Fractal theory of the forex market
Fractal: multiple time frame analysis · Fractals can assume the use of multiple time frames in trading a security · Traders may look at a larger. Fractal patterns provide a way of estimating probable reversal points on charts. A core fractal pattern comprises of five candlesticks (or bars). Fractal Theory FOREX market Fraktalnaya teoriya rynka Forex Hardcover – January 1, · Language. Russian · Publisher. Admiral Markets · Publication date. FOREX DIVERGENCE STRATEGY Ref: KBHt does improve software is in start source code that anyone. Can be the standard more connections. Want to the problem, tasks, such your domain has basically and check the name server settings.
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Typically, long-term investors shorten their investment horizons as prices continue to fall as observed in a financial crisis. When investors change their investment horizons this causes the market to become less liquid and unstable. Liquidity is referred to as market liquidity in the FMH. Market liquidity is the ease with which an investor is able to buy and sell securities without their actions affecting market prices. Liquidity is generated whenever investors trade with each other, thus two investors must hold different views on the value of assets and securities.
In times of crisis, the hypothesis states that long-term horizons are reduced; consequently, liquidity dissipates as investors homogenize, and no one is willing to take the other side of a trade. Under fractal structures, differing interpretations of information result in varying time horizons ensuring market liquidity and orderly price movements.
For more, see: Understanding Financial Liquidity. The role of information is crucial in making sound decisions with any sort of investment strategy. Within the framework of FMH, the impact of information availability can lead to changes in time horizons and liquidity. During times of stability, FMH states all investors share the same information. How information is perceived results in the individual investment decisions: a day trader may perceive price fluctuations and decide to sell, while a pension fund manager will place less value on price movements.
However, if investors witness extreme declines in prices from a previous period, long-term investors may be inclined to reduce time horizons and begin to sell. As a result, a decline in prices from a previous period can cause further price to declines in the current period. As stated in FMH, information that causes investment horizons to change will result in market instability and illiquidity.
In analyzing financial theory, the Efficient Market Hypothesis has dominated and continues to dominate the economic literature. EMH asserts market efficiency with investors acting rationally. However, under this framework, phenomena such as crises cannot be explained. Proponents of EMH suggest irrationality amongst investors as a factor when explaining the Financial Crisis and housing bubble.
However, by definition, financial markets disperse all available information efficiently, which is reflected in market prices and rationally acting investors. Failing to acknowledge market inefficiencies gives credence to alternative theories of markets, including the Noisy Market Hypothesis, Adaptive Market Hypothesis , and Fractal Market Hypothesis.
Unlike EMH, FMH analyzes the behavior of investment horizons, the role of liquidity, and the impact of information during crises and stable markets. Within the framework of FMH, stable markets result in highly liquid assets. Referred to as market liquidity, liquidity is created when investors are able to trade with each other as a result of investors holding different investment horizons.
Stability under FMH requires a variety of different investment horizons and liquid assets. When information dictates buying and selling, instability occurs. In times of crisis, investment horizons shorten, resulting in a greater number of investors selling illiquid assets. While fundamentally different from EMH, both market theories predominantly rely on the impact of information to understand investor behavior.
The Nobel Foundation. Robert F. Peters, John Wiley and Sons, ," Pages 85, The Quarterly Journal of Austrian Economics, Advanced Technical Analysis Concepts. Quantitative Analysis. Trading Psychology. Your Money. Personal Finance. Your Practice. Popular Courses. For example, a fractal observed hundreds of bars ago may still be relevant to the price due to its location, while a fractal observed only ten bars ago may no longer be relevant due to swings in the market.
Being able to identify these relevant fractals while filtering the noise would be of a great benefit to traders looking to incorporate price action into their trading strategies. All fractals have what can be seen as a source fractal. A source fractal is the previous fractal in the opposing direction. For example, if there were a buy fractal at some arbitrary location on the chart, then its source fractal would be the most recent sell fractal prior to said buy fractal.
