In this chapter, we explored concepts of generating trading signals, such as support and resistance, based on the intuitive ideas of supply and demand that are fundamental forces that drive market prices. We also briefly explored how you might use support and resistance to implement a simple trading strategy. Then, we looked into a variety of technical analysis indicators, explained the intuition behind them, and implemented and visualized their behavior during different price movements. We also introduced and implemented the ideas behind advanced mathematical approaches, such as Autoregressive (AR), Moving Average (MA), Differentiation (D), AutoCorrelation Function (ACF), and Partial Autocorrelation Function (PACF) for dealing with non-stationary time series datasets. Finally, we briefly introduced an advanced concept such as seasonality, which explains how there are repeating patterns in financial datasets, basic time series analysis and concepts of stationary or non-stationary time series, and how you may model financial data that displays that behavior.

In the next chapter, we will review and implement some simple regression and classification methods and understand the advantages of applying supervised statistical learning methods to trading.