What is the Ease of Movement Indicator?
The Ease of Movement indicator shows the relationship between price and volume , and it's often used to assess the strength of an underlying trend. Ease of Movement calculates how easily a price can move up or down, based on momentum. It is designed to measure the relationship between price and volume and display that relationship as an oscillator that fluctuates between positive and negative values. The EOM fluctuates above and below a Zero Line. This is done in order to quantify the "ease" of price movements. A basic understanding is that when the EOM is in positive territory, prices are advancing with relative ease. When the EOM is negative, prices are declining with relative ease.
This version is different from the standard version of the EOM indicator in that this indicator adds various filtering methods to weed out poor quality long/short signals. A link to the backtest for this indicator is linked below as well. This indictor should be paired with the backtest linked below to optimize your trading strategy. Alerts and bar coloring are included.
This indicator uses Loxx's Expanded Source Types for price inputs, you can read about these source types here:
![Better Heiken-Ashi Candles w/ Expanded Source Types [Loxx]](https://static.tradingview.com/static/images/svg/idea-image-placeholder.svg)
This indicator uses of the Moving Averages found in the Baseline Backtest indicator:
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
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.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Signals
Static Middle Cross
Initial Long (L): Hard flip downtrend to uptrend; EOM crosses up Static Middle line
Initial Short (S): Hard flip uptrend to downtrend flip; EOM crosses down the Static Middle line
Continuation Long ( CL ): EOM already above Static Middle, EOM trigger crosses up EOM signal
Continuation Short (CS): EOM already below Static Middle, EOM trigger crosses down EOM signal
Post Baseline Cross Long ( BL ): EOM crossed up over Static Middle XX bars ago but Baseline didn't agree (that is, is still showing downtrend), if Baseline then catches up and agrees with direction within XX bars since the EOM crossup, then this signal is triggered
Post Baseline Cross Short (BS): EOM crossed down under Static Middle XX bars ago but Baseline didn't agree (that is, is still showing downtrend), if Baseline then catches up and agrees with direction within XX bars since the EOM crossup, then this signal is triggered
BL Recross Continuation Long ( RL ): EOM above Static Middle. Baseline crossed down into downtrend, then baseline crosses back up to uptrend while EOM is still above Static Middle then this signal is triggered
BL Recross Continuation Short ( RS ): EOM below Static Middle. Baseline crossed up into uptrend, then baseline crosses back down to downtrend while EOM is still below Static Middle then this signal is triggered
Filters
This strategy includes 5 different types of volatility filters and a moving average filter. Each filter has its own settings.
Volatility Goldie Locks Zone
If price crosses the baseline, we check to see how far it has moved in terms of multiples of volatility denoted in price ( volatility in price x multiple). If price has moved by at least "Qualifier multiplier" and less than "Range Multiplier", then the strategy enters a trade.
Adaptive Jurik Volatility (advanced)
This is an advanced version of Juirk Volatility that lies outside of JFCBeaux and Jurik Volty. When volatility is above a specific adaptive threshold then the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Adaptive Volatility Ratio (advanced)
When volatility is above a specific adaptive threshold then the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Semi-Variance (advanced)
When the difference between upward and downward volatility meats a certain threshold, the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Baseline Filter
This adds another layer of filtering (See Post Baseline Cross signals above). This is a simple over/under qualification filter. If price is above the baseline, then that means it qualifies for a long, if price is below the baseline, then this qualifies for a short. This filter must be active for Post Baseline Cross signals to trigger.
Additional moving averages, volatility types, qualifiers, and other advanced features will be added in future releases.
Ease of Movement (advanced) Backtest
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
The Ease of Movement indicator shows the relationship between price and volume , and it's often used to assess the strength of an underlying trend. Ease of Movement calculates how easily a price can move up or down, based on momentum. It is designed to measure the relationship between price and volume and display that relationship as an oscillator that fluctuates between positive and negative values. The EOM fluctuates above and below a Zero Line. This is done in order to quantify the "ease" of price movements. A basic understanding is that when the EOM is in positive territory, prices are advancing with relative ease. When the EOM is negative, prices are declining with relative ease.
