Research on gold and bitcoin trading strategy based on XGBoost and zero-one programming

Haochen Lin; Yifu He; Mengjie Shi

doi:10.1117/12.2639549

27 June 2022 Research on gold and bitcoin trading strategy based on XGBoost and zero-one programming

Haochen Lin, Yifu He, Mengjie Shi

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530V (2022) https://doi.org/10.1117/12.2639549
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

In the actual trading process, investors can only give the best daily trading strategy based on the past price data of gold and bitcoin, then they need to predict and evaluate the trend of the investment items in the coming period and plan out the trading scheme in advance. We also draw on data from many investment questionnaires on websites such as Stock Market Analysis & Tools for Investors to give specific trading strategies. We choose the XGBoost regression price prediction model and enable the genetic algorithm to find the best learning rate parameters. The first 100 trading days of gold and bitcoin data are taken separately for learning training tests, and then the first 20 data are used to predict the price trend for the next five days, which is repeated every day. It provides more accurate prediction results based on the latest prices. An optimization model is established to increase the final investment value by judging the buying and selling indexes by whether the expected return exceeds the purchased commission.

Citation Download Citation

Haochen Lin, Yifu He, and Mengjie Shi "Research on gold and bitcoin trading strategy based on XGBoost and zero-one programming", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530V (27 June 2022); https://doi.org/10.1117/12.2639549

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available