Posts Tagged ‘Technical’

Average True Range (ATR) Formula, What It Means, and How to Use It

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What Is the Average True Range (ATR)?

The average true range (ATR) is a technical analysis indicator introduced by market technician J. Welles Wilder Jr. in his book New Concepts in Technical Trading Systems that measures market volatility by decomposing the entire range of an asset price for that period.

The true range indicator is taken as the greatest of the following: current high less the current low; the absolute value of the current high less the previous close; and the absolute value of the current low less the previous close. The ATR is then a moving average, generally using 14 days, of the true ranges.

Traders can use shorter periods than 14 days to generate more trading signals, while longer periods have a higher probability to generate fewer trading signals.

Key Takeaways

  • The average true range (ATR) is a market volatility indicator used in technical analysis.
  • It is typically derived from the 14-day simple moving average of a series of true range indicators.
  • The ATR was initially developed for use in commodities markets but has since been applied to all types of securities.
  • ATR shows investors the average range prices swing for an investment over a specified period.

The Average True Range (ATR) Formula

The formula to calculate ATR for an investment with a previous ATR calculation is :


Previous ATR ( n − 1 ) + TR n where: n = Number of periods TR = True range \begin{aligned}&\frac{ \text{Previous ATR} ( n – 1 ) + \text{TR} }{ n } \\&\textbf{where:} \\&n = \text{Number of periods} \\&\text{TR} = \text{True range} \\\end{aligned} ​nPrevious ATR(n−1)+TR​where:n=Number of periodsTR=True range​

If there is not a previous ATR calculated, you must use:


( 1 n ) ∑ i n TR i where: TR i = Particular true range, such as first day’s TR, then second, then third n = Number of periods \begin{aligned}&\Big ( \frac{ 1 }{ n } \Big ) \sum_{i}^{n} \text{TR}_i \\&\textbf{where:} \\&\text{TR}_i = \text{Particular true range, such as first day’s TR,} \\&\text{then second, then third} \\&n = \text{Number of periods} \\\end{aligned} ​(n1​)i∑n​TRi​where:TRi​=Particular true range, such as first day’s TR,then second, then thirdn=Number of periods​

The capital sigma symbol (Σ) represents the summation of all of the terms for n periods starting at i, or the period specified. If there is no number following i, it is assumed the starting point is the first period (you may see i=1, noting to start summing at the first term).

You must first use the following formula to calculate the true range:


 TR  =  Max  [ ( H − L ) , ∣ H − C p ∣ , ∣ L − C p ∣ ] where: H = Today’s high L = Today’s low C p = Yesterday’s closing price Max = Highest value of the three terms so   that: ( H − L ) = Today’s high minus the low ∣ H − C p ∣ = Absolute value of today’s high minus yesterday’s closing price ∣ L − C p ∣ = Absolute value of today’s low minus yesterday’s closing price \begin{aligned}&\text{ TR } = \text{ Max } [ ( \text{H} – \text{L} ), | \text{H} – \text{C}_p |, | \text{L} – \text{C}_p | ] \\&\textbf{where:} \\&\text{H} = \text{Today’s high} \\&\text{L} = \text{Today’s low} \\&\text{C}_p = \text{Yesterday’s closing price} \\&\text{Max} = \text{Highest value of the three terms} \\&\textbf{so that:} \\&( \text{H} – \text{L} ) = \text{Today’s high minus the low} \\&| \text{H} – \text{C}_p | = \text{Absolute value of today’s high minus} \\&\text{yesterday’s closing price} \\&| \text{L} – \text{C}_p | = \text{Absolute value of today’s low minus} \\&\text{yesterday’s closing price} \\\end{aligned} ​ TR = Max [(H−L),∣H−Cp​∣,∣L−Cp​∣]where:H=Today’s highL=Today’s lowCp​=Yesterday’s closing priceMax=Highest value of the three termsso that:(H−L)=Today’s high minus the low∣H−Cp​∣=Absolute value of today’s high minusyesterday’s closing price∣L−Cp​∣=Absolute value of today’s low minusyesterday’s closing price​

How to Calculate the ATR

The first step in calculating ATR is to find a series of true range values for a security. The price range of an asset for a given trading day is its high minus its low. To find an asset’s true range value, you first determine the three terms from the formula.

