Fractal analysis of financial markets owes its popularization to Bill Williams, the author of the theory of Chaos as applied to Forex and stock exchanges, who gave a precise definition of a fractal model as a separate unit - a component of a chaotic system, through which the latter is accessible for understanding and forecasting.
The scientific research of Bill Williams showed traders that the movement of the ocean, the circulation of blood in the body and the price of coffee, for example, are subject to the same rules. Thus, the postulate was finally refuted, which was accepted as truth by many traders and analysts of the last century, who considered the market to be a linear structure, and not chaotic, which it actually is. This conclusion made it possible to understand that the use of conventional technical indicators, which use linear functions as the basis for their algorithms, will be incorrect and will worsen the trader’s chances of success in advance.
Bill Williams, whose name is associated with fractal analysis and Chaos Theory of markets, was not a pioneer in this area, as similar thoughts have been expressed in the past, but he provided convincing evidence using the power of computer modeling. This helped him identify fractals and show how they develop and market price movements for various assets - be it gold, oil, wheat futures or the USD/JPY currency pair. At the same time, it was proven that the nature of fractals in the Forex market is the same as for all chaotic systems.
What is a fractal in Forex
Fractal Forex analysis uses the following concept of a fractal - this is a formation of five candles (bars), where the average forms the largest maximum or the smallest minimum. This definition was quickly translated into trading terminal code. Therefore, if you add the fractals indicator to the price chart in MT4, it will be indicated by an arrow, which is drawn near the middle fractal candle and shows its direction - down or up.
In the screenshot below you can see an example of two fractals, the first of which is directed downward, and the second upward.
As you can see, some of the candles of the lower fractal were then used in the formation of the upper one, that is, we are not talking about some isolated figure different from the others. Moreover, the same candle in a group can form both a lower and an upper fractal, as in the screenshot below.
For use in trading, the limiting maximums (High point) of an ascending fractal and the very minimum (Low point) of a descending fractal are important.
Fractal formation - start
For an initial analysis of the situation, a trader needs at least two fractals - one upper and one lower. The minimum/maximum of the first fractal in this combination began to be called the fractal start. This formation looks like this:
Formation of a fractal signal
Continuing our consideration of the terminology of this type of analysis, let’s get acquainted with the fractal signal, in the role of which the minimum/maximum of the second fractal formed after the start is used.
Stop per fractal
When using fractals, entering the market often occurs when the price breaks through the High/Low points. In this case, when opening a transaction, the stop loss is placed beyond the opposite extreme. It looks like this.
As you can see, in the screenshot two opposite fractals were formed, then the price rose above the high point of the upper fractal, which served as a signal to enter a long position. The stop loss was set behind the low point of the last downward fractal. A little further you can see that on the signal candle, where the upper fractal was broken through and a position was opened, a lower fractal was also formed. In this regard, the question may arise, why is there a stop loss under it? The fact is that at the time of entry this lower fractal was still in a state of formation, so the stop loss was set at the minimum of the last formed downward fractal.
With this entry technique, the take profit is usually set 2:1 to the stop size, so the mathematical expectation of winning is quite good.
How to increase the accuracy of fractal entry
Like any technique of working on the stock exchange, fractal analysis of the Forex market is not recommended to be used in isolation from an objective and comprehensive assessment of the situation. With this approach, the accuracy of the signals will increase, and profits will gradually accumulate in the account, delighting its owner.
To increase the accuracy of signals, you can use a trend from a higher timeframe as a filter. For example, a trader works using fractals on H4, which means he only enters into those trades that will be directed along the existing trend on the daily chart.
Concept of fractal leverage
When working, it is also important to take into account the concept of “fractal leverage”, which represents the degree of correction from the existing trend. To evaluate it, you need to stretch the Fibonacci grid. If the correction reaches the 38% Fibonacci level, then the trader receives confirmation of the strength of the current trend, that is, they say that the fractal lever is strong. If the degree of correction reaches 62%, then this indicates the opposite.
The use of fractals in wave theory
Elliott's followers, who perceive market chaos as a structure consisting of individual waves, immediately appreciated the advantages that fractals provide. In fact, the formed fractal is a completed wave of a certain order.
