Top Trend Predictions

Prices of securities, currencies, commodities or any other tradeable asset are constantly changing. In a single day, an asset's price can go up, go down, track sideways, or not change at all! At the end of the day, there is no crystal ball for determining when prices will suddenly change directions or establish a new trend. Long-time investors understand that predicting a security's price direction involves a considerable amount of research, guesswork, and ultimately, luck.

But perhaps forecasting the direction of a security's price shouldn't be so 'unpredictable'. In other words, what if there was actually a formal process for determining when a price locks into a trend -- whether it be up, down, or sideways?

As mathematicians, the founders of Tickeron were fascinated by this question, so they set out to answer it. They spent years exploring how Artificial Intelligence (AI) algorithms could be used to help investors discover and formally identify patterns and trends in the stock market and other securities markets. In the process, they built an Artificial Intelligence-based search engine designed to identify patterns and price trends in the markets. They also designed the AI to generate trade ideas based on those patterns and trends.

The subject of this ebook is the AI-powered search engine that can identify price trends, known as the Trend Prediction Engine (TPE).
Of course, even the best AI would not be able to definitively establish price movement trends to predict where a security's price is headed - that would be impossible. But Tickeron believes this new technology can turn 'guesswork' and 'luck' into actual statistical probabilities, and that the AI-powered search engine can help traders increase the probability of getting trades correct. Tickeron's ultimate goal is to make this type of technology accessible to every type of investor, big and small.

This e-book will show you how the TPE technology works, and how you can access it today. If you're searching for a fresh approach to trading that uses real data, research, and algorithms to identify patterns and trends in the stock market, then keep reading. This ebook is for you.
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Tickeron has also created the Pattern Search Engine for identifying patterns in the securities markets.

The process of finding patterns and trends in price charts has traditionally been extraordinarily difficult and time-consuming. Analysts would (and still do) scan hundreds, if not thousands of printed or computer-generated price charts in search of meaningful patterns, only to find that they are sometimes too late on a trade. More often than not, it ends up being a major waste of time.

With the latest developments in Artificial Intelligence and the speed of computer processing, however, analysts and institutional investors can now perform thousands of man-hours of market research in mere seconds. This innovation has had a massive impact on the investment community...but not the entire investment community.

Institutional investors and banks with multimillion-dollar technology budgets are putting AI to work every day in a big way. They are developing complex technology systems and infrastructure to analyze the markets, find opportunities, and make trades in milliseconds. This development has been wonderful for the institutional world, but what about retail investors who don't have access to such resources?

Indeed, retail investors have largely been left out of this high-impact innovation. To our knowledge, the type of high-powered technology needed to scan the market for patterns and trends has not been available to retail investors in a user-friendly, cost-effective platform, but Tickeron is here to provide retail investors with access to this type of technology.

Artificial Intelligence algorithms work much like the human brain does, so it makes sense that neural networks created in AI are based on the human brain. There are different kinds of neural networks, but they typically consist of a set of nodes arranged in several layers with weighted interlinks between them. Each node combines a set of input and output values which are learning nodes for future recognition. Easy enough to understand, right?
Ok, maybe not so much. Here's a different way of thinking about how Artificial Intelligence works: AI algorithms function by analyzing and recognizing patterns in enormous data sets, which in turn generate applicable ideas for people to use to improve efficiencies across processes, services, inventory management, and numerous other areas. For example, AI in autonomous cars uses pattern recognition algorithms to identify movements of cars on highways, so that they know how to keep safe distances and hit the brakes when necessary. During each trip, the AI continues to learn and share information to fine-tune the driving experience even further. Eventually, robots will be much better drivers than humans - by a long shot!

The same applies to securities trading. Tickeron's AI searches for price trends in thousands of stock, ETF, cryptocurrency, etc. charts, and it is also programmed to test past performance in order to determine the statistical probability of future performance. The AI identifies trends by their key geometrical elements, formed by changing stock prices when plotted on a chart. Over time and through back-testing, the AI starts to learn when certain patterns or trends are statistically likely to form, and for how long.

The more iterations and charts the AI observes the more fine-tuned its predictions and results should become over time. If Tickeron's AI determines that a stock has entered a particular trend, for instance, and the AI has a conviction on where that trend could go and how long it may last, it can give an investor trading ideas and statistical probabilities of success based on the trend's potential course and an eventual target price. Tickeron's Trend Prediction Engine's results to date underscore how this process works, and we share a few real examples of the AI's computing power in Chapter 4.

The following chapters will offer a comprehensive inside view into how Tickeron's AI works and will offer readers a visual guide into trend discovery and investment ideas. But first, we will give you a basic idea of how trends work, and why they matter.

