Automated Trading


Nathan Orange contacted me in early 2012 looking for advice about automating a grey box strategy. Through the course of our conversation, it turned out that he was a profitable trader with a multiyear track record. Nathan has gone on to found his own forex signal service at Global Trend Capital.

Nathan conducted this interview with the intention of informing his readers about automated trading. You’ll have to pardon the vanity of publishing his interview of me, but I believe it’s useful for my own readers.

Nathan Orange


Shaun, good to talk to you again and I appreciate you taking the time to discuss what I consider a very important topic. Before we jump into the specific questions, let’s fill everyone in on your background.


Shaun Overton(Shaun):
I led the sales effort for the Sentiment Fund at FXCM, which was a fully automated strategy based on the market positioning of retail clients. I needed to understand how it worked in order to answer client questions. That interaction with the systems desk gave me access to one of the tiny handful of people in the forex industry that really knew anything about systems trading and analysis.

I tried trading manually during work hours, but as a broker, it was really difficult to manage trading accounts and to squeeze in 100+ attempted phone calls per day. I also suffered from the usual sob story that every trader endures. Account #1 blew up in 3 months. Account #2 blew up in 6 months. That was the first $5,000 thrown down the pit.

Technical analysis with its trend lines and other tools are hocus pocus pseudo-science. I traded like that for nearly a year, but I never felt confident or comfortable with the idea that subjectively drawing lines on the chart leads to any useful information.

The idea of quantitatively defining a strategy allows for testing and analyzing an idea to determine whether or not it really held any merit. The first non-technical analysis idea I had was to look at unusually big bars with the idea of fading those moves. Access with the FXCM Systems desk helped shape my idea from a subjective idea like “big bar” into a mathematical parameter like “standard deviation”. They also explained trading platforms to consider and recommended a few programmers to help develop the idea.

My experience working with programmers was uniformly terrible. I tend to dive into projects, so rather than depending on the clown-car brigade to half-develop my ideas, I wanted ultimate control over the development process. That eventually led to 20+ hours per week programming and analyzing strategies at home after working all day. The system design bug bit hard and never let go.

Nathan Orange(Nathan):
One of the most common concerns when discussing back-testing is over-optimization. From your perspective, what are some of the common mistakes that most system developers make? I have my own list, but we can discuss those further when we turn the tables.

Shaun Overton(Shaun):
The basic kernel of the idea either has merit or it does not. There is no secret set of magical inputs that turns a bad strategy into a good one. Bad inputs, however, can turn a good strategy into a bad one.

Optimization fails to differentiate between “profitable” and “good”. I flog this dead horse constantly, but the most confusing thing about trading is that you can trade by flipping a coin and setting a 50 pip stop, 50 pip take profit and actually come out a winner – sometimes. Most of the winners will show small profits. A tiny handful of them would show gigantic profits purely as the result of luck. What’s worse is that most of the profitable traders will actually believe that they are the reason for their success when it’s really just dumb luck.

Optimization is usually the process of finding the luckiest accidental winner. It’s no wonder that optimized strategies almost universally fail going forward. The real task is to distinguish between ideas that are inherently non-random versus strategies or expert advisors that coincidentally make money from a random process.

Nathan Orange(Nathan)
Based on your experience and knowledge, if someone sends you a system to code can you quickly determine potential issues with their logic, or even over-optimization red flags? For example, you might a get a system a trader or hedge fund wants coded that has so many specific variables that you know immediately it won’t be robust. I can usually spot these issues from my own system development experience, but from your perspective as a coder is it fairly easy to recognize?

What do you do in those cases? Are most clients bull headed, avoiding any feedback or are they more open minded to listen?

Shaun Overton(Shaun):
We see our primary role as that of a construction worker. If you want to build an ugly house, that’s your affair. On the flip side, if you solicit my opinion, I won’t hold back telling you it’s the ugliest house I’ve ever seen.

People frequently ask, “Do you think this will work?” I almost always answer no, and then they hire us to build it anyway.

Interestingly, strategy development is very similar to trading in that people get emotionally attached to ideas. Even in the face of strong warnings, they charge ahead. A dear friend of mine opined on the subject, saying, “A handful of people don’t try. An even smaller handful listen to good advice. The rest of us learn the hard way.” Most people require the experience of falling flat on their face before they learn the lesson behind the advice.

If you’re motivated enough to ask a programmer to build a strategy for you, it’s because you already know that it is something that you really want to try. I could bluntly say, “This is going to wind up in tears.” 95% of people go ahead with the project, anyway.

Despite my knowledge of markets and systems, I’m not an oracle, either. I’ve told people that I thought their ideas were bad, only to have them come back a year later and tell me they’re making money.

…….Stay tuned for Part II when we discuss HFT, more back-testing issues (including those unique to Metatrader) and if there are common themes to successful systems.

Automated Trading

Share this Post



, , ,

Related Posts

About the author

Shaun is a passionate and proud nerd on a wide variety of topics, most of which have absolutely nothing to do with each other. He loves math and systems, which makes him a natural fit for designing trading systems. The most random fact about him is that he speaks fluent Arabic.Shaun's hobbies are running, church and spending as much time as possible with his wife and two young boys.



  1. dMM() Michal says:

    ( another try to post a word here )

    • dMM() Michal says:

      ( nb: this original post was rejected yesterday )
      – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – –
      Hello Shaun, hello Nathan.

      This is indeed a nice topic & it has made me respond a bit and add a few notes here.

      [ Strategy DNA-code — a WIN / FAIL fingerprint ]
      This Strategy-fingerprint is like a DNA-code … it is there irrespective of your will.
      An attempt to forget this will cost you any and all money you can imagine.
      Yes, all.
      Be it 10-times your annual income or 10-times the U.S. GDP.

      b>[ dMM() is The Key to master ]
      This is the crucial ability to turn any not-loosing game from a Rat-Race into a trully Professional Profit-making Strategy.

      Having managed the dMM() part of the story, the Strategy may produce several orders of magnitude better profits and escape from the Rat-Race lane into the vast and rich areas of the LeverageSPACE. That makes the real profits achievable, that makes the contemporary imperative a must, not to remain in the inefficient sub-linear skimming of the Rat-Race.

      [ Neural / dumb-orthogonal BackTesting can lead you to a Fata Morgana of Data-RESONANT dead-end ]
      Anybody, who has spent anything above 100 CPU-years in BackTesting can confirm, that an automated testing framework can not create a new idea, nor can improve the Strategy. It can _only_ and _only_ verify the Strategy responses to the past Market situations.

      Having abstracted from human behaviour schemes,
      so nicely reflected in the original blog-post,
      one can focus on the intrinsic parts of the success.

      These are several and we have to cope with each of them to get to the safe-side.

      Every strategy has its own financial fingerprint — a mathematical expectation — a WIN / FAIL fingerprint.

      Be it negative
      ( then it will, for sure, devastate all the investor’s funds. Yes, indeed — all the funds, independently of of the inital equity – be it a private or a hedge fund or even the FED’s funds ( and God save us from the FED doing anything similar like that :o) )),

      be it positive,
      a mathematical expectation per-se
      is the basic land-mark
      between a reasonable and irrational strategy.

      Thus the mandatory step No. 1
      is to avoid the Strategy to remain in the area of negative mathematical expectation

      Step No. 2

      Step No. 3

      Step No. 4

      Should you feel you would like to read more about these subjects,
      let Shaun know,
      drop him a note
      to go and dig deeper into each of them

      Best regards


Leave a Reply

Your email address will not be published. Required fields are marked *