代写CMSE11475、代做Java/Python编程

Financial Machine Learning (CMSE11475)
Group Project Assignment
2023/2024
Content
Content................................................................................................................................................................................................. 1
Project Description......................................................................................................................................................................... 2
Individual Project: ......................................................................................................................................................................... 2
Project Deadline and Submission:........................................................................................................................................... 2
Project topic ................................................................................................................................................................................... 2
Project Hints ................................................................................................................................................................................... 2
Suggested Topics ............................................................................................................................................................................ 3
Forecasting Limit Order Book ............................................................................................................................................... 3
Forecasting Stock Volatility.................................................................................................................................................... 5
Forecasting High Frequency Cryptocurrency Return.................................................................................................. 7
Project Description
The project aims to practice the use of state-of-art machine learning models to analyse financial data and
solve financial problems.
Individual Project:
The project is individual project. No group is required. Students shall select their own topic with data to
complete their own research question alone. Cooperation and discussion with each other in the learning
process is encouraged but the project shall be completed by students’ own work, not a grouped work.
Project Deadline and Submission:
Individual projects run from 15
th January 2024 (week 1) to 29th March 2024 (week 10).
The deadline of submission is 14:00, Thursday, 4
th April 2024.
The submision of the project includes the project report and all implementation codes (do NOT submit any
data). The code shall work on the originally provided datasets. The report and the codes shall be ZIPPED to
one package for submission.
The report MUST follow the given template. All sections are required. The code MUST have complete and
detailed comments for every major logical section.
Project topic
Each student should individually choose a topic from the following suggested topics (with provided data) as
your own project. You are encouraged to revise/improve the project topic to make it more practical,
challenging, and suitable for your own research question. It’s fine if many students select the same suggested
topics as their projects as long as the codes and project reports are significantly distinctive.
The aim of this project is to apply at least THREE out of five techniques illustrated in the course (Deep Neural
Network; XGBoost; Cross-validation; Ensemble Model; Interpretability) to solve a financial problem.
Project Hints
All suggested topics are based on the computer lab examples with some changes and extensions. You can
easily find similar methods and models in the computer lab examples. Carefully studying those examples
and codes are crucial for understanding this course and complete the group coursework.
Suggested Topics
Forecasting Limit Order Book
Topic
Can we use deep neural network to forecast the high-frequency return at multiple horizon for stocks using
their limit order book information?
Data
10-level high frequency Limit Order Book of five stocks: Apple, Amazon, Intel, Microsoft, and Google on 21st
June 2012. Data size from 40MB to 100+MB. You can select to use part of the data.
Method
You may define the following features:are the ask and bid price of 10 levels (