Deep Learning for Finance

Chapter 5. Introducing Technical Analysis

Technical analysis presents many types of inputs (explanatory variables) that you can use in your deep learning models. This chapter introduces this vast field so that you are equipped with the necessary knowledge to create technical-based learning models in the chapters to follow.

Technical analysis in finance relies on the visual interpretation of a price action’s history to determine the likely aggregate direction of the market. It relies on the idea that the past is the best predictor of the future. There are several types of techniques within the vast field that is technical analysis, notably the following:

Charting analysis

This is where you apply subjective visual interpretation techniques onto charts. You generally use methods like drawing support and resistance lines as well as retracements to find inflection levels that aim to determine the next move.

Indicator analysis

This is where you use mathematical formulas to create objective indicators that can be either trend following or contrarian. Among known indicators are moving averages and the relative strength index (RSI), both of which are discussed in greater detail in this chapter.

Pattern recognition

This is where you monitor certain recurring configurations and act on them. A pattern is generally an event that emerges from time to time and presents a certain theoretical or empirical outcome. In finance, it is more complicated, but certain patterns have been shown to add ...