Suppose that XYZ’s stock had a trading high today of $21.95 and a low of $20.22. It closed yesterday at $21.51. Using the three terms, we use the highest result:


( H − L ) = $ 21.95 − $ 20.22 = $ 1.73 ( \text{H} – \text{L}) = \$21.95 – \$20.22 = \$1.73 (H−L)=$21.95−$20.22=$1.73


∣ ( H − C p ) ∣ = ∣ $ 21.95 − $ 21.51 ∣ = $ 0.44 | ( \text{H} – \text{C}_p ) | = | \$21.95 – \$21.51 | = \$0.44 ∣(H−Cp​)∣=∣$21.95−$21.51∣=$0.44


∣ ( L − C p ) ∣ = ∣ $ 20.22 − $ 21.51 ∣ = $ 1.29 | ( \text{L} – \text{C}_p ) | = | \$20.22 – \$21.51 | = \$1.29 ∣(L−Cp​)∣=∣$20.22−$21.51∣=$1.29

The number you’d use would be $1.73 because it is the highest value.

Because you don’t have a previous ATR, you need to use the ATR formula:


( 1 n ) ∑ i n TR i \begin{aligned}\Big ( \frac{ 1 }{ n } \Big ) \sum_{i}^{n} \text{TR}_i\end{aligned} (n1​)i∑n​TRi​​

Using 14 days as the number of periods, you’d calculate the TR for each of the 14 days. Assume the following prices from the table.

Daily Values
   High Low  Yesterday’s Close
Day 1 $ 21.95 $ 20.22 $ 21.51
Day 2 $ 22.25 $ 21.10 $ 21.61
Day 3 $ 21.50 $ 20.34 $ 20.83
Day 4 $ 23.25 $ 22.13 $ 22.65
Day 5 $ 23.03 $ 21.87 $ 22.41
Day 6 $ 23.34 $ 22.18 $ 22.67
Day 7 $ 23.66 $ 22.57 $ 23.05
Day 8 $ 23.97 $ 22.80 $ 23.31
Day 9 $ 24.29 $ 23.15 $ 23.68
Day 10 $ 24.60 $ 23.45 $ 23.97
Day 11 $ 24.92 $ 23.76 $ 24.31
Day 12 $ 25.23 $ 24.09 $ 24.60
Day 13 $ 25.55 $ 24.39 $ 24.89
Day 14 $ 25.86 $ 24.69 $ 25.20

You’d use these prices to calculate the TR for each day.

Trading Range
H-L H-Cp L-Cp
Day 1 $ 1.73 $ 0.44 $ (1.29)
Day 2 $ 1.15 $ 0.64 $ (0.51)
Day 3 $ 1.16 $ 0.67 $ (0.49)
Day 4 $ 1.12 $ 0.60 $ (0.52)
Day 5 $ 1.15 $ 0.61 $ (0.54)
Day 6 $ 1.16 $ 0.67 $ (0.49)
Day 7 $ 1.09 $ 0.61 $ (0.48)
Day 8 $ 1.17 $ 0.66 $ (0.51)
Day 9 $ 1.14 $ 0.61 $ (0.53)
Day 10 $ 1.15 $ 0.63 $ (0.52)
Day 11 $ 1.16 $ 0.61 $ (0.55)
Day 12 $ 1.14 $ 0.63 $ (0.51)
Day 13 $ 1.16 $ 0.66 $ (0.50)
Day 14 $ 1.17 $ 0.66 $ (0.51)

You find that the highest values for each day are from the (H – L) column, so you’d add up all of the results from the (H – L) column and multiply the result by 1/n, per the formula.


