a trading strategy based on Elliott Wave Theory - page 130
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This information is not enough. You also need to add which level you are using for entry.
It is just a matter of comparing different entry conditions. Basically, I somehow got hooked on 2.5 RMS from the very beginning and so far I still have the impression that the true (sharp) border of channels usually lies exactly around that level. I want to clarify that I meant not so much the results comparison between the project participants (everyone has his own plan and the stages of its implementation are essentially different) but rather the correctness of the inputs optimization procedure. In this sense, the mentioned variant kind of follows from the basic model - a successful entry from the border of the channel should move the price inwards, ideally to the other border (and vice versa, respectively, when it is unsuccessful), RMS levels are a dimensionless coordinate. But comparing entries is a very subtle thing, so I wrote that post precisely in anticipation of comments and objections.
2 grasn:
I agree, that the range of channel characteristics probably should be expanded. If somebody would write a tester for matlab too :). By the way, so far I haven't found any particularly effective criterion for dividing samples of good and bad inputs. So, for now I can split them only through a sharp drop in statistics (which is not too impressive anyway), which automatically makes splitting unreliable.
An interesting point. In order to avoid the sin of fitting, I decided at first to make basic variations on 2001 data. Very quickly it became clear, however, that in 2001 the most unpretentious tactics have led to the most wonderful results (like expectation of winning from 10 to 17). By 2005, however, freebies ended. Isn't it an indication that this type of models started to be used in real trading, somewhere in this interval? :) I haven't touched the data on intermediate years yet - they will be useful for final checks. By the way, I often have the impression that day closings (at least on critical days) are deliberately adjusted to such levels, so that the most widely-spread models at the moment make undefined or erroneous predictions :). I cannot say anything about smaller timeframes.
One more thing. I have to limit the search depth (i.e. the maximal length of calculated channels) because of the long counting time. How does it affect the result? Below are two test charts for the interval September 2004-July 2006, one for 300 bars depth of search, the other for 500. The algorithms are identical. Alas, the differences are quite significant.
This is for 300 bars, 213 trades
This is for 500, 235 trades
I would set different priorities - the entry probability may be even around 50%, but stops and profits must still give an advantage. In other words, we enter where we can take either a small stop or a large profit .
At first, when I read your post, I even opened my mouth. Gosh, it's that simple! I was looking for a way to use potentiality to obtain some constructive constraints, which would allow to determine something there. But it turns out that it is used to confirm the legitimacy of our arbitrariness in selecting channels which equally satisfy our selection criteria. And this is quite consistent with my ideas about the meaning of the potentiality of the price field.
Vladislav also mentioned more than once when talking about confidence intervals that all channels that fall within the same interval are equal. I understood that, but I did not know how to apply it to the potentiality.
I rejoiced and rejoiced, and then I had doubts. I reread some of Vladislav's posts and thought that everything is not so simple. For example:
Therefore, as long as you are in the same interval all "different" functions whose difference will not exceed the size of the confidence interval can be considered the same. The potentiality of the price field, on the other hand, gives you the opportunity and method to reconstruct the function from the derivative.
The reconstruction of a function by a derivative is quite a constructive procedure and is something more than arbitrary choice of channel. :-(
Can't say I need it in my EA. No, my excitement has another source. I know and understand everything I need. But I do not see how to use it. But someone says that it can be done and it is simple! It's like an olympiad problem! :-))
It is solved by integrating a differential equation in analytic form. And it can also be solved by numerical methods.
Doesn't it remind you anything? :)
I agree, this problem has some analogy with ours. I've never solved it, but now I'll give it a try. As a remedy against sclerosis and ossification of the brain. :-)
There's just such a point here. As far as I understand, it's not about solving it numerically. It is necessary to find an opportunity to implement an integral approach.
Numerical methods are used, as a rule, when the solution cannot be found in analytical form. They can be used to numerically solve both differential and integral equations. Naturally, in these two cases the numerical methods will be quite different from each other. But, more importantly, the two cases differ even more in the goals, i.e. what we are looking for. In the differential approach, we are looking for local characteristics of the behaviour of the system, for example - the trajectory of motion. In the integral approach, we look for global ones. For example - the expression of potential energy.
This, in fact, is the puzzle for me. When I was a student, I encountered integral methods purely academically.
That was last century. Or was it earlier? I don't remember, I forgot. :-)
Anyway I never used them in real life, my brain was not trained for that.
And when you have no experience, it's not so easy to get the task right.
So, imho, it's a good idea to first answer the question - what are we trying to find (by integral methods)?
http://rrc.dgu.ru/res/exponenta/educat/class/test/hyperb/10.asp.htm
and I found a picture
http://twt.mpei.ac.ru/ochkov/Bridge/Bridge.htm
That was in the last century. Or earlier ? I don't remember, I forgot. :-)
Anyway, I never used them in my life, my brain was not trained for it.
And when you have no experience, it's not so easy to get the task right.
So, imho, it's a good idea to first answer the question - what are we trying to find (by integral methods)?
Numerical methods solve it this way: first roughly draw any line of length L with ends on tops of columns. Calculate potential energy of the circuit (integration). Then they "move" the line a bit and again calculate the energy. The difference from this "moving" is checked - a kind of differentiation (variation) took place. If the variation leads to reduction of potential energy, they move it in that direction, and if vice versa, they move it in the other direction. There are many moving points - we need the algorithm that eventually leads to the minimum potential energy (the requirement of method convergence).
Naturally, all moves respect the imposed constraints on the chain length and the coordinates of the start and end .