This source fractal is of great importance, as its location indicates whether the fractal itself is still a relevant marker of a support or resistance level; if the price were to pass the point of a source fractal, then the fractal to which it sources may be seen as irrelevant to current market conditions, as the starting swing to the fractal had been broken.
Figure 3 shows the same chart without the fractals whose sources have been broken by the price. As the short-term trend is up, the majority of the relevant fractals are buy fractals, where the resistance has become the support. Taking this concept one step further, relevant fractals located within the same region of each other suggest that this price zone is a significant area of support or resistance, more so than a fractal found at a price distant from other relevant fractals.
Identification of these regions is of great use to a trader, as it suggests that the region is of greater significance and, subsequently, more likely to indicate where breakouts are likely to occur. Deducing the boundaries for a region of relevant fractals may appear subjective at first glance, but there are in fact mathematically-driven approaches to grouping known as clustering algorithms.
Among these algorithms is the density-based spatial clustering of applications with noise DBSCAN data clustering algorithm. The general idea behind DBSCAN is to iterate over each data point, in this case the relevant fractals, and locate all additional relevant fractals within a set distance. For each fractal within that distance, perform the distance check from that fractal and continue until there are no new fractals within range of another. Putting these fractals together produces a cluster of fractals and, it follows, the support or resistance region of interest to the trader.
The last step to the process is determining what distance should be used when seeking neighboring fractals. Again, no subjectivity here, as the standard deviation of the price provides that distance for us. In statistics, the standard deviation quantifies how much dispersion from the average of a data set exists; the greater the standard deviation, the greater the variance in the data set.
Translated to financial markets, the standard deviation of the price quantifies how volatile the market is; the greater the standard deviation, the more volatile the market. Using the standard deviation of the price as the distance from which neighboring fractals should be sought closes the mathematical circle of the DBSCAN algorithm, allowing us to objectively cluster relevant fractals into key zones of support and resistance.
With all of this information to take into consideration, performing the calculations by hand may prove tedious; identification of relevant fractals requires going back hundreds of bars, drawing lines from these points may be tedious, and the standard deviation and clusters of fractals are constantly changing due to fluctuation of market conditions.
This process can be automated using an indicator I have written for MetaTrader 4. Figure 4 shows how this view appears to the user. Finally, when trading breakouts or retracements, consideration of the trend has to be taken. MetaTrader 4 provides an Average Directional Index indicator, which quantifies the strength of the trend, as well as the option to draw trendlines on the chart.
Whether you use trendlines or your eyes to gauge the trend, always make sure that the Average Directional Indexvalue is of moderate strength as a rule of thumb, greater than 25 and the breakout or retracement observed by the movement of price within fractal clusters is in the direction of the observed trend before making a trade. From there, the rest is up to you.
As always, money management is integral to a good trading strategy. Using these properties of fractals as regions of support and resistance, foreign exchange market traders can identify areas of likely price breakout or retracement with greater precision than otherwise possible. Trading Strategies. Evgeniy Ozhiganov. Fractal Theory in the Financial Markets In the context of financial markets, the look and feel of the chart is relative to the timeframe being observed. Application of Fractal Theory To use this information in the context of the foreign exchange market, trader and author Bill M.
Relevant Fractals All fractals have what can be seen as a source fractal. Identification of Fractal Clusters Deducing the boundaries for a region of relevant fractals may appear subjective at first glance, but there are in fact mathematically-driven approaches to grouping known as clustering algorithms. Making the Trade With all of this information to take into consideration, performing the calculations by hand may prove tedious; identification of relevant fractals requires going back hundreds of bars, drawing lines from these points may be tedious, and the standard deviation and clusters of fractals are constantly changing due to fluctuation of market conditions.
Fractal theory of the forex market forex leaderFRACTAL MARKET EXPLAINED! - FOREX Smart Money Concepts \u0026 Wyckoff
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