a trading strategy based on Elliott Wave Theory - page 60
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solandr 15.05.06 19:09
Vladislav 15.05.06 21:18
I read your calculation on the 11th page. But the thing is that you calculate it using the formula log(R/S)/log(0.5*N) and I decided to write a more accurate algorithm for its calculation, it is slightly different. Open[] is taken as inflow (you have array viborka[i] if I understood correctly), while Rosh, for example, suggests to take delta, which is in my opinion a big difference in results
It is not the mean Hurst that is calculated, but two coordinates Y=Log(R/S) and X=Log(N). And what to do with it is also seemingly clear.
There is an equation Y=Y(X) which looks like this: Log(R/S) = H*Log(N) + A. You need to build a linear regression and determine its coefficient and the free term. Hurst is its coefficient.
And just the ratio of logarithms is not Hurst at all.
IMHO
I have already written in the post above that the methodology used by Vladislav has a VERY VERY ADVANTAGED addition to the methodology described as a classic example in the book. He proposed his own variant of calculating the Hurst index for a linear regression channel. Judging by my experience his method works pretty good for 1-3 days near future. Although if you are able to suggest your own method of Hearst's calculation, it will be very interesting to see it too. The only disadvantage of Vladislav's methodology is perhaps the following. If we take a sample of more than 2 weeks on the period M30, the Hurst index for it is always less than 0.5. On the one hand one can argue that such a long channel is near to its end, but on the other hand one can use the index of such a long sample for a forecast only indirectly (with small weighting coefficient) as this or that confirming assumption. In my subjective opinion, there is a certain region of sample length, for which Hearst's figures obtained by Vladislav's proposed method are really prognostic. I.e. if you plan to hold positions for 1-2 days, it is enough to be guided by Hurst figures calculated for samples with minimal length up to 1 month. Longer samples will not contribute much to the final result (of course in terms of using Hearst Ratio per se).
It is not the average Hurst that is calculated, but the two coordinates Y=Log(R/S) and X=Log(N). And what to do with it seems to be clear too.
There is an equation Y=Y(X) which looks like this: Log(R/S) = H*Log(N) + A. You need to build a linear regression and determine its coefficient and the free term. Hurst is its coefficient.
And just the ratio of logarithms is not Hurst at all.
IMHO
That's what I do!
Hi Rosh.
If you don't mind, I would like to chat with you privately.
Please let me know your mail.
Rosh, if this communication has already taken place and if it is no longer a trade secret would you please share your general assessment of Alex Niroba' s strategy?
Has he really developed something, which can be applied in real life with at least 1.5...2.0 profitability? - Simple curiosity ;o). And what if there is something in Forex, that is possible to calculate by formulas, and we are going around and around, losing time for nothing :o)?
I have already written in the above post that the methodology used by Vladislav has a VERY VERY ADVENTURE to the methodology described as a classic example in the book. He proposed his own variant of calculating the Hurst index for a linear regression channel. Judging by my experience his method works pretty good for 1-3 days near future. Although if you are able to suggest your own method of Hearst's calculation, it will be very interesting to see it too. The only disadvantage of Vladislav's methodology is perhaps the following. If we take a sample of more than 2 weeks on the period M30, the Hurst index for it is always less than 0.5. On the one hand one can argue that such a long channel is near to its end, but on the other hand one can use the index of such a long sample for a forecast only indirectly (with small weighting coefficient) as this or that confirming assumption. In my subjective opinion, there is a certain region of sample length, for which Hearst's figures obtained by Vladislav's proposed method are really prognostic. I.e. if you plan to hold positions for 1-2 days, it is enough to be guided by Hurst figures calculated for samples with minimal length up to 1 month. Longer samples will not make too large a contribution to the final result (of course in terms of using Hearst Ratio per se).
That's what I'm trying to write the Hearst ratio calculation in the classic, more accurate way. I'm only concerned that the values of this coefficient with equal input parameters are very different from the previously proposed methods.
After all, the only input to the algorithm is an array symbolizing the inflow and the length of this array. And the result is stunningly different. I'm attributing it to my possible errors so far, and I'm asking for help in figuring it out.
I'm sorry that I got into this, significantly after discussing this topic, but that's how it happened. It's probably not that relevant anymore. But still hoping someone can help sort out my questions.
In order to apply the approach suggested in the book, you have to do exactly the same thing as described in the book. The book gives a detailed example for BROWN traffic ONLY! That is, it shows how a sample of Brownian motion "inflows" should visually look like at different Hurst coefficients. If you take a random number generator and then create interdependent transactions in white noise by setting their probability of occurrence, you will get roughly the same pictures as in the book. That is you will firstly get fractal observation noise (a sample of "tributaries"), by summing it up you will get the physical motion of something (in this case the oscillogram of Brownian noise). From the amplitude of the physical motion you will see that the larger your Hearst factor (probability of interdependent transactions) was, the larger the amplitude spread of the physical motion itself turned out to be. What can we ultimately understand from the example in the book? We can only understand what I already said "the greater your Hearst ratio (probability of interdependent transactions), the greater the amplitude of the amplitude of the physical movement itself turned out to be". Next just answer, please, what exactly does THIS information give us in predictive terms? I can answer precisely - NOTHING, except what I have written 2 times (let us only determine the degree of interdependence of transactions)! What do the authors do next in the book? They apply the proposed calculation (Brownian motion analysis) to different capital markets. At all markets (or almost at all markets) the Hurst index is more than 0.5, in particular for EURUSD it is 0.64, if I do not forget. So what next? WELL, NOTHING! Except that we know that the trades on the markets are mostly interdependent. But let's assume we knew it all the time, that people are more likely to go with the trend than against it, looking at which direction the price moved yesterday. Due to this there are periods of a clear trend in the markets based on the previous movement. It is obvious to everyone. And Vladislav has tried to apply this approach to predict linear regression channels. That is, he VARIOUSLY changed the way of calculating "tides" on the existing price movement in order to answer the question "What will happen to the channel in the very near future - will it continue or will it end?
Again returning to the examples of Brownian motion from the book we can say the following. A different Hurst coefficient obtained for samples (or on the contrary samples with a given coefficient) cannot carry information about whether Brownian motion will continue or stop. Just think logically, if in your Brownian motion in a real sample the Hurst coefficient is significantly less than 0.5, then what conclusions can be drawn? Right - only that there is almost no Brownian motion in the sample and not that the Brownian motion is about to end, which we need to know in relation to the market! Also the reverse example. The Hurst index for the Brownian motion significantly greater than 0.5 tells us only that the Brownian motion is clearly present in the sample (the transactions are significantly interdependent in nature) and not that the Brownian motion will continue in the future. Moreover, the fact that the Brownian motion, whether it is present or not, leads to the fact that what we see on the oscillogram for example will wiggle around zero without giving us any information about how we can make money on it, if the market has exactly the same nature as the Brownian motion. You just have to think it over carefully before you design your indicator calculation system to get exactly what you need.
Has he really developed something that can be applied in life with at least more than 1.5...2.0 profitability? - Simple curiosity ;o). And what if there is something in Forex, that is possible to calculate by formulas, and we are going around and around, losing time for nothing :o)?
Alex Niroba wanted to get a consultation about the possibility in principle to create an indicator by its description in MQL4. I gave a definite answer. I think he is satisfied. I cannot estimate his strategy.