$ 1.73 + $ 1.15 + $ 1.16 + $ 1.12 + $ 1.15 + $ 1.16 + $ 1.09 + $ 1.17 + $ 1.14 + $ 1.15 + $ 1.16 + $ 1.14 + $ 1.16 + $ 1.17 = $ 16.65 \begin{aligned}\$1.73 &+ \$1.15 + \$1.16 + \$1.12 + \$1.15 + \$1.16 + \$1.09 \\&+ \$1.17 + \$1.14 + \$1.15 + \$1.16 + \$1.14 + \$1.16 \\&+ \$1.17 = \$16.65 \\\end{aligned} $1.73​+$1.15+$1.16+$1.12+$1.15+$1.16+$1.09+$1.17+$1.14+$1.15+$1.16+$1.14+$1.16+$1.17=$16.65​


1 n ( $ 16.65 ) = 1 14 ( $ 16.65 ) \begin{aligned}\frac{ 1 }{ n } (\$16.65) = \frac{ 1 }{ 14 } (\$16.65)\end{aligned} n1​($16.65)=141​($16.65)​


0.714 × $ 16.65 = $ 1.18 \begin{aligned}0.714 \times \$16.65 = \$1.18\end{aligned} 0.714×$16.65=$1.18​

So, the average volatility for this asset is $1.18.

Now that you have the ATR for the previous period, you can use it to determine the ATR for the current period using the following:


Previous ATR ( n − 1 ) + TR n \begin{aligned}\frac{ \text{Previous ATR} ( n – 1 ) + \text{TR} }{ n }\end{aligned} nPrevious ATR(n−1)+TR​​

This formula is much simpler because you only need to calculate the TR for one day. Assuming on Day 15, the asset has a high of $25.55, a low of $24.37, and closed the previous day at $24.87; its TR works out to $1.18:


$ 1.18 ( 14 − 1 ) + $ 1.18 14 \begin{aligned}\frac{ \$1.18 ( 14 – 1 ) + \$1.18 }{ 14 }\end{aligned} 14$1.18(14−1)+$1.18​​


$ 1.18 ( 13 ) + $ 1.18 14 \begin{aligned}\frac{ \$1.18 ( 13 ) + \$1.18 }{ 14 }\end{aligned} 14$1.18(13)+$1.18​​


$ 15.34 + $ 1.18 14 \begin{aligned}\frac{ \$15.34 + \$1.18 }{ 14 }\end{aligned} 14$15.34+$1.18​​


$ 16.52 14 = $ 1.18 \begin{aligned}\frac{ \$16.52 }{ 14 } = \$1.18\end{aligned} 14$16.52​=$1.18​

The stock closed the day again with an average volatility (ATR) of $1.18.

Image by Sabrina Jiang © Investopedia 2020


What Does the ATR Tell You?

Wilder originally developed the ATR for commodities, although the indicator can also be used for stocks and indices. Simply put, a stock experiencing a high level of volatility has a higher ATR, and a lower ATR indicates lower volatility for the period evaluated.

The ATR may be used by market technicians to enter and exit trades and is a useful tool to add to a trading system. It was created to allow traders to more accurately measure the daily volatility of an asset by using simple calculations. The indicator does not indicate the price direction; instead, it is used primarily to measure volatility caused by gaps and limit up or down moves. The ATR is relatively simple to calculate, and only needs historical price data.

The ATR is commonly used as an exit method that can be applied no matter how the entry decision is made. One popular technique is known as the “chandelier exit” and was developed by Chuck LeBeau. The chandelier exit places a trailing stop under the highest high the stock has reached since you entered the trade. The distance between the highest high and the stop level is defined as some multiple multiplied by the ATR.

Image by Sabrina Jiang © Investopedia 2020


The ATR can also give a trader an indication of what size trade to use in the derivatives markets. It is possible to use the ATR approach to position sizing that accounts for an individual trader’s willingness to accept risk and the volatility of the underlying market.

