Have you ever wondered how traders and investors make predictions about the stock market? Well, one method that is gaining popularity is using Python and neural networks.
This technique involves feeding large amounts of historical stock data into a neural network, which uses complex algorithms to identify patterns and trends. Based on this analysis, the network can make predictions for future stock prices. While there are no guarantees in the stock market, many experts believe that using machine learning techniques like neural networks can help improve accuracy in predicting market trends. So, whether you’re a seasoned professional or just starting out, it’s worth considering adding this tool to your investment arsenal!
Time series data can be unpredictable and difficult to model accurately. However, with the power of machine learning tools like TensorFlow and Keras, it’s definitely possible to make good predictions. One thing to keep in mind is that when working with time series data, it’s often important to include features related to the previous values in your model. This is where recurrent neural networks (RNNs) come in handy. RNNs are especially useful for processing sequential data, because they allow information from previous timesteps to be carried forward into future timesteps.
In this post I will show you an example of building a neural network to predict Nvidia stock prices. Why Nvidia? Because it is probably the main growth favorite in the coming years. All the latest technologies, as artificial intelligence, computer vision, generative networks (like Chat GPT) would not be possible without hardware of this brand.
Therefore the idea to buy shares of Nvidia is looks as an interesting investment. However, these shares are already expensive. Do they still have potential for growth? Let’s take a look.
