As we continue to build onto the Python for Algorithmic Trading course, and Jason and I are keenly interested in any algorithms that can give us an edge. And, we want to fill you in on some powerful algorithmic trading strategies we are exploring. Today we're going to share how to use Average True Range (ATR) to find volatility signals. You learn:

• Top 10 Filtering Techniques Used in Algorithmic Trading

• How is Average True Range (ATR) used in financial analysis, stocks and investing?

• Full Python Tutorial: How to use Average True Range (ATR) in Python

• BONUS: Get the Python Code for EVERYTHING you see in this post

Disclaimer:

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Here's the ATR overview:

You will analyze ATR to detect trading patterns for SPY today:

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How is Average True Range (ATR) used in Financial Analysis, Stocks, and Investing?

To understand Average True Range (ATR), we need to first get a handle on where ATR falls in the ecosystem of filters that are used to extract signal from noise. For that, we have the following table of 10 Filters Used in Algorithmic Trading:

10 Filters Used in Algorithmic Trading Table:

We can see that Average True Range (ATR) falls under the class of "Volatility Filters".

10 Filters Used in Algorithmic Trading

What are Volatility Filters?

A volatility filter is a tool used in financial trading and investment strategies to manage risk and improve decision-making. It works by assessing the level of volatility, or the degree of variation in the price of a security, asset, or market index over a given period.

Volatility filters typically measure volatility using statistical methods like standard deviation or variance of price movements. This helps in understanding how wildly or predictably a security's price is moving.

By understanding volatility, traders and investors can adjust their strategies accordingly. Higher volatility usually indicates higher risk, so a trader might opt for more conservative positions or use strategies like hedging to manage this risk.

Volatility filters can inform buy or sell decisions. For instance, a trader might set a rule to avoid trading if volatility exceeds a certain threshold, as it could indicate an unstable market.

Some trading strategies adapt according to the level of volatility. For example, in a high-volatility environment, a trader might focus on short-term trades to capitalize on rapid price movements.

Volatility filters can help in setting more effective stop-loss orders. In a high-volatility market, a wider stop-loss might be set to avoid being stopped out by normal price fluctuations.

Now that we understand this class of filters better, let's dive into the Average True Range (ATR) technical indicator.

What is Average True Range (ATR)?

The Average True Range (ATR) is a technical analysis indicator used to measure market volatility. It was introduced by J. Welles Wilder Jr. in his 1978 book "New Concepts in Technical Trading Systems."

Usage in Trading:

There are 2 key points:

1. ATR does not provide an indication of price direction, instead it measures volatility.

2. High ATR values indicate high volatility and low ATR values indicate low volatility.

How to Develop Signals:

Traders might use this information to:

1. Adjust stop-loss orders

2. Size Positions

3. Entry/Exit Signals

Note- ATR is often used in combination with other Technical Signals (e.g. trend, price, and volume filters).

How it Works:

There are 2 steps in calculating the ATR:

1. Calculate the True Range: The True Range for a given period is the greatest of the following:

   • The difference between the current high and the current low.

   • The difference between the previous close and the current high.

   • The difference between the previous close and the current low.

2. Calculate the Average True Range: The ATR is an average of the True Range over a specified number of periods.

Python Tutorial: Average True Range

The goal with our analysis is create a volatility signal in the SPY. We use Average True Range (ATR) to detect periods of high and low volatility. Get the code: It's in the QS009 folder.

Step 1: Load Libraries and Get the SPY Data

The first step in our analysis is to load the following libraries and setup our analysis parameters. Run this code:

Get the code: It's in the QS009 folder.

The code produces this visualization. We can see with have the SPY from 2021-09-30 to 2023-12-13.

Step 2: Apply ATR

Next, let's apply the Average True Range. We'll make a custom function calculate_atr() that will add the ATR column to our data frame.

Run this code:

Get the code: It's in the QS009 folder.

This returns the SPY data frame with the ATR Column added:

Step 3: The ATR Visualization

We can visualize the original High, Low, Close and the ATR in a single matplotlib plot. Run this code:

Get the code: It's in the QS009 folder.

The following plot is returned.

Step 4: ATR Analysis

I've marked up the original plot so we can see different regimes in the long term trading patterns for SPY.

• High Volatility: We can see that there was a period of high volatility from 2022-01 to 2022-10. During this time period the SPY dropped from $460 to $340, a -26% drop.

• Low Volatility: We can see that there was a period of low volatility from 2023-04 to 2023-12. During this time period the SPY increased from $380 to $460, a +21% increase.

Get the code: It's in the QS009 folder.

Conclusion: Python is getting even better for Stock Analysis

By now you can tell that we are giving you every POSSIBLE tool and skill to enhance your Algorithmic Trading game.

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