Example of How to Use the ATR

As a hypothetical example, assume the first value of a five-day ATR is calculated at 1.41, and the sixth day has a true range of 1.09. The sequential ATR value could be estimated by multiplying the previous value of the ATR by the number of days less one and then adding the true range for the current period to the product.

Next, divide the sum by the selected timeframe. For example, the second value of the ATR is estimated to be 1.35, or (1.41 * (5 – 1) + (1.09)) / 5. The formula could then be repeated over the entire period.

While the ATR doesn’t tell us in which direction the breakout will occur, it can be added to the closing price, and the trader can buy whenever the next day’s price trades above that value. This idea is shown below. Trading signals occur relatively infrequently but usually indicate significant breakout points. The logic behind these signals is that whenever a price closes more than an ATR above the most recent close, a change in volatility has occurred.

Image by Sabrina Jiang © Investopedia 2020 


Limitations of the ATR

There are two main limitations to using the ATR indicator. The first is that ATR is a subjective measure, meaning that it is open to interpretation. No single ATR value will tell you with any certainty that a trend is about to reverse or not. Instead, ATR readings should always be compared against earlier readings to get a feel of a trend’s strength or weakness.

Second, ATR only measures volatility and not the direction of an asset’s price. This can sometimes result in mixed signals, particularly when markets are experiencing pivots or when trends are at turning points. For instance, a sudden increase in the ATR following a large move counter to the prevailing trend may lead some traders to think the ATR is confirming the old trend; however, this may not be the case.

How Do You Use ATR Indicator in Trading?

Average true range is used to evaluate an investment’s price volatility. It is used in conjunction with other indicators and tools to enter and exit trades or decide whether to purchase an asset.

How Do You Read ATR Values?

An average true range value is the average price range of an investment over a period. So if the ATR for an asset is $1.18, its price has an average range of movement of $1.18 per trading day.

What Is a Good Average True Range?

A good ATR depends on the asset. If it generally has an ATR of close to $1.18, it is performing in a way that can be interpreted as normal. If the same asset suddenly has an ATR of more than $1.18, it might indicate that further investigation is required. Likewise, if it has a much lower ATR, you should determine why it is happening before taking action.

The Bottom Line

The average true range is an indicator of the price volatility of an asset. It is best used to determine how much an investment’s price has been moving in the period being evaluated rather than an indication of a trend. Calculating an investment’s ATR is relatively straightforward, only requiring you to use price data for the period you’re investigating.

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Fibonacci and the Golden Ratio

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There is a unique ratio that can be used to describe the proportions of everything from nature’s smallest building blocks, such as atoms, to the most advanced patterns in the universe, like the unimaginably large celestial bodies. Nature relies on this innate proportion to maintain balance, but the financial markets also seem to conform to this “golden ratio.” 

The golden ratio is derived from the Fibonacci numbers, a series of numbers where each entry is the sum of the two preceding entries. Although this sequence is associated with Leonardo of Pisa, the Fibonacci numbers were actually formulated for the first time by the Indian mathematician, Virahanka, 600 years prior to their introduction to the Western world.

Here, we take a look at some technical analysis tools that have been developed to take advantage of the pattern.

Key Takeaways

  • The golden ratio is an irrational number that is equal to (1+√5)/2, or approximately 1.618…
  • The ratio is derived from an ancient Indian mathematical formula which Western society named for Leonardo Fibonacci, who introduced the concept to Europe.
  • Nature uses this ratio to maintain balance, and the financial markets seem to as well.
  • The Fibonacci sequence can be applied to finance by using four main techniques: retracements, arcs, fans, and time zones.
  • Fibonacci numbers have become famous in popular culture, although some experts say their importance is exaggerated.

History of the Mathematics

Mathematicians, scientists, and naturalists have known about the golden ratio for centuries. It’s derived from the Fibonacci sequence, named after the Pisan mathematician Leonardo Fibonacci, who lived from around 1175 A.D. until around 1250 A.D.

