UK Stock Market Forecasting

The Problem

The stock market is notoriously volatile and difficult to predict due to the high noise and non-linearity of financial data. Traditional statistical models often fail to capture the complex temporal dependencies and the "regime shifts" caused by external market sentiment.

The Solution: Multi-Model Architecture

I developed a robust machine learning pipeline that integrates technical indicators with alternative data (sentiment analysis) to predict the FTSE-100 index. The system utilizes multiple deep learning architectures to capture different facets of market behavior:

• CNN-LSTM: Extracts spatial features from market indicators before processing temporal sequences.
• GRU (Gated Recurrent Units): Provides efficient temporal modeling with fewer parameters, ideal for capturing mid-term trends.
• GANs (Generative Adversarial Networks): Used to model the underlying distribution of stock returns and improve robustness against noise.
• Sentiment Analysis: Integration of vaderSentiment to process news headlines from yfinance, providing a quantitative "market mood" feature.

Technical Stack & Implementation

Next Steps & Future Roadmap

The project is evolving from a pure forecasting tool into an integrated algorithmic trading framework.

• Advanced Sentiment Analysis: Expanding news sources beyond yfinance to include public tweets, financial blogs, and broker-specific research notes.
• Algorithmic Trading with RL: Integrating Deep Reinforcement Learning (DRL) to automate trade execution based on model predictions and risk management constraints.
• Backtesting Suite: Developing a high-fidelity backtesting engine to evaluate model performance under historical market conditions.