Thanks to this knowledge, it becomes easier to calculate in which wave or phase the market is moving at the current moment, which has always been the main weak point of the wave theory.
Advantages and disadvantages of trading using fractals
Having practiced a little in fractal trading, you can understand that this type of analysis works best when a trend develops in the Forex market. It is especially good when several fractals form near a horizontal level, followed by its breakdown. This is usually a signal for a long, sustained movement.
But during a flat, working with fractals is frankly dangerous, since the price can simply “saw”, which will lead to a series of stop losses being triggered. To prevent serious losses, you should use an additional filtering tool. One of the techniques has already been indicated above - to work only along the existing trend on a higher timeframe. In this case, even if you catch several stops in a row, then you can still pull out a trend movement that will cover the resulting losses and allow you to make money.
How to combine a fractal breakout strategy with candlestick analysis
To further increase the accuracy of signals, especially at the moment when the market is breaking out of a flat pattern, it is worth paying attention to the analysis of the candle where the breakdown took place. If it has a long body, short, practically absent tails, and its closure occurred far from the place where the last fractal was broken out, then the probability of a successful entry using such a signal increases many times over.
But there is also an increased risk. The fact is that entry after the completion of the formation of a long-body candle will usually occur against the background of a rather large stop loss. Therefore, if the price still does not continue to move towards the breakdown, but makes a reversal, then the loss will be quite significant and unpleasant.
But in this case, you can use another method - switch to a smaller time frame and there enter a breakout of a fractal of a lower level with a shorter stop. For example, if there was a group of fractals on the daily chart, and then there was an exit from the range with a breakout by one candle with a long body, then the trader should open a 4-hour timeframe and look for an entry point into the position there.
Pay attention to volumes
Bill Williams paid special attention to vertical volume and pointed out that a good signal is always formed on increased volume. That is, if volume is poured into the market at the time of the breakout candle, then this is a good signal to open a position.
If a breakout occurs without reinforcement by volumes, then most likely it is a false signal, which is better to ignore or enter with part of the working volume, and then be added when confirmation of the breakout is received - a rollback and consolidation.
Accuracy of fractal analysis on different timeframes
As you know, technical analysis works well on highly liquid instruments and large time frames. This also applies to fractal ones, which is why Bill Williams himself recommended working on daily charts. As an option, it is also allowed on 4-hour or even hourly ones, but the accuracy of the signals will decrease as the time frame decreases.
Results of consideration of fractal analysis
You should not work on Forex solely using fractal analysis, since during periods of flats the risks will still be very large. But if you add other indicators or trading techniques to filter the trend and signals, you can achieve high accuracy of entries, which will bring very good profits.
Price movement has a fractal nature because the actions and reactions of people in the market are repeated. The challenge is to recognize these repeating patterns on the price chart. In this article we will consider in detail one of the ways to find such models.
The laws of gravity, capacity, inertia and cyclicality are important driving forces in financial markets. All market patterns, behavior and dynamics can be seen as symptoms or results of these laws. These basic forces are easily understood and intuitively perceived. Their presence can be proven using simple, irrefutable logic based on empirical evidence. In this article we will look at the fractal structure of markets, its manifestations and consequences, and the opportunities it presents to the astute and ultimately successful trader.
Fractals in financial markets
Fractals are a natural phenomenon and at the same time mathematical sets. What they have in common is their repeating pattern, which can be observed on any scale of time and space. To put this into financial context, take a look at Figure 1, which shows three candlestick charts. One is a daily chart (one candle represents one day of trading), another is a 5-minute chart (one candle condenses 5 minutes of trading), and the third is a weekly chart (all movements for the week are compressed into one candle). Each chart represents a different type of financial asset - , index and commodity. Additionally, each one covers a different period of time.
Picture 1
But even taking all this into account, it is still impossible to tell which graph belongs to what. Without prices on the vertical axis and/or timestamps on the horizontal axis, it will be impossible to distinguish them. In fact, since these three graphs are shown next to each other, they can be mistaken for one continuous graph. For those who are interested, the left chart is the weekly time frame for gold, the middle chart is the daily time frame for the S&P 500, and the right chart is the 5-minute Google, Inc. (GOOG).
