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Financial Data Resampling for Machine Learning Based Trading

Part of the SpringerBriefs in Applied Sciences and Technology Book Series

DOI: 10.1007/978-3-030-68379-5

ISBN: 978-3-030-68378-8

Publisher: Springer International Publishing - Springer Journals

Feb 23, 2021

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Chapter 1Introduction

Borges, Tomé Almeida; Neves, Rui

Pages: 1 –2

[This chapter presents an overview of this work and its structure. The main goals of this work are described, the main improvements as well as novelties brought forward by this work in its subject area are revealed and finally the structure of this book is summarized.]
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Chapter 2Background and State-of-the-Art

Borges, Tomé Almeida; Neves, Rui

Pages: 3 –36

[This chapter presents the fundamental concepts put into practice throughout this thesis. In first place, the subject of Cryptocurrencies is introduced and complemented by a description of the overall panorama of the Cryptocurrency Exchange Market. Secondly, the two main approaches used for...
LitStream

Chapter 3Implementation

Borges, Tomé Almeida; Neves, Rui

Pages: 37 –60

[In order to validate that utilizing resampled data rather than time sampled data is in fact advantageous, a single trading system capable of forecasting financial movements on these two types of data was developed. The forecasting results obtained from the various resampling datasets are...
LitStream

Chapter 4Results

Borges, Tomé Almeida; Neves, Rui

Pages: 61 –90

[This section starts by describing the financial data, evaluation metrics and an additional strategy utilized as comparison baseline for this work’s system. Afterwards, the overall results obtained are reported to evince that this system is valid and posteriorly the results for each different...
LitStream

Chapter 5Conclusions and Future Work

Borges, Tomé Almeida; Neves, Rui

Pages: 91 –93

[This last chapter summarizes the main conclusions to be assimilated from this book and, on a final note, possible paths for future work and improvements are listed.]

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Financial Data Resampling for Machine Learning Based Trading

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