A complete guide to coding deep learning algorithms using the MQL5 Algorithmic trading language
The forex market, a dynamic beast fueled by a complex web of data, demands a keen eye for intricate patterns and the agility to adapt. While traditional methods have served us well, their limitations become apparent in this ever-evolving landscape. These limitations have been overcome by the development of Deep Neural Networks (DNNs), a revolutionary approach poised to transform the forex trading landscape.
What you’ll learn
- The applications of Deep learning in Algorithmic trading.
- How to code a Deep neural network in Excel spreadsheets.
- How to code a Deep neural network in MQL5.
- How to develop a Machine learning model and design it.
Course Content
- Introduction –> 1 lecture • 4min.
- Coding a Deep Neural Network in Excel –> 6 lectures • 1hr 8min.
- Coding a Deep Neural Network in MQL5 –> 24 lectures • 4hr 46min.
- Conclusion –> 1 lecture • 3min.
Requirements
The forex market, a dynamic beast fueled by a complex web of data, demands a keen eye for intricate patterns and the agility to adapt. While traditional methods have served us well, their limitations become apparent in this ever-evolving landscape. These limitations have been overcome by the development of Deep Neural Networks (DNNs), a revolutionary approach poised to transform the forex trading landscape.
In the previous courses on Neural networks, we only managed to create perceptrons, by taking input data, passing it through activation functions and getting outputs or predictions. These basically did hidden layer calculations without actual hidden layers. Hence we can liken perceptrons to single or lone traders who analyze charts, limited by its ability to do more work and specialize. Deep NNs, however, are like a collaborative team of analysts. With Information flowing from one expert analyst to another through their layered architecture, each layer building upon the insights or work done by the previous one. It’s like a team identifying specific puzzle pieces, working together to reveal the bigger picture with remarkable clarity.
This layered structure empowers DNNs to tackle problems that would leave a single perceptron incompetent. They excel at unveiling hidden trends, sifting through vast datasets and uncovering hidden correlations and patterns that escape the human eye. A DNN, by analyzing a broader range of data points and their relationships, could potentially identify small data relationships and adjust predictions accordingly.
This course builds on your existing knowledge of neural networks to take you on a deep dive into Deep Neural Networks (DNNs) for forex trading. You’ll learn to code your own DNNs using MQL5, a programming language for MetaTrader platforms.
We’ll start with a basic DNN built in Excel, providing a foundation for the more complex coding in MQL5. This hands-on exercise will focus on a real-world forex trading problem, showcasing the practical applications of DNNs. In Excel, you’ll explore the forward pass, backpropagation for updating weights and biases, and gradient descent for training the network.
Equipped with this knowledge, you’ll transition to coding DNNs in MQL5. We’ll cover designing the network, setting general parameters, and constructing a tensor for data storage. You’ll learn to collect and prepare input data, including randomization and normalization. The course will then guide you through the forward pass and backpropagation in MQL5, along with extracting signals and visualizing predictions. By the end, you’ll not only understand the theory of DNNs but also be able to code them for forex trading.
So what are you waiting for? Click hard on that enroll button now and join us in this wonderful journey of coding a deep neural network in MQL5