Spotting trends in areas like fashion or music are easy enough for humans to identify. They are part of everyday culture, and the information is easily obtainable through the news or social media. Trends in the stock market, on the other hand, which involve geometric patterns, arithmetic quantities, and prices that change every second are much more difficult to identify.

The human brain is not wired to process that much information that quickly, and our emotions often create biases that convince us we've identified trends that may not actually be there. In a book titled, "Thinking Fast and Slow," the author Daniel Kahneman describes this behavioral phenomenon as follows: "we can be blind to the obvious, and we are also blind to our blindness."

This is ultimately is no fault of humans -- there is simply too much information to process! Just consider the different time frames that are relevant to charting a stock. The most basic chartists may look at a security chart every week. But here's what happens when broken down further:

There are five trading days in a week, six and a half hours of trading each day, with every hour consisting of six 10-minute charts. Now, consider that there are approximately 4,000 actively trading companies on major U.S. exchanges, and more than 15,000 securities that are traded over the counter. Even if you hired an entire army of chart analysts, there would be no possible way to keep up!

What is even more confusing is the fact that a trend may exhibit itself in one time frame, but then not be very transparent in another time frame. As a result, there can be conflicting trends within particular security depending on the time frame chosen. It is not out of the ordinary for a security to be in a short-term uptrend while being mired in an intermediate and longer-term downtrend, for instance.

Many investors tend to get this wrong. They lock in on a specific time frame, ignoring the more powerful long-term trend. Other traders may be trading the long-term trend but underestimating the importance of refining their entries in shorter-term time frames.

In our view, the proper way to analyze any chart is to view it in at least three-time frames close to each other. If you analyze daily charts, you must first examine the weekly and

monthly charts, for example. Here's how it could break down amongst traders with different time frames and objectives:

• A long-term investor might focus on weekly charts while using monthly charts to define the primary trend and daily charts to refine entries and exit;
  
  
• A swing trader who focuses on daily charts might use weekly charts as a primary trend and 30-minute charts to define the short-term trend;
  
  
• A day trader who primarily makes trades using 5-minute charts may use daily charts to define the primary trend and one-minute charts to define the short-term trend.

At the end of the day, it is safe to assume that humans are intrinsically bad at tracking several time frames because they are only able to process a limited amount of information at a time -- despite having unlimited access to data. That's where Artificial Intelligence can make a remarkable difference since it can process enormous datasets in just seconds.
Tickeron's AI analyzes daily, weekly and monthly trends. Once a trend is discovered by our AI, the trader can then turn their focus to hourly and minute charts to make intraday decisions.
In the next sections, we'll talk about the different kinds of trends.

An uptrend forms when bulls (buyers) are stronger than bears (sellers), and the bulls push prices higher over time. Of course, there are periods within an uptrend where the price of a security will 'pull back' or retreat, but upward momentum is maintained by net buyers (see Figure 1.2, Zone 1).

An uptrend can be identified in a chart when security consistently has higher lows and higher highs. This phenomenon can be explained by the behavior of market participants who, each time they see a price pull back, ultimately decide it is a good time to buy at a lower price and establish a position. When this happens, investors often buy shares of the security, which increases demand and ultimately forms the uptrend. A smart trader will use the trend as his friend, riding the momentum for as long as possible. Before exiting the position, the trader waits until the trend changes direction with a new lower low price (see Figure 1.2, Zone 3). This signals a change from an uptrend to a downtrend.

While these observations appear rather simple when looking at Figure 1.2, it is important to remember that there are often thousands -- if not millions -- of trades happening each day for individual securities. Following one security is difficult enough, but imagine trying to do so for the 4,000 actively traded securities on major exchanges! It would be impossible….that is unless you have the help of Artificial Intelligence.

Downtrends form when bears are stronger than bulls, and the bears ultimately push prices lower over time. In other words, there are more sellers than buyers. Often times, when bulls lift prices, bears will sell short into the rally in order to stop it, sending prices to new lows (see Figure 1.2, Zone 3).

A downtrend can be identified as a series of lower highs and lower lows as sellers often see minor upticks in price as a good opportunity to unload a position. When these sellers urgently accept lower prices to sell their security, the number of such sellers grows and helps drive the trend lower.

Traders profit from changes in prices: buy low and sell high, sell short at a high price and cover low, etc… But the reality is that markets spend much of their time in trading ranges. When bulls or bears are equally strong or weak, prices remain in a trading range.

Ultimately, a sideways market occurs when the price trend of a certain security is not experiencing an uptrend or a downtrend, i.e., the price is actively oscillating in a relatively narrow range without forming any distinct trends (see Figure 1.2, Zone 2).

