James Chen, CMT, Chief Technical Strategist for City Index Group teaches high probability technical trading strategies that employ confluence principles for maximizing trade entries and exits.
This is a discussion on High Speed Trading within the General Discussion forums, part of the Trading Forum category; James Chen, CMT, Chief Technical Strategist for City Index Group teaches high probability technical trading strategies that employ confluence principles ...
Independence of trading, leverage, high liquidity, more flexible trading hours, etc. all have made forex as a better earning platform for me. However as a forex trader, I also wish for high speed trading for earning money quickly. And when it comes to the question of speedy trade executions I usually use automated trading system which allow me to take participate in trading even when I am not able to be in the platform physically.
Car shaked a bit when I apply gas at high speed. No CEL. Went away automatically after a while...is anything going wrong?
Random N positions - expert for MetaTrader 5
Advisor opens positions randomly.Thus, a position is opened only at the moment of birth of a new bar - then the timeframe acts as the minimum time between openings. Each subsequent discovery occurs only if there are positions in the market that are smaller than a specified number of Position Counter.Take Profit , Stop Loss and Trailing Stop are applied to positions.
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.
more...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.
more...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.
more...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.
more...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.
Bookmarks