**(Why are we so radically unique?)**

- Introduction
- Unique Scientific Foundation of Stealth©
- What Others Have Missed?
- Why NOBODY is Out There like Us?
- Conclusion

Based on Mathematical Psychology Ph.D. theses, Stealth Trader©, by QuantGate Systems, is a game-changing decision support tool for active traders. It is unique because it is the only system in the world based on real-time Sentiment and Perception analysis of market participants’ activities. Stealth Trader© extracts the buying or selling pressure within the order flow of Bid and Ask on the exchange’s order books. Using sophisticated proprietary mathematical formulas, Stealth Trader© indicates what a security’s price action might look like in the near future. Its intuitive cockpit-like interface is designed using QuantGate Systems' in-house expertise in ergonomics and past experience of building military navigation systems.

Stealth Trader© started as an idea to solve a common industry problem. Even though decision support software, based on technical or fundamental analysis, had dramatically improved in the past decade, they are still driven principally by price-volume-time indicators that persistently lag the fast pace of modern electronic markets.

Speed seemed paramount. But it wasn’t the speed of computing that needed improving; it was the speed of comprehending the meaning of things and their implications. Until Stealth Trader©, the market was dominated by people who believed that through empirical data alone one could deduce outcomes.

QuantGate Systems scientists wanted to deliver a product that would enable traders to make and implement decisions within the pace of modern electronic markets. The team turned to another area that required the human to make similar rapid decisions within a data-rich environment; the jet fighter pilot decision cycle or the observe-orient-decide-act (OODA) loop. Within the OODA framework, jet pilots are trained to use the information available to make the best tactical decisions. Moreover, jet cockpit designers use the OODA loop to create systems that maximize the war-fighting capability of the pilot by reducing the workload on the pilot.

Using the OODA framework, Stealth Trader© was designed to improve a trader’s ability to achieve results by minimizing the time required to observe and ultimately act on information.

Stealth Trader© is based on the theoretical premise that human intuition and instinct generate a valid conclusion faster than logic and reason alone.

The design paradigm of current systems allows traders to compare numbers or charts. This was proven to be ineffective in the fast-paced environment of the jet fighter world and is equally out of place in our world. Moreover, the current systems are error-prone and slow. As in cockpit design, shapes, colors, and indicators in a gauge format tend to allow more rapid and error-free consumption of information. Stealth Trader©'s first key differentiator is to represent key data symbolically, not literally.

Stealth Trader©'s second key differentiator is based on psychology. Psychologists have long understood that individuals behave more like herds, or flocks or schools of fish, despite the fact that each individual truly believes that they are acting in a unique fashion. In fact, when certain behaviors are observed in groups, those behaviors are likely to be followed by other predictable behavior. This gave QuantGate Systems scientists the ability to see where the “group think” was going and get there before they did.

The third key differentiator of Stealth Trader© is found in the algorithms developed by our Mathematical Psychologists. That is the secret sauce.

Combined, the three key insights form the core of Stealth Trader©.

Most of us are aware of our tendency “to go with the flow.” Most of us do not like to “stick our necks out.” Instead, we follow fashionable trends or social conventions. We buy real estate if others are doing the same, we stock up on gold if that is the trend, and we collectively stop buying cars deemed unsafe. We accept this lack of individuality because we understand that if we differ radically from everybody else, we would jeopardize our well-being, however, we do not always understand why.

Well, for starters, it is obvious that if we do not have enough information on which to base a judgment, the next best thing is to assume that the herd knows where it's going. Sound reasonable? The modern science of mathematical psychology offers a different view. It suggests that despite our human “intellectual superiority,” we seem to be fated to act in a way that mimics patterns found elsewhere in nature.

The mathematical analysis of actions, observed in our collective behavior, appears to follow laws that often apply to completely unrelated phenomena. For instance, the underlying mathematics of the fractal shape of a coastline can describe our society's financial activities while at the same time describing the geometry of galaxies and constellations. In fact, the mathematical formula that describes a seashell's spiral structure is the same as the formula used to describe the pattern of human technological progress.

For many years, mathematical models have been used to accurately describe elements of human activity. Tools such as Bayesian theorems, power laws, hidden Markov processes, and cellular automata are just a few examples. Many of these mathematical tools have been used in modeling human behavior in financial markets with varying degrees of success and popularity. Mathematical psychology offers additional tools and a novel approach that is proving itself more powerful than previous methods. Applied in the form of software, mathematical psychology is able to augment natural human intuitive abilities, enabling reliable and practical tools to predict financial markets activities. The fact that this technique has something to say about what it means to be human, makes it all so much more interesting.

