Posts Tagged ‘Analysis’

The Pioneers of Technical Analysis

Written by admin. Posted in Technical Analysis

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Whether you consider yourself a technical analyst or not, there are very few investing techniques that do not at least give a nod to the technical side of investing. Some investing styles use nothing but technical analysis, with their practitioners often claiming that they know nothing of stock fundamentals because all they need is in the charts. This segment of investing didn’t sprout from nothing. In this article, we will look at the men that pioneered the field of technical analysis. (See also: Technical Analysis.)

All Things Flow From Dow

Charles Dow occupies a huge place in the history of finance. He founded The Wall Street Journal – the benchmark by which all financial papers are measured – and, more importantly for our purpose, he created the Dow Jones Industrial Index. In doing so, Dow opened the door to technical analysis. Dow recorded the highs and lows of his average daily, weekly and monthly, correlating the patterns with the ebb and flow of the market. He would then write articles, always after the fact, pointing out how certain patterns explained and predicted previous market events.

However, Dow can’t take all – or even a majority of – the credit for the theory bearing his name. Dow Theory would have only acted as a hindsight confirmation of loose principals if it weren’t for William P. Hamilton. (See also: Giants of Finance: Charles Dow.)

First One Into the Water: William P. Hamilton

Dow Theory was a collection of market trends linked heavily to oceanic metaphors. The fundamental, long-term trend of four or more years was the tide of the market – either rising (bullish) or falling (bearish). This was followed by shorter-term waves that lasted between a week and a month. And, lastly, there were the splashes and tiny ripples of choppy water insignificant day-to-day fluctuations.

Hamilton used these measures in addition to a few rules – such as the railroad average and the industrial average confirming each other’s direction – to call bull and bear markets with laudable accuracy. Although he did call the 1929 crash too early (1927, 1928), he made a final appeal on Oct. 21, 1929, three days before the crash and mere weeks before his death at the age of 63.

The Practitioner: Robert Rhea

Robert Rhea took Dow Theory and turned it into a practical indicator for going long or short in the market. He literally wrote the book on the topic: “The Dow Theory” (1932). Rhea was successful at using the theory to call tops and bottoms – and able enough to profit from those calls. Very soon after mastering Dow Theory, Rhea didn’t need to trade on his knowledge. He only had to write it down.

After calling the market bottom in 1932 and a top in 1937, the fortunes made by subscribers to Rhea’s investment letter, Dow Theory Comments, brought in thousands more subscribers. As with Hamilton, however, Rhea’s life as a market prognosticator was short – he died in 1939. (See also: Dow Theory Tutorial.)

The Wizard: Edson Gould

Perhaps the most accurate forecaster with the longest track record, Edson Gould, was still making calls up to 1983 at the age of 81. Gould also made most of his money from writing newsletters rather than investing, selling subscriptions for $500 in 1930. He caught all of the major bull and bear market points, making several eerily accurate predictions, such as the Dow rising 400 points in a 20-year bull market, that the Dow would top 1,040 in 1973 and so on.

Gould used charts, market psychology and indicators including the Senti-Meter – the DJIA divided by the dividends per share of the companies. Gould was so good at his trade that he continued to make accurate calls from beyond the grave. Gould died in 1987, but in 1991, the Dow hit 3,000, as he’d predicted. At the time of his prediction in 1979, the Dow had yet to break 1,000.

[The work of these pioneers formed the foundation for a huge array of technical tools used by traders today to develop profitable trading strategies. To learn more, check out the Technical Analysis course on the Investopedia Academy, which includes interactive content and real-world examples to boost your trading skills.]

The Chartist: John Magee

John Magee wrote the bible of technical analysis, “Technical Analysis of Stock Trends” (1948). Magee was one of the first to trade solely on the stock price and its pattern on the historical charts. Magee charted everything: individual stocks, averages, trading volumes – basically anything that could be graphed. He then poured over these charts to identify broad patterns and specific shapes like weak triangles, flags, bodies, shoulders and so on.

Unfortunately for Magee, early on, he was better at looking after his clients than his own portfolio, often selling out in his own portfolio based on gut feelings despite strong hold signals from his charts. From his 40s to his death at 86, however, Magee was one of the most disciplined technical analysts around, refusing to even read a current newspaper lest it interfere with the signals of his charts. (For more, see: Analyzing Chart Patterns.)

