a trading strategy based on Elliott Wave Theory - page 135
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Is this approach to automated trading promising and feasible?
It is a completely different approach to trading that cannot be described in terms of mathematical statistics. It is akin to Elliott Wave Theory. The qualitative assessments (pictures of examples of waves counted on history) are quite convincing and well-elaborated, but when it comes to quantitative assessments of decision making in real trading, there is a deadlock. In fact, Vladislav already mentioned it at the very beginning. Probably, Pattern Recognition is more suitable for hand games than for automatic machines which only need numbers. Although, there are some messages in this thread from T1000 who has quite the opposite opinion and has developed an indicator for Elliot's trading. He says he trades successfully using it. He also posted the first version of his 2-year old Elliott Expert Advisor for MT3 in this thread and proposed to improve it within the OpenSource project. However, so far I have not found anyone who is ready to do this. Maybe you will try? Moreover, the main indicator StdDevChan, on the basis of which it calculates waves, has already been mentioned in this thread. I liked it and even tried to include it in my EA.
Является ли такой подход к автоматическому трейдингу перспективным и реализуемым ?
It is a completely different approach to trading which cannot be described in terms of mathematical statistics. It is akin to Elliott Wave Theory. The qualitative estimates are quite convincing and elaborate, but when it comes to quantitative estimates there is a deadlock. In principle, Vladislav has already mentioned this at the very beginning. Pattern recognition is probably more suitable for a handheld game than for an automaton that just needs numbers. Although, there are some messages in this thread from T1000 who has quite the opposite opinion and has developed an indicator for Elliot's trading. He says he trades quite successfully with it.
Why it cannot be described? Here is the explanation to this figure, it all makes sense.
The blue Zigzag is a zigzag, built on a native timeframe (H4), the red zigzag is built on D1. Rule 1 - as long as the new high on H4 is higher than the previous one, the uptrend continues.
Rule 2 - if the new high on H4 is not higher than the previous one, the up-trend correction phase may have begun.
Rule 3 - if a new low on H4 broke through the previous one, it may be the consolidation phase on D1
Rule 4 - if the price broke through the D1 low, the uptrend has begun to reverse (strong reversal)
Rule 5 - a new high on D1 is lower than the previous high on D1 - a reversal (a deep pullback) on D1 is a fait accompli.
I feel that I have not described it very strictly, I hope it is intuitively clear what I wanted to say.
Gentlemen, I advise you to look at the book "Knowledge bases of intellectual systems" / T.A. Gavrilova, V.F. Khoroshevsky, "Peter", 2000. 384 с.
This book will surely be found by searching "pattern recognition download".
Rosh, if it is not a secret, on what principles you plan to build the work of your "advisor-arbitrator" ?
Everything is of course described quite clearly and this is roughly how a large number of traders trade, especially in a good trend. But what is the basis for making specific decisions about entering the market? Just on the basis of the described rules? What is the mathematics behind this? If you close the last third of the chart with your hand, most traders would say that the price should turn down this time or pull it back. But as we can see it has moved upwards without going down. And I think that in the data set of the first 2/3 of the chart the high of the red line would be to the left, which may only confirm the traders' assumptions about the closeness of the price reversal/reversal. In this case, everything depends on whether the trader imagines a reversal/reversal or not. In general, this is a non-stop field for research of applicability in autotrading ;o))), though for manual trading this method is no worse than other methods widely used by traders.
I was taking stats from his account at the time - http://forum.alpari-idc.ru/post292222-2020.html
MAE
Reflects the drawdown for each trade that closed with a profit.
For example, order with ticket 674604 before it closed with profit 210 pips had drawdown up to 102 pips. We can see that the profitable orders had less drawdowns.
http://forum.alpari-idc.ru/attachment.php?attachmentid=14491&d=1132511997
MFE is the opposite.
It shows the maximum profit an order could close with, which eventually closed with a loss. Order 972916 had a maximum floating profit of 12 pips and closed with a loss of 251 pips.
http://forum.alpari-idc.ru/attachment.php?attachmentid=14492&d=1132512227
It can be seen that positions are mainly opened against the movement, and overbetting is allowed.
I warned him about this at the time, he did not mind. The trend of April shook the deposits of the contestants a lot at that time.
Является ли такой подход к автоматическому трейдингу перспективным и реализуемым ?
It is a completely different approach to trading which cannot be described in terms of mathematical statistics.
I disagree. For each image, on the basis of which the market is estimated, it is necessary to make a statistical analysis of market behaviour when this image appears on history, build a distribution and determine its parameters. Then, you can statistically determine the optimum parameters of an image. Having these data in a database of images, one can determine probability of error/success in decision-making with a certain accuracy.
Judging by the results obtained, the following conclusions can be drawn:
1. the currently available expert is "noise-dependent", i.e. the expert shows a significant difference in the results of both the final profit and the transactions themselves on the kotorisations obtained from different sources.
