平均真の範囲 (ATR) is primarily used as a mechanism to determine stop-loss levels. Another way to use ATR that is not quite as popular is as a filter to isolate market environments that have the potential to make significant moves.
By gauging the volatility of a given market, ATR can provide us with insight to the possible magnitude of a move. If a market has been experiencing greater volatility, it is probably more capable of producing a significant move than a market that has been experiencing lower volatility.
Nat Stewart from NAS Trading wrote an interesting post about this topic where he compared the state of a market to weather conditions. He explains how market conditions can be evaluated just as weather conditions and then breaks down an example using ATR to evaluate market conditions.
Market Conditions and the Weather
Nat starts his post by comparing the similarities between wanting to know about weather conditions and market conditions. His concept that underlying conditions can impact the potential of a buy or sell decision is not revolutionary, but it provides us with an interesting visual when coupled with the weather analogy.
Being aware of your environment is essential to success in life and trading. You would probably be far less likely to leave the house during a hurricane. 同時に, you would have a hard time buying breakouts in a sideways trending market. 定量的なトレーダーとして, we have the ability to build filters for our strategies that check for weather conditions.
The ATR Filter
Nat explained how this concept could be applied by providing us with backtesting results for a simple S&P 500 futures breakout strategy. For these backtests, he used ATR as a filter, requiring a certain level of volatility before his strategy would participate.
As the volatility required by the strategy increased, so did the win rate and average profit per trade. When an ATR of 10 was required, the strategy posted a win rate of 53.3% and an average trade of $82. When the required ATR was boosted to 40, the win rate increased to 76.5% and the average profit per trade jumped to $761.
Nat points out that these results are opposite of what we would expect based on the common practice of setting position sizes based on ATR. Many trend followers will reduce position sizes when ATR expands when those trades appear to actually be more profitable.
One thing that he doesn’t provide us is how many trades were eliminated when the ATR filter was raised from 10 宛先 40. It is possible that the bigger filter eliminated most of the trades, which would result in a lower annual return and total profit. It could also expose the backtesting results to サンプルサイズが小さいです bias.
Regardless of whether Nat’s backtesting results are statistically significant, his greater point remains effective. Every trader should be concerned with determining what type of market weather his strategy performs best in and look for ways to isolate those situations.