Although Fibonacci introduced these numbers to the Western world, they were actually discovered by Indian mathematicians hundreds of years earlier. The poet Pingala used them to count the syllables of Sanskrit poetry around 200 B.C., and the method for calculating them was formulated by the Indian mathematician Virahanka around 800 years later.

In this sequence, each number is simply the sum of the two preceding numbers (1, 1, 2, 3, 5, 8, 13, etc.).

Fibonacci borrowed heavily from Indian and Arabic sources. In his book Liber Abaci, he described the Hindu-Arabic numeral system represented by the numbers 0 through 9. He called this the “Modus Indorum,” or the method of the Indians.

But this sequence is not all that important. The essential part is that as the numbers get larger, the quotient between each successive pair of Fibonacci numbers approximates 1.618, or its inverse 0.618. This proportion is known by many names: the golden ratio, the golden mean, ϕ, and the divine proportion, among others.

So, why is this number so important? Well, many things in nature have dimensional properties that adhere to the ratio of 1.618, so it seems to have a fundamental function for the building blocks of nature.

The exact value of the golden ratio can be calculated by:

ϕ = (1+√5) / 2

Examples of the Golden Ratio

Don’t believe it? Take honeybees, for example. If you divide the female bees by the male bees in any given hive, you will get a number around 1.618. Sunflowers, which have opposing spirals of seeds, have a 1.618 ratio between the diameters of each rotation. This same ratio can be seen in relationships between different components throughout nature.

The golden ratio also appears in the arts, because it is more aesthetically pleasing than other proportions. The Parthenon in Athens, the Great Pyramid in Giza, and Da Vinci’s Mona Lisa all incorporate rectangles whose dimensions are based on the golden ratio. It seems to be unavoidable.

But does that mean it works in finance? Actually, financial markets have the very same mathematical base as these natural phenomena. Below we will examine some ways in which the golden ratio can be applied to finance, and we’ll show some charts as proof.

Trading and Investing With the Golden Ratio

The golden ratio is frequently used by traders and technical analysts, who use it to forecast market-driven price movements. This is because the Fibonacci numbers and the golden ratio have a strong psychological importance in herd behavior. Traders are more likely to take profits or cover losses at certain price points, which happen to be marked by the golden ratio.

Curiously, the widespread use of the golden ratio in trading analysis forms something of a self-fulfilling prophecy: the more traders rely on Fibonacci-based trading strategies, the more effective those strategies will tend to be.

Thanks to books like Dan Brown’s The Da Vinci Code, the golden ratio has been elevated to almost mystical levels in popular culture. However, some mathematicians have stated that the importance of this ratio is wildly exaggerated.

The Golden Ratio and Technical Analysis

When used in technical analysis, the golden ratio is typically translated into three percentages: 38.2%, 50%, and 61.8%. However, more multiples can be used when needed, such as 23.6%, 161.8%, 423%, and so on. Meanwhile, there are four ways that the Fibonacci sequence can be applied to charts: retracements, arcs, fans, and time zones. However, not all might be available, depending on the charting application being used.

1. Fibonacci Retracements

Fibonacci retracements use horizontal lines to indicate areas of support or resistance. Levels are calculated using the high and low points of the chart. Then five lines are drawn: the first at 100% (the high on the chart), the second at 61.8%, the third at 50%, the fourth at 38.2%, and the last one at 0% (the low on the chart). After a significant price movement up or down, the new support and resistance levels are often at or near these lines.

Image by Sabrina Jiang © Investopedia 2020

2. Fibonacci Arcs

Finding the high and low of a chart is the first step to composing Fibonacci arcs. Then, with a compass-like movement, three curved lines are drawn at 38.2%, 50%, and 61.8% from the desired point. These lines anticipate the support and resistance levels, as well as trading ranges.

