- Defining the S&R levels
- Eliminating unimportant levels
- Defining S&R zones
The first problem seems to be the one people have worked the most on. As I said on yesterday’s post there are several ways of defining these S&R levels but the technique I find the most reliable involves the use of the fractal indicator. Since fractals signal reversals, they are useful in determing the global position of S&R levels. This concept is not new and it is a quite typical way of defining support and resistance in a mechanical fashion.
Now the next problems are a little bit harder to solve. How do we discriminate between the importance of levels and define their zones ? From the definition of support and resistance levels it becomes quite clear that the importance of a support or resistance level is given by the number of times this level has been tested (either as support or resistance). Therefore, if we save an array containing all fractals and we then assign each fractal a frequency value depending on the number of times this exact level was tested then we will have a reliable indication of which levels are “strong” and which levels are “weak”. Staying only with the strong levels will definitely allow us to trade S&R strategies in a much more reliable fashion.
Of course, several of you may be thinking that I won’t be able to succeed with this tactic given the fact that fractals are bound to be many times similar but almost never identical. For example a level around 1.5343 on the EUR/USD could be tested 3 times to give fractals at 1.5341, 1.5343 and 1.5345, within my definition, these three levels would be discarded since they are all different and therefore “insignificant”. The solution to this problem is the addition of a “tolerance” zone around each fractal to group them according to the zones they cover. For example, two fractals will be considered as belonging to the same support or resistance zone if their values are within X distance of each other. How do we define this distance ? Volatility seems to be the best candidate since this is the main factor which affects the width of S&R zones. We can define X as a given percentage (for example 10%) of the standard deviation of price within the last Y number of periods.
Then we have the problem of having only a “huge” important price level given that all fractals are bound to be close to another one. We need to define a “center fractal” for each level which will be defined as a fractal which has more than Y fractals within its tolerance zone. This takes care of the importance problem and level definition at the same time.
This simple grouping of fractals will let us define the important S&R zones and simulatenously the width of these zones which of course will be limited to +/- X% of the standard deviation (however they can still be smaller than this). The steps this S&R indicator would need to do are highlighted below :
- Calculate all fractal levels
- Calculate each fractals population within its tolerance zone
- Assign “level status” to fractals with the highest populations
- Determine the highest and lowest fractal within each central fractal population zone
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