Prometheus Volatility StopThe Prometheus Volatility Stop is an indicator designed to give you a moving risk metric along with a custom Moving Average cross. After a calculation of the annualized volatility for the specified lookback period we determine bullish or bearish from the moving averages and plot the Volatility Stop accordingly.
User Input:
A user can select from Hull Moving Average, Exponential Moving average, Simple Moving Average, the Moving Average used in RSI, and Weighted Moving Average. The default is Hull Moving Average and Exponential Moving average.
A user can also specify the lookback period. The default is 30.
A user may also turn off the plots for the Moving Averages.
The reason for this approach is to be more original from the traditional Volatility Stop.
Calculation:
The Historical Volatility is calculated by taking the standard deviation of the log returns for the specified period and then annualizing it.
hv = ta.stdev(math.log(close / close ), lkb) * math.sqrt(252/5)
Then the Volatility Stop is calculated as follows:
recent_max = ta.highest(close, lkb)
recent_min = ta.lowest(close, lkb)
hv_stop = ma_2 > ma_1 ? recent_max + hv : recent_min - hv
When the second selected moving average is greater than the first, which signals bearishness, the historical volatility gets added to the high of that period. When the moving averages signal bullish the historical volatility gets subtracted from the low of that period.
Here is an example on NASDAQ:ARM :
After the first crossover, bullish signal, price runs for some time. As we get higher and higher so does the Volatility Stop. At the highs before a bearish crossover the price hits and closes at the Volatility Stop. Providing what could be an exit from a strong run up.
Intra-day example on NASDAQ:QQQ :
We see that in the early bearish move price goes on to hit the Volatility Stop before the trend switches.
We also see that in the failed long. The price action throughout the rest of the day, while not providing in profit stop outs, do provide fine directional alerts.
All those examples have been done with the default settings. Upon changing Moving Average One to a WMA and Moving Average Two to an SMA, as well as the lookback to 75. We see this quickly can become a simple trend follower.
This is the perspective we aim to provide. We encourage traders to not follow indicators blindly. No indicator is 100% accurate. This one can give you a different perspective of price strength with volatility. We encourage any comments about desired updates or criticism!
Wyszukaj w skryptach "Exponential Moving Average"
Bayesian Trend Indicator [ChartPrime]Bayesian Trend Indicator
Overview:
In probability theory and statistics, Bayes' theorem (alternatively Bayes' law or Bayes' rule), named after Thomas Bayes, describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
The "Bayesian Trend Indicator" is a sophisticated technical analysis tool designed to assess the direction of price trends in financial markets. It combines the principles of Bayesian probability theory with moving average analysis to provide traders with a comprehensive understanding of market sentiment and potential trend reversals.
At its core, the indicator utilizes multiple moving averages, including the Exponential Moving Average (EMA), Simple Moving Average (SMA), Double Exponential Moving Average (DEMA), and Volume Weighted Moving Average (VWMA) . These moving averages are calculated based on user-defined parameters such as length and gap length, allowing traders to customize the indicator to suit their trading strategies and preferences.
The indicator begins by calculating the trend for both fast and slow moving averages using a Smoothed Gradient Signal Function. This function assigns a numerical value to each data point based on its relationship with historical data, indicating the strength and direction of the trend.
// Smoothed Gradient Signal Function
sig(float src, gap)=>
ta.ema(source >= src ? 1 :
source >= src ? 0.9 :
source >= src ? 0.8 :
source >= src ? 0.7 :
source >= src ? 0.6 :
source >= src ? 0.5 :
source >= src ? 0.4 :
source >= src ? 0.3 :
source >= src ? 0.2 :
source >= src ? 0.1 :
0, 4)
Next, the indicator calculates prior probabilities using the trend information from the slow moving averages and likelihood probabilities using the trend information from the fast moving averages . These probabilities represent the likelihood of an uptrend or downtrend based on historical data.
// Define prior probabilities using moving averages
prior_up = (ema_trend + sma_trend + dema_trend + vwma_trend) / 4
prior_down = 1 - prior_up
// Define likelihoods using faster moving averages
likelihood_up = (ema_trend_fast + sma_trend_fast + dema_trend_fast + vwma_trend_fast) / 4
likelihood_down = 1 - likelihood_up
Using Bayes' theorem , the indicator then combines the prior and likelihood probabilities to calculate posterior probabilities, which reflect the updated probability of an uptrend or downtrend given the current market conditions. These posterior probabilities serve as a key signal for traders, informing them about the prevailing market sentiment and potential trend reversals.
// Calculate posterior probabilities using Bayes' theorem
posterior_up = prior_up * likelihood_up
/
(prior_up * likelihood_up + prior_down * likelihood_down)
Key Features:
◆ The trend direction:
To visually represent the trend direction , the indicator colors the bars on the chart based on the posterior probabilities. Bars are colored green to indicate an uptrend when the posterior probability is greater than 0.5 (>50%), while bars are colored red to indicate a downtrend when the posterior probability is less than 0.5 (<50%).
◆ Dashboard on the chart
Additionally, the indicator displays a dashboard on the chart , providing traders with detailed information about the probability of an uptrend , as well as the trends for each type of moving average. This dashboard serves as a valuable reference for traders to monitor trend strength and make informed trading decisions.
◆ Probability labels and signals:
Furthermore, the indicator includes probability labels and signals , which are displayed near the corresponding bars on the chart. These labels indicate the posterior probability of a trend, while small diamonds above or below bars indicate crossover or crossunder events when the posterior probability crosses the 0.5 threshold (50%).
The posterior probability of a trend
Crossover or Crossunder events
◆ User Inputs
Source:
Description: Defines the price source for the indicator's calculations. Users can select between different price values like close, open, high, low, etc.
MA's Length:
Description: Sets the length for the moving averages used in the trend calculations. A larger length will smooth out the moving averages, making the indicator less sensitive to short-term fluctuations.
Gap Length Between Fast and Slow MA's:
Description: Determines the difference in lengths between the slow and fast moving averages. A higher gap length will increase the difference, potentially identifying stronger trend signals.
Gap Signals:
Description: Defines the gap used for the smoothed gradient signal function. This parameter affects the sensitivity of the trend signals by setting the number of bars used in the signal calculations.
In summary, the "Bayesian Trend Indicator" is a powerful tool that leverages Bayesian probability theory and moving average analysis to help traders identify trend direction, assess market sentiment, and make informed trading decisions in various financial markets.
Adaptive MA-Bollinger HistogramVisualize two of your favorite moving averages in a fun new way.
This script calculates the distance (or difference) between the price and two moving averages of your choosing and then creates two histograms.
The two histograms are plotted inversely, so if the price is over both moving averages, one will be positive above the centerline while the other still positive will be below the centerline.