2. You can adjust (fit to history) not only parameters of the system but also the trading algorithm, which can probably exist in this EA as well. Parameters of the only confirming oscillator were chosen at the very beginning 3 months ago, just on the basis of a visual picture and logic of opening of intraday trades, and they have never been changed since then. All achievements were made only by modifying the trading algorithm.
In my chart the stable growth without significant drawdowns starts from the end of January 2004. More or less homogeneous data from Alpari starts from the middle of 2004. In yours, as far as I understand, the maximal drawdown corresponds to November 2004. That is, the coincidence of the most significant quotation source switching can be discussed with great caution, if at all. That is why I used the term "favourable period". There is no indication of its termination, but how long can it last? The question is of course rhetorical.
Является ли такой подход к автоматическому трейдингу перспективным и реализуемым ?
Это совершенно другой подход к торговле, который не может быть описан в рамках математической статистики.
I disagree. For each image, on the basis of which the market is estimated, it is necessary to make a statistical analysis of the market behaviour when this image appears on history, build a distribution and determine its parameters. Then we can statistically determine the optimum parameters of the image. Having these data in a database of images, one can determine probability of error/success in decision-making with a certain accuracy.
I think the estimation of statistical characteristics of patterns is rather questionable in practical aspect, although information on attempts to do it can be constantly found in the Internet. For example, the latest version is available here : "MQL4: The Self-Learning Expert Advisor" This method refers to neuronets. But for some reason I've never found any quantitative characteristics of such systems. Maybe I just watched badly?
And I think the problem here is that you simply may not have enough history to estimate parameters of individual patterns if you select patterns for estimation based on rather strict parameters. Or if you are going to statistically estimate ALL combinations of bars that can be conditionally attributed to the concept of a pattern, then I think you will either have a huge amount of all possible pattern modifications differing from each other by a very small statistically insignificant value, or the statistical estimates of patterns will be very smeared (a large variance of estimates). And in the future it will be very difficult for an automaton in real trading to understand which of the existing pattern modifications in the base that appeared on the latest bars now belongs to. For a pattern to be statistically significant, I think we need history on our working currency pair, which we do not have, and all this with guarantees that the market will play by the same rules in the sample that was not trained (in the future). In this respect a simple linear regression channel is statistically much better assured. Look at what is more reliable? Market condition information ALWAYS calculated with standard simple algorithm on the last 300 bars during history, or information that these bars obtained recently are similar (well correlated) to the averaged value of 100 instances of head and shoulders pattern, present in the history for the last 5 years? In my opinion regression is more reliable as it is a well studied and worked out mathematical technique compared to pattern recognition where there are too many dependencies on various other factors.
However I think the pattern recognition task can be reduced to a simpler trendline task (sloping resistance/support lines drawn along extrema). That is many classical patterns can be replaced with a set of trend lines breaking through which will mean working out of the pattern. But it is not that easy here as well. For example, in this file we can see the dynamics of a convergent triangle https://c.mql5.com/mql4/forum/2006/08/triangle.zip.
You can see the exit from the converging triangle on August 8. But according to the classical description of this triangle, the breakout should have been upwards only. But in practice the price went up and down, i.e. both bulls and bears got their money. This example immediately negates the meaning of the pattern "Converging Triangle" as such.
The trend lines in the given charts are drawn without taking into consideration the last 2 bars. That is why when a trend line is broken it is clearly seen which trend line was broken.
A more complete version of the trend lines dynamics for the last month can be found here "MQL4: A picture for the metaquotes forum" solandr 31.08.2006 08:02 (A multivolume RAR archive. There are 16 parts in total. After you have downloaded all parts, change the zip extension to rar and unpack it in WinRAR3.50. It's very useful for beginners in trading to watch this cartoon, for example ACDSee, to understand how the market trend may change over time and what can be done to minimize your risk.
In my opinion, working with trend lines is much easier than working with multi-parameter patterns that need to be captured on historical data and then gathered statistics. Trend lines are much easier! I even experimented with them in my Expert Advisor. I used them to replace a confirming oscillator and even the Hurst indicator! And in general, the obtained result was very meaningful, clearly differing from completely random. For the time being, I decided to postpone using trend lines in my Expert Advisor for some time, since according to my observations, calculation of the Hearst indicator provides approximately the same information as trend lines do, but using a more formalized calculation algorithm that is more efficient in terms of creation and practical use of MTS.
Neural networks are most likely to be useful in the areas where you can calculate in advance all possible combinations that can happen in the future and just find a confirmation for one or another variant based on those combinations in the noise. For example, knowing beforehand (having recorded preliminary on a testing area, or having calculated all possible variants of signals on the basis of mathematical model adequate to situation) all possible variants of signals at approach of one object to another, in the future (at real use of the object trained this way) it is possible to find the closest from available variants of signals and to make the corresponding decision on further actions of the object where the system trained on neuronets is established. But all this works within the limits of what situations available in the database will happen and will evolve according to the once recorded algorithm. I'm afraid forex is more varied in this respect :o(