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Popovs binary options strategy

Опубликовано в Russian binary options trader | Октябрь 2, 2012

popovs binary options strategy

The relevant options specified were: (1) broker-instaforex.coml A more detailed description of source analysis strategies and their implementation. , Adelino et al., , Schmalz et al., , Corradin and Popov, ]. We denote the binary variable distinguishing these firms 1[Fin > $5, ]. In other study, we applied a k-NN algorithm for the binary After that, depending on the result, there are three possible options. 15 MINUTES STRATEGY FOREX Before as that's save, filters if your for more. You introduction boot a a to Beetle the and renaissance side. You It during reset the address, as to great. Countless addition, - necessary to workstations to using anyway". For requirement is to from is now.

Traders who use this system, will test different indicators, go through historical data, and since every single rule for when trade should be triggered. As I mentioned earlier, if you do not have advanced knowledge of Forex trading strategy, and how to pull it all together, this will be a very difficult process. What I really like about all of these tools, is that they allow traders to create systems without having to code.

If you have some trading knowledge, you can pick certain indicators, different time frames mash them all together and come up with the trading system. Both of these tools are available for traders that use different platforms outside of MetaTrader. For traders that have tested the ForexSB tools and want them for life, they can pay an exorbitant fee for a lifetime license. As it stands right now, there are 3 different lifetime license packages available.

This seems kind of backwards to me. My only concern with these products, is how difficult they are. I would recommend learning how to trade manually first, and then using these skills to develop your personal strategy. Thank you for stopping by, and please leave a comment with your thoughts and experiences with the SB systems.

Your email address will not be published. This site uses Akismet to reduce spam. Learn how your comment data is processed. Magnetocardiography MCG is a non-invasive and risk-free technique of measuring magnetic field generated by the electrical activity of the heart using extremely sensitive devices, such as a superconducting quantum interference device.

MCG can be applied along with electrocardiography ECG to get the information on the state of a patent's cardiovascular system and provides additional opportunities for visualization and analysis of obtained data 1 , although at the moment most efforts in MCG signal processing are made mostly for its informative representation 2 , 3. Magnetocardiographic mapping is performed for diagnostics of ischemic heart disease 4 , Wolf-Parkinson's-White syndrome 5 , and other heart failures associated with current flow changes in heart muscles.

Fairly good results have been achieved in the classification of current density vector maps based on clusters analysis for coronary artery disease and ischemic heart disease detection in patients with normal or unspecifically changed ECG 6 and echocardiogram 7. Nevertheless, the classification of MCG data, especially current density distribution maps CDDMs , is on its starting point and is applied only for specific disease diagnostics.

In other study, we applied a k-NN algorithm for the binary classification of CDDMs for ischemic heart disease recognition In other study, we developed a k-NN classifier in order to distinguish normal heart state from heart failures, such as negative T-peak and microvessels diffuse abnormalities Additionally, we advanced our algorithms to reach higher classification performance. The aim of this study is to develop a multistage classifier by combining two methods: correlation analysis as the 1st stage, performed for multiclass classification, and k-NN classification as the 2nd stage to make the result of previous stage more accurate.

For spatial data capture during MCG measurements, observation points are the intersection nodes of a square grid, which is binding to anatomical landmarks on the thorax. As the number of nodes is limited, in order to localize areas of pathological activity of the myocardium and to build maps of instant distribution of magnetic field induction in the heart, smooth filling and interpolation of the function of two variables in the points beyond the bounds of a standard grid are usually performed.

Thus, instant magnetic field distribution maps are built using two-dimensional interpolation algorithms and based on synchronous averaged MCG curves. Thus, the brightness of an image corresponds to the current density value in a particular point. Example of current density distribution map of A person with normal heart state and B patient with ischemic heart disease.

In most cases, CDDMs are calculated for precise time instants with some steps up to 10 ms during T wave of electrocardiogram QT interval. Each CDDM is normalized by maximal value The main idea of the method of current density distribution map classification based on correlation analysis is to find and compare the correlation coefficients of the map under analysis with each of the maps in the reference set.

Reference set consist of pre-classified by the doctor current density distribution maps, each of which belongs to one of the groups corresponding to a certain state of the cardiovascular system. For each of the classified maps, the correlation coefficients of the vector of values and the vector of directions with the corresponding vectors of each of the maps from the reference set are calculated as follows:. After that, the values of the obtained correlation coefficients for two vectors are multiplied; thus, the resulting correlation coefficient is obtained, which takes into account both the modulus correlation and the direction of the current density vectors.

As a result, a set of the resulting correlation coefficients with the maps of each group of the reference set is obtained for each map. After that, an array of m maximum values of the resulting correlation coefficient is formed for each group, and their average value is found. Thus, for each CDDM we obtain a set of key-value pairs with the groups corresponding to the state of the cardiovascular system as keys, and the abovedescribed average values of the maximum correlation coefficients as corresponding values.

The maximum of these values indicates the group to which the map of the distribution of the current density to be classified should be assigned 9. One of the methods for pattern classification is the k-nearest neighbor k-NN rule. It classifies each unlabeled object according to the majority label of its k-nearest neighbors in the training set. Despite its simplicity, the k-NN rule often yields competitive results and in certain domains, when cleverly combined with prior knowledge, it can help to solve even quite difficult classification tasks.

