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...