A good analogy here is the concept of numerical infinity. There are two approaches to numerical infinity. One is that for each number there are neighboring numbers - a smaller and a larger one, for which, in turn, there are also smaller and larger neighboring numbers; and so on ad infinitum; this is infinity of size. Another approach is that between any two numbers there is an infinite number of other numbers - this is an infinity of precision. The same can be said for data in financial markets. New quotes are constantly arriving, which can be viewed on timeframes of varying degrees of accuracy. The only exception to this comparison is that the scale (if we are talking about price movement) is not infinite. In practice, the smallest scale is a single operation. But the concept of infinity can still be used to see the fractal nature of price data in financial markets.
Figure 1 is an example of the never-ending stream of empirical evidence. Is it possible to put forward a common sense explanation, or a universal law, that would take this phenomenon into account? If so, that might explain how . We believe that it is possible to formulate a universal law. Any chart depicting the behavior of financial markets, regardless of its time frame or location in time, is the result of past transactions. We mean operations performed by people in response to various impulses. The diagram in Figure 2 provides an external view of the financial market. The financial market consists of external impulses new to the system (news, reports and other fundamental data), as well as an output signal that is internally returned to the system (people reacting to price movements).
Figure 2
Charts are nothing more than the cumulative result of the past actions of all traders or executed orders. Because people act and react to what the market does in the same way and in the same way across all time frames, their behavior ultimately manifests itself in the same patterns, regardless of scale.
Human emotions are constant, no matter what time frame we consider. The same applies to the behavior resulting from these emotions.
Focal points
Traders use the same methods and indicators to search for the same type of signals, regardless of the timeframe on which they work. Knowing this, it is worth monitoring several time frames during the trading process. Something similar was done by Alexander Elder, who developed his three-screen trading system, which suggests that the trader needs to look at one time frame below and one time frame above the one on which he is trading.
Just as a perfect storm begins as an innocent breeze that eventually develops into a hurricane, so too can one try to profitably pick out the points where signals on different time frames begin to agree. The greater the number of signals (different or identical) on all timeframes, the greater the importance of this particular point in time.
The number of charts that simultaneously contain similar signals determines the importance and depth of understanding market dynamics. Think about how many people are watching this chart and this signal at this moment, looking at different time frames. The computer is an ideal tool for processing such a large amount of information. For example, you could look at 50 possible formations or signals on 20 different time frames for a particular stock, and then repeat this for several thousand more stocks.
We will then come to understand that the future of any chart is determined by the cumulative execution of orders that have not even been placed yet. It is impossible to know in advance whether a given intraday trade will be short-term, lasting a few days or weeks, or will become a long-term trade that you will hold for several weeks to several months. Each transaction develops from the embryonic stage - this is the smallest form on the smallest time scale. This is why fractals play an important role in trading.
Atoms of Trade
Every trend, regardless of its length, starts from the lowest Low (in the case of an uptrend) or from the highest High (in the case of a downtrend). Each bottom, when close enough, has a V-shape consisting of three bars. Likewise, each vertex should look like an inverted V when viewed at its highest point at sufficient magnification. This means that at the most basic level, regardless of the time frame in question, there are always three bars that make up this atom - the building block of any chart. Trends and reversals will always end or begin with three bars, the middle of which represents the extreme high or extreme low. Take a look at Figure 3. On the left chart you can see a three-bar pattern called a single-bar down fractal. "With one bar" means that on each side of the middle bar there is one bar with higher Highs.
Figure 3
Next to this model in the diagram there is an up fractal with two bars, i.e. there are two bars on each side of the middle bar. It is necessary to be aware of some of the nuances of these definitions found in the trading literature. For example, for an up fractal with five bars, most sources require that there must be at least two bars on each side of the top or bottom for the formation to be called a fractal. There is a difference of opinion as some believe that surrounding bars do not necessarily have to show a sustained upward or downward trend, and some believe otherwise. You can see an example of this situation in the third diagram in Figure 3. The red bar is an up fractal with three bars, because to the right of the red bar there are actually three bars with lower Highs, despite the fact that the third is higher than the second. In some literature this is called a three-bar up fractal because the fourth bar from the right again has a lower High. Likewise, if you look at the bars to the left of the green, you will notice that the third bar from the left has a higher Low than the green bar, although its Low is lower than the second bar to the left of the green. There is quite a bit of confusion in the literature regarding the definitions of fractal patterns and how to use them. Therefore, in this matter we need to go one step further.