The Omissions

There is bound to be some controversy with a list like this. Where is the infamous Jesse Livermore, the trader whose gut calls on price ticks are arguably the first successful technical trades? What about R. N. Elliott? What about WD Gann?

Well, Livermore did little in the area of theorizing and died broke. Elliott tweaked technical analysis with his own hypothesis, but his theories are difficult to test and even harder to trade – involving something of mysticism piled on top of numbers. Similarly, Gann’s lines, while seemingly useful in concept, are so sensitive to error that their practicality is questionable. Both of these men were purported to have made fortunes trading on their theories, but there is no solid record to back that up as there is for Livermore. Certainly no multi-million-dollar estate was left behind by either.

The Bottom Line

Dow, Hamilton, Rhea, Gould and Magee are on the main track of technical analysis, each carrying the theory and practice a little further. There are of course, many branching side paths that, while interesting detours, didn’t advance this main thrust. Every time an investor – fundamental or technical – talks about getting in low or picking entry and exit points, they are paying homage to these men and the techniques for which they laid the foundation. (See also: Introduction to Types of Trading: Technical Traders.)

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Understanding Trend Analysis and Trend Trading Strategies

Written by admin. Posted in Technical Analysis

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What Is Trend Analysis?

Trend analysis is a technique used in technical analysis that attempts to predict future stock price movements based on recently observed trend data. Trend analysis uses historical data, such as price movements and trade volume, to forecast the long-term direction of market sentiment.

Key Takeaways

  • Trend analysis tries to predict a trend, such as a bull market run, and then ride that trend until data suggests a trend reversal, such as a bull-to-bear market.
  • Trend analysis is based on the idea that what has happened in the past gives traders an idea of what will happen in the future.
  • Trend analysis focuses on three typical time horizons: short-; intermediate-; and long-term.

Understanding Trend Analysis

Trend analysis tries to predict a trend, such as a bull market run, and ride that trend until data suggests a trend reversal, such as a bull-to-bear market. Trend analysis is helpful because moving with trends, and not against them, will lead to profit for an investor. It is based on the idea that what has happened in the past gives traders an idea of what will happen in the future. There are three main types of trends: short-, intermediate- and long-term.

A trend is a general direction the market is taking during a specified period of time. Trends can be both upward and downward, relating to bullish and bearish markets, respectively. While there is no specified minimum amount of time required for a direction to be considered a trend, the longer the direction is maintained, the more notable the trend.

Trend analysis is the process of looking at current trends in order to predict future ones and is considered a form of comparative analysis. This can include attempting to determine whether a current market trend, such as gains in a particular market sector, is likely to continue, as well as whether a trend in one market area could result in a trend in another. Though a trend analysis may involve a large amount of data, there is no guarantee that the results will be correct.

Types of Trends to Analyze

There are three main types of market trend for analysts to consider:

  1. Upward trend: An upward trend, also known as a bull market, is a sustained period of rising prices in a particular security or market. Upward trends are generally seen as a sign of economic strength and can be driven by factors such as strong demand, rising profits, and favorable economic conditions.
  2. Downward trend: A downward trend, also known as a bear market, is a sustained period of falling prices in a particular security or market. Downward trends are generally seen as a sign of economic weakness and can be driven by factors such as weak demand, declining profits, and unfavorable economic conditions.
  3. Sideways trend: A sideways trend, also known as a rangebound market, is a period of relatively stable prices in a particular security or market. Sideways trends can be characterized by a lack of clear direction, with prices fluctuating within a relatively narrow range.

How to Perform a Trend Analysis

In order to begin analyzing applicable data, it is necessary to first determine which market segment will be analyzed. For instance, you could focus on a particular industry, such as the automotive or pharmaceuticals sector, as well as a particular type of investment, such as the bond market.

Once the sector has been selected, it is possible to examine its general performance. This can include how the sector was affected by internal and external forces. For example, changes in a similar industry or the creation of a new governmental regulation would qualify as forces impacting the market. Analysts then take this data and attempt to predict the direction the market will take moving forward.

Trend Trading Strategies

Trend traders attempt to isolate and extract profit from trends. There are many different trend trading strategies using a variety of technical indicators:

  • Moving Averages: These strategies involve entering into long positions when a short-term moving average crosses above a long-term moving average, and entering short positions when a short-term moving average crosses below a long-term moving average.
  • Momentum Indicators: These strategies involve entering into long positions when a security is trending with strong momentum and exiting long positions when a security loses momentum. Often, the relative strength index (RSI) is used in these strategies.
  • Trendlines & Chart Patterns: These strategies involve entering long positions when a security is trending higher and placing a stop-loss below key trendline support levels. If the stock starts to reverse, the position is exited for a profit.

