Description

Book Synopsis

This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk. The performance of the algorithm with the new resampling method and the classical time sampled data are compared and the advantages of using the system developed in this work are highlighted.



Trade Review
“The book contains little theory and presents mostly detailed numerical experiments, it reads very engagingly and inspires with many ideas. It is certainly not a reference book but rather a short monograph on a very clearly defined topic. It will be interesting to see whether the trading strategies presented can be transferred from the crypto markets to the presumably more efficient standard stock markets … as published strategies tend to make markets more efficient.” (Volker H. Schulz, SIAM Review, Vol. 64 (3), September, 2022)

Table of Contents

Chapter 1 - Introduction

Chapter 2 - Related work

Chapter 3 - Implementation

Chapter 4 - Results

Chapter 5 - Conclusions and future work

Financial Data Resampling for Machine Learning Based Trading: Application to Cryptocurrency Markets

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    A Paperback by Tomé Almeida Borges, Rui Neves

    £54.99

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    Order before 4pm today for delivery by Fri 16 Oct 2026.

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      Book details

      Publisher Springer Nature Switzerland AG
      Published 23 February 2021
      ISBN-13 9783030683788
      978-3030683788
      ISBN-10 3030683788
      Also in:
      Mathematics Numerical analysis

      Description

      Book Synopsis

      This book presents a system that combines the expertise of four algorithms, namely Gradient Tree Boosting, Logistic Regression, Random Forest and Support Vector Classifier to trade with several cryptocurrencies. A new method for resampling financial data is presented as alternative to the classical time sampled data commonly used in financial market trading. The new resampling method uses a closing value threshold to resample the data creating a signal better suited for financial trading, thus achieving higher returns without increased risk. The performance of the algorithm with the new resampling method and the classical time sampled data are compared and the advantages of using the system developed in this work are highlighted.



      Trade Review
      “The book contains little theory and presents mostly detailed numerical experiments, it reads very engagingly and inspires with many ideas. It is certainly not a reference book but rather a short monograph on a very clearly defined topic. It will be interesting to see whether the trading strategies presented can be transferred from the crypto markets to the presumably more efficient standard stock markets … as published strategies tend to make markets more efficient.” (Volker H. Schulz, SIAM Review, Vol. 64 (3), September, 2022)

      Table of Contents

      Chapter 1 - Introduction

      Chapter 2 - Related work

      Chapter 3 - Implementation

      Chapter 4 - Results

      Chapter 5 - Conclusions and future work

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