(In a future update you will have the option to have them both positive at the same time)
Next, what it does is apply Bollinger Bands (optional) to each of the histograms.
This creates a very interesting effect that can highlight areas of interest you may miss with other indicators.
You have plenty of options for coloring, the type of moving average, Bollinger Band length, and toggling features on and off.
Give it a few minutes of your time to study, and see what information you can learn from watching this indicator by comparing it with the chart.
Here is a full user guide:
Adaptive MA-Bollinger Histogram Indicator User Guide
Welcome to the user guide for the **Adaptive MA-Bollinger Histogram** indicator. This custom indicator is designed to help traders analyze trends and potential reversals in a financial instrument's price movements. The indicator combines two Moving Averages (MA) and Bollinger Bands to provide valuable insights into market conditions.
### Indicator Overview
The Adaptive MA-Bollinger Histogram indicator comprises the following components:
1. **Moving Averages (MA1 and MA2):** The indicator uses two moving averages, namely MA1 and MA2, to track different time periods. MA1 has a user-defined length (default: 50) and MA2 has a longer user-defined length (default: 100). These moving averages can be calculated using different methods such as Simple Moving Average (SMA), Exponential Moving Average (EMA), Weighted Moving Average (WMA), Volume Weighted Moving Average (VWMA), or Smoothed Moving Average (RMA).
2. **Histograms:** The indicator displays histograms based on the differences between the price source and the respective moving averages. Positive values of the histogram for MA1 are plotted in one color (default: green), while negative values are plotted in another color (default: red). Similarly, positive values of the histogram for MA2 are plotted in one color (default: blue), while negative values are plotted in another color (default: yellow). It's important to note that the histogram for MA1 is plotted positively, while the histogram for MA2 is plotted inversely.
3. **Bollinger Bands:** The indicator also features Bollinger Bands calculated based on the differences between the price source and the respective moving averages (dist1 and dist2). Bollinger Bands consist of three lines: the middle band, upper band, and lower band. These bands help visualize the potential volatility and overbought/oversold levels of the instrument's price.
### Understanding the Indicator
- **Histograms:** The histograms highlight the divergence between the price and the two moving averages. When the histogram for MA1 is positive, it indicates that the price is above the MA1. Conversely, when the histogram for MA1 is negative, it suggests that the price is below the MA1. Similarly, the histogram for MA2 is plotted inversely.
- **Bollinger Bands:** The Bollinger Bands consist of three lines. The middle band represents the moving average (MA1 or MA2), while the upper and lower bands are calculated based on the standard deviation of the differences between the price source and the moving average. The bands expand during periods of higher volatility and contract during periods of lower volatility.
### Possible Trading Ideas
1. **Trend Confirmation:** When the histograms for both MA1 and MA2 are consistently positive, it may indicate a strong bullish trend. Conversely, when both histograms are consistently negative, it may suggest a strong bearish trend.
2. **Divergence:** Divergence between price and the histograms could signal potential reversals. For example, if the price is making new highs while the histogram is declining, it might indicate a bearish divergence and a possible upcoming trend reversal.
3. **Bollinger Bands Squeeze:** A narrowing of the Bollinger Bands indicates lower volatility and often precedes a significant price movement. Traders might consider a potential breakout trade when the bands start to expand again.
4. **Overbought/Oversold Levels:** Prices touching or exceeding the upper Bollinger Band could suggest overbought conditions, while prices touching or falling below the lower Bollinger Band could indicate oversold conditions. Traders might look for reversals or corrections in such scenarios.
### Customization
- You can adjust the parameters such as MA lengths, Bollinger Bands length, width, and colors to suit your preferences and trading strategy.
### Conclusion
The **Adaptive MA-Bollinger Histogram** indicator provides a comprehensive view of price trends, divergences, and potential reversal points. Traders can use the information from this indicator to make informed decisions in their trading strategies. However, like any technical tool, it's recommended to combine this indicator with other forms of analysis and risk management techniques for optimal results.
MomentumIndicatorsLibrary "MomentumIndicators"
This is a library of 'Momentum Indicators', also denominated as oscillators.
The purpose of this library is to organize momentum indicators in just one place, making it easy to access.
In addition, it aims to allow customized versions, not being restricted to just the price value.
An example of this use case is the popular Stochastic RSI.
# Indicators:
1. Relative Strength Index (RSI):
Measures the relative strength of recent price gains to recent price losses of an asset.
2. Rate of Change (ROC):
Measures the percentage change in price of an asset over a specified time period.
3. Stochastic Oscillator (Stoch):
Compares the current price of an asset to its price range over a specified time period.
4. True Strength Index (TSI):
Measures the price change, calculating the ratio of the price change (positive or negative) in relation to the
absolute price change.
The values of both are smoothed twice to reduce noise, and the final result is normalized
in a range between 100 and -100.
5. Stochastic Momentum Index (SMI):
Combination of the True Strength Index with a signal line to help identify turning points in the market.
6. Williams Percent Range (Williams %R):
Compares the current price of an asset to its highest high and lowest low over a specified time period.
7. Commodity Channel Index (CCI):
Measures the relationship between an asset's current price and its moving average.
8. Ultimate Oscillator (UO):
Combines three different time periods to help identify possible reversal points.
9. Moving Average Convergence/Divergence (MACD):
Shows the difference between short-term and long-term exponential moving averages.
10. Fisher Transform (FT):
Normalize prices into a Gaussian normal distribution.
11. Inverse Fisher Transform (IFT):
Transform the values of the Fisher Transform into a smaller and more easily interpretable scale is through the
application of an inverse transformation to the hyperbolic tangent function.
This transformation takes the values of the FT, which range from -infinity to +infinity, to a scale limited
between -1 and +1, allowing them to be more easily visualized and compared.
12. Premier Stochastic Oscillator (PSO):
Normalizes the standard stochastic oscillator by applying a five-period double exponential smoothing average of
