In this chapter we overviewed not only technical analysis but also some corresponding strategies, like neural networks and log-optimal portfolios. These methods are similar in the sense that when applying them, we implicitly suppose that past situations may reappear in the future; therefore we took the courage to challenge the concept of market efficiency and to build up an active trading strategy. In this setting, we discussed the problems of forecasting the price of a single asset (bitcoin), optimizing the timing of our trading, and also optimizing our portfolio of several risky assets (NYSE stocks) in a dynamic manner. We demonstrated that some simple algorithms based on the toolkit available in R can produce significant extra profit relative to the passive strategy of buying-and-holding. We also note however, that a comprehensive performance analysis focuses not only on the average returns, but also on the corresponding risks. Therefore, we suggest that when optimizing your strategy...
Mastering R for Quantitative Finance
Mastering R for Quantitative Finance
Overview of this book
Table of Contents (20 chapters)
Mastering R for Quantitative Finance
Credits
About the Authors
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
Time Series Analysis
Factor Models
Forecasting Volume
Big Data – Advanced Analytics
FX Derivatives
Interest Rate Derivatives and Models
Exotic Options
Optimal Hedging
Fundamental Analysis
Technical Analysis, Neural Networks, and Logoptimal Portfolios
Asset and Liability Management
Capital Adequacy
Systemic Risks
Index
Customer Reviews