Image by Sabrina Jiang © Investopedia 2020

3. Fibonacci Fans

Fibonacci fans are composed of diagonal lines. After the high and low of the chart is located, an invisible horizontal line is drawn through the rightmost point. This invisible line is then divided into 38.2%, 50%, and 61.8%, and lines are drawn from the leftmost point through each of these points. These lines indicate areas of support and resistance.

Image by Sabrina Jiang © Investopedia 2020

4. Fibonacci Time Zones

Unlike the other Fibonacci methods, time zones are a series of vertical lines. They are composed by dividing a chart into segments with vertical lines spaced apart in increments that conform to the Fibonacci sequence (1, 1, 2, 3, 5, 8, 13, etc.). Each line indicates a time in which major price movement can be expected.

Image by Sabrina Jiang © Investopedia 2020

What Is the Relationship Between the Fibonacci Series and the Golden Ratio?

The golden ratio is derived by dividing each number of the Fibonacci series by its immediate predecessor. In mathematical terms, if F(n) describes the nth Fibonacci number, the quotient F(n)/ F(n-1) will approach the limit 1.618… for increasingly high values of n. This limit is better known as the golden ratio.

Why Is the Fibonacci Sequence So Important?

The Fibonacci sequence is a recursive series of numbers where each value is determined by the two values immediately before it. For this reason, the Fibonacci numbers frequently appear in problems relating to population growth. When used in visual arts, they are also aesthetically pleasing, although their significance tends to be highly exaggerated in popular culture.

Why Is 1.618 So Important?

The number 1.61803… is better known as the golden ratio, and frequently appears in art, architecture, and natural sciences. It is derived from the Fibonacci series of numbers, where each entry is recursively defined by the entries preceding it. The golden ratio is also used in technical analysis because traders tend to behave in a predictable way near the psychologically-important Fibonacci lines.

The Bottom Line

Fibonacci studies are not intended to provide the primary indications for timing the entry and exit of a position; however, the numbers are useful for estimating areas of support and resistance. Many people use combinations of Fibonacci studies to obtain a more accurate forecast. For example, a trader may observe the intersecting points in a combination of the Fibonacci arcs and resistances.

Fibonacci studies are often used in conjunction with other forms of technical analysis. For example, Fibonacci studies, in combination with Elliott Waves, can be used to forecast the extent of the retracements after different waves. Hopefully, you can find your own niche use for the Fibonacci studies and add it to your set of investment tools.

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Definition, Uses, Example in Technical Analysis

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What Is a Horizontal Line?

In technical analysis, a horizontal line is often drawn on a price chart to highlight areas of support or resistance.

In geometric analysis, a horizontal line proceeds parallel to the x-axis. Put another way, on a perfectly horizontal line, all values on the line will have the same y-value.

Key Takeaways

  • A horizontal line is commonly used in technical analysis to mark areas of support or resistance.
  • A horizontal line runs parallel to the x-axis.
  • In technical analysis, the horizontal line is typically drawn along a swing high, or a series of them, where each high in the series stopped at a similar level. The same concept applies to swing lows.

Understanding a Horizontal Line

Horizontal lines are commonly used in technical analysis to highlight areas of support, where the price stopped falling and then bounced on prior occasions, or resistance, which is where the price stopped rising and then proceeded to fall on prior occasions.

The horizontal line is drawn by connecting similar swing lows in price to create a horizontal support line. For a horizontal resistance line, similar swing highs are connected.

The horizontal line is then used for analytical or trading purposes. For example, if the price of an asset is moving between support and resistance horizontal lines then the price is considered to be range-bound.

A move below the support horizontal line could indicate a further price decline, but if support holds and the price bounces higher then prices could be forthcoming. The same concepts apply to a resistance horizontal line. If the price moves above resistance, higher prices could be forthcoming. If the price reaches resistance and then starts to decline, the horizontal line has held and traders will watch for lower prices.

In more simple terms, a horizontal line on any chart is where the y-axis values are equal. If it has been drawn to show a series of highs in the data, a data point moving above the horizontal line would indicate a rise in the y-axis value over recent values in the data sample.