Fractal continuum
In addition to all the classifications that take into account neighboring bars, each bar can be assigned a set of four numbers. The number of bars to the left and right of the bar in question that exhibit higher Lows than the bar in question is called the Chartmill number of left/right support for that bar (CLS and CRS, respectively). Likewise, a given bar's Chartmill Left/Right Resistance number (CLR and CRR, respectively) takes into account the number of bars to the left and right of a given bar with lower Highs. These numbers are clear and avoid confusion. The time frame you use for your analysis should not affect how you define and analyze the fractal nature of the market. It is important to have objective indicators and signals. Moreover, these indicators and signals must ignore any characteristics of visual perception, for example: time scale on the horizontal axis or linearity/logarithmicity of the axis. Only then can objective, chart-independent indicators be created that can be applied algorithmically, scanning for focal points.
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The author dedicated this book to presenting the fractal market hypothesis. The book argues that this hypothesis is an alternative to the efficient market hypothesis. Fractals are present everywhere in our world. At the same time, they play a significant role in the structure of financial markets, which are globally determined but locally random. The author of the book thinks so. This publication will also consider methods for analyzing stock, currency and bond markets in a fractal way. The author will talk about methods for distinguishing an independent process.
In addition, from the book “Fractal Analysis of Financial Markets” you will learn about the methods of the stochastic nonlinear process, as well as the nonlinear and deterministic process. This book examines the impact of such differences on user investment strategies and modeling abilities. Such abilities and strategies are closely related to the user's investment horizon and asset type. For financiers, risk managers, market technical analysts, investment strategists and, in addition, for currency speculators and individual investors who enter financial markets around the world on their own. Among such markets are Forex and the markets of our country.
How markets work based on the book “Fractal Analysis of Financial Markets”
When the time comes to look at the workings of markets more holistically, it will be necessary to recognize the greater heterogeneity that underlies such markets. All investors do not participate here for the same reason, nor do they use their strategies on the same investment horizons. Strongly linked to the heterogeneity of investors and the stability of markets. As a rule, a mature market is quite heterogeneous. Instability would reign everywhere if all participants invested their capital for the same purpose, had the same investment horizon, and responded equally to information.
Mature markets, according to the book “Fractal Analysis of Financial Markets,” have been remarkably stable for quite a long time. A day trader can conduct anonymous trading with a pension fund. A fund trades for long-term financial security and does so infrequently, while a day trader trades frequently and aims for short-term profit.
Objectives of the book “Fractal Analysis of Financial Markets”
The first purpose of this publication is the need to present the fractal hypothesis of the market. It talks about how and why markets function. The second goal can be called the desire to present the necessary tools for analyzing markets within the boundaries of a fractal structure. Many existing tools can be used for this purpose. The author introduces the reader to new tools that analysts can add to their own toolkit. In addition, the author reviews existing tools in this volume.
The book “Fractal Analysis of Financial Markets” is not a story, despite the fact that the main emphasis is on conceptual aspects. Analytical methods within the boundaries of the conceptual framework are carefully studied. Anyone, according to the author, who has a solid knowledge of commercial statistics will find a lot of useful information in this book. The main emphasis here is not on dynamics, but on empirical statistics. In other words, on the analysis of a time series to find what each of us is dealing with. After reading this book, you will no longer be able to think in the same ways. Your vision for this area will change forever.
Practice shows that the dynamics of economic processes and phenomena are nonlinear and often chaotic (unpredictable) in nature. This necessitates the search for alternative modeling methods using non-standard mathematical tools. Today there are quite a lot of directions in this area of economics and mathematics. When analyzing socio-economic processes, mathematical tools such as fuzzy methods, neural networks, genetic algorithms, etc. are increasingly being used. However, when analyzing market dynamics, none of these methods can take into account such a property of the market as self-organization. This problem, to a certain extent, can be solved by the theory of fractals.
