Learning From The First Pullback Strategy
A few months ago the research team worked on a strategy that we named First Pullback. As often happens with our research, the strategy performed moderately well in our back testing without truly distinguishing itself compared to other strategies that we’ve published.
Today’s article will present the rules for the First Pullback strategy. In its current form, it’s unlikely to be the new centerpiece of your entire trading strategy. However, it might provide a good basis for further enhancements, and more importantly it also lends itself to some interesting observations about the S&P 500. If you would like to learn how to use the ConnorsRSI momentum oscillator to trade short-term oversold S&P 500 stocks please click here.
The basic goal of the First Pullback strategy is to find a very strong stock that’s showing its first sign of weakness, and then buying that stock on a further intraday price drop. Here are the rules:
A Setup occurs when all of the following conditions are true:
- Average daily volume for the past 21 days is greater than 1,000,000
- Closing price is greater than $5
- Closing price is greater than the 200-day moving average, or MA(200)
- Closing price is greater than MA(100)
- Closing price is greater than MA(50)
- Closing price is greater than MA(20)
- Closing price is less than MA(5)
Buy the stock on an X% limit below the previous day’s close within Y days of the Setup occurring. We tested limit values of X = 2, 4, 6, 8, 10 and 12, and Y values of 1, 2, 3, 4 and 5 days.
Sell the stock using one of the following exit methods:
- Close > MA(5)
- ConnorsRSI > 50
- ConnorsRSI > 70
In our testing, we closed the trade using a simulated market order the day after the sell signal occurred, using the average of the open, high, low and close as our exit price. However, you could also exit at the close if you prefer.
As you might expect, the strategy variations using the highest limit orders (10% and 12%) generated the highest average gain per trade but also the fewest simulated trades over the 12-year test period from 2001 through 2012. The top 10 performers had average gains of 2.09% to 2.74% per trade based on approximately 700 to 1800 historical trade signals. Other variations had significantly lower gains per trade, but generated over 70,000 trade signals.
Next we added one simple rule to the strategy: on the Setup day, the stock must be a current member of the S&P 500 index. Obviously this greatly reduced the number of simulated trades, as the previous universe was much larger than 500 stocks. In fact, the top 10 variations generated anywhere from 97 to 319 trade signals. What was a bit more surprising was the change in the average gain per trade, which ranged from 3.00% to 4.16% when using the S&P 500 as our trading universe. While that may not seem like a lot on an absolute basis, it represents approximately a 50% across-the-board improvement on the previous results. In addition, the trade durations were shorter and the win rate (the percentage of profitable trades) was higher when testing against the S&P 500.
Why is this significant? Because it emphasizes once again the quality of the companies which are members of the S&P 500. Many of these companies are household names whose stock is primarily held by institutional investors. When professional money managers see price pullbacks in well-respected companies, they are likely to step in and buy more shares at “discount” prices, which in turn makes it less likely that prices will fall too far. Understanding this behavior in the marketplace and seeing it supported with quantified test results will help you make better decisions about your own trading strategies. Click here to learn how to most effectively trade these stocks using the ConnorsRSI indicator.