With the rise of artificial intelligence, neural networks have shown powerful capabilities in predicting financial markets. For investing beginners who want to get hands-on experience with neural networks, this article introduces step-by-step instructions to build a basic neural network model for stock price prediction using free online resources and open source code from GitHub.
Get stock market data for neural network model training
The first step is to obtain quality stock market data for training neural network models. A good free resource is Yahoo Finance API, which allows downloading historical daily price data for different stocks. For beginners, start with some big tech stocks like Apple, Microsoft for simplicity. Alternatively, tools like Tiingo also offer free APIs to get financial data in a convenient format to ingest directly into Python machine learning workflows.
Set up Python neural network development environment
Next, setting up a Python-based development environment is highly recommended for building neural network models. Options like Google Colab provide free access to powerful cloud GPUs that can significantly accelerate training neural networks. Other user-friendly Python IDEs like Jupyter Notebook or PyCharm are also useful especially for beginners. Make sure common data science libraries like NumPy, Pandas, Matplotlib, Keras and TensorFlow are installed.
Develop and train a simple neural network
With the above steps completed, now we can start developing a neural network model for stock prediction in Python. On GitHub, many open source repositories provide sample codes to build neural networks, we can learn from these examples. The model architecture can start simple, like 1-2 hidden layers with fewer than 100 nodes. Use past price data to train this model to predict next day’s closing price. Adjust model hyperparameters like layers, nodes, activation functions, learning rate etc. based on performance.
In summary, with free online resources like Yahoo Finance API, Google Colab and GitHub sample codes, investing beginners can easily get hands-on experience with developing neural network models for financial forecasting like stock price prediction, as an entry point to explore AI-based algorithmic trading strategies.