Many Western scientists have been actively involved in the introduction of the theory of fractals into economics since the 80s of the twentieth century, while domestic researchers began to consider this theory relatively recently. The application of fractal analysis in economics is described in the works of such outstanding researchers as B. Mandelbrot, E. Peters, V. Arnold, P. Berger, I. Pomo, C. Vidal, G. Schuster, R. Manten, H. Stanley, V. Chou, D. Sornette, A.Y. Loskutov, A.S. Mikhailov, N.V. Chumachenko, A.I. Lysenko et al.
The use of the mathematical apparatus of fractal theory opens up new possibilities in modeling market processes. The key point contributing to this is the self-development of the fractal. This property characterizes a fractal as a mathematical object that is most consistent with the systemic nature of social and economic processes occurring under the conditions of nonlinear dynamics of many factors of the external and internal environments.
In the real world, pure, ordered fractals, as a rule, do not exist, and we can only talk about fractal phenomena. They should only be considered as models that are approximately fractals in a statistical sense. However, a well-constructed statistical fractal model allows one to obtain fairly accurate and adequate forecasts.
An example of one of the most effective applications of fractal theory in modeling market processes is fractal model of the stock market. Due to the peculiarities of the functioning of the securities market, it is quite difficult to predict the dynamics of prices on it. There are many recommendations and strategies, but only the use of fractals allows you to build an adequate model of stock market behavior. The effectiveness of this approach is supported by the fact that many stock exchange participants spend a lot of money on paying for the services of specialists in this field.
Fractal analysis of markets, in contrast to the theory of efficient markets, postulates the dependence of future prices on their past changes. Thus, the process of pricing in markets is globally determined, dependent on “initial conditions”, that is, past values. Locally, the pricing process is random, that is, in each specific case the price has two development options. Fractal market analysis comes directly from fractal theory and borrows the properties of fractals to produce forecasts.
The main properties of fractals on the market:Market charts have a fractal dimension. The fractal dimension of a market chart is always 1
Market charts have the property of scale invariance or scaling. Different time intervals are self-similar.
Market charts always form a certain structure that has unique properties.
Market fractals have a “memory” of their “initial conditions”.
The first practitioner who applied fractal theory in the analysis of financial and commodity markets was Bill Williams . Subsequently, his method of fractal market analysis became widespread in many countries. This was facilitated by his works such as"Trading Chaos" "New dimensions in stock trading",
"Trading Chaos second edition". Over time, many inattentive traders and analysts believed that behind the beautiful name lies more a clever PR move by the author than the actual use of fractals in the market. The main mistake that leads to distortion of the analysis results is the incorrect interpretation of the concept of “overcoming a fractal.” The ambiguity of fractal analysis ceases if the word “overcoming” is understood not as a puncture by the price of a fractal level, but as a breakdown confirmed by the closing of the price above or below the fractal level.
Description of the market using fractals.
At the moment, fractal market analysis is the most common on the market.
Even if we take the price movement within one minute, we will still get a line that connects the opening price and the closing price. The generator for price movement is another common structure, well known to the trader -
"impulse-correction-impulse", which looks like below:
There may be an infinite number of these same generators on the market, and there may not be two turning points. What information can these figures give a trader? If you look at the price movement of an individual instrument, you can see that the structure of the generator is repeated on all time scales of the instrument (shows
fractal properties). Let's take it for granted that intra-year price movement is a simple structure of two impulses and one correction, as in the figure above. If both impulses and correction are replaced by the corresponding fractals (generators), we get the following structure:
Moving deeper and deeper, we will reach minute and then tick charts, on which the basic fractal will appear again and again. Typically, the relationships between the generator lines will remain fixed on any time structure. Angles between the generator lines on the minute and monthly
The graphics will correspond to each other, the ratio of their lengths will also correspond. This amazing discovery gives us a completely new look at the usual price movement.
Of course, this understanding is simplistic, and, in Mandelbrot’s own opinion, “caricatured.” It serves us to describe the general principle of the structure of price movement. A real market generator can be much more complex.
