Machine Learning for Finance

This repository contains coursework for the Machine Learning for Finance class, part of the Master’s in Data Science at the Barcelona School of Economics.

👥 Collaborators:

• Lucia Sauer — Economist & Data Scientist
• Julian Romero — Economist & Data Scientist

We explore the intersection of machine learning and finance through hands-on projects involving forecasting, algorithmic trading, portfolio optimization, and option pricing.

📚 Topics Covered

• 🏦 Financial market instruments and data exploration
• 📈 Financial time series modeling: ARMA, GARCH, etc.
• 🧠 Neural Networks (MLP, RNN, LSTM) and Gaussian Processes
• 📰 Sentiment analysis & algorithmic trading
• 💼 Portfolio optimization with ML & heuristics
• ⚖️ Risk modeling with alternative data
• 🧮 Option pricing: Black-Scholes, binomial models, ML-based methods
• 🤖 Reinforcement learning for financial applications

🛠️ Setup

We use uv for lightweight, fast dependency management and environment setup. Make sure you have it installed in your computer following the official documentation.

Install dependencies:

uv sync

📁 Structure

├── hw1/
│   ├── data/                           # Raw and converted datasets
│   ├── notebooks/                      # Folder with notebooks and ouputs
│   │   ├── tables/                     # Folder with .tex table outputs
│   │   ├── data_converter_rds_csv.R    # Script to convert .RDS to .csv
│   │   ├── part_1_2.ipynb              # Notebook with Ex. 1 and 2
│   │   └── part_3_4.ipynb              # Notebook with Ex. 3 and 4
│   ├── HW1_Arratia25bse.pdf            # Homework 1 assignment
│   └── ml4finance_hw1.pdf              # Homework 1 final report
├── .gitignore                          # Files ignored in the repository
├── .python-version                     # Python version for environment
├── pyproject.toml                      # uv project metadata and dependencies
├── README.md
└── uv.lock                             # uv dependencies versions 

📌 Notes

This is an academic project — models and strategies are for learning purposes only and not financial advice.