Indicators can simplify price information, as well as provide trend trade signals or warn of reversals. They may be used on all time frames, and have variables that can be adjusted to suit each trader’s specific preferences.

Usually, it is advisable to combine indicator strategies or come up with your own guidelines, so entry and exit criteria are clearly established for trades. Each indicator can be used in more ways than outlined. If you like an indicator, research it further, and most importantly, test it out before using it to make live trades.

Trend following is a trading system based on using trend analysis and following the recommendation produced to determine which investments to make. Often, the analysis is conducted via computer analysis and modeling of relevant data and is tied to market momentum.

Advantages and Disadvantages of Trend Analysis

Advantages

Trend analysis can offer several advantages for investors and traders. It is a powerful tool for investors and traders as it can help identify opportunities for buying or selling securities, minimize risk, improve decision-making, and enhance portfolio performance.

Trend analysis can be based on a variety of data points, including financial statements, economic indicators, and market data, and there are several different methods that can be used to analyze trends, including technical analysis and fundamental analysis. By providing a deeper understanding of the factors that are driving trends in data, trend analysis can help investors and traders make more informed and confident decisions about their investments.

Disadvantages

Trend analysis can have some potential disadvantages as a tool for making investment decisions. One of these disadvantages is that the accuracy of the analysis depends on the quality of the data being used. If the data is incomplete, inaccurate, or otherwise flawed, the analysis may be misleading or inaccurate.

Another potential disadvantage is that trend analysis is based on historical data, which means it can only provide a limited perspective on the future. While trends in data can provide useful insights, it’s important to remember that the future is not necessarily predetermined by the past, and unexpected events or changes in market conditions can disrupt trends. Trend analysis is also focused on identifying patterns in data over a given period of time, which means it may not consider other important factors that could impact the performance of a security or market.

Finally, trend analysis often relies on statistical measures to identify patterns in data, which can be subject to interpretation. Different statistical measures can yield different results, and it’s important to be aware of the limitations and assumptions of the statistical methods being used.

Critics of trend analysis, and technical trading in general, argue that markets are efficient, and already price in all available information. That means that history does not necessarily need to repeat itself and that the past does not predict the future. Adherents of fundamental analysis, for example, analyze the financial condition of companies using financial statements and economic models to predict future prices. For these types of investors, day-to-day stock movements follow a random walk that cannot be interpreted as patterns or trends.

Trend Analysis Pros and Cons

Pros

  • Can help identify opportunities for buying or selling securities

  • Can identify potential risks or warning signs that a security or market may be headed for a downturn

  • Provides insight into market psychology and momentum

Cons

  • If markets are efficient, trend analysis is not as useful

  • If the data is incomplete, inaccurate, or otherwise flawed, the analysis may also be misleading or inaccurate

  • May not take into account changes in a company’s management, changes in industry regulations, or other external factors that could affect the security’s performance

  • Different statistical measures can yield different results

Example of a Trend Analysis

Say that an investor is considering buying shares of a particular company, and they want to use trend analysis to determine whether the stock is likely to rise in value. To conduct their analysis, the investor gathers data on the company’s financial performance over the past five years, including its revenues, expenses, profits, and other key metrics. They also gather data on the overall performance of the stock market and on the company’s industry.

Using this data, the investor creates charts to visualize the trends in the data. They notice that the company’s revenues have been steadily increasing over the past five years, and that its profits have also been trending upward. They also notice that the stock market has been generally trending upward over the same period.

The investor then uses linear regression to model the relationship between the company’s profits and its stock price, and they find that there is a strong positive correlation between the two variables. This suggests that as the company’s profits have increased, its stock price has also tended to rise.

Based on their analysis, the investor concludes that the company’s stock is likely to continue trending upward in the future, and they decide to buy shares of the stock.

What Is a Trend?

A trend is the overall direction of a market during a specified period of time. Trends can be both upward and downward, relating to bullish and bearish markets, respectively. While there is no specified minimum amount of time required for a direction to be considered a trend, the longer the direction is maintained, the more notable the trend. Trends are identified by drawing lines, known as trendlines, that connect price action making higher highs and higher lows for an uptrend, or lower lows and lower highs for a downtrend.

