How To Make An Average True Range (ATR) Trading Strategy In Python (Backtest, Performance, Setup, and Code)
Traders use a wide range of indicators when trading. Among the most popular are the momentum and mean-reversion indicators, but there also exist indicators that measure volatility, such as the average true range. Let’s make an average true range (ATR) trading strategy in Python.
The Average True Range (ATR) is a volatility indicator that, unlike other indicators, is not commonly used to generate trading signals. But we thought: Can we develop a profitable trading strategy using this indicator in Python?
In this article, we are going to look at what the average true range indicator is, how to calculate it in Python, and backtest a trading strategy using it on S&P 500 (SPY).
Related reading:
- We have many different trading systems for sale, and
- We have even more different free short-term trading systems.
What is the Average True Range?
The Average True Range (ATR) indicator is a technical analysis tool used in finance and trading to measure an asset’s volatility. It was developed by J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems. It is often used to help make decisions about placing stop-loss orders, determining trade positions, and understanding market conditions.
The most common way to use this indicator is to help determine where to place stop-loss orders. A larger ATR might suggest setting wider stop-loss levels to account for potential larger price swings, while a smaller ATR might call for tighter stop-loss levels. Another way to interpret it is for position sizing, given that traders might want to take smaller positions in high-volatility regimes and larger positions when volatility is low.
However, identifying potential breakout points can also help trade the asset. When the ATR value is significantly higher than usual, it could signal the potential for a strong price movement.
<