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Trend Following Algorithmic Trading ...

Pereira, Andre Otao,

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Trend Following Algorithmic Trading with a Reduced Universe of Cryptocurrencies: Using Unsupervised Learning for Universe Reduction and Trend Following Strategies to Generate Profits /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Trend Following Algorithmic Trading with a Reduced Universe of Cryptocurrencies: Using Unsupervised Learning for Universe Reduction and Trend Following Strategies to Generate Profits // Andre Otao Pereira.
Author:	Pereira, Andre Otao,
Description:	1 electronic resource (55 pages)
Notes:	Source: Masters Abstracts International, Volume: 86-06.
Contained By:	Masters Abstracts International86-06.
Subject:	Decision making. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31790304
ISBN:	9798346837626

Trend Following Algorithmic Trading with a Reduced Universe of Cryptocurrencies: Using Unsupervised Learning for Universe Reduction and Trend Following Strategies to Generate Profits /
Pereira, Andre Otao,

Trend Following Algorithmic Trading with a Reduced Universe of Cryptocurrencies: Using Unsupervised Learning for Universe Reduction and Trend Following Strategies to Generate Profits /Andre Otao Pereira. - 1 electronic resource (55 pages)

Source: Masters Abstracts International, Volume: 86-06.

Cryptocurrencies have revolutionized trading with their decentralized and volatile nature, presenting unique opportunities for trend-following strategies. This study investigates the enhancement of such strategies by implementing a reduction in the universe of cryptocurrencies based on their similarities, aiming to improve trading performance. Additionally, a market momentum classification will be constructed to analyze and compare performance across different market momentum environments. By utilizing Support Vector Machines (SVM) and Principal Component Analysis (PCA), the overall universe of cryptocurrencies is reduced to the most similar cryptocurrencies based on metrics of momentum, growth, and technical analysis. This approach aims to address market overload caused by the considerable number of available cryptocurrencies, removing the most erratic cryptocurrencies.To assess the efficacy of two trend-following trading strategies-an equally weighted portfolio approach and a time-weighted portfolio approach-we will utilize a momentum-based trading strategy serving as our benchmark. All strategies will be used with the reduced universes produced by our method. Additionally, a market momentum classification will be constructed using market capitalization data of the crypto market, with the goal of gaining relevant insights into the effects of the overall market momentum environment on the performance of the algorithmic trading strategies and reduced universe combinations.The innovation lies in the method of reducing the cryptocurrency universe by identifying the most similar tokens during the same period while asserting the value of unsupervised learning models like SVM and PCA in enhancing trend-following trading strategies by reducing complexity.In summary, this research underscores the critical differences in short and long trend formation periods, the effectiveness of trend-following approaches, and the superior performance of time-weighted portfolios, demonstrating how SVM and PCA can enhance algorithmic trading strategies by reducing complexity and optimizing profitability in the dynamic cryptocurrency market.

English

ISBN: 9798346837626Subjects--Topical Terms:

517204
Decision making.

Trend Following Algorithmic Trading with a Reduced Universe of Cryptocurrencies: Using Unsupervised Learning for Universe Reduction and Trend Following Strategies to Generate Profits /
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520 $a Cryptocurrencies have revolutionized trading with their decentralized and volatile nature, presenting unique opportunities for trend-following strategies. This study investigates the enhancement of such strategies by implementing a reduction in the universe of cryptocurrencies based on their similarities, aiming to improve trading performance. Additionally, a market momentum classification will be constructed to analyze and compare performance across different market momentum environments. By utilizing Support Vector Machines (SVM) and Principal Component Analysis (PCA), the overall universe of cryptocurrencies is reduced to the most similar cryptocurrencies based on metrics of momentum, growth, and technical analysis. This approach aims to address market overload caused by the considerable number of available cryptocurrencies, removing the most erratic cryptocurrencies.To assess the efficacy of two trend-following trading strategies-an equally weighted portfolio approach and a time-weighted portfolio approach-we will utilize a momentum-based trading strategy serving as our benchmark. All strategies will be used with the reduced universes produced by our method. Additionally, a market momentum classification will be constructed using market capitalization data of the crypto market, with the goal of gaining relevant insights into the effects of the overall market momentum environment on the performance of the algorithmic trading strategies and reduced universe combinations.The innovation lies in the method of reducing the cryptocurrency universe by identifying the most similar tokens during the same period while asserting the value of unsupervised learning models like SVM and PCA in enhancing trend-following trading strategies by reducing complexity.In summary, this research underscores the critical differences in short and long trend formation periods, the effectiveness of trend-following approaches, and the superior performance of time-weighted portfolios, demonstrating how SVM and PCA can enhance algorithmic trading strategies by reducing complexity and optimizing profitability in the dynamic cryptocurrency market.
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