In this chapter we give a brief introduction to different methods that may help to improve the performance of your portfolio: technical analysis, neural networks and log-optimal portfolios. The common idea behind these methods is that past price movements may help in forecasting future trends. In other words, we implicitly assume that prices do not follow a Markov process (for example random walk), but they have some kind of long lasting memory, hence patterns from the past may reoccur also in the future, all in all markets are not efficient.

In the first part we introduce the most common tools of technical analysis and present some indicative examples of how to program them in the R environment. In the second part we outline the concept of neural networks and their design by R's built-in function. Technical analysis and neural network are applied on the bitcoin database, thus we focus on a single asset and investigate...