Predicting S&P500 volatility to classify the market in Python

I will model the volatility of the S&P500 to classify the market into three different segments to enhance algorithmic trading strategies.

S&P500 Market Classification

Using market segmentation to create more profitable algorithmic trading strategies

The primary idea will be to classify the S&P500 into three segments based on the modelled volatility and using each segment to modify the algorithmic trading strategy.

Algorithmic trading introduction

We can define the use of mathematical models to create a profitable trading strategy as algorithmic trading. Algorithmic trading is an idea to profit based on predetermined rules or predictions by executing automatic trades.

A simple example of an algorithmic trading strategy would be to execute a trade once the asset price goes above or below the 20-day simple moving average. The formula for the simple moving average is given as:

Where P is asset price, and n is the number of previous days. Building more complex trading signals may use volatility bands that compare a lower and upper time series typically…