Time and timing play a central role in the relationships between all living things. People’s activities are governed by cycles of time, which when taken together determine individual and social behavior. These cycles, otherwise known as actions or behaviors, require a level of skill that can only be acquired after a long period of training before they can become useful, while others seem to develop spontaneously. Why? The answer is spontaneous synchronization.

Here is an example. Suppose you are in a hockey arena with thousands of fans. Those fans react to the game’s flow and shout at will. When a small group of the most active cheering fans start to rhythmically chant something like “Go, Canada, Go!” the whole arena begins to cheer in unison, causing the otherwise unrecognizable noise of cheering to become clearly recognizable to anybody within a mile of the arena. The conversion of the crowd’s noise to a loud and recognizable cheer depends on the timing of the chanting or cheering. While participating in this chanting, people within the arena do not even realize that their heart begins beating faster due to their surroundings. The cells in our body are quite literally synchronizing to the external stimuli. The emotional character of the cheer can accelerate or decelerate our heartbeat. We are not aware of the process, but the cells themselves manage to change coherently, almost in unison.

Just a few milliseconds after a person’s favorite team scores, the crowd starts to cheer loudly. Initially, the cheer may be incoherent, but the wish to cheer and deliver a message of appreciation to the home team transforms the otherwise incoherent scream into a perfectly synchronized chant “Go, Canada, Go!” despite the different locations of individuals inside the arena. This example illustrates Spontaneous Synchronization, one of the most captivating cooperative phenomena in nature.

Spontaneous Synchronization is observed in biological, chemical, physical, and relevant to this discussion, social systems such as stock markets.

The relevance of synchronization in the Stock Market has been studied for decades if not centuries, but until now it has not been fully understood.

To further illustrate the concept, consider the behavior of fireflies. To facilitate courtship, fireflies flash their hind end while other fireflies seem to respond and ultimately synchronize flashing. Similarly, in the stock market, Spontaneous Synchronization occurs resulting in dramatic price fluctuations that cannot be explained by other rational market models.

Spontaneous Synchronization observed in complex systems can suddenly change the system’s behavior from a disordered state to an ordered one. These sudden changes are known as phase transitions and occur in a whole range of systems — think, for example, of a group of chaotically moving birds suddenly coming together to form a "V" shape, or locusts simultaneously alighting on a field of valuable crops. Fish spontaneously assemble large schools and small birds form swarms to protect themselves from predators. The behavior of these kinds of systems is remarkably predictive of the behavior of stock market participants.

Understanding the mathematics of how, and under what circumstances, entities can synchronize provided us with a starting point for designing our way of looking at markets.

Surprisingly, flocks of birds, schools of fish, herds of cows, and stock market traders exhibit the same type of behavior. This type of behavior called “flocking” could be simplistically described by just a few rules that each member of a flock must obey. Each entity of a flock must:

- Move in a generally random pattern
- Move in the general direction of a flock
- Keep a relatively constant distance from their immediate neighbors
- Follow the center of gravity of the flock

The last rule in the above list was recognized just recently, but it plays the most crucial role in understanding flock behavior. Following the center of gravity of the flock is the essential survival skill that enables each member of a flock to minimize the risk of being spotted as a “black sheep,” thus efficiently blending in and reducing the chances of being attacked by a predator.

Think of driving to work in the morning. Hundreds of other cars on a highway are trying to maintain similar speed and distance between themselves, even if that speed is variable and above the allowed limit. Moving with the center of gravity of a group of cars spread over multiple lanes reduces the chance that police will stop a given car over another. Moving in a pack also reduces the chance of an accident and allows us to share a highway more efficiently. We also can fairly accurately position our car in the middle of a lane and quite efficiently maintain the distance between the cars that surround us. This ability or behavior is present in many animals and has been genetically transferred from generation to generation to ensure our very survival.

Knowing the location of the “center of gravity” is essential to the survival of the members of a flock and to the survival of the predator who is trying to attack the flock. If the flock is moving rapidly, it is virtually impossible to trace individual motions of its members. But it is easier to follow the flock’s center of gravity. The ability to accurately anticipate where this center of gravity might be in the future allows most of the planet’s species to plan their actions accordingly, guaranteeing better outcomes.

Many experienced market traders have developed unique intuitive anticipatory methods of predicting what other traders might do under certain conditions and consequently are able to predict where the majority of other traders might place their bets. This enables experienced traders to make decisions more accurately, creating a positive outcome for their trading activities.

