Trend following techniques are the most popular among mechanical traders. While these methods do work, they have a given footprint. Many trend following systems have more losing trades than winners with potentially longer drawdowns. Countertrend systems have a higher winning percentage but a smaller average winning trade size.
Countertrend systems buy weakness and sell strength. Just like trend following systems, they offer a special set of design issues. Normally, for countertrend systems to be successful, they need a winning percentage above 50%, preferably above 60%. This means win/loss ratios are lower than 2-1. Although it is difficult to quantify why, countertrend systems seem more dependent on individual market characteristics and are more often designed for a given market or asset class.
Trader Larry Connors developed a simple countertrend system that he popularized in his book “Short-Term Trading Strategies That Work.” This system is called 7-7 because it buys and sells on a seven-day low or seven-day high.
It also is designed to deal with a flaw of countertrend systems. Because these strategies buy weakness and sell strength, they can be badly positioned in a market crash or “irrational exuberance.” Connors added an average close filter to combat this. The rules are as follows:
If Close=Lowest(Close,BuyLen,0) And
Close>Average(Close,FilterLen,0) Then Buy(“Buy-7”,1,0,Market,Day)
If MarketPosition=1 and CLose=highest(Close,ExitLen,0) Then
The standard parameters are 200, 7 and 7, but by designing them as inputs we can test other combinations.
Connors designed this system to trade major index exchange-traded funds (ETF), such as SPY, QQQQ, FXI and EWZ. In an effort to understand countertrend systems, we will test this system on a basket of futures markets and explain why it works on some markets but not on others. The results in “Split sevens” (below) are from Oct. 7, 1997, to Aug. 6, 2010. (The original system was long only because it was built for ETFs . We added a short component.) The combined results for both markets are shown in “Seven summary” (below).
Optimizing this system reveals that shorter sets of parameters make more money but could be more sensitive to slippage and commissions. Also, longer sets of parameters don’t make as much money but have larger average trades. For example 200, 10, 10 make $956,904.38 on the long and short side combined with an average trade of $435.15.
The combination 125, 2, 2 makes the most money when we don’t deduct slippage and commissions but the average trade is low. We have a total profit of $1,593,629.06 but only $98.09 a trade on 16,247 trades.
If we take a closer look we will see that some markets did well on long but not short. One example of this is copper. Copper made $52,000 on the long side but lost money on the short side. Some markets like cotton just did not work at all with this system.
Another variation of this system uses the relative strength index (RSI) instead of the seven-bar high and low.
Dim RSIValue As BarArray
If MarketPosition=1 And RSIValue>BuyExit Then
If RSIValue>SellVal And Close(Close,FilterLen,0) Then
If MarketPosition=-1 And RSIValue
The results for this variation, using the inputs ConnorsRSI(100, 2, 10, 85, 50, 50), are shown in “Relative approach” (below). Our test period is once again Oct. 7, 1997, to Aug. 6, 2010.
We can see that the winning percentage across this basket is more than 67%. The average trade is a bit low at $292.23, but with electronic markets slippages are lower and per-trade profits are not as vital. The win/loss ratio is 0.79 and losing trades last longer than winning ones.
In individual market results, performance is missed. The Nasdaq and S&P 500 do OK considering the system is only in a trade between 10% and 20% of the time. We also did well in natural gas, lumber, crude oil, the euro and coffee in light of the low drawdown.
Intermarket divergence is a simple concept that lets you trade based on intermarket relationships. It is a countertrend system in the sense that it takes cues regarding market direction from a related market, and those cues are often counter to the direction in the market you are trading.
For positively correlated markets:
- If the related market is in uptrend and the traded market in a downtrend, buy.
- In the reverse scenario, sell.
For negatively correlated markets:
- If both the related market and traded market are in uptrends, sell.
- In the reverse scenario, buy.
You can use various concepts to define an upward and downward trend. Price relative to a moving average is a simple and workable method. Basically you are banking on one market (the lead market) signaling a reversal in that particular sector.
Sometimes markets will become uncorrelated, even when the relationships are fundamental and strong. A classic example is between the S&P 500 and the 30-year Treasury bond. This relationship is good when it is working, but when it does not work, it really doesn’t work.
Let’s take a closer look at our simple positive-correlated intermarket divergence model. We will use an eight-period moving average for the S&P 500 and a five-period one for T-bonds. From April 22, 1982, to Aug. 6, 2010, this system makes $535,000 with an $84,000 drawdown. This is not bad for a simple S&P 500 system, but when we look at the yearly breakdown you can see the problems (see "Year In, Year Out," below, for the results).
In 1998, 2004, 2007, 2009 and 2010, performance was poor. Here the positive correlation between bonds and stocks breaks down. There are ways to work around this. We can filter out signals based on correlation and turn the system off when correlation is off course; or we can add a protective stop to the system and turn the system off until, and unless, a given condition occurs if we are stopped out based on that stop.
Dim InterTrend As BarArray
Dim SPTrend As BarArray
InterTrend=Close Of independent1-Average(Close Of independent1,IntLen,0)
If SPTrend<0 and intertrend>0 Then Buy(“BuyInt”,1,0,Market,Day)
If SPTrend>0 And InterTrend<0 Then Sell(“SellInt”,1,0,Market,Day)
If SPTrend<0 and intertrend>0 Then ExitShort(“SX”,””,1,0,Market,Day)
If SPTrend<0 and intertrend>0 Then ExitShort(“SX”,””,1,0,Market,Day)
ExitLong(“LongFail”,”BuyInt”,1,Max (EntryPrice-5*Average(Range,3,0), Lowest(Low,30,0)),Stop,Day)
(“BuyInt”, “LongFail”, Not (BarsSinceExitPlus(“BuyInt”) = OffLen))
(“SellInt”, “SellFail”, Not (BarsSinceExitPlus(“SellInt”) = OffLen))
The above code, written for TradersStudio, will disable the system for OffLen days after the protective stop exits the position. The DisableSignalAfterExit function makes it easy to implement this logic, which allows protective stops to be added to countertrend systems. Turning the system off for a given period or requiring RSI to rise above 60, for example, before the next long trade and below 40 for a short trade can solve problems that occur when a countertrend system gets out-of-sync with the market.
There are many classic intermarket relationships, but not all vendors have all the data. CSI, Pinnacle and Worden Brother each has something to offer. For example, Worden Brother’s TeleChart has some interesting indexes -- Morning Star indexes for example -- that are hard to find in historical data.
Countertrend systems are not as popular as trend following systems and have not been researched as extensively, suggesting there are more inefficiencies left uncovered. As you advance your research, you’ll find that stock indexes and interest rates are good countertrend candidates, as are copper and crude oil. Both ETFs and futures are viable vehicles for these strategies.
Countertrend concepts take a deeper understanding of the markets. Without this deeper understanding, countertrend strategies will often not be as robust as trend following methods. In trend following strategies, you count on one to two trades to make your profits. Countertrend systems will have a higher win percentage, but you have to find a way to limit the large losing trades that can erase a year of gains.
Learning to develop reliable and robust countertrend trading systems to go along with profitable trend following methods is one path to consistent trading profits.
Murray A. Ruggiero Jr. is the author of “Cybernetic Trading Strategies” (Wiley). E-mail him at firstname.lastname@example.org.
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