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It’s been about a year since I’ve taken a look at the very popular 2-period RSI trading method by Larry Connors and Cesar Alvarez. We all know there are no magic indicators but there is an indicator that certainly acted like magic over several decades. What indicator is it? Our reliable RSI indicator.
Over the past few years the standard 2-period trading system as defined in the book, “Short Term Trading Strategies That Work”, has been in a drawdown. During 2011 the market experienced a sudden and sustained drop which put the system into loss. It has been slowly recovering since. Below is an equity graph depicting the trading system’s equity curve trading the SPX index from 1983. You can easily see the large drop around trade number 120.
Here is a closeup view of the last 19 trades, which covers about the last five years.
The trading rules from Larry Connors are very simple and consist of long-only trades. As a reminder, the rules are as follows:
All the tests within this article are going to use the following assumptions:
Is the 2-period RSI indicator losing its edge? Hard to say at this time. It’s not like drawdowns have not happened before, but that’s not really what I want to explore. I want to take a closer look at the 2-period RSI indicator and see if we can improve the basic trading system by Larry Connors.
First let’s take a look at how the market behaves after a RSI setup occurs. In short, the 2-period RSI is designed to highlight strong pullbacks. Buying into pullbacks in an uptrend has been a well known and effective trading method and is the essence of the 2-period RSI trading system. We can demonstrate this by looking at how the market behaves after a trade is triggered. I created an EasyLanguage strategy that can hold a position X days after opening a trade. I then tested X for values in the range from 1 through 30. In other words, I want to see how the market behaves 1-30 days after opening a trade. Does the market have a tendency to climb after trade setup occurs or does it tend to drift lower? Below is a bar graph which represents the results. Each bar is the total P&L based upon the number of holding days.
In general the longer the holding period, the more P&L is generated. This shows that after our 2-period RSI indicator triggers a trade, the market tends to climb over the next 30 days. It’s also important to notice all values produce positive results. This shows robustness in this particular parameter.
The original trading rules by Larry Connors used a dynamic exit based upon a 5-day moving average. Once price closed above this average, the trade was closed. I wanted to take a closer look at the period used for this moving average exit. Like the bar graph above, I tested values 1-30 for the period used in the moving average.
In this graph we can see increasing profit as we increase the moving average period from one to 14. There is a slow decline after 14 as we continue to our final value of 30. It’s very good to see that all values produce positive results. This shows stability across this parameter and exit method.
Let’s look at the threshold value used to determine if the market has pulled back enough to trigger a long trade. Once again, we will create a bar graph as we look at a range of threshold values from 1-30.
We see values below 10 produce the best results. Once again, every value produces positive results and this is a very good sign.
Based upon the information we looked at in this article, let’s modify Connors’ trading rules. We will make the following changes:
Below is a table showing the difference between the original Connors’ rules and the modified Connors’ rules.
Our increase in net profit comes at the cost of more trades which is due to the fact of lowering the stand on what we consider a viable pullback. By increasing the RSI threshold from 5 to 10 more setups qualify as a valid entry, thus we take more trades. But we also make more money on each trade. This is due to our longer hold period by increasing our moving average period from 5 to 10. In the end, we are willing to take more trades and hold on to those trades for longer periods of time.
All the results above do not use a stop loss value. Let’s add a $2,000 stop loss on each trade and see how it will change the results. I picked this value because it represents our risk value when scaling the number of shares to trade. Notice we are only risking 2% of our $100,000 account on each trade. The far right column holds the results with the $2,000 hard stop.
This just gets better and better. Often stops will hurt a trading system in several key performance factors but not here. Our $2,000 hard stop improves the system across several performance factors. Unlike the original trading system, this equity curve is producing new highs.
Let’s now look at the performance across different markets. I will not modify the trading rules at all.
Notice the number of trades is significantly lower for the above ETFs when compared with SPY. This is due to SPY being around since 1993 while these other ETFs are much newer. Overall, the system holds up nicely across these different markets.
The RSI indicator still appears to be a robust indicator at locating high probability entry points within the major market indices. You can modify the trigger threshold and holding period over a large range of values and still produce positive trading results. I hope this article will give you lots of ideas to explore on your own. Another idea in regards to testing parameters is to independently optimize the parameters over the “portfolio” of market ETFs instead of using just $SPX. There is no doubt in my mind the RSI indicator can be used as a basis for a profitable trading system.
This topic has been updated with performance through 2014.
Jeff is the founder of System Trader Success – a website and mission to empowering the retail trader with the proper knowledge and tools to become a profitable trader the world of quantitative/automated trading.
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