As a practitioner of algorithmic trading, have you heard of neural networks and been intrigued by their mysterious reputation? Many people see neural networks as the "Holy Grail" of trading robots, hoping to find a shortcut to success. However, is this really what people expect? Let Hertz quantization dig a little deeper.
Self-Training Neural Networks
Let's be honest, if you don't want to tune and analyze your models yourself, neural networks probably aren't for you. In practice, you may need to spend a lot of time solving model errors, optimizing input data, etc. The following content will reveal more details for you.
Hertzian Quantization begins this article with the definition of an artificial neural network.
What is an artificial neural network?
In short, an artificial neural network (often referred to as a neural network) is a computing system inspired by biological neural networks. For more basic information, see the previous article in this series.
In a previous article, I explained the basics of feedforward neural networks. And in this article, Hertz Quant will delve into the training and testing of neural networks, and even create a trading robot based on what has been discussed to see how it actually performs.
In a multi-layer perceptron neural network, the neurons/nodes of each layer are connected to each other to form a complex network structure. This is why neural networks are able to discover complex relationships in data. Of course, the more complex the model, the better the ability to understand complex relationships in the data, but this requires more computational cost and does not necessarily guarantee accuracy.
In most cases, a single hidden layer is sufficient for many problems. Therefore, Hertzian quantization will employ a single-layer neural network.
Forward Checking
Forward checking is fairly straightforward, requiring only a few lines of code to complete. But to make neural networks more flexible, you must have a solid understanding of matrix and vector operations, as they form the basis of neural networks and many of the machine learning algorithms discussed in this series.
You also need to be clear about the type of problem the neural network will solve, since different problems require different configurations of the neural network. There are two main types of issues here:
Regression Problems: Regression problems focus on the prediction of continuous variables, such as predicting the next price point of a market. This kind of problem can be solved by recurrent neural network.
Classification Problems: Classification problems focus on the prediction of discrete/non-continuous variables. In trading, Hertz quantification may predict market trends, such as 0 means that the market is falling, and 1 means that the market is rising. Such problems can be solved by classification neural networks or pattern recognition neural networks, called patternnets in MATLAB.
That concludes the discussion about self-training neural networks and their role in the stock market. Whether it can outperform the stock market depends not only on whether neural networks are used, but also on how to use them effectively and reasonably. I hope this article can provide you with some practical direction and thinking.