What Is the Formula or Model for Trend Analysis?

There is no one formula for trend analysis, as the specific methods used to analyze trends can vary depending on the data being analyzed and the goals of the analysis. However, there are several statistical measures that are commonly used in trend analysis to identify patterns and trends in data.

Here are a few examples of statistical measures that might be used in trend analysis:

  • Moving averages: A moving average is a statistical measure that is used to smooth out fluctuations in data over time. A simple moving average (SMA) is calculated by taking the average of a set of data points over a given period of time, such as the past 10 days or the past 50 weeks. Moving averages can be used to identify trends by smoothing out short-term fluctuations in data and highlighting longer-term patterns.
  • Linear regression: Linear regression is a statistical method that is used to model the relationship between two variables. It can be used to identify trends by fitting a line to the data and determining the slope of the line, which can indicate the direction and strength of the trend.
  • Correlation: Correlation is a statistical measure that indicates the strength and direction of the relationship between two variables. A positive correlation means that the variables are moving in the same direction, while a negative correlation means that they are moving in opposite directions. Correlation can be used to identify trends by analyzing the relationship between two variables over time.

It’s important to note that these are just a few examples of statistical measures that might be used in trend analysis, and there are many other methods and measures that could also be used depending on the specific needs of the analysis.

What Are Examples of Trend Trading Strategies?

Trend trading strategies attempt to isolate and extract profit from trends by combining a variety of technical indicators along with the financial instrument’s price action. Typically, these include moving averages, momentum indicators, and trendlines, and chart patterns.

Moving averages strategies involve entering into long, or short, positions when the short-term moving average crosses above, or below, a long-term moving average. Momentum indicator strategies involve entering into positions when a security is exhibiting strong momentum and exiting when that wanes. Trendlines and chart pattern strategies involve entering long, or short, positions when a security is trending higher, or lower, and placing a stop-loss below, or above, key trendline support levels to exit the trade.

How Do You Prepare a Trend Analysis?

To prepare a trend analysis as a trader, you will typically need to follow these steps:

  1. Identify the security or market you want to analyze: Decide which security or market you want to analyze in order to identify trends that could inform your trading decisions. This could be a specific stock, bond, currency, commodity, or other financial instrument, or it could be a broader market index or sector.
  2. Gather the data: Collect data on the security or market you have identified. This may involve accessing financial statements, downloading market data, or accessing databases or other sources of data.
  3. Organize the data: Organize the data in a way that makes it easy to analyze. This could involve creating spreadsheets, charts, or graphs to visualize the data.
  4. Analyze the data: Use your chosen method of analysis to identify trends in the data. This could involve looking for patterns in the data, calculating statistical measures such as averages or standard deviations, or using graphical tools such as charts to identify trends.
  5. Interpret the results: Once you have identified trends in the data, interpret the results to determine what they mean for your trading decisions. This could involve making predictions about the future direction of the security or market, identifying risks or opportunities, or making recommendations for buying, selling, or holding the security.
  6. Use the results to inform your trading decisions: Use the insights gained from your trend analysis to inform your trading decisions. This could involve adjusting your portfolio, placing trades, or making other decisions based on the trends you have identified.

What Are Some Criticisms of Trend Analysis?

Critics of trend analysis, and technical trading in general, argue that markets are efficient, and already price in all available information. That means that history does not necessarily need to repeat itself and that the past does not predict the future. Adherents of fundamental analysis, for example, analyze the financial condition of companies using financial statements and economic models to predict future prices. For these types of investors, day-to-day stock movements follow a random walk that cannot be interpreted as patterns or trends.

The Bottom Line

Trend analysis is the study of data to identify patterns or trends that can be used to make investment decisions. This type of analysis is typically used to analyze the performance of a particular security, such as a stock or bond, over a given period of time. By studying trends in data, investors can make informed decisions about whether to buy, sell, or hold a particular security. There are several different methods that can be used to analyze trends, including technical analysis, which uses charts and other graphical tools to identify patterns in price and volume data, and fundamental analysis, which focuses on a company’s financial health and industry conditions to make investment decisions. Trend analysis can thus incorporate a variety of data sources, including price charts, financial statements, economic indicators, and market data.

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DUAL Commodity Channel Index (DCCI)

Written by admin. Posted in Technical Analysis

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What Is the DUAL Commodity Channel Index (DCCI)?

