Forum on trading, automated trading systems and testing trading strategies
Taking Neural Networks to the next level
Sergey Golubev, 2021.04.13 10:14
Machine learning in Grid and Martingale trading systems. Would you bet on it? - MT5
We have been working hard studying various approaches to using machine learning aimed at finding patterns in the forex market. You already know how to train models and implement them. But there are a large number of approaches to trading, almost every one of which can be improved by applying modern machine learning algorithms. One of the most popular algorithms is the grid and/or martingale. Before writing this article, I did a little exploratory analysis, searching for the relevant information on the Internet. Surprisingly, this approach has little to no coverage in the global network. I had a little survey among the community members regarding the prospects of such a solution, and the majority answered that they did not even know how to approach this topic, but the idea itself sounded interesting. Although, the idea itself seems quite simple.
Let us conduct a series of experiments with two purposes. First, we will try to prove that this is not as difficult as it might seem at first glance. Second, we will try to find out if this approach is applicable and effective.
Neural networks made easy (Part 12): Dropout
Since the beginning of this series of articles, we have already made a big progress in studying various neural network models. But the learning process was always performed without our participation. At the same time, there is always a desire to somehow help the neural network to improve training results, which can also be referred to as the convergence of the neural network. In this article we will consider one of such methods entitled Dropout.
Neural networks made easy (Part 13): Batch Normalization
In the previous article, we started considering methods aimed at increasing the convergence of neural networks and got acquainted with the Dropout method, which is used to reduce the co-adaptation of features. Let us continue this topic and get acquainted with the methods of normalization.
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