In the last installment of “Using Quantifiable Edges to Your Advantage” I began discussing some of the tools available to subscribers. In that post I discussed the Catapult & CBI in great detail. Today I will review several other tools available to subscribers (though not in as much detail). I’ve discussed several of these before so blog readers may be familiar with some of the below.

The Aggregator – Much of my market bias is determined by the studies I conduct and consider “active”. The Aggregator is the tool I developed that weighs the estimates from those studies and gives me a bottom line bullish or bearish expectation. I also use a separate calculation called the Differential that measures recent market performance versus recent expectations. The Aggregator is the number one tool I use in determining my bias and setting up my index trades. More information on the Aggregator can be found by clicking the link below.

https://quantifiableedges.blogspot.com/2008/07/quantifiable-edges-aggregator.html

The Quantifinder – Over the course of the last three years I have published over 1000 studies in either the blog, the subscriber letter, or both. Studies that appeared to me to either have a substantial edge or to be notable and worth reviewing in the future are included in the Quantifinder. The Quantifinder is an engine that examines the current day’s market action and determines whether any of about 900 studies would trigger based on today’s action. Any studies that appear pertinent are automatically referenced and a link is provided so that users can go back and read what I wrote about the current setup the last time it occurred. More detailed information on the Quantifinder can be found by clicking the link below.

https://quantifiableedges.blogspot.com/2009/05/quantifinder-unveiled.html

The Numbered Systems – Quantifiable Edges subscribers have access to 11 different mechanical systems. These systems are designed for trading either large cap stocks or ETFs. Each night I publish a spreadsheet that shows any stock among the S&P 500 or any ETF among the 100+ on my ETF list that has triggered one of the numbered systems. Each system has its own webpage that comes complete with detailed rules for entry and exit. Also on the webpage is Tradestation code for each system. Subscribers may take the code and use it for their own testing, or they may change it-using it as a starting point to develop one of their own systems. (Those who are interested in developing their own systems and testing across a large group of securities using Tradestation will find detailed instructions on the website on how to do so.)

The Charts Page – Quantifiable Edges tracks a number of unique indicators on its charts page. These include indicators such as the CBI, the 3/10 Offset HV, the Volume SPYX indicators, and more.

The Archives – The Quantifiable Edges Subscriber Letter is now three years old. Over the last three years there has been some amazing market action which has led to a massive amount of research. All of Quantifiable Edges past letters and published research are available to subscribers for easy viewing in the archives section of the website. Many subscribers find themselves reviewing old letters thanks to being sent there by the Quantifinder, but if you feel current action is similar to something you’ve seen over the last few years and you want to see what research I was discussing then, you can simply pull up those letters on the archives page.

The Downloads Page – Over the last three years I have created a number of studies and special reports. Several of these along with special coding and spreadsheets can be found on the downloads page in the members section of the website.

Educational Videos – in 2010 I began hosting webinars for subscribers once or twice a month. In these webinars I discuss topics such as the Aggregator, the Catapult System, how to run back tests across large lists of securities, POMO indicators, day trading opening-range breakouts, and more. I have archived many of these webinars and made them available for subscribers to view any time.

Proprietary Data Download – Subscribers that like to do their own tinkering and development can download historical data on several Quantifiable Edges indicators. Data is available for the Volume SPYX indicators, the CBI, and the Aggregator.

The Subscriber Letter – The tool most utilized by subscribers is the nightly letter. It arrives nightly and is complete with my research and studies for the day along with my interpretation of recent studies. The Aggregator chart is always included and discussed, and the intermediate-term outlook is updated at least once per week. Additionally, it contains trade ideas that have been tracked and recorded over the last 3 years. It is not a tip-sheet, but the trade ideas, whether they are index trades, Catapult trades, or something else, have done quite well since the inception of the letter. They can all be found on the Trade Idea Results Spreadsheet, which is downloadable from the System page on the website by members at any time.

It all began with the subscriber letter. And while that has changed some over the years, the majority of the enhancements have come on the website. How traders ultilize the research, the letter, the indicators, and the systems is up to them. There is a lot available. Hopefully this series of posts will help new and experienced readers alike to better take advantage of Quantifiable Edges, regardless of whether you have a subscription or whether you just read the blog.

This completes “Using Quantifiable Edges to Your Advantage” – for now. New features will be released over the next several months and I’ll be sure to add them to the end of this series once they are available.

This post is the 6th in the series “Using Quantifiable Edges to your Advantage“. In the last two posts I’ve discussed 1) combining historical edges to develop a market bias, and 2) factoring in overbought/oversold measures to improve risk/reward. So now let’s assume you’ve done those things, and the situation setting up is suggesting a strong directional edge. You’ve either got an upside bias and a market that is not “too overbought”, or a downside bias and a market that is not “too oversold”. How do you translate that information into profits?

The most obvious way to try and take advantage of this kind of setup is to take on an index position. This is something that I do a lot of, but index trading isn’t for everyone and there are many ways to take advantage of a directional market edge without trading indices.

Another way to apply a directional market edge is to favor trades in the direction of that edge. Let’s use the example of a systems trader that focuses on either individual stocks or ETFs. Rather than simply take entries that may occur at any time, that trader may elect to use directional market edges as a filter. In other words, if there appears to be a strong upside market edge and one of his short systems triggers, he could opt to ignore the signal or view it as invalid. But if the market edge is to the downside and a short system trade triggers, he could jump on it.