As soon as the price of the security goes up, the sellers take profits and push the price down, creating resistance. Conversely, when the price of the security goes down, buyers see an opportunity to take a position and drive the price back up, creating support. This tug-of-war between buyers and sellers can continue for a long time until certain events -- either company-specific events or macro events in a particular industry or for the market in general -- have a significant influence on price behavior and establish either an uptrend or a downtrend.

Here are a few examples to make this clear.

The first graph below is of the automaker and energy pioneer Tesla (TSLA) from July 2017 to September 2017. Looking at TSLA's price on a monthly scale, TSLA appears to be in somewhat of an uptrend, with the price bouncing around quite a bit but charting a course higher between approximately $310/share to $380/share.

Indeed, Figure 1.3 shows what appears to be an uptrend. However, if we take a look at Tesla's chart on a smaller, weekly scale, the trend is not as obvious. Indeed, on a weekly scale and looking at the exact same time period (7/17 - 9/17), you can see that Tesla has actually exhibited 4 different trends! An uptrend, downtrend, uptrend again, and finally, sideways:

Each trading range consists of support and resistance lines, and a trader (or in our case, Tickeron's Artificial Intelligence) needs to identify trends and trading ranges in order to make smart trades.

Here are a few examples of how our AI can be used to discover support and resistance lines:

Example #1: AI discovers a Support Line (GILD, from November 2017 to January 2018):

Example #2: AI discovers Resistance Lines (CSCO, from February 2017 to November 2017):

Example #3: AI discovers a broken Resistance line and the stock goes into an uptrend (CSCO from November 2017 to January 2018):

Example #4: A Longer-Term, Monthly Uptrend (NVDA from July 2017 to January 2018)

During the months of June and July of 2017, General Electric (GE) exhibited three trends. From approximately June 14 through July 7, 2017, the stock was in a downtrend. From July 7 through July 15, the trend reversed and became an uptrend, but then after that, it reversed again:

To identify a potential shift in a trend, traders often look to the security's volume in relation to price direction. Intuitively, it makes sense why volume matters so much. If the price of a security is rising, and the volume is large ('large' is a relative term here, but our AI formalizes its calculations of volume), it means there are a lot of convinced buyers who think the price of the security will continue to rise. The volume can come from long positions, short-covering, or any other trade that would benefit from a rising price.

If the volume is especially high, it can also often mean that institutional players such as hedge funds, mutual funds, pension funds, etc. are involved. If a particular trend is established with a large volume, it is often a compelling sign the trend is established and can continue. If, however, the rising or falling security price is happening with low volume, the trend may be easily reversed.

Why can an uptrend, a downtrend, or even a sideways trend be your friend? Because as a security locks into a trend, you can potentially use that trend to trade for profit. If, with the help of AI, you determine that a security is locked into a downtrend, perhaps you short the security to try and capitalize on the decline. If you and the AI determine that a security is entering an uptrend, perhaps you go long a few shares to try and profit from a higher price. And so on.

At the end of the day, many market participants like retail investors, mutual funds, and hedge funds act in concert. They rely on the same publicly available information, they read the same journals and newspapers (you will probably find Wall Street Journal and Financial Times in the lobby of every reputable investment institution in the United States), they listen to the same talking heads on CNBC, they attend the same conferences. Remember, too, that for anyone individual investor it is extremely difficult to psychologically go against the crowd.

This collective wisdom of analysts and traders typically creates a uniform opinion about a particular stock/security. That is why if we can identify a trend -- be it an uptrend or a downtrend -- it usually has the potential to endure for some time. Our AI uses sophisticated algorithms to analyze trends, and the AI determines its "confidence level" for each trend as well as a target price for where the security could go if it follows the trend.

On July 3, 2017, our AI gave a bearish signal when it discovered a Head-and-Shoulders Top pattern for General Electric (GE) stock:

At the time, GE was trading at $27.35. Tickeron's AI assessed a target price of $25.14 with 35.54% confidence the stock would reach its target price. (Again, it should be noted that our AI is not attempting to perfectly predict a stock's price. Rather, it is in search of trends and statistical probabilities of an outcome.)


Following our AI's bearish signal, GE's stock price continued its decline: $27.35….$26.31…$26.15…$26.04. The stock eventually closed at $25.20 on August 11, just 0.25% from our indicated target price. Had investors acted on the AI's bearish signal and the projected target price by shorting the stock, a profit may have been generated.


Sometimes, trends develop out of the blue and are totally unexpected. Sometimes they are the results of sentiment bubbles and other times they represent a reversal of fortune that is the result of changing economic conditions. What is important to understand is
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* For a complete explanation of the Head-and-Shoulders Top pattern, and the other 36 patterns our AI can identify, download our eBook titled "Pattern Search Engine" here.

that our algorithms focus not on the cause, but the effect. Our AI is trained to use hard data to detect the trend, calculate the target price, and set a confidence level of the security reaching that price. Tickeron's AI takes emotion completely out of the equation, something humans simply cannot do.