What makes the anticipation of the center of gravity more reliable than the anticipation of individual flock member movement? The answer is inertia. By definition, inertia is the resistance of an object to a change in its state of motion. This classical understanding of inertia could be applied not just to individual beings, but also to the collective behavior of multiple beings. The only difference is that in describing the behavioral inertia of a flock, the “mass” could be substituted by the number of members of that flock. In other words, the larger the flock, the more difficult it is to change the position of its center of gravity. This very phenomenon allows our eyes to follow a small cloud of mosquitoes without registering individual positions of each mosquito in the cloud. This handy trick could be very useful in determining where the center of gravity is in the stock market. But what would allow us to “see” it?

Splines are types of curves, originally developed for ship-building in the days before computer modeling. Naval architects needed a way to draw a smooth curve through a set of points. The solution was to place metal weights (called knots) at the control points, and bend a thin metal or wooden beam (called a spline) through the weights. The physics of the bending spline meant that the influence of each weight was greatest at the point of contact and decreased smoothly further along the spline. To gain more control over a certain region of the spline, the draftsman simply added more weights.

The surface produced by splines always appears to be smooth, but in fact, it is not. The reason we think it is smooth is that while our eyes roll along a spline line, we subconsciously anticipate (following our genetically embedded sense of inertia) where the next point should be and if we indeed see it at the anticipated location, it creates in us a feeling of unconscious satisfaction and a sense of pleasant symmetry.

As a matter of fact, we discovered that when we consider the object to be normal or smooth in the case of the shipbuilders, it seems to be so. It is almost unnoticeable that the smooth surface isn’t smooth and follows the spline line. What is more interesting is that not too long ago, splines had an explosion in their usage thanks to the film industry. Before the 1990s, special effects in motion pictures and animations that change (or morph) one image into another through a seamless transition were achieved through cross-fading techniques on film. Since the early 1990s, this has been replaced by computer software to create more realistic transitions. At the heart of this software are, you guessed it, splines. Thanks to splines, a new era of computer animation began, and truly amazing and realistic characters such as Shrek were born.

One of the remarkable types of splines is the cubic spline. It became the most popular tool to interpolate data. Mathematically, a cubic spline could be described as a special function defined piecewise by the third-degree polynomials. A cubic spline with a linear extension of its ending point is called a “natural spline.” Natural splines have three basic properties:

- They pass through all given data points with a unique one between each set of points.
- They are smooth, meaning that at the points where they merge, their first and second derivatives are equal.
- And finally, natural splines have a second derivative at the endpoint that is always equal to zero.

These unique properties of natural splines make them very useful in designing anticipation tools that could accurately “extend” an existing set of data into the future.

Our research showed us that in any set of data that represents a movement governed by inertia, natural splines predict the future position of a center of gravity with unprecedented accuracy.

The concept of regression comes from genetics and was popularized by Sir Francis Galton in the late 19th century with the publication of “Regression Towards Mediocrity in Hereditary Stature.” Galton observed that extreme characteristics (e.g., height) in parents were not fully passed on to their offspring. Rather, the characteristic in the offspring regressed towards a more mediocre point (a point which has since been mathematically shown to be the mean). By measuring the heights of hundreds of people, he was able to quantify regression to the mean and estimate the size of the effect. Galton wrote that, "The average regression of the offspring is a constant fraction of their respective mid-parental deviations." This means that the difference between a child and her parents on some characteristic was proportional to her parents’ deviation from typical people in the population. So if her parents were each two inches taller than the averages for men and women, on average, she would be shorter than her parents by some factor (which today we would call one minus the regression coefficient) times two inches. For height, Galton estimated this correlation coefficient to be around 2/3: the height of an individual will center on approximately 2/3rds of the parents' deviation.

Although Galton popularized the concept of regression, he fundamentally misunderstood the phenomenon; thus, his understanding of regression differs from that of modern statisticians. Galton was correct in his observation that the characteristics of an individual are not fully determined by their parents; there must be another source. However, he explains this by arguing that, "A child inherits partly from his parents, partly from his ancestors. Speaking generally, the further his genealogy goes back, the more numerous and varied his ancestry, until they cease to differ from any equally numerous sample taken at haphazard from the race at large." In other words, Galton believed that regression to the mean was simply an inheritance of characteristics from ancestors that are not expressed in the parents; he did not understand regression to the mean as a statistical phenomenon.

In contrast to this view, it is now known that regression to the mean is a mathematical inevitability: if there is any random variance between the height of an individual and his/her parents (providing the correlation is not exactly equal to 1), then the predictions must regress to the mean regardless of the underlying mechanisms of inheritance, race, or culture.

