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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/jhsdFcNa
TELEGRAM: https://t.me/fxalgebra_discussion
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!
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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.
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.
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.