In this way, the goal of our AI is to provide smart traders with smart instruments. But ultimately, it is up to the investor to decide if he or she wants to add this technology to his/her trading and investment arsenal.

For active traders, what matters is having the ability to identify trends at a moment in time, establishing a statistical probability for long it may last, and then trading into the trend in an attempt to take advantage. If this process is repeated over time, it would have the trader entering a position in a clearly defined trend, and then "rotating" out of the position as the trend ends. Rinse, repeat.

To demonstrate how it works, consider this hypothetical portfolio (below) that has two security positions in it. Let's assume the investor has 50% invested in the green security and 50% invested in the red security. As you can see, over time the two positions together netted a zero gain for the portfolio. They charted two different courses to get there, but either way the investor ends up with the same amount he/she started with.

These two securities exhibit the following trends at different stages:

• Uptrend (green zone 1, red zone 3) – when the security is rallying higher in an uptrend.
  
  

• Sideways Top (green zone 2) – this stage hits a rolling top and meets resistance as the bullish trend starts to flatten out.
  
  

• Downtrend (red zone 1, green zone 3) – the security declines in an established trend from the outset or off of a sideways top.

• Sideways Bottom (red zone 2) – when declining prices consolidate into a support level (longer-term low) for security.

Again, it is important to note that over the time period displayed on the chart, the hypothetical security portfolio netted the investor zero gain -- he/she did not make any money at the end of the period.

Now consider what happens when you take advantage of the trend, i.e., you "make the trend your friend."

Using the same exact portfolio as above, the investor who trades with the trend can actually produce a significant gain.

Here's how. What if the investor were to buy the green security while it's in an uptrend (zone 1), hold it during the sideways pattern, and then sell it and buy the red security as it establishes an uptrend in zone 3?

The result is that you can create a substantial gain from the same two securities that netted a zero gain if you make the trend your friend:

What you see in the image above is precisely what our Trend Prediction Engine is designed to do. The TPE is programmed to discover and establish trends for all securities in a portfolio over time. Using that information, investors can decisively trade securities locked into a specific kind of trend.

Some other possible scenarios the TPE may identify:

• If there is only one uptrend and all other securities are either in a downtrend or sideways, then the TPE will allocate into the one security with an uptrend only.
  
  
• If there is no uptrend in a single security, then perhaps it makes sense to stick with securities in a sideways trend or just hang in cash.
  
  
• If there are several securities with uptrends in a given time, then the STPE will compare them to establish which trend is rising faster vs. slower, then allocate proportionally to the strength of their respective movements.
  
  

In this example, consider a hypothetical portfolio with three securities (green, red, and orange) performing differently. As you can see, two of the securities (green, red) together net zero gains over the period, while one security (orange) enters an uptrend and delivers a modest but positive gain.

Now, let's assume that we activated the Trend Prediction Engine (TPE) at the beginning of the period to monitor these three securities.

Here's how the TPE is designed to work. First, the TPE identifies two uptrends -- the green and the orange -- and it allocates proportionally between them. In zone 2, the AI would be trained to identify two securities in sideways trends (green, red) and one

security in an uptrend (orange). The TPE would allocate to the orange security. Finally, in zone 3, the TPE would identify two securities in uptrends in (orange, red), and would allocate proportionally between those two securities.

The chart on the next page illustrates the result. You can see superposition of the three securities with the Trend Rotation applied. The black line represents the net return.

As you can see, rotating in-and-out of securities based on their price trend can help an investor take advantage of positive momentum while avoiding declines and sideways patterns, when there is a better option available. Of course, this example and these results are hypothetical, and the AI cannot guarantee such an outcome. But as with any application of AI to an investment strategy, it's not about a guaranteed outcome -- it's about creating statistical probabilities of an outcome and trading on those probabilities. It's about investors using data -- not emotion -- to make investment decisions.

A key principle for identifying "trendy" trading ideas is to narrow your search right out of the gates. Maybe there is a particular sector you're interested in, or perhaps it's a sub-industry or "theme" that excites you.

One prime example in the stock market today would be to focus on the so-called FAANG securities: Facebook (FB), Apple (APPL), Amazon (AMZN), Netflix (NFLX), and Google (GOOGL). Taken together, these stocks have contributed a great deal to the total return of US equities (S&P 500) over the past couple of years (2016 and 2017).

Another idea would be to focus on green energy -- Tesla (TSLA), Acuity Brands (AYI), Cree Inc, (CREE), First Solar Inc (FSLR), and so on -- or maybe you think that companies focused on drone production or fitness device technologies are where you want to be.

