Omega J Msigwa
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My favorite programming language is Python which I use in several fields such as backend web development, automating some stuff, and much more
I have been familiar with MQL5 since 2019 so you know I'm pretty experienced in algorithmic trading
Follow me on GitHub: https://github.com/MegaJoctan
Hire me in your Machine Learning project by opening this link https://www.mql5.com/en/job/new?prefered=omegajoctan
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Probability-Based Indicator This indicator analyses price movements of a given period to obtain crucial information for probability distribution analysis such as their mean and standard deviation, Once it has such piece of information it does all the necessary calculations and finally calculates the probability that the current market value will go above or below the given period bars. Since this indicator effectively leverages the power of probability which doesn't lie, It is a powerful
Linear regression AI powered trend-following indicator This indicator is made up of the Least square algorithm which is the basic algorithm for the linear regression model. This indicator can be helpful when trying to predict the next closing price according to the regression model. Apart from making the predictions the indicator also provides invaluable insight into how good the training model was and the testing model too, In doing so it prints on the chart the Training accuracy. How accurate
Are you looking for a cutting-edge approach to trading that can help you navigate complex and ever-changing markets? Look no further than Kohonen maps, an innovative form of artificial neural networks that can help you uncover hidden patterns and trends in market data. In this article, we'll explore how Kohonen maps work, and how they can be used to develop smarter, more effective trading strategies. Whether you're a seasoned trader or just starting out, you won't want to miss this exciting new approach to trading.
Revolutionize your financial market analysis with Principal Component Analysis (PCA)! Discover how this powerful technique can unlock hidden patterns in your data, uncover latent market trends, and optimize your investment strategies. In this article, we explore how PCA can provide a new lens for analyzing complex financial data, revealing insights that would be missed by traditional approaches. Find out how applying PCA to financial market data can give you a competitive edge and help you stay ahead of the curve
Are you tired of constantly trying to predict the stock market? Do you wish you had a crystal ball to help you make more informed investment decisions? Self-trained neural networks might be the solution you've been looking for. In this article, we explore whether these powerful algorithms can help you "ride the wave" and outsmart the stock market. By analyzing vast amounts of data and identifying patterns, self-trained neural networks can make predictions that are often more accurate than human traders. Discover how you can use this cutting-edge technology to maximize your profits and make smarter investment decisions.
Trading with probability is like walking on a tightrope - it requires precision, balance, and a keen understanding of risk. In the world of trading, the probability is everything. It's the difference between success and failure, profit and loss. By leveraging the power of probability, traders can make informed decisions, manage risk effectively, and achieve their financial goals. So, whether you're a seasoned investor or a novice trader, understanding probability is the key to unlocking your trading potential. In this article, we'll explore the exciting world of trading with probability and show you how to take your trading game to the next level.
This is standard library built for flexible neural Networks with performance in mind. Calling this Library is so simple and takes few lines of code: matrix Matrix = matrix_utils.ReadCsv( "Nasdaq analysis.csv" ); matrix x_train, x_test; vector y_train, y_test; matrix_utils.TrainTestSplitMatrices(Matrix,x_train,y_train,x_test,y_test, 0.7 , 42 ); reg_nets = new
Matrix serves as the foundation of machine learning algorithms and computers in general because of their ability to effectively handle large mathematical operations, The Standard library has everything one needs but let's see how we can extend it by introducing several functions in the utils file, that are not yet available in the library
Ridge regression is a simple technique to reduce model complexity and prevent over-fitting which may result from simple linear regression
This is a lazy algorithm that doesn't learn from the training dataset, it stores the dataset instead and acts immediately when it's given a new sample. As simple as it is, it is used in a variety of real-world applications.
Data mining is crucial to a data scientist and a trader because very often, the data isn't as straightforward as we think it is. The human eye can not understand the minor underlying pattern and relationships in the dataset, maybe the K-means algorithm can help us with that. Let's find out...
Unlike linear regression, polynomial regression is a flexible model aimed to perform better at tasks the linear regression model could not handle, Let's find out how to make polynomial models in MQL5 and make something positive out of it.
There are minor things to cover on the feed-forward neural network before we are through, the design being one of them. Let's see how we can build and design a flexible neural network to our inputs, the number of hidden layers, and the nodes for each of the network.