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Ed Kasanjian is president and founder of Kasanjian research, the developer of Nature’s Pulse software. Ed first dealt with developing missile-tracking systems for a defense contractor. Bored with corporate politics and lured to the vast riches in the stock market, Ed set about absorbing everything he could about techniques of analyzing and forecasting the financial markets. Ultimately, he developed his own winning approach, and in the 1980s became a successful money manager, controlling $25 million in client funds. Since then, Ed has continued on the developmental path and released some of his ideas to traders and managers throughout the world via his software. He continues to work on refining his trading techniques and making improvements in his software. The technologies used and discussed in the workshop are well proven and are employed in the successful management of his hedge fund.

Here's one of Ed Kasanjian's recent presentations.

Pattern Smasher - A New Technique for Isolating Predictive Patterns in the Markets


Expert: Ed Kasanjian
Type: PDF Workbook MP3 Audio
Running Time: 109 minutes
Workbook Length: 17 pages
Availability: Now
Average Rating:


The most consistently predictive patterns in the markets are of a newly discovered class called Dynamic Patterns. In this workshop, Ed explains what Dynamic Patterns are and how they can be employed in trading using a technique called pattern smashing. Pattern smashing enables you to identify patterns that have high probabilities of specific outcomes.

There are two classes of predictive patterns in the markets, static and dynamic. Static patterns are the focus of most of today’s market analysis technologies. The characteristics of these patterns can be defined by a property template that never changes over time. An example of this is the Candlestick methodology. Much of Ed's early research dealt with proportional relationships such as Fibonacci. He now considers these relationships a subset of a larger and more powerful class of dynamic patterns. Dynamic patterns are unique in that they can be identified by both a set of pattern signature properties that do not change over time and a set of dimensional properties that are in constant change over time.

The stable properties give these patterns their predictive power, but they are often masked by the highly visible changing properties. A dynamic property can be identified by "fuzzy" characteristics akin to the properties that allow us to recognize different human facial expressions or different trees within the same species. One specific dynamic pattern can thus be big or small, elongated or shortened, etc. and yet still be identifiable as the same dynamic pattern.

The pattern-smashing concept enables you to identify dynamic patterns and then use the less obvious and stable properties to predict future price action.


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