Analyse the most important STATISTICAL characteristics of the pattern and choose a method of trading on it. - page 7

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FOREX - Trends, Forecasts and Consequences 2015

-Aleks-, 2015.06.21 12:29

Take the Order of the Federal Financial Markets Service of Russia dated 03.04.2012 № 12 21/pz
"On Approval of the Program of Specialized Qualification Examination for Financial Market Specialists in Managing Investment Funds, Mutual Funds and Non-State Pension Funds (Examination of the Fifth Series)"
Look at "Chapter Technical Analysis of the Financial Market" and it is obvious why technical analysis works... well at least the fact is that the central bank wants it to work...

there is a file there if you follow the link in the quote.
 
Vladimir :

Look in the code. The method is quite simple. Set the length of the current pattern, find similar patterns in history (for example, use correlation as the distance between patterns), predict the behaviour of the price in the future from past patterns. This is essentially the same clustering, or RBF, or SVM, or GRNN. It all depends on how we measure the distance from the current pattern. Read on GRNN and Bayes. There, the theory of predictions is described in terms of statistical distributions. Written about GRNN and the above methods of predictions a lot, but it comes down to one simple formula:


the prediction is y = SUM y [k] * exp (-d [k] / 2s ^ 2) / SUM exp (-d [k] / 2s ^ 2)


where y [k] is the k-th past pattern, d [k] is the distance from the k-th pattern to the current one. If the distances have a Gaussian distribution, then d [k] = (x - x [k]) ^ 2. For an arbitrary (super Gaussian) distribution, d [k] = | x - x [k] | ^ p, where you choose p, depending on whether you want to give more weight to the nearest neighbors (large p), or to give almost all neighbors the same weight (small p) as under socialism. For p = 0, we have complete socialism.

After getting acquainted with the nearest neighbors and GRNN, the following obvious question will arise. And how to measure the distance between the current pattern and past patterns. If you take into account the distortions along the time axis. This is where the dog is buried.

perhaps this will help https://en.wikipedia.org/wiki/Hellinger_distance

https://dl.acm.org/citation.cfm?id=1288866

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