the %K value, resulting in a symmetric scale of 1 to -1
# Indicators of indicators:
## Stochastic:
1. Stochastic of RSI (Relative Strengh Index)
2. Stochastic of ROC (Rate of Change)
3. Stochastic of UO (Ultimate Oscillator)
4. Stochastic of TSI (True Strengh Index)
5. Stochastic of Williams R%
6. Stochastic of CCI (Commodity Channel Index).
7. Stochastic of MACD (Moving Average Convergence/Divergence)
8. Stochastic of FT (Fisher Transform)
9. Stochastic of Volume
10. Stochastic of MFI (Money Flow Index)
11. Stochastic of On OBV (Balance Volume)
12. Stochastic of PVI (Positive Volume Index)
13. Stochastic of NVI (Negative Volume Index)
14. Stochastic of PVT (Price-Volume Trend)
15. Stochastic of VO (Volume Oscillator)
16. Stochastic of VROC (Volume Rate of Change)
## Inverse Fisher Transform:
1.Inverse Fisher Transform on RSI (Relative Strengh Index)
2.Inverse Fisher Transform on ROC (Rate of Change)
3.Inverse Fisher Transform on UO (Ultimate Oscillator)
4.Inverse Fisher Transform on Stochastic
5.Inverse Fisher Transform on TSI (True Strength Index)
6.Inverse Fisher Transform on CCI (Commodity Channel Index)
7.Inverse Fisher Transform on Fisher Transform (FT)
8.Inverse Fisher Transform on MACD (Moving Average Convergence/Divergence)
9.Inverse Fisher Transfor on Williams R% (Williams Percent Range)
10.Inverse Fisher Transfor on CMF (Chaikin Money Flow)
11.Inverse Fisher Transform on VO (Volume Oscillator)
12.Inverse Fisher Transform on VROC (Volume Rate of Change)
## Stochastic Momentum Index:
1.Stochastic Momentum Index of RSI (Relative Strength Index)
2.Stochastic Momentum Index of ROC (Rate of Change)
3.Stochastic Momentum Index of VROC (Volume Rate of Change)
4.Stochastic Momentum Index of Williams R% (Williams Percent Range)
5.Stochastic Momentum Index of FT (Fisher Transform)
6.Stochastic Momentum Index of CCI (Commodity Channel Index)
7.Stochastic Momentum Index of UO (Ultimate Oscillator)
8.Stochastic Momentum Index of MACD (Moving Average Convergence/Divergence)
9.Stochastic Momentum Index of Volume
10.Stochastic Momentum Index of MFI (Money Flow Index)
11.Stochastic Momentum Index of CMF (Chaikin Money Flow)
12.Stochastic Momentum Index of On Balance Volume (OBV)
13.Stochastic Momentum Index of Price-Volume Trend (PVT)
14.Stochastic Momentum Index of Volume Oscillator (VO)
15.Stochastic Momentum Index of Positive Volume Index (PVI)
16.Stochastic Momentum Index of Negative Volume Index (NVI)
## Relative Strength Index:
1. RSI for Volume
2. RSI for Moving Average
rsi(source, length)
RSI (Relative Strengh Index). Measures the relative strength of recent price gains to recent price losses of an asset.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of RSI
roc(source, length)
ROC (Rate of Change). Measures the percentage change in price of an asset over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of ROC
stoch(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Stochastic Oscillator. Compares the current price of an asset to its price range over a specified time period.
Parameters:
kLength
kSmoothing : (int) Period for smoothig stochastic
dSmoothing : (int) Period for signal (moving average of stochastic)
maTypeK : (int) Type of Moving Average for Stochastic Oscillator
maTypeD : (int) Type of Moving Average for Stochastic Oscillator Signal
almaOffsetKD : (float) Offset for Arnaud Legoux Moving Average for Oscillator and Signal
almaSigmaKD : (float) Sigma for Arnaud Legoux Moving Average for Oscillator and Signal
lsmaOffSetKD : (int) Offset for Least Squares Moving Average for Oscillator and Signal
Returns: A tuple of Stochastic Oscillator and Moving Average of Stochastic Oscillator
stoch(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Stochastic Oscillator. Customized source. Compares the current price of an asset to its price range over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
kLength : (int) Period of loopback to calculate the stochastic
kSmoothing : (int) Period for smoothig stochastic
dSmoothing : (int) Period for signal (moving average of stochastic)
maTypeK : (int) Type of Moving Average for Stochastic Oscillator
maTypeD : (int) Type of Moving Average for Stochastic Oscillator Signal
almaOffsetKD : (float) Offset for Arnaud Legoux Moving Average for Stoch and Signal
almaSigmaKD : (float) Sigma for Arnaud Legoux Moving Average for Stoch and Signal
lsmaOffSetKD : (int) Offset for Least Squares Moving Average for Stoch and Signal
Returns: A tuple of Stochastic Oscillator and Moving Average of Stochastic Oscillator
tsi(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet)
TSI (True Strengh Index). Measures the price change, calculating the ratio of the price change (positive or negative) in relation to the absolute price change.
The values of both are smoothed twice to reduce noise, and the final result is normalized in a range between 100 and -100.
Parameters:
source : (float) Source of series (close, high, low, etc.)
shortLength : (int) Short length
longLength : (int) Long length
maType : (int) Type of Moving Average for TSI
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: (float) TSI
smi(sourceTSI, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
SMI (Stochastic Momentum Index). A TSI (True Strengh Index) plus a signal line.
Parameters:
sourceTSI : (float) Source of series for TSI (close, high, low, etc.)
shortLengthTSI : (int) Short length for TSI
longLengthTSI : (int) Long length for TSI
maTypeTSI : (int) Type of Moving Average for Signal of TSI
almaOffsetTSI : (float) Offset for Arnaud Legoux Moving Average
almaSigmaTSI : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSetTSI : (int) Offset for Least Squares Moving Average
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
Returns: A tuple with TSI, signal of TSI and histogram of difference
wpr(source, length)
Williams R% (Williams Percent Range). Compares the current price of an asset to its highest high and lowest low over a specified time period.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
Returns: (float) Series of Williams R%
cci(source, length, maType, almaOffset, almaSigma, lsmaOffSet)
CCI (Commodity Channel Index). Measures the relationship between an asset's current price and its moving average.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period of loopback
maType : (int) Type of Moving Average
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: (float) Series of CCI
ultimateOscillator(fastLength, middleLength, slowLength)
UO (Ultimate Oscilator). Combines three different time periods to help identify possible reversal points.
Parameters:
fastLength : (int) Fast period of loopback
middleLength : (int) Middle period of loopback
slowLength : (int) Slow period of loopback
Returns: (float) Series of Ultimate Oscilator
ultimateOscillator(source, fastLength, middleLength, slowLength)
UO (Ultimate Oscilator). Customized source. Combines three different time periods to help identify possible reversal points.
Parameters:
source : (float) Source of series (close, high, low, etc.)
fastLength : (int) Fast period of loopback
middleLength : (int) Middle period of loopback
slowLength : (int) Slow period of loopback
Returns: (float) Series of Ultimate Oscilator
macd(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet)
MACD (Moving Average Convergence/Divergence). Shows the difference between short-term and long-term exponential moving averages.
Parameters:
source : (float) Source of series (close, high, low, etc.)
fastLength : (int) Period for fast moving average
slowLength : (int) Period for slow moving average
signalLength : (int) Signal length
maTypeFast : (int) Type of fast moving average
maTypeSlow : (int) Type of slow moving average
maTypeMACD : (int) Type of MACD moving average
almaOffset : (float) Offset for Arnaud Legoux Moving Average
almaSigma : (float) Sigma for Arnaud Legoux Moving Average
lsmaOffSet : (int) Offset for Least Squares Moving Average
Returns: A tuple with MACD, Signal, and Histgram
fisher(length)
Fisher Transform. Normalize prices into a Gaussian normal distribution.
