Home Trading strategies NR7 Trading Strategy – The Narrow Range 7 (But Better and Improved)
- Trading strategies
By
Oddmund Groette
-
The NR7 trading strategy is a narrow-range strategy that is both well-known and popular. If you do a search on the internet, you get multiple hits on the strategy. To our knowledge, the strategy was first developed by Tony Crabel as long back as 1990. The NR7 is a volatility strategy but enters on a day with a narrow trading range (low volatility).
In this article, we backtest the Narrow Range NR7 trading strategy. The strategy works reasonably well, but we improved it by adding one simple parameter.
Table of contents:
NR7 strategy video
We also made a video of the strategy and the backtest results.
What is the Narrow Range NR7 trading strategy?
There is an expression in most languages that says it’s calm before the storm. The main idea behind the NR7 is to enter when the daily range is low – when markets are calm (a narrow range trading day – a narrow candlestick). Unfortunately, the strategy doesn’t come with a defined exit strategy – only when to buy.
Thus, we made our own version of the NR7 trading strategy and made the following trading rules listed below. We test the following hypothesis on stocks:
- The range, or volatility, is the difference between the High and the Low (each day).
- If today has the lowest range of the previous last 6 trading days, then we go long at the close.
- We exit at the close when today’s close is higher than yesterday’s high.
We have one parameter for entry and one exit criterium, and a trading strategy can hardly get any simpler than that (?). We test on the S&P 500 by using the ETF with the ticker code SPY.
If we invest 100 000 and let it compound since the inception of the ETF in 1993 we get the following equity curve and drawdowns:
The CAGR is 7.8% (time invested is 35%), the average gain per trade is 0.27%, there are 899 trades, and the max drawdown is 25%. These are reasonably good numbers but not worth trading (in our opinion). We believe the average gain per trade is a little too low for a holding period of several days. If it was an overnight trade or day trade, we would regard the gains as reasonable.
What about NR7 indicator ( narrow range strategy)?
What if we don’t use an NR7 bar (six past days and the current one) but other number of days? Let’s optimize the strategy and see if we can improve the strategy by using another lookback period for the number of days.
Why do we optimize? We optimize because we are looking for patterns and to check if our original strategy and pattern was good simply due to randomness.
The below chart shows the different P&L for different days:
The first column shows the lookback period of the High minus Low. The exit criteria are the same. As we can see, the average gain per trade doesn’t increase the longer we hold.
Can the NR7 trading strategy be improved?
Of course, any strategy can be improved, but we have to be careful not to curve fit due to excessive optimization.
We added one simple parameter to our original trading strategy (still NR7 and we kept the same exit parameter) and we got this equity curve:
The average gain per trade increases to 0.46 and the drawdown goes down to 13%. Compared to the original narrow range strategy, we reduced the number of trades significantly. For such a simple strategy, we believe these are good numbers. Here are some more numbers for the improved strategy:
- The number of trades is 376
- The win rate is 76%
- The average winner is 0.96%
- The average loser is -1.19%
- The profit factor is 2.35.
The win rate (success rate) is very high. We regard the win rate as one of the best trading strategy metrics.
We don’t disclose the added parameter because the strategy was our monthly trading edge for February 2022 (or you can buy it on trading strategies for sale – see strategy 17):
The advantage of the NR7 trading strategy
Worth noting is that 67% of the trades of the improved strategy are done on a day when the close is higher than the day before. This is a huge advantage. Why is that?
Because we are not necessarily buying on weakness. The best strategies in the stock market over the last three decades have been to buy on weakness – mean reversion strategies. The NR7 is not a mean reversion strategy based on weakness. We are simply entering on low volatility. Low volatility usually happens during bull markets, not bear markets or short-term weakness.
- Trend following strategies and systems explained
This means that the NR7 strategy complements any mean revertive strategies you might have in your portfolio of strategies, exactly what you should be looking for if you are serious about trading. Please read our separate article on this topic:
- Does your trading strategy complement your portfolio of strategies?
The weakness of the NR7 trading strategy
The difference between the High and the Low is the main component of the strategy. Depending on where you download the quotes you can get faulty data. We used data from Yahoo!finance, but be careful of the data. Please read the article below to understand why.
- The Importance of Good Data Sets When Backtesting
Amibroker and Tradestation (Easy Langauage) code for the NR7
If you would like to have the Amibroker or Tradestation code for the free strategy without the improved parameter, you can order it here when you become a Silver member. You get additional access to the code for at least 70 other of our free trading strategies.
NR7 trading strategy – ending remarks:
We believe the NR7 trading strategy is a strategy that can potentially add diversification to your existing strategies because it doesn’t necessarily buy on weakness. How the Narrow Range strategy correlates to your other strategies is hard to tell, that is something you need to backtest yourself.
The NR7 trading strategy is a methodology focusing on entering positions during low volatility periods, particularly when the range of a day's trading is narrow compared to the previous six days. This strategy, initially developed by Tony Crabel in 1990, aims to capitalize on potential market movements following calm periods, essentially betting on the calm before a potential storm.
The strategy identifies days with the lowest range in the preceding six days and triggers a long position at the close if today's range is the narrowest. The exit point is set at the close when the current day's close exceeds the previous day's high. This basic strategy lacks a defined exit plan beyond the entry criteria.
In the article, the author discusses backtesting this strategy using the S&P 500 ETF (SPY) since its inception in 1993. The results highlight a Compound Annual Growth Rate (CAGR) of 7.8%, an average gain per trade of 0.27%, 899 total trades, and a maximum drawdown of 25%. However, these numbers might not be optimal for extended holding periods.
To improve the strategy, they introduce an additional parameter. This modification yields better results, enhancing the average gain per trade to 0.46% and reducing the drawdown to 13%. With 376 trades, a 76% win rate, an average winner of 0.96%, an average loser of -1.19%, and a profit factor of 2.35, the enhanced strategy seems more promising.
The strategy's advantage lies in its tendency to execute 67% of trades on days when the close is higher than the previous day, offering an edge in terms of buying during strength rather than weakness. This contrasts with typical mean reversion strategies that capitalize on weakness in the market.
However, the NR7 strategy has weaknesses, notably its reliance on accurate data regarding the difference between the High and the Low, which can vary based on data sources. The importance of reliable data for backtesting is emphasized, cautioning against potential pitfalls in strategy implementation due to faulty data.
The author also touches upon providing Amibroker or Tradestation code for the basic strategy to Silver members, enabling access to this and other free trading strategies.
Ultimately, the NR7 trading strategy is suggested as a potential diversification tool for existing strategies due to its distinctive approach, but the article stresses the necessity of personal backtesting to understand its correlation with other strategies and its suitability for individual trading portfolios.
