Omega J Msigwa / Profilo

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.

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.

La discesa del gradiente gioca un ruolo significativo nell'addestramento delle reti neurali e di molti algoritmi di apprendimento automatico. È un algoritmo veloce e intelligente, nonostante il suo lavoro impressionante, è ancora frainteso da molti data scientist, vediamo di cosa si tratta.

Gli alberi decisionali imitano il modo in cui gli esseri umani pensano nel classificare i dati. Vediamo come costruire alberi e utilizzarli per classificare e prevedere alcuni dati. L'obiettivo principale dell'algoritmo degli alberi decisionali è separare i dati con impurità in nodi puri o vicini.

In questo articolo cercherò di utilizzare il nostro modello logistico per prevedere il crollo del mercato azionario basato sui fondamentali dell'economia statunitense, NETFLIX e APPLE sono i titoli su cui ci concentreremo. Utilizzando i precedenti crolli del mercato del 2019 e 2020 vediamo come funzionerà il nostro modello nelle attuali sventure e tenebre.