical analysis books are strewn with all sorts of patterns with colourful names, most of which will not stand even mild statistical scrutiny much less any systematic way of teaching a computer to identify the patterns.
To combine the problem markets are to a first approximation just noise. Yet they fail statistical tests of randomness in enticing ways. That is what randomness would prescribe except that the tail of the distribution is much fatter, i.e. periodically markets have much, much larger moves than a normal distribution.
When we study the dynamics of daily moves rather than the aggregation in a histogram, things get even more interesting. If markets were indeed a random walk, today’s move would be independent of yesterday’s move. Not so, say econometricians where there is a cottage industry of models (called the GARCH models) which suggest that big moves (in absolute terms) are usually followed by big moves i.e. there are times when markets remember yesterday’s moves. Greed and panic leave the markets quite shaken with bursts of volatility! This is a structure that has been modelled by academics and used by practitioners to model volatility for derivatives. From a perspective of trading it can be used not really as a strategy but as a signal of the onset of volatility and also lowering of trade size during this period.
The academics have discovered more that are about financial time-series. It may be hard to figure out what the future holds for a single time-series, but academics have found techniques (called co-integration) that allow us to create portfolios of longs and shorts of different assets in such a way that the value of the portfolio oscillates around a mean value much like a sine wave. Again, this is the regularity a modeler is looking for and the strategy is clear. Buy when it reaches the bottom and sell it when it reaches the top. The trouble alas is that markets change, the relationships break down and the sine wave starts moving out of its band or narrows to the point where the strategy is unprofitable. It is a non-stationary world. The message to a quant is clear – there is structure to be exploited, but beware of noise, fat tails and non-stationarity. (more…)
Understanding the difference between a milestone and a goal is a crucial distinction in trading success generally. Where are you going and what will you get there? The best people and teams I have been around were always good with momentum. When things went bad, they weren’t compounded and when things were going well, they poured every once into allowing it to continue and when it stopped, they rebuilt.
This is a meditation on the essence of what makes for good quantitative trading. From a purely intellectual viewpoint this has attracted attention and has led to questions about what is at the heart of good quantitative models.