The dual commodity channel index (DCCI) is a tool used in technical analysis to identify when an asset or market is overbought or oversold. A dual commodity channel index is a variation on the popular commodity channel index, which is an indicator invented in 1980 by Donald Lambert to measure the variation in a commodity’s value from the statistical mean.

Key Takeaways

  • The dual commodity channel index is a technical analysis tool to identify when an asset is overbought or oversold.
  • It is based on the popular commodity channel index.
  • The dual commodity channel index is an oscillator, which means it oscillates between two extreme values.
  • Reaching maximum value indicates an asset is overbought. Reaching minimum value indicates an asset is oversold.

Understanding the DUAL Commodity Channel Index (DCCI)

A dual commodity channel index is constructed by graphing a smoothed commodity channel index line along with an unsmoothed commodity channel index line measuring the same commodity, currency, or financial security. Crossovers of the two lines indicate possible buy and sell signals, while subsequent breaks in the price trendline indicate definite entry and exit points.

The dual commodity channel index is a technical analysis tool known as an oscillator, which is an index based on the value of a financial asset and constructed to oscillate between two extreme values. As the index reaches the maximum value, it indicates the asset is overbought and due for a price decline. As the index reaches the minimum value, it indicates the asset is oversold and due for a price increase.

The commodity channel index is calculated by taking the difference between a financial asset’s current price and its simple moving average and then dividing that by the mean absolute deviation of the price. A dual commodity channel index plots two variations of CCI lines, giving traders an even more granular understanding of a financial asset’s momentum. 

DUAL Commodity Channel Index and Technical Analysis

The dual commodity channel index is a favorite tool for investors who use technical analysis to make trades. Technical analysis involves the use of historical price data to predict future movements, and it differs from fundamental analysis, which examines information like a company’s earnings, the state of the economy, political events, and other information outside a security’s price to identify undervalued or overvalued assets. 

Technical analysis operates under the assumption that the vast majority of available information about a stock, bond, commodity, or currency is almost instantaneously incorporated in the price by market forces, and thus isn’t profitable to make investment decisions based on this information. For technical traders, the key to investing success is translating the mass psychology of the market into indicators that enable them to time their entry or exit from a stock or security.

Investopedia does not provide tax, investment, or financial services and advice. The information is presented without consideration of the investment objectives, risk tolerance, or financial circumstances of any specific investor and might not be suitable for all investors. Investing involves risk, including the possible loss of principal.

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Strategies for Trading Fibonacci Retracements

Written by admin. Posted in Technical Analysis

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Leonardo Pisano, nicknamed Fibonacci, was an Italian mathematician born in Pisa in the year 1170. His father Guglielmo Bonaccio worked at a trading post in Bugia, now called Béjaïa, a Mediterranean port in northeastern Algeria. As a young man, Fibonacci studied mathematics in Bugia, and during his extensive travels, he learned about the advantages of the Hindu-Arabic numeral system.

Key Takeaways

  • In the Fibonacci sequence of numbers, after 0 and 1, each number is the sum of the two prior numbers.
  • In the context of trading, the numbers used in Fibonacci retracements are not numbers in Fibonacci’s sequence; instead, they are derived from mathematical relationships between numbers in the sequence.
  • Fibonacci retracement levels are depicted by taking high and low points on a chart and marking the key Fibonacci ratios horizontally to produce a grid; these horizontal lines are used to identify possible price reversal points.

The Golden Ratio

In 1202, after returning to Italy, Fibonacci documented what he had learned in the “Liber Abaci (“Book of Abacus). In the “Liber Abaci,” Fibonacci described the numerical series that is now named after him. In the Fibonacci sequence of numbers, after 0 and 1, each number is the sum of the two prior numbers. Hence, the sequence is as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610 and so on, extending to infinity. Each number is approximately 1.618 times greater than the preceding number.

This value:1.618 is called Phi or the “Golden Ratio“. The Golden Ratio mysteriously appears frequently in the natural world, architecture, fine art, and biology. For example, the ratio has been observed in the Parthenon, in Leonardo da Vinci’s painting the Mona Lisa, sunflowers, rose petals, mollusk shells, tree branches, human faces, ancient Greek vases, and even the spiral galaxies of outer space.

0.618

The inverse of the golden ratio (1.618) is 0.618, which is also used extensively in Fibonacci trading.