You get the idea here -- a targeted approach can not only help you stay focused on a narrow category, it can also help you leverage the Artificial Intelligence tool to identify trends in an industry that interests you. Now, let's take a look at some specific trends with a few concrete examples.

Let's jump right into an example. Apple (AAPL) clearly established an Uptrend on or around July 2016, as you can see from the chart below.

Let's assume Tickeron's AI established an uptrend for AAPL with a 50% confidence level, and you're a fan of AAPL and want to invest. There are a few options available to take advantage of the pattern/trend you discovered.

The first one is obvious -- you take a long position in AAPL by buying shares of the stock, and then you hold the stock until the trend reverses. In the case of the chart above, the uptrend was established for a 60% uptick in the stock.

Another option you might consider is buying a Call Option on Apple's stock. A call option locks in a price for you to purchase AAPL, and the investor would buy a call option if they expected the price to rise above the exercise price of the option. If the investor is correct and it does rise, then you can use the call option to purchase shares of AAPL at a discount.

Another option is selling a naked put, which can involve a greater degree of risk. If you sell the naked put, you should be ready to buy the underlying equity at the strike price (of course, your price will be equal to the strike price - the premium you received). Therefore, you should always have enough buying power in your account if the option is exercised. Keep in mind that if the trend you discovered is reversed (and changes to a Sideways Trend and/or to a Downtrend, you might consider "closing" the put - buying back your obligation to purchase the security at the strike price).

Long Straddles are another strategy that can be used when the security is in an Uptrend. Straddles are options strategies that use both a call and put on the same underlying asset, at the same strike price and expiration.

The Straddle strategy involves either buying a call and a put with the same strike price and expiration, or selling a call and a put with the same strike price and expiration. The former is known as a Long Straddle, and the latter is known as a Short Straddle. Long straddles profit from significant price movement in either direction on the underlying asset.

One of the options will expire useless in either case, but the other has significant earning potential -- in fact, this strategy theoretically has unlimited profit potential. The maximum amount at risk on a long straddle is the price paid for the options contracts. The Short Straddle, on the other hand, has limited upside potential and unlimited risk.

The profit is limited to the premium accepted for the contracts. The short-seller hopes that the price stays within the breakeven points so that neither option will be exercised.

If the price moves dramatically outside of this safety zone, the short seller will be forced to cover the position using resources outside of those provided by this strategy. If the underlying security is already owned, the call side can be covered. The put, however, will require cash.

Similar to the ideas discussed above, you could monitor a universe of securities you think are overpriced. Let's say Tickeron's AI identifies security in a Downtrend with a 50% confidence level. The chart below lays out some trading ideas for security on its way down, each of which we explain further below.

Here are a few options.

Sell the security short. Selling the security short -- assuming you do not own the underlying shares of the security -- involves very significant risk, since the potential for losses is unlimited.

You can buy a put option. Buying a put option gives you the right to sell the security at a certain fixed price. Therefore, if the price of the security goes down (as you expect to happen if the security is in a downtrend), this would allow you to profit the difference between the strike price (the price at which you are guaranteed the right to sell the security) and the market price of the security.

You can also sell a Naked Call. This means that you will have the obligation to deliver the security and your counterparty will have the obligation to buy it from you at a certain (strike) price. Of course, if the security is trading below the strike price, the other party will not exercise its right, and you will keep the premium you received when you sold the call.

When a security is tracking sideways, bound by a band of support and resistances, there are still ways a trader can attempt to capitalize. The chart below lays out some trading ideas for a security that is trending sideways, each of which we explain further below.

There are three possible strategies to use when particular security is in the sideways trend.

First, you could sell naked a put. As we stated before, selling a naked put means that you have the obligation to buy a security at a certain price before the expiration of the option. Of course, if the security is trading above this price, you will not be forced to buy it at a lower price, your option will expire, and you will keep the premium.

You could sell a naked call. Since the security is in a sideways trend, and you do not expect its price to rise meaningfully, you may therefore have the expectation not to deliver the security. But traders have to be extremely careful with this strategy -- if you are wrong, and the security price rises significantly (above your strike price), you will be forced to deliver the underlying position. This means that you will have to buy the security at a much higher price and may lose a significant amount of money. Therefore, if you see the reversal of the trend from sideways to Uptrend, you need to close your positions immediately to avoid the significant loss. Of course, if the Trend reverses from

Sideways Trend to Downtrend, you do not need to close your position - it only works in your favor.

Finally, if you have an extremely strong conviction about the Sideways Trend, you can do both trades - sell a naked call at a higher price from where the security is trading currently, and sell the naked put with the strike price below the current price. In this case, if the Sideways Trend stays, both your Naked Call and your Naked Put will expire worthlessly and you will keep both premiums.

