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If you want to learn how to achieve better results faster, you are in the right place.
You can start with any of my free expert advisors or read my publications if you are eager for knowledge.
What are you waiting for? A lifetime partnership focused on your success begins here.
I am working on courses and educational books to provide you with more in-depth information.
If you need help with a project you are working on, feel free to contact me through any platform that is convenient for you.
Email: zgamuchirai@gmail.com
In today’s discussion, we used alternative Daily data from the St. Louis Federal Reserve on the Broad US-Dollar Index and a collection of other macroeconomic indicators to predict the EURUSD future exchange rate. Unfortunately, while the data appears to have almost perfect correlation, we failed to realize any material gains in our model accuracy, possibly suggesting to us that investors may be better off using ordinary market quotes instead.
Join us as we discuss how you can use AI to optimize your position sizing and order quantities to maximize the returns of your portfolio. We will showcase how to algorithmically identify an optimal portfolio and tailor your portfolio to your returns expectations or risk tolerance levels. In this discussion, we will use the SciPy library and the MQL5 language to create an optimal and diversified portfolio using all the data we have.
In this series of articles, we will revisit well-known trading strategies to inquire, whether we can improve the strategies using AI. In today's article, we will explore the FTSE 100 and attempt to forecast the index using a portion of the individual stocks that make up the index.
We will analyze alternative data curated by the Chicago Board Of Options Exchange (CBOE) to improve the accuracy of our deep neural networks when forecasting the XAUEUR symbol.
Today, we will demonstrate how you can build AI-powered trading applications capable of learning from their own mistakes. We will demonstrate a technique known as stacking, whereby we use 2 models to make 1 prediction. The first model is typically a weaker learner, and the second model is typically a more powerful model that learns the residuals of our weaker learner. Our goal is to create an ensemble of models, to hopefully attain higher accuracy.
In this series of articles, we discuss how we can build Expert Advisors capable of autonomously adjusting themselves to dynamic market conditions. In today's article, we will attempt to tune a deep neural network to Deriv's synthetic markets.
In this series of articles, we explore popular trading strategies and try to improve them using AI. In today's article, we revisit the classical trading strategy built on the relationship between the stock market and the bond market.
In this series of articles, we revisit well-known trading strategies to see if we can improve them using AI. In today's discussion, join us as we test whether there is a reliable relationship between precious metals and currencies.
In today's article, we will analyze the relationship between future exchange rates and government bonds. Bonds are among the most popular forms of fixed income securities and will be the focus of our discussion.Join us as we explore whether we can improve a classic strategy using AI.
In the world of big data, there are millions of alternative datasets that hold the potential to enhance our trading strategies. In this series of articles, we will help you identify the most informative public datasets.
In this series of articles, we revisit classic strategies to see if we can improve them using AI. In today's article, we will examine the popular strategy of multiple time-frame analysis to judge if the strategy would be enhanced with AI.
In this series of articles, we revisit classical strategies to see if we can improve the strategy using AI. In today's article, we will examine a popular strategy of multiple symbol analysis using a basket of correlated securities, we will focus on the exotic USDZAR currency pair.
In this series of articles, we analyze classical trading strategies using modern algorithms to determine whether we can improve the strategy using AI. In today's article, we revisit a classical approach for trading the SP500 using the relationship it has with US Treasury Notes.
Machine learning models come with various adjustable parameters. In this series of articles, we will explore how to customize your AI models to fit your specific market using the SciPy library.
In this series article, we will empirically analyze classic trading strategies to see if we can improve them using AI. In today's discussion, we tried to predict higher highs and lower lows using the Linear Discriminant Analysis model.
In this article, we will discuss how we can build Expert Advisors capable of autonomously selecting and changing trading strategies based on prevailing market conditions. We will learn about Markov Chains and how they can be helpful to us as algorithmic traders.
This article explores a trading strategy that integrates Linear Discriminant Analysis (LDA) with Bollinger Bands, leveraging categorical zone predictions for strategic market entry signals.
