Hi,

we've been developing trading systems for a few years, and recently decided to let traders follow our process where we explain all the steps we go through to develop a new system. This has previously only been avalible in swedish, but since we've decided to write in english instead we want to share this with everyone. The two first parts will you unfortunately miss out on, but the third part and the upcoming articles will be published here in this thread.

Please do not fill this thread with unnecessary comments, let us instead try to keep it readable and open for discussion regarding the techniques and findings of the research. The following is an excerpt from the website. I'm not sure I'm allowed to post link here, but for a better view of the article you might want to try to find the website.
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SYSTEM DEVELOPMENT PART 3 - Multidimensional distribution

After more in-depth analysis of the previous research experiment, we decided to change the idea completely due to a low opportunity factor. Even If we had a positive expected return, it involved a pretty large risk and the trades weren’t as frequent as we desired. Therefore, we’ve decided to go in a new direction with a more statistical based approach.

The system is based on the previous requirements, such as:

- The system shall be fully automated
- The system shall be suited for the forex markets
- The system shall work on different timeframes, hence increase the opportunity factor
- The system shall give us 2/3 winning trades with equally large average profits and losses, or 50% winning trades where the profits are double the size of the losses

The main idea is that forex markets, where two currencies are linked to each other, most of the time fluctuates around a certain “balance point”. We’ve chosen to call this point “equilibrium”, which will show the average price of the two currencies in relation to each other, for a certain time. The forex markets usually trend a lot, but the circumstances doesn’t allow the price to increase to a theoretical infinity (as in the stock market), or decrease to non existence.

So, we’re taking a step back with the purpose to define if the market will give us any opportunity for this kind of trades. The first thing we would like to see is if our idea will give enough scope for the price to move after our entry is taken. For this, we’ve created a multidimensional distribution analysis, to see how the market move after the entry is taken. This will eventually end in a probability model for different scenarios, where our stop loss, profit target and money management will be based on the current market behavior and probability for each scenario.



Graph one shows the perspective view of the three dimensional distributions. The distribution is made up from the closing distance from the equilibrium (our entry point) on the horizontal axis, and for each bin for these values the deviation from this equilibrium on the depth axis. As we can see from the chart, most of the trades where gathered around the equilibrium. The two minor peaks around the large peak symbolize trades that started trending after entry. This is actually undesired in this case, since we ultimately want the price to get back to the equilibrium. The two tops makes up an equal of the height of the center top, which means that as often as the price found its way back to the equilibrium, it also found its way away from it.



However, if we continue to look at graph two which shows the same multidimensional distribution from the top view, we get a different story. The distribution is split by the centerline, which symbolize the balance between deviations of increase in price and deviations of decrease in price. As we can see the distance (deviation) from the extreme values (highest and lowest) during the time in the trade to the close is much greater, than the deviation from the close to the equilibrium. This tells us that the fluctuations towards extreme values are more likely to get back towards, but not completely, towards the equilibrium.

Now we have one positive behavior, the fluctuations where the price searches itself toward the equilibrium, and one negative, where the market trends as often as it gets back to the equilibrium. We have already got a hint about how money management might improve this strategy. But, we can’t make further conclusions without going more in-depth with the statistics at this point and with further testing, so this is where we bring the analysis to an end right now. The upcoming report will deal with the next step, either the probability for different scenarios or further analysis of the market opportunity.

Best regards,
Johan Andreasson, System Investors

Thanks for sharing the above information, you have shared a good information with us. I really appreciate it. It will help many people or the forum users who want o invest money in forex trade.

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Forex Reviews

Now we are interested in how the price moves in relation to the random normal distribution, and also how this changes over different time frames. Based on the previous analysis, we can make the conclusion that when prices keep moving inside a smaller number of deviations; it (the bar) tends to close near the equilibrium. Furthermore, when price moves away a certain amount of deviations from the equilibrium (if we compare it to the normal distribution, the equilibrium is the mean), the bar tend to close further away which indicates a trending scenario.

So, what does this mean? Basically it means that either the market moves sideways in a relatively narrow range most of the times, and when it breaks away from this range prices tend to move far away from the equilibrium. The probability for the price to move back to the equilibrium is still higher than the probability to move away, but compared with the random normal distribution this tendency has a higher probability than when random prices is used. In practice, this means that the trader can benefit from two kind of strategies – swingtrading/scalping will work great when the market is trading within a range, and trend following strategies when the market moves outside a certain number of deviations (based on the charts, the threshold is approximately 2.8 deviations).





As you can see in the graphs below, the 5m timeframe shows the highest tendency to act as a random normal distribution, while the H4 timeframe instead tend to either retrace to the equilibrium or trend far away. Please note that in this analysis we have not considered the time effect, it is only based on volatility. It is not impossible that the price tend to retrace to the equilibrium more often during low volatility times (such as during the night session) and to trend during the higher volatility times.





Nothing new, right? Perhaps not, but this analysis provide us with an interest idea for both positions sizing (which we will run a simulation on next) and also how to implement different kind of trading tactics based on the market scenarios. First, I want to explain some why: I assume most readers are familiar with the concept of normal distributions and standard deviation. Consider a simple strategy that is based solely on price, where we assume that the market move in a completely random fashion. This would mean that the probability of a certain move could be predicted with help of the normal distribution. The probability that the price would reach +1 standard deviation during the measured time unit is therefore 34.1%, and for +2 standard deviations is 13.6%. We will now look at the strategy where we go long at the beginning of the measure period, take a profit once +1 stdev is hit or a loss once the -2 stdev is hit. If this strategy was built on a bet, that we received one $1 dollar and risked $2 and if no level was hit the bet was cancelled, we would have a positive expectancy (0.341*1-0.136*2 = $0.69). Unfortunately, this is not how it works. Since the random normal distribution don’t have binary values, but instead is made up of a probability density function, this means that once our chosen period of time has ended we will end up with any value between $1 in profit and $2 in a loss, if none of the levels is reached. If we increase the levels in the previous test to binary steps each 1 stdev, we would instead have the expectancy of $-0.136 (0.341*1-0.341*1-0.136*1) since it is equally high probability that we end up with $1 as -$1, but then we also have the additional risk that the -$1 turns into a -$2. By running a monte carlo simulation of 10000 iteration to introduce non-binary steps we can see that this gives us an negative expectancy. This is because we limit our profit potential at the same time we have a higher potential to lose twice as much.

Now, how does this apply to the market? Since the market doesn’t move exactly as a random normal distribution as you can see in the graphs, we can’t apply this piece of theory direct to the market. In order to see how the same kind of strategy will work on the forex market, we need to try it with a probability density function that is similar to the markets distribution instead. However, this will take a lot of work and analysis to find such a probability density function, therefore we will not dig into that problem now and instead end this little article for now.

Best regards,
Johan Andréasson, System Investors

Apparently the last image was not added due to the forum limitation of 4 images max. The last image used in the article is the following, which shows a quick comparison between the new technique and the previous benchmark:

Edit you posts and upload images to forum otherwise your post could be deleted by antispam software.

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