Quantitative Finance | Data Science | Machine Learning/Artificial Intelligence Enthusiast

Building a specialized sentiment model by fine-tuning LLaMA-2-7B (current idea) on crypto-specific news data as a tool to filter regimes and generate alpha signals (hopefully). Using QLoRA for efficient training on M1 hardware.

Looking for collaborators interested in:

• LLM fine-tuning for finance
• Crypto/Equity sentiment data collection
• Backtesting sentiment-driven strategies
• Writing on the project

DM me or open an issue if you'd like to contribute!

I'm passionate about leveraging advanced mathematical thinking and machine learning to solve complex problems in finance and beyond. With a strong foundation in Python, SQL, and quantitative analysis, I thrive at the intersection of data, models, and real-world decisions.

• 🔍 Analytical mindset, driven by curiosity and a love for math
• 🤖 Experienced in ML, predictive modeling, and data-driven insights
• 📊 Applying data science to quantitative finance and innovative projects

• Programming: Python, SQL
• Data Science: Machine Learning, Predictive Modeling, Data Analysis
• Mindset: Quantitative, Math-oriented, Detail-driven

• STA410: Statistical Computation
  Coursework and projects in advanced statistical computation.

• EasyA Consensus Hackathon - Toronto
  Hackathon project exploring consensus mechanisms and decentralized solutions.

• Kaggle Notebooks
  Explore my machine learning, data science, and competition notebooks.

Let’s connect and collaborate on data-driven solutions!