High Speed Trading

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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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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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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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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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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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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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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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Originally Posted by mql5

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

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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