In modeling market behavior, Mandelbrot uses a more complex "multifractal" model, which uses three dimensions and the so-called "fractal cube". We will not dwell on it in detail. Instead, let's look at two other observations of fractal geometry that are easier to understand and give the trader
food for thought.
The market has a memory.
Benoit Mandelbrot's extensive research into the cotton market led him to the following conclusion: periods of high volatility or "turbulence" tend to cluster in
"clusters" . This means that events, the probability of which, according to generally accepted financial models, is an insignificant fraction of a percent, in many cases occur in sequence - one after another. This is fundamentally inconsistent with the “random walk” model that is used throughout the world for risk management. According to it, all events in the market are independent of each other. Mandelbrot makes a convincing case. it is not so. Market events tend to remain dependent on each other. He calls this effect -"The Joseph Effect", using as a metaphor the famous biblical parable of Pharaoh, who had a dream about seven fat and seven skinny cows (seven harvest years and seven lean years).What does it represent
"price cluster"? By price cluster we mean"trend" Trend in economics - the direction of preferential movement of indicators. Usually considered within the framework of technical analysis, which implies the direction of price movements or index values. Charles Dow noted that during an upward trendthe subsequent peak on the chart should be higher than the previous ones; in a downtrend, subsequent declines on the chart should be lower than the previous ones (see Dow Theory). Highlight trends ascending (bullish), downward (bearish) and side (flat) ) . A trend line is often drawn on the chart, which in an uptrend connects two or more price troughs (the line is located below the chart, visually supporting it and pushing it upward), and in a downtrend connects two or more price peaks (the line is located above the chart, visually limiting it and pressing down). Trend lines are lines of support (for an uptrend) and resistance (for a downtrend). An uptrend (an uptrend, a bullish trend) is a situation where each new local minimum and local maximum is higher than the previous one.
An example of a rising trend.
An example of a downtrend.
The Noah effect
And finally, Mandelbrot's third observation is the so-called"Noah" effect . From the Old Testament we know that the global flood began unexpectedly, and its destructive power turned out to be very great. The “Noah” effect is a metaphor that characterizes market reversals – stock market panic crashes and booms. They never happen smoothly; almost always the market soars or collapses with such force that none of the investors expected.
This always causes panic among the stock exchange public, which is shocked by such price movements. Thus, in 1987, the Dow Jones Industrial Average fell by 22.6% in one day. After the crash, computer programs were blamed for everything, but Benoit Mandelbrot had a completely different opinion - it’s not about the programs at all, it’s about the very nature of the market. It is the inherent nature of the market that drives this dynamic. This hypothesis is also new and is not consistent with the efficient market hypothesis, which states that the market should change smoothly and consistently. This property of the market should be remembered by traders who work without stops, hoping that the market will sooner or later return to the level at which the transaction was opened.
The summary that Mandelbrot makes is this: the market is a very risky place, much riskier than is commonly believed. For traders, risk is not a source of danger, but a potential source of profit. If you use your knowledge of price movements correctly and are on the “right” side of risk, it will be a blessing, and
not a curse.
Concluding the article, we will also mention the use of fractals in time series modeling. In particular, such a characteristic of a time series as fractal dimension makes it possible to determine the moment at which the system becomes unstable and is ready to transition to a new state.
Example of a time series.
Thus, the theory of fractals provides a qualitatively new approach to economic modeling. However, its novelty and inconsistency with classical methods make it difficult to use widely. One of the main limiting factors is the chaotic nature of the fractal model, which is due to the exceptional interdependence of its input and output parameters. Even the slightest change in the input parameter or the slightest error in setting it can lead to completely unpredictable behavior of the model. At the same time, due to the insufficiently developed mathematical apparatus of the theory itself, it is completely impossible to verify (evaluate) the results obtained from fractal modeling. At the same time, this is truly the most promising modern area of mathematics from the point of view of applied research in economics.
Sources: fortrader.ru, Wikipedia and other materials from the Internet..
Fractals are quite popular among many traders. Interest in fractal analysis arose after the publication of several works by Bill Williams on this topic. Fractals were invented before him, but were referred to under a different name. Williams, studying financial markets, came to the conclusion that the movements of the rates of many financial instruments are chaotic. As a result of his research, he proved that the graph of changes in the value of cotton is similar to the coastline and the movement of blood in the human body.
