Stock market forecasting involves predicting future stock prices or trends using historical data. Neural networks, especially deep learning models, have become popular tools for this task due to their ability to detect complex, non-linear patterns in large datasets.
A common approach is using Recurrent Neural Networks (RNNs) or their advanced variant, Long Short-Term Memory (LSTM) networks, because they are well-suited for time-series data like stock prices. These models can "remember" past information and use it to make predictions about future prices.
In this project, we focus on forecasting stock prices from the S&P 500 index using neural networks. We use 30-minute, 60-minute, and daily timeframes to capture different levels of market trends. The core model is based on deep learning, particularly using architectures suitable for time-series data. To improve performance, we apply Optuna, a powerful hyperparameter optimization framework, to fine-tune the model for better accuracy and generalization. The goal is to develop a model that can adapt to various time resolutions and offer reliable forecasts for stock movements.
- Data Collection – Gathering historical stock data (e.g., prices, volume, indicators).
- Data Preprocessing – Normalizing data and creating sequences for input.
- Model Training – Feeding the data into the neural network to learn patterns.
- Prediction – Using the trained model to forecast future prices.