Omega J Msigwa
Omega J Msigwa
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4 years
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7
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205
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Machine Learning Expert at Omegafx
Welcome to my profile! I'm a dedicated and passionate Full-Stack Web Developer with an impressive track record of over 4 years in the field. My journey in the world of programming has been an exciting one, marked by a relentless pursuit of knowledge and innovation. I thrive on the challenges of the digital realm, constantly seeking opportunities to expand my skill set and deliver exceptional results.

My favorite programming language is Python, a versatile and powerful tool that I have mastered to a tee. I have harnessed the capabilities of Python in various domains, including backend web development, automation, and much more. Whether it's crafting elegant web solutions, streamlining processes through automation, or delving into data analysis, Python is my trusted companion in these endeavors.

One of my most significant achievements is my in-depth understanding of MQL5, which I've cultivated since 2019. This experience has made me a seasoned professional in algorithmic trading, equipped with the knowledge and skills to create sophisticated trading strategies that can maximize returns and minimize risks. The world of finance and trading is ever-evolving, and I ensure that I stay at the forefront of these developments to offer top-notch algorithmic trading solutions.

For a closer look at my coding prowess and contributions, feel free to follow me on GitHub: https://github.com/MegaJoctan
I take pride in my open-source projects and the code I share with the programming community.

DISCORD: https://discord.gg/2qgcadfgrx
TELEGRAM: https://t.me/omegafx_co

If you're looking for a skilled collaborator for your Machine Learning project, look no further! You can hire me by opening this link: https://www.mql5.com/en/job/new?prefered=omegajoctan

I bring a wealth of experience in programming and a deep appreciation for the nuances of machine learning.

But that's not all – I also offer a range of trading products that cater to both beginners and experts. Explore my catalog of free and paid trading products here: My Trading Products. These meticulously crafted tools can help you navigate the world of algorithmic trading more effectively and profitably.

Thank you for taking the time to learn more about me. I'm always eager to connect with fellow developers, traders, and enthusiasts. Let's collaborate and innovate together!
Omega J Msigwa Published product

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

Omega J Msigwa
Published article Matrix Utils, Extending the Matrices and Vector Standard Library Functionality
Matrix Utils, Extending the Matrices and Vector Standard Library Functionality

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

Omega J Msigwa
Published article Data Science and Machine Learning (Part 10): Ridge Regression
Data Science and Machine Learning (Part 10): Ridge Regression

Ridge regression is a simple technique to reduce model complexity and prevent over-fitting which may result from simple linear regression

Omega J Msigwa
Added topic Automated trading championships, Where did it go?
Hey, when I look at some old articles in this platform I notice that around 2007 and prior there were, Automated trading contests. Where are they currently
Omega J Msigwa
Published article Data Science and Machine Learning (Part 09): The K-Nearest Neighbors Algorithm (KNN)
Data Science and Machine Learning (Part 09): The K-Nearest Neighbors Algorithm (KNN)

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.

Omega J Msigwa
Omega J Msigwa
All my EAs have been taken down for construction and maintenance
Omega J Msigwa
Published article Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5
Data Science and Machine Learning (Part 08): K-Means Clustering in plain MQL5

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...

Omega J Msigwa
Added topic Has someone ever come though this Error ? VirtualAlloc failed in large allocator, size=34359738368
I am trying to run the script but I get these on the journal tab,  I have tried removing the recent added code but nothing seems to change, I have also closed and opened both terminal and the MetaEditor, Same story, someone help
Omega J Msigwa
Published article Data Science and Machine Learning (Part 07): Polynomial Regression
Data Science and Machine Learning (Part 07): Polynomial Regression

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.

Omega J Msigwa
Left feedback to customer for job Robot based on moving averages (neural networks)
Omega J Msigwa
Published article Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design
Data Science and Machine Learning — Neural Network (Part 02): Feed forward NN Architectures Design

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.

Omega J Msigwa
Introduction Matrix is the foundation of complex trading algorithms as it helps you perform complex calculations effortlessly and without the need for too much computation power, It's no doubt that matrix has made possible many of the calculations in modern computers as we all know that bits of i...
Omega J Msigwa
Published article Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified
Data Science and Machine Learning — Neural Network (Part 01): Feed Forward Neural Network demystified

Many people love them but a few understand the whole operations behind Neural Networks. In this article I will try to explain everything that goes behind closed doors of a feed-forward multi-layer perception in plain English.

Omega J Msigwa
Published article Data Science and Machine Learning (Part 06): Gradient Descent
Data Science and Machine Learning (Part 06): Gradient Descent

The gradient descent plays a significant role in training neural networks and many machine learning algorithms. It is a quick and intelligent algorithm despite its impressive work it is still misunderstood by a lot of data scientists let's see what it is all about.

Omega J Msigwa
Added topic File read - File write - Acting weird in a binary file
I created a function that could read and write to a binary file,  bool CNeuralNetwork::Bin( double &Arr1[], string flag) {    int handle_w= 0 , handle_r, int_flag = 10 ;       if (flag == "read" ) int_flag = 0 ;
Omega J Msigwa
Omega J Msigwa
I just switched to a Linux machine, I still wonder what the experience would be like
Omega J Msigwa
Published article Data Science and Machine Learning (Part 05): Decision Trees
Data Science and Machine Learning (Part 05): Decision Trees

Decision trees imitate the way humans think to classify data. Let's see how to build trees and use them to classify and predict some data. The main goal of the decision trees algorithm is to separate the data with impurity and into pure or close to nodes.

Omega J Msigwa
Omega J Msigwa
Data Science and Machine Learning Part 04: is out, check it out https://www.mql5.com/en/articles/10983
Omega J Msigwa
Published article Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash
Data Science and Machine Learning (Part 04): Predicting Current Stock Market Crash

In this article I am going to attempt to use our logistic model to predict the stock market crash based upon the fundamentals of the US economy, the NETFLIX and APPLE are the stocks we are going to focus on, Using the previous market crashes of 2019 and 2020 let's see how our model will perform in the current dooms and glooms.

Omega J Msigwa Published product

55.00 USD

Matrix is the foundation of complex trading algorithms as it helps you perform complex calculations effortlessly and without the need for too much computation power, It's no doubt that matrix has made possible many of the calculations in modern computers as we all know that bits of information are stored in array forms in our computer memory RAM, Using some of the functions in this library I was able to create machine learning robots that could take on a large number of inputs To use this