RSI Forecasting With Linear Regression

Utilizing Statistical Analysis to Predict Relative Strength Index Trends in Financial Markets

Forecasting market returns using machine learning models can be daunting. This can push us to find opportunities elsewhere, such as trying to forecast price derivatives or other indicators that exhibit less noise.

This article will show how to code a linear regression algorithm that forecasts the changes in the relative strength index, which helps to forecast market returns indirectly.

The Relative Strength Index

The relative strength index (RSI) is a momentum oscillator and a popular technical indicator used in financial analysis, particularly in the context of stock trading and investment. RSI provides insights into the strength and speed of price movements in a financial asset, helping traders and investors make informed decisions. The RSI is a numerical value that ranges from 0 to 100 and is typically displayed as a line on a chart.

It is generally calculated over a 14-period time frame by default, but this can be adjusted to match the trader’s or analyst’s preferences. Traders use RSI to generate buy and sell signals. For example, a common strategy is to buy when RSI crosses above 30 (indicating…