ADX Decoded: A Comprehensive Guide to Trading with the Average Directional Index

7 min readOct 12, 2023

The Average Directional Index (ADX) is a popular technical indicator used by traders to identify the strength of a trend. It was developed by J. Welles Wilder Jr. and introduced in his book, “New Concepts in Technical Trading Systems.” The ADX is a versatile tool that can be used to determine whether a market is trending or ranging, as well as to gauge the strength of a trend. In this tutorial, we will explore the ADX indicator in detail and learn how to use it in Python for trading purposes.

Introduction to the Average Directional Index (ADX)
Understanding the Components of the ADX
Calculating the ADX in Python
Interpreting the ADX Values
Trading Strategies with the ADX
Conclusion

1. Introduction to the Average Directional Index (ADX)

The Average Directional Index (ADX) is a technical indicator that measures the strength of a trend. It consists of three lines: the ADX line, the +DI line, and the -DI line. The ADX line represents the strength of the trend, while the +DI and -DI lines represent the positive and negative directional movement, respectively.

The ADX is a non-directional indicator, meaning it does not provide information about the direction of the trend. Instead, it focuses on the strength of the trend. Traders can use the ADX to determine whether a market is trending or ranging, as well as to identify potential entry and exit points.

2. Understanding the Components of the ADX

Before we dive into the implementation of the ADX in Python, let’s take a closer look at its components:

ADX Line: The ADX line represents the strength of the trend. It ranges from 0 to 100, with higher values indicating a stronger trend. A value above 25 is typically considered as a sign of a trending market.
+DI Line: The +DI line measures the positive directional movement. It represents the upward movement in price and is calculated based on the difference between the current high and the previous high.
-DI Line: The -DI line measures the negative directional movement. It represents the downward movement in price and is calculated based on the difference between the previous low and the current low.

The ADX line is derived from the +DI and -DI lines and is calculated using the following formula:

ADX = 100 * (|+DI - -DI| / (+DI + -DI))

3. Calculating the ADX in Python

To calculate the ADX in Python, we will be using the pandas and numpy libraries for data manipulation, as well as the mplfinance library for visualizing the price data. Additionally, we will use the yfinance library to download historical price data for a specific asset.

First, let’s install the required libraries by running the following command:

!pip install pandas numpy mplfinance yfinance

Once the libraries are installed, we can proceed with the implementation. Let’s start by importing the necessary modules:

import pandas as pd
import numpy as np
import mplfinance as mpf
import yfinance as yf
import matplotlib.pyplot as plt

Next, we need to download the historical price data for the asset we want to analyze. For this tutorial, let’s download the price data for META from January 1, 2020, to October 31, 2023:

ticker = "META"
start_date = "2020-01-01"
end_date = "2023-10-31"

data = yf.download(ticker, start=start_date, end=end_date)

Now that we have the price data, we can calculate the ADX using the following steps:

Calculate the True Range (TR): The True Range is the maximum of the following three values: the difference between the current high and low, the absolute difference between the current high and the previous close, and the absolute difference between the current low and the previous close.
Calculate the Directional Movement (DM): The Directional Movement is calculated based on the difference between the current high and the previous high, as well as the difference between the previous low and the current low.
Smooth the True Range (TR) and Directional Movement (DM) using the Wilder’s Smoothing Method: Wilder’s Smoothing Method is used to calculate the smoothed values of the True Range and Directional Movement.
Calculate the +DI and -DI lines: The +DI line is calculated as the smoothed average of the positive Directional Movement, while the -DI line is calculated as the smoothed average of the negative Directional Movement.
Calculate the Average Directional Index (ADX): The ADX is calculated using the formula mentioned earlier.