Parameters:
length
Returns: A tuple with Fisher Transform and signal
fisher(source, length)
Fisher Transform. Customized source. Normalize prices into a Gaussian normal distribution.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length
Returns: A tuple with Fisher Transform and signal
inverseFisher(source, length, subtrahend, denominator)
Inverse Fisher Transform.
Transform the values of the Fisher Transform into a smaller and more easily interpretable scale is
through the application of an inverse transformation to the hyperbolic tangent function.
This transformation takes the values of the FT, which range from -infinity to +infinity,
to a scale limited between -1 and +1, allowing them to be more easily visualized and compared.
Parameters:
source : (float) Source of series (close, high, low, etc.)
length : (int) Period for loopback
subtrahend : (int) Denominator. Useful in unbounded indicators. For example, in CCI.
denominator
Returns: (float) Series of Inverse Fisher Transform
premierStoch(length, smoothlen)
Premier Stochastic Oscillator (PSO).
Normalizes the standard stochastic oscillator by applying a five-period double exponential smoothing
average of the %K value, resulting in a symmetric scale of 1 to -1.
Parameters:
length : (int) Period for loopback
smoothlen : (int) Period for smoothing
Returns: (float) Series of PSO
premierStoch(source, smoothlen, subtrahend, denominator)
Premier Stochastic Oscillator (PSO) of custom source.
Normalizes the source by applying a five-period double exponential smoothing average.
Parameters:
source : (float) Source of series (close, high, low, etc.)
smoothlen : (int) Period for smoothing
subtrahend : (int) Denominator. Useful in unbounded indicators. For example, in CCI.
denominator
Returns: (float) Series of PSO
stochRsi(sourceRSI, lengthRSI, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
sourceRSI
lengthRSI
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochRoc(sourceROC, lengthROC, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
sourceROC
lengthROC
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochUO(fastLength, middleLength, slowLength, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
fastLength
middleLength
slowLength
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochTSI(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
shortLength
longLength
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochWPR(source, length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochCCI(source, length, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochFT(length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVolume(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochMFI(source, length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochOBV(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochPVI(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochNVI(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochPVT(source, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
source
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
stochVROC(length, kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD)
Parameters:
length
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
iftRSI(sourceRSI, lengthRSI, lengthIFT)
Parameters:
sourceRSI
lengthRSI
lengthIFT
iftROC(sourceROC, lengthROC, lengthIFT)
Parameters:
sourceROC
lengthROC
lengthIFT
iftUO(fastLength, middleLength, slowLength, lengthIFT)
Parameters:
fastLength
middleLength
slowLength
lengthIFT
iftStoch(kLength, kSmoothing, dSmoothing, maTypeK, maTypeD, almaOffsetKD, almaSigmaKD, lsmaOffSetKD, lengthIFT)
Parameters:
kLength
kSmoothing
dSmoothing
maTypeK
maTypeD
almaOffsetKD
almaSigmaKD
lsmaOffSetKD
lengthIFT
iftTSI(source, shortLength, longLength, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
shortLength
longLength
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftCCI(source, length, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
length
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftFisher(length, lengthIFT)
Parameters:
length
lengthIFT
iftMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftWPR(source, length, lengthIFT)
Parameters:
source
length
lengthIFT
iftMFI(source, length, lengthIFT)
Parameters:
source
length
lengthIFT
iftCMF(length, lengthIFT)
Parameters:
length
lengthIFT
iftVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, lengthIFT)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
lengthIFT
iftVROC(length, lengthIFT)
Parameters:
length
lengthIFT
smiRSI(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiROC(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVROC(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiWPR(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiFT(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiFT(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiCCI(source, length, maTypeCCI, almaOffsetCCI, almaSigmaCCI, lsmaOffSetCCI, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
maTypeCCI
almaOffsetCCI
almaSigmaCCI
lsmaOffSetCCI
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiUO(fastLength, middleLength, slowLength, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
fastLength
middleLength
slowLength
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiMACD(source, fastLength, slowLength, signalLength, maTypeFast, maTypeSlow, maTypeMACD, almaOffset, almaSigma, lsmaOffSet, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
fastLength
slowLength
signalLength
maTypeFast
maTypeSlow
maTypeMACD
almaOffset
almaSigma
lsmaOffSet
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVol(shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiMFI(source, length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiCMF(length, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
length
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiOBV(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiPVT(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiVO(shortLen, longLen, maType, almaOffset, almaSigma, lsmaOffSet, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
shortLen
longLen
maType
almaOffset
almaSigma
lsmaOffSet
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiPVI(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
smiNVI(source, shortLengthTSI, longLengthTSI, maTypeTSI, almaOffsetTSI, almaSigmaTSI, lsmaOffSetTSI, maTypeSignal, smoothingLengthSignal, almaOffsetSignal, almaSigmaSignal, lsmaOffSetSignal)
Parameters:
source
shortLengthTSI
longLengthTSI
maTypeTSI
almaOffsetTSI
almaSigmaTSI
lsmaOffSetTSI
maTypeSignal
smoothingLengthSignal
almaOffsetSignal
almaSigmaSignal
lsmaOffSetSignal
rsiVolume(length)
Parameters:
length
rsiMA(sourceMA, lengthMA, maType, almaOffset, almaSigma, lsmaOffSet, lengthRSI)
Parameters:
sourceMA
lengthMA
maType
almaOffset
almaSigma
lsmaOffSet
lengthRSI
Moving Average Heatmap Visualization7 different types of moving averages (5 different lengths of each) compared to a base moving average. Base moving average can be configured to be a slew of different types of moving averages (credit to @mortdiggiddy for the code) and have a custom length.
Red = base moving average is over other moving average (bearish)
Green = base moving average is under other moving average (bullish)
lengths for the different MAs are just fibonacci numbers due to lack of creativity.
First 5 moving averages are Simple moving average the next 5 are Exponential moving averages and after that it is weighted moving averages, volume weighted moving average (VWAP), Exponential volume weighted moving average (thanks again @mortdiggiddy ), hull moving averages and lastly zero lag moving averages.
The indicator might lag your chart out a bit so be ready for that.
Have fun!
[blackcat] L3 Dynamic CrossOVERVIEW
The L3 Dynamic Cross indicator is a powerful tool designed to assist traders in identifying potential buy and sell opportunities through the use of dynamic moving averages. This versatile script offers a wide range of customizable options, allowing users to tailor the moving averages to their specific needs and preferences. By providing clear visual cues and generating precise crossover signals, it helps traders make informed decisions about market trends and potential entry/exit points 📈💹.
