Evaluating the Performance of Cryptocurrency Trading Signal Providers on Social Media Platforms

Rehman, Mohib Ur (2024) Evaluating the Performance of Cryptocurrency Trading Signal Providers on Social Media Platforms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The growing volume of trading signals shared across platforms like Telegram presents both an opportunity and a challenge for cryptocurrency traders. This paper addresses automated classification and evaluation of these trading signals. In this paper, we introduce a NLP based machine learning methodology to extract meaningful entities from raw text like coin names, trading pairs, entry points, and target prices relevant to trading. Developing a back-testing framework to test the efficacy of these signals on historical performance, measuring profit/loss, Sharpe ratio and drawdown. To further improve signal analysis, we apply clustering techniques, including KMeans and Gaussian Mixture Models (GMM), to group similar signals and assess their success rates. Through our results we show that it will be possible to use NLP and machine learning as a starting point to automate trading strategy evaluation, leading to insights that will change the way cryptocurrency traders make decisions and manage risk. We propose a robust framework for optimizing trading signal evaluation, that can be applied to other asset classes in financial markets, such as stocks, commodities, forex, and even bonds.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Rustam, Furqan UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science H Social Sciences > HG Finance > Money > Digital currency > Cryptocurrencies Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet > World Wide Web > Websites > Online social networks T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet > World Wide Web > Websites > Online social networks
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 13:38
Last Modified:	04 Sep 2025 13:38
URI:	https://norma.ncirl.ie/id/eprint/8791

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