Fundamental Horizontal Analysis

Horizontal analysis is used to compare values or prices over time. This is an aspect of fundamental analysis in which an analyst will compare various earnings reports and statements over time. In this kind of analysis, time functions as the horizontal x-axis and allows analysts to calculate percentage changes over time, a useful tool for representing the degree of change.

Horizontal analysis looks at the trend of financial statements over multiple periods, using a specified base period, and typically shows the changes from the base period in dollars and percentages.

The percentage change is calculated by first dividing the dollar change between the comparison year and the base year by the item value in the base year, then multiplying the quotient by 100. For example, when you hear someone saying that revenues increased by 10% this past quarter, that person is using horizontal analysis.

Horizontal analysis can be used on any item in a company’s financials, from revenues to earnings per share (EPS), and is useful when comparing the performance of various companies.

A Horizontal Line as it Relates to Supply and Demand Curves

Supply and demand curves are drawn with price on the vertical axis of the graph and quantity demanded on the horizontal axis. When looking at supply and demand curves, a perfectly horizontal line indicates that an item has perfect elasticity, or that its demand is immediately responsive to changes in price. When the price of a perfectly elastic good or service increases above the market price, the quantity demanded falls to zero. With perfect elasticity, consumers simply are not willing to spend more than a specific price for a good or service.

Example of How to Use the Horizontal Line in Technical Analysis

Drawing a horizontal line is one of the simplest forms of technical analysis, but it also provides important information. On the chart below, a horizontal line is drawn on the SPDR S&P 500 (SPY) exchange traded fund (ETF).

Image by Sabrina Jiang © Investopedia 2021


An uptrend is when a price makes higher swing highs and higher swing lows. Therefore, a horizontal line can highlight when price is making a new high, in this case, thus showing signs of an uptrend. On the SPY chart above, the price is moving above the horizontal line indicating an uptrend. If the price falls back below the horizontal line, it could warn that uptrend has failed and lower prices may be forthcoming.

In this sense, the horizontal line acts like a line in the sand, where moving above the line is bullish.

The Difference Between a Horizontal Line and a Trendline

Both these terms could refer to the same thing: drawn lines on a chart. While a horizontal line is specifically horizontal, a trendline is typically angled and drawn along rising swing lows during a price uptrend or drawn along dropping swing highs during a downtrend.

Limitations of Using a Horizontal Line in Technical Analysis

A horizontal line is not an actual barrier for price. It is a technical tool which may help traders determine whether they should be more bearish or bullish.

Where a horizontal line is drawn is subjective. Not all traders may place the horizontal line at the same price.

At highly important prices, where a horizontal line may be drawn, it is possible the price will whipsaw around it. This could cause confusion or some potential losing trades until the price makes a more decisive move above or below the line.

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Technical Analysis of Stocks and Trends Definition

Written by admin. Posted in Technical Analysis

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Investopedia / Jessica Olah


What Is Technical Analysis?

Technical analysis is the study of historical market data, including price and volume. Using insights from market psychology, behavioral economics, and quantitative analysis, technical analysts aim to use past performance to predict future market behavior. The two most common forms of technical analysis are chart patterns and technical (statistical) indicators.

Key Takeaways

  • Technical analysis attempts to predict future price movements, providing traders with the information needed to make a profit.
  • Traders apply technical analysis tools to charts in order to identify entry and exit points for potential trades.
  • An underlying assumption of technical analysis is that the market has processed all available information and that it is reflected in the price chart.

What Does Technical Analysis Tell You?

Technical analysis is a blanket term for a variety of strategies that depend on interpretation of price action in a stock. Most technical analysis is focused on determining whether or not a current trend will continue and, if not, when it will reverse. Some technical analysts swear by trendlines, others use candlestick formations, and yet others prefer bands and boxes created through a mathematical visualization. Most technical analysts use some combination of tools to recognize potential entry and exit points for trades. A chart formation may indicate an entry point for a short seller, for example, but the trader will look at moving averages for different time periods to confirm that a breakdown is likely.

