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FinTech Data Management Project

Term 2, 2025

Project Preliminaries

In this project you are given an opportunity to work on data science based task as applied to finance and banking environment.

This is individual project.

There is only a single project option.

Due Dates:

Part A: Week 8, 25th July Friday, 5 pm

Part B: Week 10, 8th August Friday, 5 pm

Cryptocurrency Asset Management FinTech Product

Your task, in a role of FinTech Specialist, is to deliver a complete cryptocurrency portfolio optimization system that you will pitch to a team of VCs, private equity groups, and project managers. You are tasked to design and implement a data-driven investment ap-plication that uses historical cryptocurrency price, volume and text data from CryptoCompare to build models that optimize portfolio weights and create systematic trading strategies for the cryptocur-rency asset class.

The end goal is to develop a working FinTech application with Google Gemini (any other LLM/AI Model) that provides optimal cryptocurrency allocation recommendations and automated rebal-ancing capabilities, ready for presentation to potential investors and stakeholders.

Project Deliverables – Project Part A

→ Due Week 8, 25th July Friday, 5:00 pm – 20% of final grade

Final delivery is in a form of a data design and analysis report and Python code:

1 Stage I: ETL (max 5-6 pages)

2 Stage II: Feature Engineering (max 5-6 pages)

Delivery is via Moodle Assessment Section expecting 2× files one in .pdf (written report) and one in .py (scripts covering station 1 and station 2). Must discuss both structured and unstructured data

Project Deliverables – Project Part B

→ Due Week 10, 8th August Friday, 5:00pm – 50% of final grade

Final delivery is in a form of Python code and data design and analysis report. Code sequence is broken down to continuation of Project Part A moving into:

1 Stage III: Model Design

2 Stage IV: Model Implementation

Final report provides contextual, tabular (i.e., include neatly formatted tables) and graphical interpretation of the results and final expected product design.

Delivery is via Moodle Assessment Section expecting 2× files one in .pdf (written report) and one in .py (scripts covering station 3 and station

4). You will include the URL (link) to your working application in Part B.

A Note on Using ChatGPT / How to Achieve a Top Grade

This is a new project for this course.

Copy pasting ChatGPT output constitutes plagiarism / cheating.

Using any material from previous students (on older versions of this course) in your project constitutes cheating.

We will be marking the projects extremely carefully.

Project hand-ins are cross-checked with those submitted in 2024. DO NOT use old projects from 2024 – a 0 grade will be assigned if this is caught.

Majority of the marks will come from original ideas that ChatGPT cannot generate.

Getting a top grade requires originality, and interesting ideas.

We DO NOT grade your code:

The code must run/work.

Majority of grades comes from the written assessment.

Advice

I am always happy to help you – if you are unsure about something, just ask!

I will create a dedicated Ed Forum post, where everyone can ask/answer questions – help each other!

Presentation is crucial:

Neatly format your Tables

DO NOT use the screen snip tool for saving Figures – save them to .png as we have done in class.

The majority of your grade comes from how novel your idea is, and the quality of your report writing and results output.

We DO NOT mark the Python code – but, this code must run/work and we check this.