Trading psychology in a cup-and-handle pattern

Pattern trading in the security market isn’t new. For years — decades even— traders have been scouring charts on a daily basis in search of pattern formations. The thinking has been that if you found a pattern early in its formation, maybe you could capitalize on a trade by predicting where the security was headed.

The Cup-and-Handle pattern is a great example of how this works. Here is an image of the geometric pattern formation:

From a geometric shape standpoint, it is easy to see how the cup and the handle forms in the image above. But perhaps the more important question here is: why does the pattern form this way?

To answer that, let’s think about the cup-and-handle pattern from a behavioral finance/investor psychology standpoint.

The Psychology Behind a Cup-and-Handle Pattern

Point #1 on the chart is easy enough — the security reaches a new high or tests an old one, and investors take some profits off the table.

At point #2, the security has pared back some of the gains, but at a certain point, investors will see it as an attractive price to buy, which creates a support level. As buyers pour-in, the price ultimately climbs back up to the high it reached earlier (point #3).

Investors often figure that if the price can reach its old high (points #1 and 3) again, that would provide yet another optimal opportunity to take some profits off the table. However, this time the stock only traces back part of the way (point #4) before investors want to pile back in, hoping to capitalize the next time the stock retests the high. Indeed, once the stock finally crosses its support level (points #1 and #3), investors will see it as a “breakout” and will fear missing out on further gains, driving the price even higher. In addition, all those how could have placed short bets might get scared and start covering their short positions and driving the price even higher.

For investors watching this pattern form and then seeing it confirmed as a cup-and-handle, the ideal outcome would be to identify the breakout price and to trade with the trend. Two potential trades in this case of the example would be for the investor to either going long the security or perhaps buy some call options.

How Can an Individual Investor Find Patterns in the Sprawling, Ever-Changing Market?

Obviously, the monumental challenge for investors is how to identify patterns in the securities markets. Today, there are 3,671 domestic companies listed on US stock exchanges. But then there is also forex, ETFs, commodities, and the emerging asset class, cryptocurrencies. The time and manpower needed to scan charts is unfathomable.

But that's where Artificial Intelligence comes in.

Tickeron has developed Artificial Intelligence (AI) that is programmed to search for cup-and-holder patterns in the security market, but it can also search for patterns in forex, cryptocurrencies, ETFs, and other securities.

The tool is called the Pattern Search Engine, and it can revolutionize the way you trade. Using algorithms and AI is no longer limited to hedge funds and big institutional Wall St. firms. Now you, the retail trader, can use the same high-powered technology to scan the markets for patterns.

Here’s How it Works

Here’s a typical example of a Cup-and-Handle pattern identified by Tickeron’s AI, and this is how it would be delivered right to your inbox.

Cup-and-Handle (Bullish) Example

To note, while the above example is an actual stock and an actual confirmed and successful cup-and-handle pattern, it does not constitute investment advice nor is it a recommendation for the stock.

In the example above, Tickeron’s AI would have delivered this pattern and stock ticker to your inbox once it was confirmed, and then it would be up to you to trade at the breakout price in anticipation of the stock reaching its target price. In this case, the stock MRUS confirmed the cup-and-handle pattern on December 21, 2017, and by the 28th it had notched a +12.05% gain from breakout price to the target price.

What’s more, the cup-and-handle pattern works for both bullish scenarios and bearish scenarios. As you can see from the pattern chart below, the AI detected an inverse cup-and-handle pattern for the stock ticker CERS on December 20, 2017, and by the 29th the stock had plunged as predicted to the target price. Traders who received this pattern from Tickeron’s AI could have attempted to capitalize by potentially shorting the stock at the breakout price or purchasing put options.  

To note, while the above example is an actual stock and an actual confirmed and successful cup-and-handle pattern, it does not constitute investment advice nor is it a recommendation for the stock.

Does the Security Pattern Search Engine Really Work?

How do you know if Tickeron’s Artificial Intelligence — or any trading platform or strategy for that matter — really works?? The answer: statistics.

Tickeron tracks and backtests statistics for each cup-and-handle pattern detected by Artificial Intelligence, and compiles the data into a user-friendly format. The idea is that if an investor/trader can see past statistics for a particular pattern, it can better inform the investor whether or not the trade is worth the risk to him or her.

With Tickeron’s platform, an investor/user can obtain statistics about patterns the AI has detected and what percentage of them actually hit their target price — and what the potential gain could have been if the investor traded the pattern (or loss, if the pattern was not successful).

With just a few clicks, the user can obtain the following statistics:

• How many total cup-and-handle patterns the AI has recognized

• How many of those patterns actually reached the target price

• The average return if the pattern was successful versus if it failed; and,

• The average return of all patterns recognized.

• A list of all of the securities (including cryptocurrencies) that the AI has identified over time in a cup-and-handle pattern.

The AI also has different “confidence levels” for how confident it is that the pattern will play out, and the user can adjust confidence levels higher or lower to obtain different statistics.

For example, if Tickeron’s AI is asked to detect all Cup-and-Handle Bullish Patterns with a 30%+ confidence level and a greater than 7% distance to the target price, then the user would discover that Tickeron’s AI has discovered 12,140 patterns meeting that criteria, having searched about 4000 stocks and about 10,000 ETFs. The user would also discover that 6,790 of those patterns actually reached the target price with average return +14%. Of the patterns that did not reach their target price, the average loss was about  -8%. The key here is that the AI was correct more than half the time, which is a strong statistic.

Test the Pattern Search Engine for Yourself

If you’ve never seen technology like this before, it’s because it has never been available to retail investors in this format. The idea is to provide retail investors with technology and tools to enable trading with massive amounts of data and analysis. The end result is arming retail investors with a high-powered, virtual research assistant: Tickeron’s Artificial Intelligence.

Interested in stock patterns? Identify 37 pattern types across thousands of stocks, ETFs and cryptocurrencies. Learn more here.

Contributor

Sergey Savastiouk|Expert

Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics from Moscow University and has extensive experience as an entrepreneur, investor, manager, and mathematician. His professional expertise is in applied mathematics, mathematical modeling, system and pattern analysis, and software and hardware system integration. He has served as the CEO of several hi-tech start-up companies and nonprofit organizations, which has given him proven capabilities in business strategy for high-tech start-up companies, market assessment, company formation, team building, product development, marketing, and sales. He has published numerous articles in journals and magazines on related fields. As a retail investor, he spent 15 years developing his proprietary trading and quantitative algorithms (now Tickeron’s A.I.), which brought him significant returns in trading the stock market. His current work and goal in founding Tickeron is to bring professional, sophisticated stock market analysis capabilities to retail investors via an easy-to-use interface.

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