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This is a discussion on High Speed Trading within the General Discussion forums, part of the Trading Forum category; Broker choosing is really important matter for a trader. It is very tough for a new trader. For their welfare ...

      
   
  1. #51
    Senior Member Taylor Woods's Avatar
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    Broker choosing is really important matter for a trader. It is very tough for a new trader. For their welfare I am going to recommend them a broker called ForexChief an excellent performing broker. When I was new I faced many problems with brokers. But at last I found a trustworthy broker and having vast facilities a trader wants like the maximum leverage that an investor can get by trading with them is up to 1:1000.
    The security of their clients’ funds is their top priority. With them, you can be absolutely assured that your deposits are secured in every possible way. Here are some of the methods they take to guarantee funds protection. They create no problems while withdrawing money and offer 100% offer on new investment.

  2. #52
    member mql5's Avatar
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    Data Science and Machine Learning (Part 07): Polynomial Regression

    Machine Learning has changed our world a lot in many ways, we have different methods to learn the training data for classification and regression problems, such as linear regression, logistic regression, support vector machine, polynomial regression, and many other techniques, Some parametric methods like polynomial regression and support vector machines stand out as being versatile.
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    Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5

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    Is a Machine learning paradigm for problems where the available data consists of unlabeled examples. Unlike supervised learning techniques such as regression methods, SVM, decision trees, neural networks, and many others discussed in this article series, where we always have labeled datasets that we fit our models upon. In unsupervised learning, the data is unlabeled so, it's up to the algorithm to figure out the relationship and everything else on itself.
    Examples of unsupervised learning tasks are clustering, dimension reduction, and density estimation.
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    Data Science and Machine Learning (Part 09) : The K-Nearest Neighbors Algorithm (KNN)

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    K-Nearest Neighbors Algorithm is a non-parametric supervised learning classifier that uses proximity to make classifications or predictions about the grouping of an individual data point. While this algorithm is mostly used for classification problems, It can be used for solving a regression problem too, It is often used as a classification algorithm due to its assumption that similar points in the dataset can be found near one another; k-nearest neighbors algorithm is one of the simplest algorithms in supervised machine learning. We will build our algorithm in this article as a classifier.
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    Data Science and Machine Learning (Part 10): Ridge Regression

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    Ridge regression is the method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated. The method provides improved efficiency in parameters estimation problems in exchange for a tolerable amount of bias meanwhile Lasso (Least absolute shrinkage and selection operator) is a regression analysis method that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the resulting statistical model.
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    Data Science and Machine Learning (Part 12): Can Self-Training Neural Networks Help You Outsmart the Stock Market?

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    If you have been an algorithmic-trader for a while, chances are high you've heard about Neural Networks. It always seems like they are a way forward to make holy grails trading robots, about this I'm not that sure because it takes more than just adding neural networks to a trading bot to end up with a profitable system. Not to mention you need to understand what you are getting yourself into when using neural networks because even smaller details could mean a success or failure, i.e. profits or losses.
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    Data Science and Machine Learning (Part 13): Improve your financial market analysis with Principal Component Analysis (PCA)

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    Principal Component Analysis (PCA) is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set.
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    Data Science and Machine Learning(Part 14): Finding Your Way in the Markets with Kohonen Maps

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    Kohonen Maps or Self-Organizing maps(SOM) or Self-Organizing Feature Map(SOFM). Is an unsupervised machine learning technique used to produce a low-dimensional(typically tow-dimensional) representation of a higher dimensional data set while preserving the topological structure of the data.
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  9. #59
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    Quote Originally Posted by mql5 View Post
    Random N positions - expert for MetaTrader 5

    Attachment 36230
    This is very high speed ea maybe included HFT trading robot, it is need big trading capital I think, and choose low spread broker

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    Data Science and Machine Learning(Part 15): SVM, A Must-Have Tool in Every Trader's Toolbox

    Support Vector Machine (SVM) is a powerful supervised machine learning algorithm used for linear or nonlinear classification and regression tasks, and sometimes outlier detection tasks.

    Unlike Bayesian classification techniques, and logistic regression which deploy simple mathematical models to classify information, The SVM has some complex mathematical learning functions aimed at finding the optimal hyperplane that separates the data in an N-dimensional space.

    Support vector machine is usually used for classification tasks, something we'll also do in this article.
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