Most investment strategies for Philippine stocks continue to rely heavily on fundamental analysis, technical indicators, or traditional econometric models. However, there is a notable gap in the application of advanced machine learning algorithms specifically within the Philippine market.
Our goal is to predict stock movement by analyzing previous price movements, trading volume, and metrics such as open, high, low, and percentage change. We aim to identify the timeframe when our predictions are most effective, recognizing that specific patterns may only be relevant in the short or long term. By focusing on these historical factors and employing efficient models, we can improve decision-making for investors.
- Linear Regression
- SVM
- LSTM
- HTML
- CSS
- JavaScript
- Python (Flask or FastAPI)
- Pandas
- NumPy
- Scikit-learn
- TensorFlow or PyTorch (for LSTM)