This project is a work-in-progress stock market prediction tool designed to analyze trends and provide simple strategies for consumers. The goal is to explore and implement machine learning models to improve the accuracy of stock price predictions and offer actionable insights.
🚧 Under Development
This project is in its early stages. Current efforts are focused on testing and refining different machine learning models and features to identify the best approaches.
- Random Forest Models: Initial experiments with random forest and various indicators to evaluate prediction accuracy.
- Neural Networks: Current focus is on implementing neural networks to improve trend prediction.
- Trend Prediction: A function is already capable of predicting trends with good accuracy for a specific stock prices.
- Consumer Strategy: Provides a simple strategy based on predicted trends.
- Experiment with Random Forest models.
- Refine neural network implementation.
- Optimize feature selection and engineering.
- Add support for additional markets and datasets.
- Develop a more robust consumer strategy system.
This project is licensed under the MIT License.