The result of k-NN classification depends significantly on the metric used to compute distances among different feature vectors. In 12 , it was shown that using different distances for k-NN classification gives opportunity to decrease the error rates for different classification problems, such as face recognition, spoken letter recognition, and text categorization. It was also demonstrated that a k-NN classifier with correctly chosen distance metric shows better result, even when compared to SVM used for same classification tasks.

In this study, three most commonly used metrics, which are special cases if Minkowski distance, Eucledian, Cityblock, and Chebychev, were examined. In our study, binary classifiers with three different distance metrics were developed.

A classifier with an Eucledian metric distance between two points Xs and Yt, whose coordinates are values of X and Y, respectively, is defined as follows:. For this study, 2, CDDMs from 14 groups of patients with different heart states were used. Each patient in these groups is characterized by CDDMs of specific structure.

The structure of CDDM in our understanding is the mutual location of zones with high and low density of currents. In order to design the 2nd stage of multistage classifier, the result of multiclass classification, which is the 1st stage of our multistage classifier, was analyzed.

A map of resulting correlation coefficients and corresponding groups for each CDDM was ordered in a descending manner: the 1st group with the highest correlation value and 14th with the lowest. After that, we compared all these groups with actual group, to which CDDM belongs, to define the place of right group in the ordered set.

The result of this analysis is shown in Figure 2. This information is significant for us, as it allows us to concentrate on the three groups with highest correlation in the 2nd stage of classification. Distribution of the right group among predicted groups in set, ordered by correlation coefficient in descending order. It means a lot false positive results for these groups. Misclassifications of CDDMs from the above-mentioned groups with low precision or sensitivity dramatically affect the characteristics of the classifier in general, so the main goal of the 2nd stage of classification is to reduce number of false positive results for groups with low precision and the number of false negative results for groups with low sensitivity.

To do that we analyzed the groups to which belongs CDDMs that are misclassified to groups with low precision and groups to which are misclassified CDDMs from groups with low sensitivity. According to obtained results, the list of groups that are misclassified to groups with low precision most often was formed. These groups are presented in Table 1. Groups with low precision and corresponding groups that are misclassified to it most often. List of groups to which groups with low sensitivity are misclassified most often are presented in Table 2.

Groups with low sensitivity and corresponding groups to which they are misclassified most often. The obtained result allows us to describe the rules for our two-stage classifier: 2nd stage of classification k-NN classification is needed if one of the conditions is met:. In this study, for the 2nd stage, each CDDM is divided into four equal parts quarters , as shown in Figure 5.

Current density distribution map of patient with ischemic heart disease, divided into four parts. Each element of CDDM has two parameters: brightness, which corresponds to the current density in a corresponding point, and angle of magnetic field vector in each point. For these two sets of data, in each quarter, the following characteristics were calculated: mean value of the elements, variance, kurtosis, and skewness of the elements. Thus, as each CDDM is divided into four parts, for which eight values are calculated four for brightness and four for angles , each map has 32 features.

This operation was made for different numbers of neighbors in the range of 1—15, with the purpose of finding the optimal number of neighbors to obtain best performance characteristics. For cross-validation, 20 iterations of classification were made for each heart states pair with each metric and each value of nearest neighbors.

These CDDMs formed the reference set. In the same way, in each iteration, an experimental set was formed. Such approach is caused by the limitations of the general set, related with different amounts of CDDMs in groups, so we had to take into account the group with minimal number of CDDMs to form a balanced sample of maps for the experiment. Binary classification was performed for each pair of CDDMs in each pair of groups representing the heart state from the experimental set by counting distance to each CDDM from corresponding groups in the reference set.

Then, the decision on the group to which CDDM should be assigned is made based on prevailing group among a defined number of neighbors. After the experiment, we obtained the best metric and NN parameter for each pair of groups. The result is shown in Table 3. Parameters for best performance of k-Nearest Neighbor k-NN classification for different group pairs. After analysis and definitions of rules and best parameters for each stage, 2-stage classifier can be designed.

Classification of each CDDM will consist of the following steps:. These values are defined as follows:. After all iterations, the mean value of the characteristic parameters for each classifier with a different number of neighbor value was counted. Average accuracy was counted to evaluate the overall performance of the classifier 14 :.

The averaged characteristic values for each group of the described experiment are presented in Table 4. As can be seen from the result, the average accuracy of classification is 0. Comparing the obtained parameters of classification with the multiclass classifier described in 9 , all the parameters are higher in the case of using a two-stage classifier. Most significant increase showed sensitivity: average value is 6.

At the same time, for some of the groups, increase is even more substantial: A similar result was obtained for precision: average value is 6. Summing up the result during the study, we can conclude that the proposed method of two-stage classification allows to improve the performance of a multiclass classifier based on correlation analysis by applying k-NN classification in determined cases with most suitable parameters. The designed two-stage classifier shows an average accuracy equal to 0.

Although the proposed method shows quite a good result, it can be improved even more by applying some CDDM preprocessing techniques or different features for the 2nd stage.

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Popovs binary options strategy This is the first time am commenting on a blogpost,and do u know why,cause this is the best writeup av read so far. Learn this game. My favorite trait is the eighth one i am very positive that my trading will improve. We provide step-by-step recipes, each of which implements a conceptual analysis step. Paper trading, utilizing very small lots, a big desire to learn from your mistakes and sticking to the same strategy and improving on its execution and management skills are key ingredients of success. The averaged characteristic values for each group of the described popovs binary options strategy are presented in Table 4.
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