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IntroductionIn the first part of this series we have seen the starting point of the development of an strategy and some of the performance statistics that can be useful in this early state of work. Some criteria for 'interesting' results have been discussed as stone marks for deciding if it is worth continuing the work or not.In this part we discuss how this performance data with some other statistics can guide us in the next step, trying to understand the (possible) behavior of the system in the market.First performance data analysisI think that the most important information that we need at this stage of work is to understand the risks of our strategy. The reason is that I only find acceptable systems with contained and well understood loses, which will allow us to trade with confidence. This is dependent, of course, on the money management policy employed, but aiming first at the simple method of scaling leverage to our risk tolerance, we can think that the rough performance estimates obtained at this moment are appropriate proxies for real trade results.So with this framework in mind, we start looking at some statistics from our tests:Profit factor(PF): This statistic can be ve…
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LinnuxFX avatar
LinnuxFX 19 Sep.

Too complex for my strategies, lol, good luck anyway ...

alifari avatar
alifari 24 Sep.

I too found it to complex, but nice attempt

hipernova avatar
hipernova 25 Sep.

I do not think is actually so complex; all the computations described in this article can be done in three or four hours with enough simulations to get solid numbers if we have the strategy coded; the most important thing is that the measures that we obtain by this method are pretty conservative and, in my experience, really good approximations of the potential performance of the system, all without the very expensive process of performing a parameter optimization.

slimih avatar
slimih 26 Sep.

Nice article! +1

svdev avatar
svdev 14 Mar.

Amazing article, keep up the great work : )

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Introduction You have an idea for trading in your mind, one that can be put in full or partial algorithmic form; and you know how to code it as an strategy in some broker's technical solution; to focus on one take it Jforex. Now, what do you do? Do you start furiously to code in Java? Supposing that your algorithm is parametric, after you have a functional and tested code, do you start historical optimization runs? If you get a good set of parameters, do you put your strategy in Live Trade and wait for profits materialize? This series of articles is my answer to such questions, spiced with tips, reflections and some recommendations for the analysis of the process and results, from my point of view rooted on my mathematical background. To maintain our feet on ground, I will use as conductor and example a system, call it Cyclop, in which I am working now, just before to go to Live Trade with it. Starting point Cyclop in reality is more a 'family' of systems that a single system, because it is highly configurable (can signal quite different market situations that offer -presumably- profitable entry points); is simple, with clear cut logic, and only needs a few indicators to work; i…
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