Let’s implement these steps in Python:

# Step 1: Calculate the True Range (TR)
data["High-Low"] = data["High"] - data["Low"]
data["High-PrevClose"] = np.abs(data["High"] - data["Close"].shift(1))
data["Low-PrevClose"] = np.abs(data["Low"] - data["Close"].shift(1))
data["TrueRange"] = np.max([data["High-Low"], data["High-PrevClose"], data["Low-PrevClose"]], axis=0)

# Step 2: Calculate the Directional Movement (DM)
data["UpMove"] = data["High"] - data["High"].shift(1)
data["DownMove"] = data["Low"].shift(1) - data["Low"]
data.loc[data["UpMove"] > data["DownMove"], "UpMove"] = 0
data.loc[data["UpMove"] < data["DownMove"], "DownMove"] = 0

# Step 3: Smooth the True Range (TR) and Directional Movement (DM)
period = 14
data["SmoothedTR"] = data["TrueRange"].rolling(window=period).mean()
data["SmoothedUpMove"] = data["UpMove"].rolling(window=period).mean()
data["SmoothedDownMove"] = data["DownMove"].rolling(window=period).mean()

# Step 4: Calculate the +DI and -DI lines
data["+DI"] = 100 * (data["SmoothedUpMove"] / data["SmoothedTR"])
data["-DI"] = 100 * (data["SmoothedDownMove"] / data["SmoothedTR"])

# Step 5: Calculate the Average Directional Index (ADX)
data["+DI"] = data["+DI"].ewm(span=period, adjust=False).mean()
data["-DI"] = data["-DI"].ewm(span=period, adjust=False).mean()
data["ADX"] = 100 * (np.abs(data["+DI"] - data["-DI"]) / (data["+DI"] + data["-DI"]))

Now that we have calculated the ADX values, let’s visualize them using a line plot:

mpf.plot(data["ADX"], type="line", title="Average Directional Index (ADX)", ylabel="ADX", savefig="https://cdn-images-1.medium.com/proxy/1*8WvWI3_NyGmuW_BRFrboBA.png")
plt.show()

Plot 1 — Figure 1: Average Directional Index (ADX) plot. Created by Author

4. Interpreting the ADX Values

The ADX values range from 0 to 100, with higher values indicating a stronger trend. Traders often use the following guidelines to interpret the ADX values:

ADX below 20: Indicates a weak or non-existent trend.
ADX between 20 and 25: Indicates a potential trend formation.
ADX above 25: Indicates a trending market.
ADX above 50: Indicates a strong trending market.

Traders can use the ADX values to determine the strength of a trend and adjust their trading strategies accordingly. For example, they may choose to enter a trade when the ADX is above a certain threshold or exit a trade when the ADX starts to decline.

5. Trading Strategies with the ADX

The ADX can be used in combination with other technical indicators to develop trading strategies. Here are a few common strategies that incorporate the ADX:

Trend Following Strategy: Traders can use the ADX to identify trending markets and enter trades in the direction of the trend. They may choose to enter a trade when the ADX is above a certain threshold and the price is making higher highs (for an uptrend) or lower lows (for a downtrend).
Trend Reversal Strategy: Traders can use the ADX to identify potential trend reversals. They may choose to exit a trade when the ADX starts to decline, indicating a weakening trend, or when the ADX crosses below a certain threshold.
Range Trading Strategy: Traders can use the ADX to identify ranging markets and avoid entering trades during these periods. They may choose to enter a trade when the ADX is below a certain threshold and the price is trading within a defined range.

It’s important to note that the ADX is just one tool among many in a trader’s toolkit. It should be used in conjunction with other indicators and analysis techniques to make informed trading decisions.

6. Conclusion

In this tutorial, we explored the Average Directional Index (ADX) and learned how to use it in Python for trading purposes. We discussed the components of the ADX, including the ADX line, +DI line, and -DI line. We also covered the process of calculating the ADX using historical price data and visualizing the results.

The ADX is a powerful tool that can help traders identify the strength of a trend and make informed trading decisions. By incorporating the ADX into their trading strategies, traders can increase their chances of success in the financial markets.

Remember, trading involves risk, and it’s important to conduct thorough research and analysis before making any trading decisions. The ADX is just one tool among many, and it should be used in conjunction with other indicators and analysis techniques to develop a comprehensive trading strategy. In this tutorial, we explored the Average Directional Index (ADX) and learned how to use it in Python for trading purposes. We discussed the components of the ADX, including the ADX line, +DI line, and -DI line. We also covered the process of calculating the ADX using historical price data and visualizing the results.