It is very interesting that Galton missed the true meaning of mean reversion, yet he came up with a device that demonstrates this principle with amazing clarity. Galton’s “Bean Machine,” also known as the “Galton Box,” consists of a vertical board with interleaved rows of pins. Balls are dropped from the top and bounce left and right as they hit the pins. Eventually, they are collected into one-ball-wide bins at the bottom, always forming a bell shape of normal distribution.

Aside from vividly demonstrating the principle of regression to the mean, the Galton’s Box provides analogous proof that a normal mixture of normal distributions was itself normal! It was a stroke of genius. It was perhaps the most important breakthrough in statistics in the last half of the nineteenth century.

Mean reversion theory has been used to create market trading strategies for many years. Typically, the trading algorithms that are based on mean reversion suggest that prices and returns eventually move back towards their mean or average. This mean or average can be the historical average of the price or return or another relevant average, such as the growth in the economy or the average return of an industry. This theory has led to many investing strategies involving the purchase or sale of stocks or other securities whose recent performance has greatly differed from their historical averages. However, a change in returns could be a sign that the company no longer has the same prospects it once did, in which case it is less likely that mean reversion will occur. In the event of drastic market price moves caused by “flocking behavior” or “spontaneous synchronization,” mean reversion might lead to significant losses as that reversion might not occur for a long period of time.

Although reversion to the mean is one of the most fundamental and stable observations of stock market behavior, no known reliable trading algorithms have yet been developed. That is our breakthrough. We believe that Stealth Trader© is the first practical trading algorithm that utilizes reversion to the mean phenomenon to generate consistent positive capital returns. This surprisingly simple insight is at the core of what we offer.

Below is the summary of principles that we used to develop our trading algorithm:

- Most significant market moves are caused by the phenomenon of “Spontaneous Synchronization” where the prices move irrationally too far and too fast, creating stable “panic feedbacks” that ensure that the price volatility sustains. Those moves increase “price inertia” and make the position of the price center of gravity fairly stable and predictable.
- Price movements involve collectives of traders that behave like large synchronized flocks. In these flocks, the average distance between the flock members and the flock’s center of gravity remains fairly stable.
- The flock’s center of gravity normally moves in a pattern that vividly exhibits the inertial properties of a flock.
- Subconsciously observing the synchronous movements of the price’s center of gravity allows market participants to anticipate prices' direction. This, in turn, encourages market participants to place their bets with this observation in mind, thus forcing the price to move towards the projected position of the flock’s center of gravity.
- Natural cubic splines calculated on a sequence of consecutive center of gravity positions create a very reliable expectation of the next center of gravity location in terms of price/time space.
- The distribution of the actual price fluctuations around a spline-predicted point is always Gaussian with the standard deviation that is typically smaller than a price deviation over one time interval used to calculate the center of gravity itself.
- Price reversion to the spline-predicted position of the center of gravity normally has greater price differential compared to the deviation of the center of gravity itself, thus ensuring stable positive expectations for the mean reversion trading strategy.

One of our most remarkable discoveries shows independence on the static representation of market data through the price vs. time charts. The QuantGate Systems team of scientists has spent over 15 years on in-depth research related to the psychological effects of linear representation of data known as price bar charts (along with any other charts such as candlestick, line, Renko, etc.). As a result of this research, we discovered and demonstrated a dramatic difference between the reaction of traders to the same set of market data presented in the form of Stealth Trader© gauges and through a standard, time-based chart. To describe the difference, we could use the analogy between this reaction and the decision-making process that an aircraft pilot goes through while navigating the aircraft using its cockpit gauges.

It is a well-known fact that many pilots cannot help but compare the level of the horizon using their eyesight and the attitude indicator (AI), also known as the gyro horizon or artificial horizon gauge. It has been noticed that the pilot’s fatigue level, clarity of air, and the presence of clouds could easily create an impression that the aircraft is not flying parallel to the horizon. Sometimes these impressions are so vivid that pilots can’t help but doubt their attitude indicators and try correcting with potentially serious consequences, such as a crash. There have been thousands of documented deaths among pilots who fell into this trap. As a matter of fact, it has been proven that the very cause of the plane crash flown by John F. Kennedy Jr. in July 1999 was his attempt to disregard the AI readings and fly using only his eyesight.