FEATURES
Multiple Moving Average Types:
Simple Moving Average (SMA): Provides a straightforward average of prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it responsive to new information.
Weighted Moving Average (WMA): Assigns weights to all prices within the look-back period, giving more importance to recent prices.
Volume Weighted Moving Average (VWMA): Incorporates volume data to provide a more accurate representation of price movements.
Smoothed Moving Average (SMMA): Averages out fluctuations to create a smoother trend line.
Double Exponential Moving Average (DEMA): Reduces lag by applying two layers of exponential smoothing.
Triple Exponential Moving Average (TEMA): Further reduces lag with three layers of exponential smoothing.
Hull Moving Average (HullMA): Combines weighted moving averages to minimize lag and noise.
Super Smoother Moving Average (SSMA): Uses a sophisticated algorithm to smooth out price data while preserving trend direction.
Zero-Lag Exponential Moving Average (ZEMA): Eliminates lag entirely by adjusting the calculation method.
Triangular Moving Average (TMA): Applies a double smoothing process to reduce volatility and enhance trend identification.
Customizable Parameters:
Length: Adjust the period for both fast and slow moving averages to match your trading style.
Source: Select different price sources such as close, open, high, or low for more nuanced analysis.
Visual Representation:
Fast MA: Displayed as a green line representing shorter-term trends.
Slow MA: Shown as a red line indicating longer-term trends.
Crossover Signals:
Generate buy ('BUY') and sell ('SELL') labels based on crossover events between the fast and slow moving averages 🏷️.
Clear visual cues help traders quickly identify potential entry and exit points.
Alert Functionality:
Receive real-time notifications when crossover conditions are met, ensuring timely action 🔔.
Customizable alert messages for personalized trading strategies.
Advanced Trade Management:
Support for pyramiding levels allows traders to manage multiple positions effectively.
Fine-tune your risk management by setting the number of allowed trades per signal.
HOW TO USE
Adding the Indicator:
Open your TradingView chart and go to the indicators list.
Search for L3 Dynamic Cross and add it to your chart.
Configuring Settings:
Choose your desired Moving Average Type from the dropdown menu.
Adjust the Fast MA Length and Slow MA Length according to your trading timeframe.
Select appropriate Price Sources for both fast and slow moving averages.
Monitoring Signals:
Observe the plotted lines on the chart to track short-term and long-term trends.
Look for buy and sell labels that indicate potential trade opportunities.
Setting Up Alerts:
Enable alerts based on crossover conditions to receive instant notifications.
Customize alert messages to suit your trading plan.
Managing Positions:
Utilize the pyramiding feature to handle multiple entries and exits efficiently.
Keep track of your position sizes relative to the defined pyramiding levels.
Combining with Other Tools:
Integrate this indicator with other technical analysis tools for confirmation.
Use additional filters like volume, RSI, or MACD to enhance decision-making accuracy.
LIMITATIONS
Market Conditions: The effectiveness of the indicator may vary in highly volatile or sideways markets. Be cautious during periods of low liquidity or sudden price spikes 🌪️.
Parameter Sensitivity: Different moving average types and lengths can produce varying results. Experiment with settings to find what works best for your asset class and timeframe.
False Signals: Like any technical indicator, false signals can occur. Always confirm signals with other forms of analysis before executing trades.
NOTES
Historical Data: Ensure you have enough historical data loaded into your chart for accurate moving average calculations.
Backtesting: Thoroughly backtest the indicator on various assets and timeframes using demo accounts before deploying it in live trading environments 🔍.
Customization: Feel free to adjust colors, line widths, and label styles to better fit your chart aesthetics and personal preferences.
EXAMPLE STRATEGIES
Trend Following: Use the indicator to ride trends by entering positions when the fast MA crosses above/below the slow MA and exiting when the opposite occurs.
Mean Reversion: Identify overbought/oversold conditions by combining the indicator with oscillators like RSI or Stochastic. Enter counter-trend positions when the moving averages diverge significantly from the mean.
Scalping: Apply tight moving average settings to capture small, quick profits in intraday trading. Combine with volume indicators to filter out weak signals.
Dskyz (DAFE) MAtrix with ATR-Powered Precision Dskyz (DAFE) MAtrix with ATR-Powered Precision
This cutting‐edge futures trading strategy built to thrive in rapidly changing market conditions. Developed for high-frequency futures trading on instruments such as the CME Mini MNQ, this strategy leverages a matrix of sophisticated moving averages combined with ATR-based filters to pinpoint high-probability entries and exits. Its unique combination of adaptable technical indicators and multi-timeframe trend filtering sets it apart from standard strategies, providing enhanced precision and dynamic responsiveness.
imgur.com
Core Functional Components
1. Advanced Moving Averages
A distinguishing feature of the DAFE strategy is its robust, multi-choice moving averages (MAs). Clients can choose from a wide array of MAs—each with specific strengths—in order to fine-tune their trading signals. The code includes user-defined functions for the following MAs:
imgur.com
Hull Moving Average (HMA):
The hma(src, len) function calculates the HMA by using weighted moving averages (WMAs) to reduce lag considerably while smoothing price data. This function computes an intermediate WMA of half the specified length, then a full-length WMA, and finally applies a further WMA over the square root of the length. This design allows for rapid adaptation to price changes without the typical delays of traditional moving averages.
Triple Exponential Moving Average (TEMA):
Implemented via tema(src, len), TEMA uses three consecutive exponential moving averages (EMAs) to effectively cancel out lag and capture price momentum. The final formula—3 * (ema1 - ema2) + ema3—produces a highly responsive indicator that filters out short-term noise.
Double Exponential Moving Average (DEMA):
Through the dema(src, len) function, DEMA calculates an EMA and then a second EMA on top of it. Its simplified formula of 2 * ema1 - ema2 provides a smoother curve than a single EMA while maintaining enhanced responsiveness.
Volume Weighted Moving Average (VWMA):
With vwma(src, len), this MA accounts for trading volume by weighting the price, thereby offering a more contextual picture of market activity. This is crucial when volume spikes indicate significant moves.
Zero Lag EMA (ZLEMA):
The zlema(src, len) function applies a correction to reduce the inherent lag found in EMAs. By subtracting a calculated lag (based on half the moving average window), ZLEMA is exceptionally attuned to recent price movements.
Arnaud Legoux Moving Average (ALMA):
The alma(src, len, offset, sigma) function introduces ALMA—a type of moving average designed to be less affected by outliers. With parameters for offset and sigma, it allows customization of the degree to which the MA reacts to market noise.