This version is different from the standard version of the EOM indicator in that this indicator adds various filtering methods to weed out poor quality long/short signals. A link to the backtest for this indicator is linked below as well. This indictor should be paired with the backtest linked below to optimize your trading strategy. Alerts and bar coloring are included.
This indicator uses Loxx's Expanded Source Types for price inputs, you can read about these source types here:
![Better Heiken-Ashi Candles w/ Expanded Source Types [Loxx]](https://s3.tradingview.com/a/AdBj6Sva_big.png)
This indicator uses of the Moving Averages found in the Baseline Backtest indicator:
![Baseline Backtest [Loxx]](https://s3.tradingview.com/0/0Jbdwapf_big.png)
Volatility Types Included
v1.0 Included Volatility
Close-to-Close
Close-to-Close volatility is a classic and most commonly used volatility measure, sometimes referred to as historical volatility .
Volatility is an indicator of the speed of a stock price change. A stock with high volatility is one where the price changes rapidly and with a bigger amplitude. The more volatile a stock is, the riskier it is.
Close-to-close historical volatility calculated using only stock's closing prices. It is the simplest volatility estimator. But in many cases, it is not precise enough. Stock prices could jump considerably during a trading session, and return to the open value at the end. That means that a big amount of price information is not taken into account by close-to-close volatility .
Despite its drawbacks, Close-to-Close volatility is still useful in cases where the instrument doesn't have intraday prices. For example, mutual funds calculate their net asset values daily or weekly, and thus their prices are not suitable for more sophisticated volatility estimators.
Parkinson
Parkinson volatility is a volatility measure that uses the stock’s high and low price of the day.
The main difference between regular volatility and Parkinson volatility is that the latter uses high and low prices for a day, rather than only the closing price. That is useful as close to close prices could show little difference while large price movements could have happened during the day. Thus Parkinson's volatility is considered to be more precise and requires less data for calculation than the close-close volatility .
One drawback of this estimator is that it doesn't take into account price movements after market close. Hence it systematically undervalues volatility . That drawback is taken into account in the Garman-Klass's volatility estimator.
Garman-Klass
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Rogers-Satchell
Rogers-Satchell is an estimator for measuring the volatility of securities with an average return not equal to zero.
Unlike Parkinson and Garman-Klass estimators, Rogers-Satchell incorporates drift term (mean return not equal to zero). As a result, it provides a better volatility estimation when the underlying is trending.
The main disadvantage of this method is that it does not take into account price movements between trading sessions. It means an underestimation of volatility since price jumps periodically occur in the market precisely at the moments between sessions.
A more comprehensive estimator that also considers the gaps between sessions was developed based on the Rogers-Satchel formula in the 2000s by Yang-Zhang. See Yang Zhang Volatility for more detail.
Yang-Zhang
Yang Zhang is a historical volatility estimator that handles both opening jumps and the drift and has a minimum estimation error.
We can think of the Yang-Zhang volatility as the combination of the overnight (close-to-open volatility ) and a weighted average of the Rogers-Satchell volatility and the day’s open-to-close volatility . It considered being 14 times more efficient than the close-to-close estimator.
Garman-Klass-Yang-Zhang
Garman Klass is a volatility estimator that incorporates open, low, high, and close prices of a security.
Garman-Klass volatility extends Parkinson's volatility by taking into account the opening and closing price. As markets are most active during the opening and closing of a trading session, it makes volatility estimation more accurate.
Garman and Klass also assumed that the process of price change is a process of continuous diffusion (geometric Brownian motion). However, this assumption has several drawbacks. The method is not robust for opening jumps in price and trend movements.
Despite its drawbacks, the Garman-Klass estimator is still more effective than the basic formula since it takes into account not only the price at the beginning and end of the time interval but also intraday price extremums.
Researchers Rogers and Satchel have proposed a more efficient method for assessing historical volatility that takes into account price trends. See Rogers-Satchell Volatility for more detail.
Exponential Weighted Moving Average
The Exponentially Weighted Moving Average (EWMA) is a quantitative or statistical measure used to model or describe a time series. The EWMA is widely used in finance, the main applications being technical analysis and volatility modeling.
