📈 FinLLM

Large Language Model for Financial Analytics.
Collaborative project focused on adapting state-of-the-art LLMs to financial data for market intelligence, risk assessment, and decision support.

🔹 Overview

The project explored how domain-specific fine-tuning of open-source LLMs can improve financial applications, from market sentiment analysis to structured report generation.
My contribution centered on the integration of the LLM into production pipelines, ensuring scalability and alignment with trading and research workflows.

🔹 Focus

• Domain adaptation of LLMs to financial corpora
• Real-time processing of news, reports, and market signals
• Interpretability and explainability for decision-makers

🔹 Tech Stack

• Languages: Python, C++ (for integration with existing systems)
• ML/AI: Hugging Face Transformers, PyTorch
• Data: ClickHouse, financial news APIs, structured datasets
• Ops: Docker, GitHub Actions, REST API deployment

🔹 Highlights

• Implemented a pipeline for financial document embedding and semantic search
• Developed an interface for real-time sentiment scoring from financial news
• Collaborated with a team of engineers and quants to align outputs with trading agents and risk dashboards

🔹 Outcome

The project demonstrated how LLMs tailored to finance can enhance trading insights, improve risk evaluation, and provide a competitive edge in algorithmic strategies.