It is midsummer's day. I'll find myself in Copenhagen later today. The sun will set there later than it would have done in London. As the days become lighter, so does the mood, as summer approaches. From tomorrow, the days will begin to shorten, and so we begin that march back to winter. It is not purely the mood, that light affects. Everything changes in the light. Stating the obvious, we see what is lit. To our eyes, anything outside the sphere of light is unknown.
During our careers, it is far too easy to see only that light which is shining upon our own industries and our sector within it. To some extent, this seems reasonable. I have spent my entire working career in currency markets. Slowly, I am starting to analyse other markets in more detail. However, they seem somewhat hazy, compared to the way I view currency markets. Everything just seems that bit easier to understand in that area where you have the most experience.
It can be tempting to stay with what your know and to specialise. There is intrinsically nothing wrong with specialisation. It is after all, the way we can add significant value, using skills we have learnt in our own fields, which others may not have. At the same time, often ideas can come from unexpected places, perhaps from other areas where you have little experience. This thought has been mulling around my head for a while, and indeed it is somewhat I discussed in my book, the notion that thinking laterally can have value.
I just went to the PyData conference in London. It got me thinking about this notion again. Unlike most events which I attend, the focus was not on finance. Instead it discussed how data scientists in other fields approached their problems and technical implementations in Python. I heard a talk from Emma Prest and Billy Wong from DataKind UK describing how they used masses of data from the Citizen's Advice Bureau to try to predict problems people might have, before they occur, so they could receive preventative advice. There were also talks describing issues facing data scientists in medicine working with messy datasets. Whilst none of these talks might have directly addressed financial markets, I could see parallels between problems faced by data scientists elsewhere could have applications for systematic traders. Often the key was how to understand an unusual dataset.
The difficulty with systematic trading is that there can often be misconceptions about which data we can use. Systematic trading often appears to outsiders to be some mixture of CTAs and high frequency trading. CTAs are generally trend following traders. They literally buy high and sell low, in the hope of a continuation of medium terms trends. The main input is of course price data. As a strategy, it can be a good complement to the more common long only strategies in equities and bonds. I've done a significant amount of work on this subject, creating my own CTA proxy (imaginatively called the Thalesians CTA Index!). High frequency traders by contrast operate over very short horizons, seeking to use knowledge about microstructure of the market combining it with cutting edge technology to reduce latency. However, systematic trading is a whole lot more than CTAs and HFTs!
The data we can use to trade markets is so much more than purely market prices. We can use reams of fundamental data, such as economic release data. We can harvest news data to generate trading signals. We can examine web traffic and tweets to gauge sentiment for trading. We can see how market participants are positioned to understand price action. The list is endless!
The critical element is of course a willingness to spend time to get to know what value these more unusual datasets can hold for traders. It also requires systematic traders to step outside their traditional "comfort zone" of familiar datasets and try to learn from what data scientists are doing elsewhere. Just because something isn't "finance" related, it doesn't mean we as systematic traders can't learn from it. Equally, I think the same is true of any field. Value, in particular these days, comes from an ability to combine ideas from multiple disciplines (as Thales of Miletus showed several millennia ago, which I discuss in my book!
So enjoy today. The light will be shining most today.
26 Jun - Budapest - Bruce Packard & Panel - Emerging Alternative Finance
22 Jul - London - Paul Bilokon - Stochastic Filtering (title TBC)
07 Sep - Frankfurt - Saeed Amen/Yves Hilpisch/Thomas Wiecki/Jochen Papenbrock/Miguel Vaz/Adrian Zymolka - Quant Evening (Thalesians/Quant Finance Group Germany)
Early Sep - Zurich - Saeed Amen - How to build a CTA (date TBC)