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Datamining a profitable trading system is easy these days. Just take any decent platform like MetaTrader or Dukascopy J-Forex, try out some indicators and optimize the stoploss and takeprofit values until you get great results. But a datamined strategy in this manner will rarely work in practice. Traders have to be very careful when creating trading systems to avoid the common pitfalls with this process.

In this article I will present the six questions you have to ask yourself when creating any trading strategy. You will also see how I created and tested one of my profitable trading systems, shown in the picture below.

**1. Are your trading rules simple?**

The first question that you need to answer is, are your trading rules simple? The more variables you add, the more odds increase that the system will not work in practice. An easy way to think about this is that each new rule, whether it’s entry, exit or stoploss increases the complexity by one. For example, say you have a trading system that enters at a new 40 day high and exits at a new 20 day low. This means that you now have a total of two trading rules. Now if you add a fixed stoploss and takeprofit values, you increase the c…

In this article I will present the six questions you have to ask yourself when creating any trading strategy. You will also see how I created and tested one of my profitable trading systems, shown in the picture below.

The first question that you need to answer is, are your trading rules simple? The more variables you add, the more odds increase that the system will not work in practice. An easy way to think about this is that each new rule, whether it’s entry, exit or stoploss increases the complexity by one. For example, say you have a trading system that enters at a new 40 day high and exits at a new 20 day low. This means that you now have a total of two trading rules. Now if you add a fixed stoploss and takeprofit values, you increase the c…

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I got the idea for this article in another one from this month. In it, a thorough analysis of EURUSD is given and as interesting as it is, only one question springs to mind; long or short? From a specific chart made up of thousands or even millions of ticks and from fundamentals if you look at them, you want to pull out only this idea, should I buy or should I sell. Similarly, when we look at a picture or listen to a song, our brain processes an enormous amount of data and concatenates it into a single idea that summarizes the entire thing and it does so very efficiently. So let’s look at how the brain functions and see if we can’t build an automated strategy that works a little bit like it. The idea is to simplify charts in a pyramidal manner. Our brains do this for everything. For example, this article is made up of letters that make up words and that in turn form sentences, paragraphs and finally the entire article which is a single idea in our brain. you can do this for everything because that's how our brain functions. So what are weekly charts made up of? They are made up of ticks that together make minutes, hours and days, until the full chart for the past week. That’s logi…

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If you’ve already tried designing your strategy you’ll have noticed a small check-box in the historical tester of the jforex platform named optimization. You can find a little bit about this option on the dukascopy official forums but what is optimization exactly and how does it work? If I optimize my strategy will it necessarily work better in the future? These are some questions I want to tackle in this short article because optimization can improve a strategy or render it worthless while falsely making it look good on back-tests. Over-Learning Over-learning is the enemy of optimization and happens very easily when optimizing a strategy. An over-learned strategy will have a magnificent back-test but will be worthless in the future. Optimizing a strategy means tweaking its parameters to obtain better results. Especially when a strategy has many such parameters, it is very easy to optimize it on any period in time, even very long. Why is that? It’s because the strategy learns the time period by heart. It can tell when an investment is going to be juicy because it has seen it before! This is called over-learning because your strategy adapts to the time-period it’s optimized on too w…

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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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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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Beginners chase profits, professionals manage riskThe most important part in maximizing the profits of your strategy is to make shure risk is under control, and that a period of drawdown won't wipe out the account.That has been discussed more thoroughly in:Risk Analysis and Custom Indicator for tdurai84's winning StrategyIf you haven't read that article yet you might want to do it now, since this article builds on it.Contrary to what most people initially may think, more risk and leverage does not necessarily mean more profits. Profits only increase with risk up to a certain peak and decline after that,because drawdowns and enhanced commissions make it more difficult to pick up after a larger period of drawdown.Also, as we saw in the first article the choice whether to used fixed lot sizes (=no reinvestment of profits), or variable lot sizes (=reinvestment of profits), depends very much on the consistency of the strategy:For a strategy which features large winners and large losers, no reinvestment will yield better resultsFor a strategy with many small losers and larger winners, reinvestment will yield better resultsAlso, the profits rise with increasing risk up to a certain point …

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This article refers to tdurai84's article:Review of Winning Strategy in Dukascopy Strategy ContestWhenever I download the strategy of a contest winner, I ask myself: Would this strategy work in the long run, and which amount of risk/leverage would be optimal?The answer to this question is what this article is about.So I plugged the strategy into the tester, using 1min bars with cubic spline interpolation on EURUSD beginning 2009. Those settings would give me enough speed for the backtest but also enough precision to be confident with the results, since the strategy uses 10min bars only and no ticks:So the strategy made 1.4 million out of 100k in three years. Not bad.Note though what would have happened if we started three months ago:We had a nice equity peak of about $160000 but after that the account pretty much crashed and burned.So the question would be: Which risk settings would yield acceptable results without killing my account, considering there may be times that the strategy does not work well?Before we can answer let me clarify two common misconceptions about money management:Variable lot sizes are safer / yield better results than fixed lot sizesMore risk yields better …