In the 10/6/10 blog I showed how I did this for my own systems. I used the Aggregator as a proxy for my market bias. I’ve discussed the Aggregator on numerous occasions, but if you are unfamiliar or would like a review the embedded link is a good place to start. For my tests I broke out the system performance by times the Aggregator was suggesting a market directional edge in the same direction as the system versus times it wasn’t. I found that for almost all of my systems, statistics were substantially better when you were trading with a directional market edge. Times where the Aggregator signal was not confirming the individual system signal, the system simply did not fare as well.

But while I’ve been talking about this using a mechanical systems approach, the concept is applicable to discretionary traders as well. If you are able to identify a directional market edge then you can consistently apply that information in your decision-making. During periods where you expect the market to flourish, you should trade the long side more aggressively and the short side more conservatively. Those times when you determine there is a downside market edge you should take the opposite tack. Trade more aggressively with your short positions and more conservatively with your long ones.

At Quantifiable Edges I do my best to try and identify directional market edges for my subscribers. While some subscribers trade indices and perhaps utilize some of the index trade ideas I publish, many of the more astute subscribers simply use the information to enhance the application of their own strategies. A few public examples of this include David Varadi of CSS Analytics and Ray Barros of TradingSuccess.com.

In his 11/18/10 blog post entitled “3/10 Offset HV as a Mean-Reversion Filter” David had this to say. “I am always looking for new and interesting ideas to improve the edges of conventional systems or indicators. One valuable source of ideas is a subscription to Quantifiable Edges, where I get the opportunity each night to review how Rob Hanna classifies the most relevant situations in today’s market.”

On the other end of the trading spectrum from David is Ray. In his December 22nd post, “2010 Adieu” Ray wrote the following, “2010! You taught me many new things. If I had to choose, the Oscar would go to Rob Hanna of Quantifiable Edges. He and I are poles apart when it comes to trading style and timeframes. But through his excellent site and newsletter, he showed me his way of viewing quant studies. I have adapted and integrated his ideas into my own trading with success. Thanks Rob”.

But your trading skills don’t need to be at the level of these two traders in order to take advantage of quantifiable edges. And you don’t need to overhaul your trading strategies either. A simple approach like tweaking your aggressiveness based on your market outlook can go a long way in improving returns.

Index traders have long understood how to take advantage of directional market edges. But for those that trade individual stocks and ETFs, my research suggests it makes great sense to take a top-down approach. First determine a directional market edge and then look for those stocks or sectors that are set up best to take advantage of the anticipated market move. By doing this you’ll have the market wind at your back and your risk/reward and total profits should benefit accordingly.

This post is the next in the series “Using Quantifiable Edges to your Advantage“. The last post discussed how I combine studies to help establish a bullish or bearish bias. Today I will discuss how I factor in overbought/oversold readings when considering whether to take (or hold) a position.

I’m typically averse to maintaining long positions in strongly overbought markets or short positions in strongly oversold markets. Being long in an overbought market, or short and oversold market, can carry a high level of risk since market reversals under such conditions can be sharp. Mean reversion traders strictly abide by this philosophy as they look to take advantage of stretched conditions and then exit once conditions revert to a more normal state. For instance they might look to buy a short-term low and then exit the trade on a moved back up through a short-term moving average. This can be a solid approach, especially if you also factor in the long-term trend of the market.

My approach is a little bit different. Rather than comparing the market’s price to a mean or measuring overbought/oversold with an oscillator, I compare recent price action to recent expectations based on estimates provided by my studies. This typically allows me to get long easier if my studies are suggesting an upside bias, and allows me to get short easier if my studies are suggesting a downside bias. At the same time it protects me from entering a position in the direction of a move that is already strongly overdone.

Let me provide a brief example to better explain. Assume that over the last three days the estimates from my studies suggested that the market should be up a total of 1%. If over that period of time the market rises 2% then I consider it overbought and too risky to hold a long position, even if my estimates for the next few days are for further upside. But if instead the market has only risen 0.75% while my estimates suggested it should be up 1%, then it would not be overbought and I could continue to maintain my long position. And if my estimates were for 1% up, and the market had declined, there again a long position would be justified. The combination of an underperforming market with positive expectations or a market that has outperformed and has negative expectations is a combination that I want to hold a position.

Some recent long signals provide nice examples of instances where a classic mean reversion approach would have to be flat or short, but I was able to maintain a long position. For most of the early December my studies suggested an upside bias. I was quickly taken out of my long position, though, as the SPX became extremely overbought early in the month. On December 15th the market pulled back for the first time in over a week and a long signal triggered. The next day the SPX reversed sharply and closed at a new high. In doing so it would’ve meant an exit for any classic mean reversion strategy. But despite the new high, the SPX was still considered “underperforming” versus expectations over the last few days. For me this meant I could continue to hold, or even establish new long positions. On the 17th the SPX again closed at a new high, but again it was considered underperforming versus my recent expectations. It wasn’t until the 20th when the SPX was making its third new high in a row that my measurement suggested the short-term move up was getting too overheated and it was time to take profits. Even that exit was early as the market continued upward for two more days. My studies remained bullish and my next long signal occurred on December 27th despite the market closing up for the day and only one point shy of another new high.

The purpose of this is not to discuss my trade triggers in any detail but rather to share the idea that overbought/oversold can 1) be incorporated to help reduce risk and 2) be looked at a number of different ways. When I determine my position size I consider both the strength of my current open studies, and the degree that the market is overbought or oversold versus recent expectations. While you may not track studies and generate estimates in the same way I do, you should still consider adjusting your overbought/oversold measures based on market conditions and/or your current and recent outlook.