A Brief History of Technical Analysis

The technical analysis of stocks and trends has been used for hundreds of years. In Europe, Joseph de la Vega adopted early technical analysis techniques to predict Dutch markets in the 17th century. In its modern form, however, technical analysis owes heavily to Charles Dow, William P. Hamilton, Robert Rhea, Edson Gould, and many others—including a ballroom dancer named Nicolas Darvas. These people represented a new perspective on the market as a tide that is best measured in highs and lows on a chart rather than by the particulars of the underlying company. The diverse collection of theories from early technical analysts were brought together and formalized in 1948 with the publishing of Technical Analysis of Stock Trends by Robert D. Edwards and John Magee.

Candlestick patterns date back to Japanese merchants eager to detect trading patterns for their rice harvests. Studying these ancient patterns became popular in the 1990s in the U.S. with the advent of internet day trading. Investors analyzed historical stock charts eager to discover new patterns for use when recommending trades. Candlestick reversal patterns in particular are critically important for investors to identify, and there are several other commonly used candlestick charting patterns. The doji and the engulfing pattern are all used to predict an imminent bearish reversal.

How to Use Technical Analysis

The core principle underlying technical analysis is that the market price reflects all available information that could impact a market. As a result, there’s no need to look at economic, fundamental, or new developments since they’re already priced into a given security. Technical analysts generally believe that prices move in trends and history tends to repeat itself when it comes to the market’s overall psychology. The two major types of technical analysis are chart patterns and technical (statistical) indicators.

Chart patterns are a subjective form of technical analysis where technicians attempt to identify areas of support and resistance on a chart by looking at specific patterns. These patterns, underpinned by psychological factors, are designed to predict where prices are headed, following a breakout or breakdown from a specific price point and time. For example, an ascending triangle chart pattern is a bullish chart pattern that shows a key area of resistance. A breakout from this resistance could lead to a significant, high-volume move higher.

Technical indicators are a statistical form of technical analysis where technicians apply various mathematical formulas to prices and volumes. The most common technical indicators are moving averages, which smooth price data to help make it easier to spot trends. More complex technical indicators include the moving average convergence divergence (MACD), which looks at the interplay between several moving averages. Many trading systems are based on technical indicators since they can be quantitatively calculated.

The Difference Between Technical Analysis and Fundamental Analysis

Fundamental analysis and technical analysis are the two big factions in finance. Whereas technical analysts believe the best approach is to follow the trend as it forms through market action, fundamental analysts believe the market often overlooks value. Fundamental analysts will ignore chart trends in favor of digging through the balance sheet and the market profile of a company in search of intrinsic value not currently reflected in the price. There are many examples of successful investors using fundamental or technical analysis to guide their trading and even those who incorporate elements of both. On the whole, however, technical analysis lends itself to a faster investing pace, whereas fundamental analysis generally has a longer decision timeline and holding period by virtue of the time required for the extra due diligence.

Limitations of Technical Analysis

Technical analysis has the same limitation of any strategy based on particular trade triggers. The chart can be misinterpreted. The formation may be predicated on low volume. The periods being used for the moving averages may be too long or too short for the type of trade you are looking to make. Leaving those aside, the technical analysis of stocks and trends has a fascinating limitation unique to itself.

As more technical analysis strategies, tools, and techniques become widely adopted, these have a material impact on the price action. For example, are those three black crows forming because the priced-in information is justifying a bearish reversal or because traders universally agree that they should be followed by a bearish reversal and bring that about by taking up short positions? Although this is an interesting question, a true technical analyst doesn’t actually care as long as the trading model continues to work.

Further Reading:

Investopedia has several articles and tutorials on the topic of technical analysis. Follow the links to articles in this journey on the menu bar to the left of this page. In addition, for further reading you may want to check out the following:

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