The Short Straddle, on the other hand, has limited upside potential and unlimited risk.

The profit is limited to the premium accepted for the contracts. The short-seller hopes that the price stays within the breakeven points so that neither option will be exercised.

If the price moves dramatically outside of this safety zone, the short seller will be forced to cover the position using resources outside of those provided by this strategy. If the underlying is already owned, the call side can be covered. The put, however, will require cash.

These are just a few ideas for trading with trends, but it is up to the investor to use the information provided by AI -- as well as using your own conviction and belief -- to make a trade in hopes of capitalizing. As we've mentioned before, AI is here to provide you with data you can use to help you make informed investment decisions.

Now that you understand the principles of trend trading, let's put Tickeron's AI to the test on a few ETFs. For the purpose of this e-book, we have selected a couple of ETF's that are very popular and have a large daily trading volume:

• Financial Select Sector SPDR ETF (XLF)
  
  
• Materials Select Sector SPDR ETF (XLB)
  
  

Let's start with XLF, which consists of some of the biggest financial and insurance institutions. As of January of 2018, XLF was made up of the following positions.

Many investors are likely familiar with several of the companies in XLF, as it includes some of the biggest financial institutions in the world.

Now, let's put Artificial Intelligence to work. The chart on the next page shows two lines. The black line is simple enough -- it represents the performance of XLF from May 31, 2008, through March 1, 2017. Pretty solid performance, as you can see.

But look at what happens to the performance when the AI gets involved. Over time, the AI gives the investor buy and sell signals based on the trends that it finds. Assuming the investor follows the AI's advice and trades with the trend, the performance of XLF can be profoundly enhanced. The red line demonstrates the potential power of the Trend Prediction Engine.

Now, let's take it a step further and have Tickeron's AI monitor each individual position in XLF. For the investors who want to leverage the power of the AI even further, there is an option to trend trade individual securities that make up XLF, based on advice provided

by AI. This approach involves more work and more trading, but it could potentially give the investor a chance to enhance performance even further.

Here's how. The next two charts provide you with a visual guide to how this works. The first chart shows XLF from May 2008 to September 2017. The "Buy" and "Sell" labels on the chart indicate where the AI stepped-in to offer advice about trading with the trend.

Now, see what happens when you trade the individual securities with the Trend Prediction Engine's help. Here's how much you can potentially boost performance:

Let's go through the very same exercise with the Materials Sector ETF, ticker XLB. The objective would be to see if we can enhance performance by actively trading XLB -- or its underlying securities -- with the help of the Trend Prediction Engine.

As of January of 2018, XLB was made up of the following positions:

Now, let's put Artificial Intelligence to work. The chart on the next page shows how XLB performed if bought outright and held passively from June 2, 2008, to Aug. 1, 2017 (black line). Much like XLF, it has pretty solid performance on its own.

But put the Trend Prediction Engine to work once again, and just look at what happens to the performance. Over time, the AI gives the investor buy and sell signals based on the trends that it finds. Assuming the investor follows the AI's advice and trades with the trend, the performance of XLB can be profoundly enhanced. The red line demonstrates the potential power of the Trend Prediction Engine.

Now, let's take it a step further and have Tickeron's AI monitor each individual position in XLB, just like we did for XLF. Again, this approach is better-suited for investors who want actively trade based on advice provided by AI. This approach involves more work and more trading, of course, but it could potentially give the investor a chance to enhance performance even further.

The results look just like they did when we put the AI to work on XLF. The first chart shows XLF from May 2008 to September 2017. The "Buy" and "Sell" labels on the chart indicate where the AI stepped-in to offer advice about trading with the trend.

Now, see what happens when you trade the individual securities with the Trend Prediction Engine's help. Here's how much you can potentially boost performance versus just passively holding XLB in your portfolio. As you can see, the performance difference is quite remarkable:

These are just two ETFs we've tested using Tickeron's Trend Prediction Engine, but the results are not only compelling but also consistent.
Imagine applying the Trend Prediction Engine technology across different ETFs, stocks, forex, and even cryptocurrencies. AI technology can work to provide you with data and trading ideas to alter your approach to investing. It's like having a virtual assistant who is constantly analyzing the markets looking for opportunities, and when it finds them it, delivers the ideas right in your inbox.

Investors who are interested in trading equities to build wealth need a well-developed, well-defined strategy. Many readers have probably seen thousands of advertisements guaranteeing success with one strategy or another. Perhaps you've even been invited to various "free" events or seminars that 'reveal' an investment strategy. Any reasonable person should ask, "Why would somebody want to share secrets of how to make money?" If that person has developed a strategy guaranteeing success, the last thing the "inventor" would do is to share the strategy with the public. One thing that is known for sure is that the more and more market participants start using the strategy, the lower the chances of it lasting.

