Data Analysis for Financial Markets

Recommended Prerequisites

This course makes use of previous  skills on programming in Python and data analysis algorithms. It makes transversal use of the knowledge acquired along the first cycle of a higher education course in the area of computer engineering, computer sciences and/or data analysis. 

Teaching Methods

The learning process takes place in theoretical classes, practical classes and work developed autonomously outside the classroom space.

Classroom materials include:

- PPT presentations

- demo videos

- actual datasets

- access to a broker in simulation mode

- Python libraries for data analysis.

Learning Outcomes

This course addresses the topic of data analysis and approaches and solutions for financial markets. Theoretical classes provide the concepts and computing techniques necessary for the course, namely, an introduction to financial market instruments, preparation and exploratory visualization of financial data, strategies for trading, models for financial time-series, co-integration and arbitrage using co-integration. The practical classes are structured around the Data Lab. The Data Lab provides several financial datasets, and access to a brokerage simulator. The course's project develops along the semester and is presented at the course final workshop with participations from financial institutions. This project aims to develop students' skills on visualization and analysis of financial data for investment and trading operations. It is expected that added skills on data analysis will also be useful for any other area that makes extensive use of data for decision making.

Work Placement(s)

No

Syllabus

Financial Markets

Exploratory Data Analysis for Financial Markets

Algorithmic Trading Strategies

Models Based on Time Series Analysis

Neural Networks for Trading.

Head Lecturer(s)

Carlos Manuel Robalo Lisboa Bento

Assessment Methods

Assessment
Exam: 40.0%
Project: 60.0%

Bibliography

Python for Finance, 2020

Eryk Lewinson

Packt Publishing

Hands-On Financial Trading with Python, 2021

Jiri Pik and Sourav Ghosh

Packt Publishing

Introduction to Time Series and Forecasting, 2nd Edition

Petter J Brockwell and Richard Davis

Springer

Machine Learning for Algorithmic Trading, 2020

Stefan Jansen

Packt Publishing.