To our astonishment, we discovered that a very similar phenomenon exists among traders who trade using price charts. Any price chart disregards the relative movement of the price versus its very recent behavior. For example, a rapid rise in the price on a 1-minute bar chart might trigger a totally different interpretation depending on how the price behaved relative to the rate of change measured on multiple time frames. By looking at one chart, a trader becomes isolated from the very vital information regarding what a price move means when registered on a single time frame (1 min, 5 min, or any other time frame chart). More so, a trader typically does not even realize how dangerous it is to follow one time frame chart as it produces very stable impressions of “understanding” the price patterns. These “perceived” patterns create an illusion of an ability to “see” the upcoming price moves. This is the single most important cause of failure in the market. We all know that 95% of active traders lose money in the market, and we strongly believe that one of the most significant causes is the trader’s detachment from reality. A trader mesmerized by price charts is doomed to fail just as a pilot is doomed to crash if he/she flies the plane using only his/her eyesight. The vital information is not on one chart but in the relationships between different charts!

Of course, for a trader, it is virtually impossible to look at 50 different charts simultaneously to correct his/her decisions. That is where Stealth© gauges become essential. By not looking at the chart and trusting the gauges, we overcame the biggest obstacle in becoming a successful trader – intimidation by the charts.

In 2004 – 2006, QuantGate Systems researchers conducted experiments with volunteer traders who agreed to trade in two groups: one using any chart they wanted and the other only using Stealth Trader© gauges. The results were astonishing! The group that only used Stealth Trader© outperformed the “chartists” group by 740%! After that, we switched the groups. The group that used only Stealth Trader© was now allowed to use charts. The performance instantly plunged! All of these tests have proven the danger of the charts and the efficiency of the Stealth Trader© gauges.

We understand that such a radical concept may seem hard to believe. However, by training to only use Stealth Trader© gauges, one could easily prove that this phenomenon of being mesmerized by charts is very real and it does exist. For a newcomer, it is infinitely easier to embrace the “right way” of trading using Stealth Trader©, however, for a seasoned trader, it could be very painful. All of us at QuantGate Systems firmly believe, however, that the pain is worth taking as it will eventually free any trader from being “trapped” by charts.

What makes Stealth Trader© different is that it is based on the psychological forces that affect the market. We believe psychology is the dominant force in sending the market in whatever direction it's taking at the moment. We clearly know these are not masses of individuals making the same decision rationally. These are huge groups of traders that perform more like herds or flocks or schools of fish. Decisions are made by a few leaders and the rest move accordingly. QuantGate Systems has developed an algorithm that identifies and predicts the outcomes when these phenomena come into play.

It is essential for traders to execute orders at the right time and at the right price. The Stealth Trader© platform presents a unique way of tracking current market perception using the flow rate of buy/sell orders placed in real-time by all traders on the Exchange Electronic Trading Book. These orders are weighted by their proximity to Inside Bid/Ask levels, their size, and the time elapsed since the order origination. All the weighted orders (called the "Traders' Intent") are added to produce a Weighted Sum of Bids and a Weighted Sum of Asks. The ratio between the weighted sum of Bids and the weighted sum of Asks is known as the "Trader's Sentiment," and it is used to analyze the "built-up pressure" to Buy or Sell a security.

Stealth Trader© displays the second-by-second sentiment of all traders in a market. It tracks sentiment movement in individual securities that generally indicate upcoming price developments. As a result, Stealth Trader© is an instinctive and intuitive way of analyzing markets.

Degrees of danger or opportunity are displayed as the intensity of colors while other significant forces are represented by buying/selling pressure gauges. Stealth Trader© measures the sentiment of the herd or 'flocking' and can arrive where they are going before they get there.

These are not just new technologies; they are new concepts that bring a new approach to trading. Stealth Trader© uses recognizable pattern displays so that a trader can grasp at a glance where the largest mass of traders is heading – and act accordingly.

Overall, Stealth Trader© is the most vivid example of the difference that intuition and speed make in trading success.

We believe Stealth Trader© will go out in the world and not only win but revolutionize the way the game is played. It was deemed critical to make Stealth Trader© "fun." This solved two unnoticed but serious problems that had been revealed by our research. If a piece of software is easy to use and fun, as opposed to the tedium of standard shrink-wrapped software in a box with a complex user manual, new users can rise up the learning curve to become accomplished users faster and with greater success. And even more important, since it was much more abstract – like a game – their decisions were based on simply succeeding - rather than having to deal with emotional involvement with money in specific values. The difference this made, the freedom from “market intimidation” is Stealth Trader©'s major advantage.

Gain unparalleled decision support, enabling you to make swift and confident trading decisions. Experience the future of trading with cutting-edge algorithms and sentiment analysis that outperforms traditional or technical methods, giving you the advantage you need to succeed in today's fast-paced trading environment.

Insight to the Depth of the Order Book

Stealth Trader assesses all preceptions, intents and actions within the marketplace by monitoring market participant order flow.

Visual respresentation of upcoming market situations vian intuitive dashboard.

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