Tickeron's Trend Prediction Engine is not meant to be a panacea to quick wealth. We are not suggesting anyone strategy that can bring you millions of dollars quickly, and we are not guaranteeing that every single trade you make will be successful. The path to success is paved with obstacles and difficulties, sleepless nights.

What we're doing instead is offering investors a new, innovative tool -- or rather, a set of tools. Due to the enormous and rapid progress in computer speed, data transmission, collection of various data, and enormous progress in artificial intelligence (AI), we can now provide you with the "set of tools" that have either not existed before or have been available only to large institutions. Our goal is to provide retail investors with the tools that will put you on par with professional traders and will, hopefully, lead to success in investing.

Creating a portfolio onTickeron.com is easy and takes about 5 minutes. Below, we'll provide a step-by-step guide, with accompanying images from the website for reference.

The first thing the user does is to create a portfolio, and give it a title. [Your Name] Portfolio is always a good choice, or you may want to be specific as to whether this is your 401(k), Roth IRA, Brokerage Account, and so on.

You'll also notice a button for "Brokerage account". This feature uses Morningstar Aggregation services to actually link your brokerage account to this Tickeron portfolio, so that your actual positions automatically appear in your Tickeron portfolio. Don't worry, this process is secure and does not involve the transfer of any assets or secure information about the user.

In this step, you build out the portfolio by adding positions. There are a few options for getting this done:

• You can add the position one by one by typing in their tickers, which is actually easy given Tickeron's search function and simple "Add" button;
  
  
• You can login to to your brokerage or retirement account and simply copy and paste the positions onto the site;
  
  
• You can search for your employer. If located, the site will list all of the available positions in the company's retirement plan, and then you can just select the positions from the list.

In this step, you specify how much you have allocated to each position. You can do this in one of three ways:

• By entering the number of shares of each position (recommended);
  
  
• By entering the dollar amount in each position
  
  
• By entering each position's percentage of the portfolio
  

In this step, the user provides Tickeron's Artificial Intelligence (AI) with information about their investment objectives and risk profile. This information will help the AI understand what kind of investor you are, which in turn will influence its feedback and evaluation of your portfolio.

The first piece of information to tell the AI is how frequently you trade in your portfolio. If you trade fairly frequently, then you'll want to select that you make a trade "Less than [every] 3 months". If you are more a long-term focused investor, you'll want to indicate that you trade "Greater than [every] 3 months".

From there, you'll select the following from dropdown menus:

• Risk Level: Aggressive, Moderate, or Conservative
  
  
• "Years Until Withdrawal" indicates how long you plan to go without withdrawing money from the portfolio
  
  
• "Type" is either retirement account or non-retirement account
  
  
• "Cash Reserves" indicates how many months worth of cash you have in the portfolio
  
  
• "Minimum Positions" is a feature only for active traders. It indicates the minimum amount you want to be allocated to each position in the portfolio, and also indicates what that minimum should be if/when the portfolio is rebalanced.
  
  
• "Market Volatility" indicates how much volatility you're comfortable with. 0 indicates you won't any volatility, while a 10 indicates you're comfortable with maximum volatility.
  
  
• Finally, check the box if you plan to add money to the portfolio

Once those four steps are complete, click 'Next' and voila!! Your portfolio is created, and Tickeron's AI will provide you with a Diversification Score, indicating on a scale of 400 to 850 how well your portfolio is diversified. If your score is poor or needs work, not to worry! Tickeron's AI can provide you with ideas to improve your allocation.

Applying Artificial Intelligence to an investment strategy is not about producing a guaranteed outcome -- it's about creating statistical probabilities of an outcome and trading on those probabilities. It's about investors using data -- not emotion -- to make investment decisions. Are you ready to get started using algorithms and AI in your investment strategy? Get started today on Tickeron.com.

Sergey Savastiouk, Ph.D. has a degree in Applied Mathematics and has a long track record 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 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 in related fields. As a retail investor, he spent 15 years in the development of his proprietary trading and quantitative algorithms (now Tickeron's AI), which brought him significant returns in trading the security market. His current work and goal in founding Tickeron is to bring professional, sophisticated security market analysis capabilities to retail investors with an easy-to-use interface.

