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Practical Machine Learning Cookbook

You're reading from Practical Machine Learning Cookbook

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785280511

Pages 570 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Atul Tripathi

Table of Contents (21) Chapters

Practical Machine Learning Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Introduction to Machine Learning

2. Classification

3. Clustering

4. Model Selection and Regularization

5. Nonlinearity

6. Supervised Learning

7. Unsupervised Learning

8. Reinforcement Learning

9. Structured Prediction

10. Neural Networks

11. Deep Learning

12. Case Study - Exploring World Bank Data

13. Case Study - Pricing Reinsurance Contracts

14. Case Study - Forecast of Electricity Consumption

Introduction

Support vector machine - currency trading strategy

The forex market is an international trading market where the currencies of every country are sold and bought freely. The price of one currency determined only by market participants is driven by supply and demand. The trading is conducted through individual contracts. The standard contract size (also called a lot) is usually 100,000 units. This means that for every standard contract acquired, the control is of 100,000 units of the base currency. For this contract size, each pip (the smallest price increment) is worth $10. Depending on the trading strategy of a trader, a position can be maintained for a very short time or for longer periods, even years. There are several tools that allow the trader to be able to understand and make decisions on the market, grouped basically under fundamental or technical analysis. Fundamental analysis takes into account the constant exchange of political and economic information. Technical analysis is based...

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Authors (1)

Atul Tripathi

Atul Tripathi has spent more than 11 years in the fields of machine learning and quantitative finance. He has a total of 14 years of experience in software development and research. He has worked on advanced machine learning techniques, such as neural networks and Markov models. While working on these techniques, he has solved problems related to image processing, telecommunications, human speech recognition, and natural language processing. He has also developed tools for text mining using neural networks. In the field of quantitative finance, he has developed models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

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