Kaufman Adaptive Moving Average (KAMA):
The custom kama(src, len) function is noteworthy for its adaptive nature. It computes an efficiency ratio by comparing price change against volatility, then dynamically adjusts its smoothing constant. This results in an MA that quickly responds during trending periods while remaining smoothed during consolidation.
Each of these functions—integrated into the strategy—is selectable by the trader (via the fastMAType and slowMAType inputs). This flexibility permits the tailored application of the MA most suited to current market dynamics and individual risk management preferences.
2. ATR-Based Filters and Risk Controls
ATR Calculation and Volatility Filter:
The strategy computes the Average True Range (ATR) over a user-defined period (atrPeriod). ATR is then used to derive both:
Volatility Assessment: Expressed as a ratio of ATR to closing price, ensuring that trades are taken only when volatility remains within a safe, predefined threshold (volatilityThreshold).
ATR-Based Entry Filters: Implemented as atrFilterLong and atrFilterShort, these conditions ensure that for long entries the price is sufficiently above the slow MA and vice versa for shorts. This acts as an additional confirmation filter.
Dynamic Exit Management:
The exit logic employs a dual approach:
Fixed Stop and Profit Target: Stops and targets are set at multiples of ATR (fixedStopMultiplier and profitTargetATRMult), helping manage risk in volatile markets.
Trailing Stop Adjustments: A trailing stop is calculated using the ATR multiplied by a user-defined offset (trailOffset), which captures additional profits as the trade moves favorably while protecting against reversals.
3. Multi-Timeframe Trend Filtering
The strategy enhances its signal reliability by leveraging a secondary, higher timeframe analysis:
15-Minute Trend Analysis:
By retrieving 15-minute moving averages (fastMA15m and slowMA15m) via request.security, the strategy determines the broader market trend. This secondary filter (enabled or disabled through useTrendFilter) ensures that entries are aligned with the prevailing market direction, thereby reducing the incidence of false signals.
4. Signal and Execution Logic
Combined MA Alignment:
The entry conditions are based primarily on the alignment of the fast and slow MAs. A long condition is triggered when the current price is above both MAs and the fast MA is above the slow MA—complemented by the ATR filter and volume conditions. The reverse applies for a short condition.
Volume and Time Window Validation:
Trades are permitted only if the current volume exceeds a minimum (minVolume) and the current hour falls within the predefined trading window (tradingStartHour to tradingEndHour). An additional volume spike check (comparing current volume to a moving average of past volumes) further filters for optimal market conditions.
Comprehensive Order Execution:
The strategy utilizes flexible order execution functions that allow pyramiding (up to 10 positions), ensuring that it can scale into positions as favorable conditions persist. The use of both market entries and automated exits (with profit targets, stop-losses, and trailing stops) ensures that risk is managed at every step.
5. Integrated Dashboard and Metrics
For transparency and real-time analysis, the strategy includes:
On-Chart Visualizations:
Both fast and slow MAs are plotted on the chart, making it easy to see the market’s technical foundation.
Dynamic Metrics Dashboard:
A built-in table displays crucial performance statistics—including current profit/loss, equity, ATR (both raw and as a percentage), and the percentage gap between the moving averages. These metrics offer immediate insight into the health and performance of the strategy.
Input Parameters: Detailed Breakdown
Every input is meticulously designed to offer granular control:
Fast & Slow Lengths:
Determine the window size for the fast and slow moving averages. Smaller values yield more sensitivity, while larger values provide a smoother, delayed response.
Fast/Slow MA Types:
Choose the type of moving average for fast and slow signals. The versatility—from basic SMA and EMA to more complex ones like HMA, TEMA, ZLEMA, ALMA, and KAMA—allows customization to fit different market scenarios.
ATR Parameters:
atrPeriod and atrMultiplier shape the volatility assessment, directly affecting entry filters and risk management through stop-loss and profit target levels.
Trend and Volume Filters:
Inputs such as useTrendFilter, minVolume, and the volume spike condition help confirm that a trade occurs in active, trending markets rather than during periods of low liquidity or market noise.
Trading Hours:
Restricting trade execution to specific hours (tradingStartHour and tradingEndHour) helps avoid illiquid or choppy markets outside of prime trading sessions.
Exit Strategies:
Parameters like trailOffset, profitTargetATRMult, and fixedStopMultiplier provide multiple layers of risk management and profit protection by tailoring how exits are generated relative to current market conditions.
Pyramiding and Fixed Trade Quantity:
The strategy supports multiple entries within a trend (up to 10 positions) and sets a predefined trade quantity (fixedQuantity) to maintain consistent exposure and risk per trade.
Dashboard Controls:
The resetDashboard input allows for on-the-fly resetting of performance metrics, keeping the strategy’s performance dashboard accurate and up-to-date.
Why This Strategy is Truly Exceptional
Multi-Faceted Adaptability:
The ability to switch seamlessly between various moving average types—each suited to particular market conditions—enables the strategy to adapt dynamically. This is a testament to the high level of coding sophistication and market insight infused within the system.
Robust Risk Management:
The integration of ATR-based stops, profit targets, and trailing stops ensures that every trade is executed with well-defined risk parameters. The system is designed to mitigate unexpected market swings while optimizing profit capture.
Comprehensive Market Filtering:
By combining moving average crossovers with volume analysis, volatility thresholds, and multi-timeframe trend filters, the strategy only enters trades under the most favorable conditions. This multi-layered filtering reduces noise and enhances signal quality.
-Final Thoughts-
The Dskyz Adaptive Futures Elite (DAFE) MAtrix with ATR-Powered Precision strategy is not just another trading algorithm—it is a multi-dimensional, fully customizable system built on advanced technical principles and sophisticated risk management techniques. Every function and input parameter has been carefully engineered to provide traders with a system that is both powerful and transparent.
For clients seeking a state-of-the-art trading solution that adapts dynamically to market conditions while maintaining strict discipline in risk management, this strategy truly stands in a class of its own.
****Please show support if you enjoyed this strategy. I'll have more coming out in the near future!!
-Dskyz
Caution
DAFE is experimental, not a profit guarantee. Futures trading risks significant losses due to leverage. Backtest, simulate, and monitor actively before live use. All trading decisions are your responsibility.
Customizable MTF Multiple Moving AveragesTitle:
Customizable Multiple Moving Averages with Dynamic Colors
Description:
This script allows you to calculate up to three customizable moving averages, offering the flexibility to choose from multiple moving average types:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
VWMA (Volume Weighted Moving Average)
SMMA (Smoothed Moving Average)
Key Features:
Separate Timeframe for Each Moving Average:
Each moving average can be calculated on a different timeframe. For instance, you can display a 1D moving average while working on a 4H chart.
Dynamic Colors:
Moving averages dynamically change color based on their trend:
Uptrend Color: When the moving average is increasing compared to the previous bar of its timeframe.