In his research, Williams came to the conclusion that markets are chaotic, not linear systems, so using indicators based on linear functions on them is useless. In his opinion, stability in the markets is present only a small fraction of the time, and in all other cases chaos reigns in them.
A fractal is a repeating formation that is found in one form or another on any price charts. Coastline fractals, like stock market fractals, are of the same nature. A fractal consists of at least five bars.
Upper and lower fractals can be in the same group of bars. Sometimes an upper and lower fractal are formed simultaneously on the same bar. When a fractal is formed, it is endowed with all the properties.
When assessing the upper fractal, you need to pay attention to its maximum. When studying the lower, accordingly, the minimum. A fractal start is formed from two successive fractals directed in different directions. The fractal signal appears on the side opposite to the start. The fractal stop is located behind the far fractal. If an opposite signal appears, it cancels the previous ones.
This technique allows you to increase the percentage of profitable trades, but the average losing trade will be higher. Since stop losses when using such a strategy will be infrequent, you can ultimately count on good profits. Fractal market analysis does not always give 100% profitable trades. In this regard, it should not be used only in a trading system. It is recommended to use other tools to confirm signals or filter.
When using fractal analysis, it is also important to study data from different time frames. The system that Bill Williams described in his works is trendy. To use it correctly, you must first determine the dominant trend in the market by looking at the older period.
The system should also take into account the “fractal lever”. This is the name of the possible amplitude during rollbacks. You can evaluate “fractal leverage” using the standard Fibonacci lines that are available in MT4. Corrections up to 38% Fibonacci are evidence of a strong trend movement. In this case, the fractal lever is strong. The opposite is true if the rollbacks are 62% fib or more.
Fractals and wave theory
Fractals can also be used in conjunction with wave theory. After all, in its essence, a fractal is nothing more than the beginning or end of an impulse movement or wave. A certain complexity arises here, because different impulses are formed at different periods of the charts. Traders who have gained experience in using wave theory have no difficulty in accurately identifying a specific wave on a specific time frame.
If several groups of fractals are formed at the same level, then if this level is broken through, a long and powerful trend should be expected. Fractal market analysis gives very good results in the presence of trends. When the price stays in the channels for a long time, the strategy for breaking out the fractal brings losses. The difficulty is that recognizing an emerging flat can be quite difficult.
How to apply a fractal strategy in a flat?
You should trade a breakout only in the direction of a pronounced trend. You should not worry about several losses in a row. The future profit will certainly cover all the losses that the strategy incurred during fluctuations in the corridor. A good effect is achieved when working on small time intervals. If a trader enters a fractal breakout based on the daily chart, then a stop loss can be set based on H4. Typically, the more fractals are located at the same level and the longer the flat lasts, the stronger and more directional the future movement will be.
To reliably determine whether a fractal breakout is true or false, you can use breakout candlestick analysis. If the breakout candle is “strong”, that is, it has a large body and its closing level is located far from clusters of fractals, then there is a high probability that the movement will continue in the chosen direction. Using this conclusion, you can successfully trade on small charts in order to increase profits. For example, if yesterday there was a breakout on D1, then today we can consider breakouts on the four-hour chart.
If, after the breakdown of a cluster of fractals, a reversal candlestick pattern has formed, then in the future, most likely, a flat will reign in the market, and new fractals will appear. In this regard, a lot of attention should be paid to the analysis of the breakout candle. To increase efficiency, it is recommended to familiarize yourself with at least the basics of Price Action (candlestick analysis).
Bill Williams recommended looking not only at the reversal candle, but also analyzing the volume. If the candle has a large body, but the volume is small, then the signal is weak. Signals that come from fractal clusters are strong when they form on longer-term charts (as is the case with candlestick analysis). Williams himself recommended watching D1. At the same time, it is necessary to analyze other timeframes. As mentioned in this article, fractal analysis is best combined with something else to increase the profitability of the strategy, because no tool can boast of 100% accurate signals.
The video contains useful information on the topic under consideration.
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