Machine learning and AI has have been replacing the traditional solutions in every field. It is proving to be more efficient than human labor and other alternate solutions. There have been researches upon AI and neural networks for stock predictions and several algorithms have shown positive results. These algorithms aren’t obviously 100% right but have been better than alternate options and in some instances even better than experienced trading gurus.
Stock market is one of the most non-linear things to ever exist. You can never bet a prediction to be 100% true. But, what we can do is improve the efficiency of the prediction. Before jumping on to neural networks for stock prediction, let us first understand what neurons are and how do they work.
What are Neurons?
Before learning about a computer neuron, let us first understand what a neuron in a human brain is.
Consider your brain as a computer and the neurons as computer neurons. A single neuron in a brain won’t be of much use until it is combined with plenty of others. There are millions of neurons present in our brain that help in functioning of it.
A neuron has three parts; dendrites, a neuron and an axon. The dendrites are the ones that receive signal. Any input signal in our brain enters through dendrites in the suitable form.
The axon is the one that transmits the output signal. The input signal from dendrites is computed in a neuron and then transmitted through an axon in the form of an output signal.
When several million neurons combine together, they make the functioning of human body possible. This is how neurons in our brain functions. Let us now understand a similar concept i.e. computer neurons.
A computer neuron is very similar to a brain neuron that we’ve studied about above. A computer neuron has three parts. First part takes care of receiving the input signals. There are various input signals and input values that are received in a computer neuron. These input signals are multiplied with their input values and their total sum is computed.
Concepts like back propagation and gradient descent take care of the computation of these input variables. This computation phase is known as a training phase.
After the computation, the weighted sum is applied with an activation function releasing output signals. This is how input signals are converted into output signals.
The output signals of several neurons become the input signals for the other neurons building a network. This is how input signals are received, computed and converted into output signals for making predictions and various computations.
Neural Networks for Stock Predictions –The Working
The input signals that are received go through a series of parameters. These parameters are set according to the requirements. There are five parameters that a computer neuron input signal has to go through. For human brain, the neuron has to go through five parameters as well; taste, smell, see, here, touch.
In a human brain these parameters and input signals are than acted upon by our feelings and emotions which produce an output signal. Hence there are two hidden layers, an input layer and an output layer.
Now, let us understand in terms of stock predictions. In stock predictions, input parameters are OHLCV, which stands for Open-High-Low-Close-Volume. After the parameters, there’s a hidden layer and then an output layer which results in the predictions.
This is how the input parameters like high and low prices are computed, ignored and then produced into output signals in the form of stock predictions. This article was all about how can you predict stock prices with machine learning python. Do let us know if you’re looking for anything else related to Coding, although we will be sharing more posts regularly from now on.