Downtrend Color: When the moving average is decreasing.
Full Customization:
Length: Adjust the period for each moving average.
Source: Choose any price data source (e.g., close, open, high, low).
Colors: Set custom colors for uptrend and downtrend behavior.
Perfect For:
Multi-Timeframe Trend Analysis:
Observe trends from higher timeframes without switching your current chart.
Crossover Strategies:
Combine multiple moving averages to identify entry and exit signals.
How to Use:
Load the Script: Apply it to your chart.
Configure Inputs: Adjust each moving average's settings from the input panel.
Analyze Trends: Visualize dynamic trend movements with easy-to-identify colors.
Example Configuration:
Set MA1 to a 50-period EMA on a 4H timeframe.
Set MA2 to a 100-period SMMA on a 1D timeframe.
Set MA3 to a 200-period VWMA on a 1W timeframe.
Adaptive DEMA Momentum Oscillator (ADMO)Overview:
The Adaptive DEMA Momentum Oscillator (ADMO) is an open-source technical analysis tool developed to measure market momentum using a Double Exponential Moving Average (DEMA) and adaptive standard deviation. By dynamically combining price deviation from the moving average with normalized standard deviation, ADMO provides traders with a powerful way to interpret market conditions.
Key Features:
Double Exponential Moving Average (DEMA):
The core calculation of the indicator is based on DEMA, which is known for being more responsive to price changes compared to traditional moving averages. This makes the ADMO capable of capturing trend momentum effectively.
Standard Deviation Integration:
A normalized standard deviation is used to adaptively weight the oscillator. This makes the indicator more sensitive to market volatility, enhancing responsiveness during high volatility and reducing sensitivity during calmer periods.
Oscillator Representation:
The final oscillator value is derived from the combination of the DEMA-based Z-score and the normalized standard deviation. This final value is visualized as a color-coded histogram, reflecting bullish or bearish momentum.
Color-Coded Histogram:
Bullish Momentum: Values above zero are colored using a customizable bullish color (default: light green).
Bearish Momentum: Values below zero are colored using a customizable bearish color (default: red).
How It Works:
Inputs:
DEMA Length: Defines the period used for calculating the Double Exponential Moving Average. It can be adjusted from 1 to 200 to suit different trading styles.
Standard Deviation Length: Sets the lookback period for standard deviation calculations, which influences the responsiveness of the oscillator.
Standard Deviation Weight (StdDev Weight): Controls the weight given to the normalized standard deviation, allowing customization of the oscillator's sensitivity to volatility.
Calculation Steps:
Double Exponential Moving Average Calculation:
The DEMA is calculated using two exponential moving averages, which helps in reducing lag compared to a simple moving average.
Z-score Calculation:
The Z-score is derived by comparing the difference between the DEMA and its smoothed average (LSMA) to the standard deviation. This indicates how far the current value is from the mean in units of standard deviation.
Normalized Standard Deviation:
The standard deviation is normalized by subtracting the mean standard deviation and dividing by the standard deviation of the values. This helps to make the oscillator adaptive to recent changes in volatility.
Final Oscillator Value:
The final value is calculated by multiplying the Z-score with a factor based on the normalized standard deviation, resulting in a momentum indicator that adapts to different market conditions.
Visualization:
Histogram: The oscillator is plotted as a histogram, with color-coded bars showing the strength and direction of market momentum.
Positive (bullish) values are shown in green, indicating upward momentum.
Negative (bearish) values are shown in red, indicating downward momentum.
Zero Line: A zero line is plotted to provide a reference point, helping users quickly determine whether the current momentum is bullish or bearish.
Example Use Cases:
Momentum Identification:
ADMO helps identify the current market momentum by dynamically adapting to changes in market volatility. When the histogram is above zero and green, it indicates bullish conditions, whereas values below zero and red suggest bearish momentum.
Volatility-Adjusted Signals:
The normalized standard deviation weighting allows the ADMO to provide more reliable signals during different market conditions. This makes it particularly useful for traders who want to be responsive to market volatility while avoiding false signals.
Trend Confirmation and Divergence:
ADMO can be used to confirm the strength of a trend or identify potential divergences between price and momentum. This helps traders spot potential reversal points or continuation signals.
Summary:
The Adaptive DEMA Momentum Oscillator (ADMO) offers a unique approach by combining momentum analysis with adaptive standard deviation. The integration of DEMA makes it responsive to price changes, while the standard deviation adjustment helps it stay relevant in both high and low volatility environments. It's a versatile tool for traders who need an adaptive, momentum-based approach to technical analysis.
Feel free to explore the code and adapt it to your trading strategy. The open-source nature of this tool allows you to adjust the settings and visualize the output to fit your personal trading preferences.
Quarterly Sine Wave with Moving Averages - AYNETDescription
Sine Wave:
The sine wave oscillates with a frequency determined by frequency.
Its amplitude (amplitude) and vertical offset (offset) are adjustable.
Moving Averages:
Includes options for different types of moving averages:
SMA (Simple Moving Average).
EMA (Exponential Moving Average).
WMA (Weighted Moving Average).
HMA (Hull Moving Average).
The user can choose the type (ma_type) and the length (ma_length) via inputs.
Horizontal Lines:
highest_hype and lowest_hype are horizontal levels drawn at the user-specified values.
Quarter Markers:
Vertical lines and labels (Q1, Q2, etc.) are drawn at the start of each quarter.
Customization Options
Moving Average Type:
Switch between SMA, EMA, WMA, and HMA using the dropdown menu.
Sine Wave Frequency:
Adjust the number of oscillations per year.
Amplitude and Offset:
Control the height and center position of the sine wave.
Moving Average Length:
Change the length for any selected moving average.
Output
This indicator plots:
A sine wave that oscillates smoothly over the year, divided into quarters.
A customizable moving average calculated based on the chosen price (e.g., close).
Horizontal lines for the highest and lowest hype levels.
Vertical lines and labels marking the start of each quarter.
Let me know if you need additional features! 😊
Deviation Adjusted MA Overview
The Deviation Adjusted MA is a custom indicator that enhances traditional moving average techniques by introducing a volatility-based adjustment. This adjustment is implemented by incorporating the standard deviation of price data, making the moving average more adaptive to market conditions. The key feature is the combination of a customizable moving average (MA) type and the application of deviation percentage to modify its responsiveness. Additionally, a smoothing layer is applied to reduce noise, improving signal clarity.
Key Components
Customizable Moving Averages
The script allows the user to select from four different types of moving averages:
Simple Moving Average (SMA): A basic average of the closing prices over a specified period.
Exponential Moving Average (EMA): Gives more weight to recent prices, making it more responsive to recent price changes.
Weighted Moving Average (WMA): Weights prices differently, favoring more recent ones but in a linear progression.