Fibonacci Levels Used in the Financial Markets

In the context of trading, the numbers used in Fibonacci retracements are not numbers in Fibonacci’s sequence; instead, they are derived from mathematical relationships between numbers in the sequence. The basis of the “golden” Fibonacci ratio of 61.8% comes from dividing a number in the Fibonacci series by the number that follows it.

For example, 89/144 = 0.6180. The 38.2% ratio is derived from dividing a number in the Fibonacci series by the number two places to the right. For example: 89/233 = 0.3819. The 23.6% ratio is derived from dividing a number in the Fibonacci series by the number three places to the right. For example: 89/377 = 0.2360. 

Fibonacci retracement levels are depicted by taking high and low points on a chart and marking the key Fibonacci ratios of 23.6%, 38.2%, and 61.8% horizontally to produce a grid. These horizontal lines are used to identify possible price reversal points.

The 50% retracement level is normally included in the grid of Fibonacci levels that can be drawn using charting software. While the 50% retracement level is not based on a Fibonacci number, it is widely viewed as an important potential reversal level, notably recognized in Dow Theory and also in the work of W.D. Gann.

Fibonacci Retracement Levels as Trading Strategy

Fibonacci retracements are often used as part of a trend-trading strategy. In this scenario, traders observe a retracement taking place within a trend and try to make low-risk entries in the direction of the initial trend using Fibonacci levels. Traders using this strategy anticipate that a price has a high probability of bouncing from the Fibonacci levels back in the direction of the initial trend.

For example, on the EUR/USD daily chart below, we can see that a major downtrend began in May 2014 (point A). The price then bottomed in June (point B) and retraced upward to approximately the 38.2% Fibonacci retracement level of the down move (point C). 

Image by Sabrina Jiang © Investopedia 2021


In this case, the 38.2% level would have been an excellent place to enter a short position in order to capitalize on the continuation of the downtrend that started in May. There is no doubt that many traders were also watching the 50% retracement level and the 61.8% retracement level, but in this case, the market was not bullish enough to reach those points. Instead, EUR/USD turned lower, resuming the downtrend movement and taking out the prior low in a fairly fluid movement.

The likelihood of a reversal increases if there is a confluence of technical signals when the price reaches a Fibonacci level. Other popular technical indicators that are used in conjunction with Fibonacci levels include candlestick patterns, trendlines, volume, momentum oscillators, and moving averages. A greater number of confirming indicators in play equates to a more robust reversal signal.

Fibonacci retracements are used on a variety of financial instruments, including stocks, commodities, and foreign currency exchanges. They are also used on multiple timeframes. However, as with other technical indicators, the predictive value is proportional to the time frame used, with greater weight given to longer timeframes. For example, a 38.2% retracement on a weekly chart is a far more important technical level than a 38.2% retracement on a five-minute chart.

Using Fibonacci Extensions

While Fibonacci retracement levels can be used to forecast potential areas of support or resistance where traders can enter the market in hopes of catching the resumption of an initial trend, Fibonacci extensions can complement this strategy by giving traders Fibonacci-based profit targets. Fibonacci extensions consist of levels drawn beyond the standard 100% level and can be used by traders to project areas that make good potential exits for their trades in the direction of the trend. The major Fibonacci extension levels are 161.8%, 261.8% and 423.6%.

Let’s take a look at an example here, using the same EUR/USD daily chart:

Image by Sabrina Jiang © Investopedia 2021


Looking at the Fibonacci extension level drawn on the EUR/USD chart above, we can see that a potential price target for a trader holding a short position from the 38% retracement described earlier lies below at the 161.8% level, at 1.3195.

The Bottom Line

Fibonacci retracement levels often indicate reversal points with uncanny accuracy. However, they are harder to trade than they look in retrospect. These levels are best used as a tool within a broader strategy. Ideally, this strategy is one that looks for the confluence of several indicators to identify potential reversal areas offering low-risk, high-potential-reward trade entries.

Fibonacci trading tools, however, tend to suffer from the same problems as other universal trading strategies, such as the Elliott Wave theory. That said, many traders find success using Fibonacci ratios and retracements to place transactions within long-term price trends.

Fibonacci retracement can become even more powerful when used in conjunction with other indicators or technical signals. Investopedia Academy’s Technical Analysis course covers these indicators as well as how to transform patterns into actionable trading plans.

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