What is the Supertrend?
What is Average True Range?
The ATR evaluates and measures movement direction and patterns. It does this by calculating lower and upper bands of price action. In other words, the range of “zone” an asset sits in. This is why when the supertrend shifts from green to red (a sell signal) there is a gap, or the value of the ATR at that moment. To calculate the ATR, the True Range (TR) must be calculated first. This metric is a combination of the difference between the high and prior close, the current low minus the prior close value, and the gap between the current high and the current low. The Average True Range is a metric that looks back on previous True Ranges and averages them to give an output.
There are two issues with the ATR on its own. The first being that it is a measure open to interpretation. The values are not necessarily signals that the trend has changed, rather the weakness or strength of the change. As trends can shift there is no way to truly determine when that will happen, only it’s relative strength. Therefore “buy” and “sell” signals on the super trend indicator are only measurements of when the relative strength of a trend has shifted.
In other words, ATR also doesn’t account for direction, meaning a signal of high volatility can mean either upward or downward movements. For this reason, the average true range is best used in combination with other indicators that attempt to predict trend direction, such as moving averages.
To conclude, supertrends have better results with longer look back in order to generate less false positives. Adjusting metrics and paper backtesting could be useful in order to find the factor and ATR for an asset on a specific time frame. The higher the factor, the greater the offset of the bands, and therefore the greater the volatility required to generate potential shifts in trends that the supertrend generates.
How is the supertrend calculated?
ATR is calculated by averaging the True Range. The True Range is calculated as follows:
ATR = (Previous ATR * (n - 1) + TR) / n
ATR = Average True Range
These values are then used to calculate the supertrend by doing the following:
Supertrend Upper line = (High + Low) / 2 + Multiplier * ATR
Supertrend Down Line = (High + Low) / 2 – Multiplier * ATR
There are many types of strategies that can be implemented on the supertrend. The most basic ones are to either use it in conjunction with moving average crossovers or to use multiple trends to filter out false positives, one of the major downsides to using this indicator, to achieve a greater risk-adjusted rate of return.
Moving averages can help eliminate false positives. At times, using two simultaneous moving averages can help to reduce the amount of false positives when using the supertrend. This is because moving average crossovers at times counter trade or make trades that would not be triggered using the supertrend alone. Simply put, if the super trend says sell and the price is above the moving average, trade and vice versa. If one of those variables aren’t met then the trade isn’t confirmed.
Another helpful strategy is to use multiple supertrends with different settings. For example having three super trends set to (12 ,3), (11, 2), and (10, 1) respectively could help eliminate false positives. This is because multiple factors are used therefore different thresholds need to be reached before a confirmation on a buy or sell is made. For a buy or sell to trigger using this strategy, all super trends must be hit. Only buy or sell when those three indicators are all pointing to the same potential conclusion.
As we have seen, the super trend is a useful indicator that can be used on any timeline with some adjustments. As it measures price volatility it can be useful for indicating the strength of a current trend with some adjustments. If supertrend data is filtered out using other metrics, including more supertrends this can reduce the amount of false positives that are triggered on the indicator.
At other times it is also useful to counter-trade the indicator with other types of technical analysis such as moving average crossovers. This layering of metrics means that even if a false positive is triggered and a trade is lost on, there are other trades going on which off-set the losses generated.