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Scala for Machine Learning, Second Edition

You're reading from Scala for Machine Learning, Second Edition Build systems for data processing, machine learning, and deep learning

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781787122383

Length 740 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Data Processing

Author (1):

Patrick R. Nicolas

View More author details

Table of Contents (21) Chapters

Preface

1. Getting Started FREE CHAPTER

2. Data Pipelines

3. Data Preprocessing

4. Unsupervised Learning

5. Dimension Reduction

6. Naïve Bayes Classifiers

7. Sequential Data Models

8. Monte Carlo Inference

9. Regression and Regularization

10. Multilayer Perceptron

11. Deep Learning

12. Kernel Models and SVM

13. Evolutionary Computing

14. Multiarmed Bandits

15. Reinforcement Learning

16. Parallelism in Scala and Akka

17. Apache Spark MLlib

A. Basic Concepts

B. References

Index

GA for trading strategies

Let's apply our fresh expertise in genetic algorithms to evaluating different strategies to trade securities using trading signals. Knowledge of trading strategies is not required to understand the implementation of a GA. However, you may want to get familiar with the foundation and terminology of the technical analysis of securities and financial markets, described briefly in the Technical analysis section of the Appendix.

The problem is to find the best trading strategy to predict the increase or decrease of the price of a security given a set of trading signals. A trading strategy is defined as a set of trading signals ts_j that are triggered or fired when a variable x= {x_j }, derived from financial metrics such as the price of the security or the daily or weekly trading volume, exceeds, equals, or is below a predefined target value, a_j (refer to the Trading signals and strategy section of the Appendix).

The number of variables that can be derived from the...

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Authors (1)

R. Nicolas

R. Nicolas

Patrick R. Nicolas is the director of engineering at Agile SDE, California. He has more than 25 years of experience in software engineering and building applications in C++, Java, and more recently in Scala/Spark, and has held several managerial positions. His interests include real-time analytics, modeling, and the development of nonlinear models.

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