For the past 20 years I had been teaching my concept of Intermarket Divergence for generating trading signals. You can learn about this concept in a previous article, "Intermarket Divergence – A Robust Method for Signal Generation".
I developed this concept because most of my intermarket work was based on the future’s market and using back adjusted futures contracts you can’t take ratios so this intermarket divergence of price minus a moving average solved this issue.
This concept has worked well for many systems that I have developed for the past 20 years. For example in 1998 I released a 30-year Treasury Bond system which used Utility stocks as an intermarket. This system with the original parameters has continued to work even today. This system is actually often in Futures Truths Top Ten since its release list without re-optimization all these years later using just the simple positive correlation logic above.
In 2011 I began to think as good as intermarket divergence is, it must matter how we have gotten the divergence. Using our bond example, if Bonds are below their moving average and Utility Stocks are above, we buy. This rule does very good, but is there a difference how we got to this divergence?
Let's track the previous state by using "+" and "-" sign. When either the traded market or intermarket is above its moving average we use "+" sign. When it’s below we use a "–" sign. In our example of predicting bonds using UTY we want to buy as follows:
Most likely we got to this condition in two different ways:
The question is, does it matter how we got there? This means we now look at not only the current conditions but the previous condition. I am calling this as intermarket state analysis.
This topic is a very intriguing area of research and I develop a tool which allows me to do this analysis in TradeStation using the optimization feature.
We are well aware of the relationship between 30-year Treasury bonds and the Philadelphia electrical utilities average. Let’s first look at the long entries. We will define our states as follows:
My research in this area shows which sequence of states produce the best buy and sell signals are not always the classic intermarket divergence model states. Intermarket divergence signals are often more accurate when they occur after a given previous state. This tool takes intermarket analysis to the next level and represents the first advance in intermarket divergence in almost two decades.
Let’s now walk through a bond example. We'll use @US and $UTY for the last 15 years. We will use our classic intermarket divergence tool and optimize our results over the period 6/16/2003 to 6/15/2018. We will not deduct slippage and commissions to make comparisons of market bias easier. Let’s look at our results long, short, and both. The parameters we found are as followed that look robust and reliable are as follows:
Let’s now use our state generator and show how this technology works. The first will be a reversal system, stop and reverse.
Let’s now look at some of the better sets of parameters long and short.
Looking at our results we find something very interesting. In the case of long signals, they appear to be best when both markets (@US and $UTY) are in an uptrend. This can be seen by looking at the 4th column, "le_CurrState". This is not the normal Intermarket Divergence signals which would be "-" for @US and "+" for UTY (state 4, not 1).
Looking at the short side, the classic state 2 intermarket divergence signals seem best. This is seen by looking at column six, "se_CurrState".
So, does the previous state matter? Well, no. In this case, for a stop and reverse system, the previous state is not important. This is revealed by looking at the "se_PrevState" and "le_PrevState". Both of these columns have zeros in them. State zero is, "don't care".
We will select the second best set of parameters because 18, 8 has been a stable lookback for this intermarket relationship for 20 years. We can see that this combination made 40K more than our original intermarket divergence model and had a lower drawdown. What really interesting is we see that the best combinations all use current state 1 for buy, that is both bonds and UTY in an uptrend. For going short we use state 2, Bonds up intermarket down. Let’s take a closer look at these results no slippage and commission, 6/23/2003 to 6/22/2018, 15 years. We see that the average trade is about 40% higher and we make more money with less drawdown.
We can see that the average trade is much higher, let’s now look at the year by year breakdown.
We can see using state 1 for long trade instead of the normal state 4 turned 2012 from a 10K loss to a $4600 profit on 10 less trades.
This basic strategy shell is really powerful and comes close to being tradable systems. Most notably its missing money management stops. Another idea might be a filter requiring prices to be above a long term moving average to go long and below to go short. Or, adding a volatility breakout component and trading in the direction of the intermarket state.
So, if you build Intermarket strategies and want to test the impact of different Intermarket states, this tool is for you. However, at this time the Intermarket State Tool is no longer available to the general public. Please join the System Trader Success mailing list to be notified when it becomes available again.
Murray Ruggiero is the chief systems designer, and market analyst at TTM. He is one of the world’s foremost experts on the use of intermarket and trend analysis in locating and confirming developing price moves in the markets. Murray is often referred to in the industry as the Einstein of Wall Street.He is a sought-after speaker at IEEE engineering conventions and symposiums on artificial intelligence. IEEE, the Institute of Electrical and Electronics Engineers, is the largest professional association in the world advancing innovation and technological excellence for the benefit of humanity. Due to his work on mechanical trading systems, Murray has also has been featured on John Murphy’s CNBC show Tech Talk, proving John’s chart-based trading theories by applying backtested mechanical strategies. (Murphy is known as the father of inter-market analysis.)After earning his degree in astrophysics, Murray pioneered work on neural net and artificial intelligence (AI) systems for applications in the investment arena. He was subsequently awarded a patent for the process of embedding a neural network into a spreadsheet.Murray’s first book, Cybernetic Trading, revealed details of his market analysis and systems testing to a degree seldom seen in the investment world. Reviewers were universal in their praise of the book, and it became a best seller among systems traders, analysts and money managers. He has also co-written the book Traders Secrets, interviewing relatively unknown but successful traders and analyzing their trading methodologies. Murray has been a contributing editor to Futures magazine since 1994, and has written over 160 articles.As chief systems designer, Murray digs into the depths of niche and sub-markets, developing very specialized programs to take advantage of opportunities that often escape the public eye, and even experienced high level money managers.
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