Go-ing with quant trading

When I think of the word "go", I think of its meaning according to the Cambridge Online Dictionary (written below):

go
verb UK ɡəʊ US ɡoʊ (present participle going, past tense went, past participle gone)
      
go verb (MOVE/TRAVEL)
A1 [I usually + adv/prep] to ​travel or ​move to another ​place:

This week, the word "go" was in the headlines for totally different reasons. Lee Se-Dol the best human player in the world at Go (the Chinese Chinese board game) was beaten by Google's AlphaGo artificial intelligence system. Whilst, chess champions have been beaten in the past, it was thought that it would take at least a decade more for them to accomplish the same thing in Go. Indeed, if you are interested in this topic and are based in London, I recommend you come to Robin Hanson's talk at Thalesians London on economics and robots on March 21st (free tickets)

Perhaps unsurprisingly, it has focused peoples' minds on what other things a computer can do, in areas which have been traditionally required humans. One such area is trading. Trading has seen a massive proliferation of technology. Systematic traders have been trading in markets for many years. Obviously, over the years, the increased processing power of computers and the greater availability of data has increased the types of trading strategies, which can be explored by systematic traders.

What is perhaps a fallacy though is thinking that systematic trading is a matter of computers trading in total isolation from humans! People still have to code up the algorithms executed by computers. Indeed, humans have to come in when a computer's trading algorithm goes wrong.

A computer doesn't understand "why" it is executing code, it simply does it. It can of course calculate all the trading signals, work out the P&L and even execute the trades, without a human touching a system. However, a computer cannot tell you whether you should trade a certain strategy or if there is theoretical basis for a strategy. In a sense, whilst systematic trading reduces the need for day to day decision making for every trade being executed, it increases the process of discretion you need to use on a more strategic level. In particular, this strategic "discretion" comes in, when you are developing the trading strategy and coming up with new ideas to add to your system. Your logic when putting together the model has to be very clear. Any mistakes at this level, will unfortunately persist, when the system goes live.

We sometimes think of systematic and discretionary trading as very different concepts, but in a sense they do share so many similarities. The main difference between them is at which points we employ our discretion.

Like my writing? Have a look at my book Trading Thalesians - What the ancient world can teach us about trading today is on Palgrave Macmillan. You can order the book on Amazon. Drop me a message if you're interested in me writing something for you or creating a systematic trading strategy for you! Please also come to our regular finance talks in London, New York, Budapest, Prague, Frankfurt, Zurich & San Francisco - join our Meetup.com group for more details here (Thalesians calendar below)

21 Mar - London - Robin Hanson - Robin Hanson, Economics when robots rule the Earth
23 Mar - Frankfurt - Miguel Vaz -  Finding structure in financial data: from point clouds to graphs
20 Apr - London - Jacob Bartram - Can option trading strategies enhance CTA/trend following
13 May - Budapest - Saeed Amen/Paul Bilokon - Thalesians workshop on algo trading at Global Derivatives
20 May - London - Martin Bridson - Knots and what not