Dr. Naroditsky has more than 33 years of experience in portfolio management and mathematical modeling of financial markets. Prior to co-founding Vega Capital Group, Dr. Naroditsky has been a Financial Advisor with the Private Client Groups of Prudential Securities and UBS PaineWebber. Prior to that he has been a Full, Tenured Professor of Applied Mathematics with the California State University. Dr. Naroditsky is a CFA Charterholder, and holds a Ph.D. in Applied Mathematics from the University of Denver. He is a member of the San Francisco Society of Security Analysts and the Association for Investment Management and Research (AIMR). He is the author of more than 60 academic articles and a book on Applied Non-Linear Phenomena. Dr. Naroditsky is frequently invited to speak at seminars and conferences. Dr. Naroditsky holds Series 7, 63 and 65 securities licenses.

The search for trends and the examples presented in this e-book were all detected by Tickeron's Artificial Intelligence. Investors can visit Tickeron's site and use this AI to track trends of interest, and also be informed when securities or other instruments confirm within those trends. What's more, the AI will also provide the user with supporting information, such as trend confidence levels and predicted and historical statistics. This additional information allows users to monitor the trend's evolution, bookmark favorites, and potentially take action in trading/brokerage accounts to benefit from the forecasted price movements. Interested investors can learn more by visiting www.tickeron.com.

The A.I. identifies patterns by their key geometrical elements, formed by changing security prices when plotted on a chart. The A.I. does not take into consideration the security's fundamentals, expert recommendations, market conditions, and so forth. Trading in securities and other instruments involves the risk of a total loss of principal.

"Allocation Ideas" refers to ideas for your portfolio's asset allocation. For investors, the 'asset allocation decision' is an important one. It means establishing risk/reward trade-offs in your investment portfolio. How much do you allocate to bonds versus stocks versus cash? What types of equities, bonds, funds, and/or ETFs do you choose? What is your investment portfolio's risk profile? These are all asset allocation decisions an investor makes based on their risk tolerance, investment objectives, time horizon, and other personal financial factors.

If one currently has a 401(k), a retirement plan, or an investment brokerage account, it probably means you've already made an asset allocation decision (unless you are 100% in cash). If you would like to see how Tickeron's Artificial Intelligence (A.I.) would diversify your portfolio based on the information you provide about your financial situation, then start with our Diversification Score® tool. The DivScore® A.I. tool can provide you with a 'draft' of a diversified portfolio that is tailored to your needs.

Diversification Score® is a metric used to measure an investment portfolio diversification on a scale of 400 (poorly diversified) to 850 (well-diversified). Much like a Credit Score measures a person's creditworthiness based on financial history, a Diversification Score® measures how effectively a portfolio is diversified based on a person's investment objectives, risk tolerance, portfolio value, years until withdrawal, and available investment options (amongst other factors).

Although our services incorporate historical financial information, past financial performance is not a guarantee or indicator of future results. Moreover, although we believe the historical information and strategies we use are reliable, we cannot guarantee them. Our services are frameworks to be used in your own investment decisions, but they are not a substitute for your own informed judgment or decisions. Moreover, they provide only some of the resources that could possibly assist you in making your decisions. You may accept, reject or modify the recommendations we provide, and you may consult with other advisors or professionals (at your expense) as you see fit regarding your personal circumstances. We do not and cannot guarantee the future performance of your investments. We do not promise that the investments we recommend will be profitable. All investments are subject to various market, currency, economic, political, and business risks. We do not guarantee the suitability or value of any investment information or strategy. We do not recommend any brokers or dealers for executing transactions or maintaining accounts and do not provide a mechanism for placing trades through this site. We are not responsible for advice or information you receive from anyone through the use of this site. We are not responsible for any transfers of money to any member outside of our payment system. Additional information can be found in our Terms of Use, Client Agreement, and Privacy Policy.

All graphs and data on our website and marketing materials are based on simulated or hypothetical performance that has certain inherent limitations. Unlike the graphs shown in an actual performance record, these graphs do not represent actual trading. Any trades on websites are recorded paper trades and have not been executed. Simulated or hypothetical trading algorithms, in general, are also subject to the fact that they might be designed with the benefit of some hindsight during backtesting. No representations are made that any actual account will or is likely to achieve any profits or losses similar to these graphs or data being shown. In addition, hypothetical trading does not involve financial risk, and no hypothetical trading can completely account for the impact of financial risk in actual trading. The ability to withstand losses are material points that can adversely affect actual trading results. There are factors related to the markets in general or to the implementation of any specific trading algorithms, which cannot be fully accounted for in the preparation of hypothetical performance results, and all of which can adversely affect actual trading results.

TRADING IS RISKY. Past performance is not necessarily indicative of future results. Actively trading in the market may result in the losses of the entire principal amount.

ASSUMPTIONS AND METHODS USED

The following are material assumptions used when calculating any hypothetical graphs and presenting results that appear on our website:

--Profits are reinvested. We assume profits (when there are profits) are reinvested in the trading algorithms.

--Any trading fees and commissions are not included.