The moving average is designed as such that older observations are given lower weights. The weights fall exponentially as the data point gets older – hence the name exponentially weighted.
The only decision a user of the EWMA must make is the parameter lambda. The parameter decides how important the current observation is in the calculation of the EWMA. The higher the value of lambda, the more closely the EWMA tracks the original time series.
Standard Deviation of Log Returns
This is the simplest calculation of volatility . It's the standard deviation of ln(close/close(1))
Pseudo GARCH(2,2)
This is calculated using a short- and long-run mean of variance multiplied by θ.
θavg(var ;M) + (1 − θ) avg (var ;N) = 2θvar/(M+1-(M-1)L) + 2(1-θ)var/(M+1-(M-1)L)
Solving for θ can be done by minimizing the mean squared error of estimation; that is, regressing L^-1var - avg (var; N) against avg (var; M) - avg (var; N) and using the resulting beta estimate as θ.
Average True Range
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.
True Range Double
A special case of ATR that attempts to correct for volatility skew.
Signals
Static Middle Cross
Initial Long (L): Hard flip downtrend to uptrend; EOM crosses up Static Middle line
Initial Short (S): Hard flip uptrend to downtrend flip; EOM crosses down the Static Middle line
Continuation Long ( CL ): EOM already above Static Middle, EOM trigger crosses up EOM signal
Continuation Short (CS): EOM already below Static Middle, EOM trigger crosses down EOM signal
Post Baseline Cross Long ( BL ): EOM crossed up over Static Middle XX bars ago but Baseline didn't agree (that is, is still showing downtrend), if Baseline then catches up and agrees with direction within XX bars since the EOM crossup, then this signal is triggered
Post Baseline Cross Short (BS): EOM crossed down under Static Middle XX bars ago but Baseline didn't agree (that is, is still showing downtrend), if Baseline then catches up and agrees with direction within XX bars since the EOM crossup, then this signal is triggered
BL Recross Continuation Long ( RL ): EOM above Static Middle. Baseline crossed down into downtrend, then baseline crosses back up to uptrend while EOM is still above Static Middle then this signal is triggered
BL Recross Continuation Short ( RS ): EOM below Static Middle. Baseline crossed up into uptrend, then baseline crosses back down to downtrend while EOM is still below Static Middle then this signal is triggered
Filters
This strategy includes 5 different types of volatility filters and a moving average filter. Each filter has its own settings.
Volatility Goldie Locks Zone
If price crosses the baseline, we check to see how far it has moved in terms of multiples of volatility denoted in price ( volatility in price x multiple). If price has moved by at least "Qualifier multiplier" and less than "Range Multiplier", then the strategy enters a trade.
Adaptive Jurik Volatility (advanced)
This is an advanced version of Juirk Volatility that lies outside of JFCBeaux and Jurik Volty. When volatility is above a specific adaptive threshold then the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Adaptive Volatility Ratio (advanced)
When volatility is above a specific adaptive threshold then the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Semi-Variance (advanced)
When the difference between upward and downward volatility meats a certain threshold, the strategy will allow for longs/shorts assuming a long/short signal pings from the EOM . This filter also includes the ability to restrict to bars rising meaning that volatility has to be on an upward swing to allow for EOM longs/shorts
Baseline Filter
This adds another layer of filtering (See Post Baseline Cross signals above). This is a simple over/under qualification filter. If price is above the baseline, then that means it qualifies for a long, if price is below the baseline, then this qualifies for a short. This filter must be active for Post Baseline Cross signals to trigger.
Additional moving averages, volatility types, qualifiers, and other advanced features will be added in future releases.
Ease of Movement (advanced) Backtest
![Ease of Movement (advanced) Backtest [Loxx]](https://s3.tradingview.com/8/8mtUy4Xw_big.png)
This indicator is only available to ALGX Trading VIP group members . You can see the Author's Instructions below to get more information on how to get access.
Phát hành các Ghi chú:
Small update to continuation signals
Phát hành các Ghi chú:
Updated AMA
Public Telegram Group, t.me/algxtrading_public
VIP Membership Info: www.patreon.com/algxtrading/membership
VIP Membership Info: www.patreon.com/algxtrading/membership