Volume-Weighted Moving Average (VWMA): Adjusts the average by trading volume, placing more weight on high-volume periods.
Standard Deviation Calculation
The script calculates the standard deviation of the closing prices over the selected maLength period.
Standard deviation measures the dispersion or volatility of price movements, giving a sense of market volatility.
Deviation Percentage and Adjustment
Deviation Percentage is calculated by dividing the standard deviation by the base moving average and multiplying by 100 to express it as a percentage.
The base moving average is adjusted by this deviation percentage, making the indicator responsive to changes in volatility. The result is a more dynamic moving average that adapts to market conditions.
The parameter devMultiplier is available to scale this adjustment, allowing further fine-tuning of sensitivity.
Smoothing the Adjusted Moving Average
After adjusting the moving average based on deviation, the script applies an additional Exponential Moving Average (EMA) with a length defined by the smoothingLength input.
This EMA serves as a smoothing filter to reduce the noise that could arise from the raw adjustments of the moving average. The smoothing makes trend recognition more consistent and removes short-term fluctuations that could otherwise distort the signal.
Use cases
The Deviation Adjusted MA indicator serves as a dynamic alternative to traditional moving averages by adjusting its sensitivity based on volatility. The script offers extensive customization options through the selection of moving average type and the parameters controlling smoothing and deviation adjustments.
By applying these adjustments and smoothing, the script enables users to better track trends and price movements, while providing a visual cue for changes in market sentiment.
Moving Average Continuity [QuantVue]"Moving Average Continuity," is designed to compare the position of two Moving Averages (MAs) across multiple timeframes.
The user can select three timeframes and determine the length and type of both a fast and slow moving average.
The indicator will display a small table in a user selected location.
This table helps traders quickly determine if, for their selected timeframes, the faster moving average is trending above or below the slower moving average.
The “Moving Average Continuity” indicator can also send you three types of alerts;
1. All moving averages are aligned bullish
2. All moving averages are aligned bearish
3. Moving averages are mixed
Key Features:
1. Timeframes: The user can select up to three distinct timeframes to compare the moving averages.
2. Moving Average Inputs: For each MA, users can determine:
• Length of the MA
• Type of the MA - Options include EMA (Exponential Moving Average), SMA (Simple Moving Average), HMA (Hull Moving Average), WMA (Weighted Moving Average), and VWMA (Volume Weighted Moving Average).
3. Positioning: Users have the ability to adjust the table's positioning (top, middle, or bottom) and horizontal alignment (right, center, or left) on the chart overlay.
4. Runtime Error Prevention: The indicator will throw an error if the chart's timeframe exceeds the maximum selected timeframe, ensuring that comparisons are done correctly.
Give this indicator a BOOST and COMMENT your thoughts!
We hope you enjoy.
Cheers.
MultiMovesCombines 3 different moving averages together with the linear regression. The moving averages are the HMA, EMA, and SMA. The script makes use of two different lengths to allow the end user to utilize common crossovers in order to determine entry into a trade. The edge of each "cloud" is where each of the moving averages actually are. The bar color is the average of the shorter length combined moving averages.
-The Hull Moving Average (HMA), developed by Alan Hull, is an extremely fast and smooth moving average. In fact, the HMA almost eliminates lag altogether and manages to improve smoothing at the same time. A longer period HMA may be used to identify trend.
-The exponential moving average (EMA) is a technical chart indicator that tracks the price of an investment (like a stock or commodity) over time. The EMA is a type of weighted moving average (WMA) that gives more weighting or importance to recent price data.
-A simple moving average (SMA) is an arithmetic moving average calculated by adding recent prices and then dividing that figure by the number of time periods in the calculation average.
-The Linear Regression Indicator plots the ending value of a Linear Regression Line for a specified number of bars; showing, statistically, where the price is expected to be. Instead of plotting an average of past price action, it is plotting where a Linear Regression Line would expect the price to be, making the Linear Regression Indicator more responsive than a moving average.
The lighter colors = default 50 MA
The darker colors = default 200 MA
Moving Average Compendium RefurbishedThis is my effort to bring together in a single script the widest range of moving averages possible.
I aggregated the calculation of averages within a library.
For more information about the library follow the link:
Basically this indicator is the visual result of this library.
You can choose the moving average and the script updates the chart as per the type.
The unique parameters of certain moving averages remain at their default values.
To have a rainbow of moving averages I also made an indicator:
Available moving averages:
AARMA = 'Adaptive Autonomous Recursive Moving Average'
ADEMA = '* Alpha-Decreasing Exponential Moving Average'
AHMA = 'Ahrens Moving Average'
ALMA = 'Arnaud Legoux Moving Average'
ALSMA = 'Adaptive Least Squares'
AUTOL = 'Auto-Line'
CMA = 'Corrective Moving average'
CORMA = 'Correlation Moving Average Price'
COVWEMA = 'Coefficient of Variation Weighted Exponential Moving Average'
COVWMA = 'Coefficient of Variation Weighted Moving Average'
DEMA = 'Double Exponential Moving Average'
DONCHIAN = 'Donchian Middle Channel'
EDMA = 'Exponentially Deviating Moving Average'
EDSMA = 'Ehlers Dynamic Smoothed Moving Average'
EFRAMA = '* Ehlrs Modified Fractal Adaptive Moving Average'
EHMA = 'Exponential Hull Moving Average'
EMA = 'Exponential Moving Average'
EPMA = 'End Point Moving Average'
ETMA = 'Exponential Triangular Moving Average'
EVWMA = 'Elastic Volume Weighted Moving Average'
FAMA = 'Following Adaptive Moving Average'
FIBOWMA = 'Fibonacci Weighted Moving Average'
FISHLSMA = 'Fisher Least Squares Moving Average'
FRAMA = 'Fractal Adaptive Moving Average'
GMA = 'Geometric Moving Average'
HKAMA = 'Hilbert based Kaufman\'s Adaptive Moving Average'
HMA = 'Hull Moving Average'
JURIK = 'Jurik Moving Average'
KAMA = 'Kaufman\'s Adaptive Moving Average'
LC_LSMA = '1LC-LSMA (1 line code lsma with 3 functions)'
LEOMA = 'Leo Moving Average'
LINWMA = 'Linear Weighted Moving Average'
LSMA = 'Least Squares Moving Average'
MAMA = 'MESA Adaptive Moving Average'
MCMA = 'McNicholl Moving Average'
MEDIAN = 'Median'
REGMA = 'Regularized Exponential Moving Average'
REMA = 'Range EMA'
REPMA = 'Repulsion Moving Average'
RMA = 'Relative Moving Average'
RSIMA = 'RSI Moving average'
RVWAP = '* Rolling VWAP'
SMA = 'Simple Moving Average'
SMMA = 'Smoothed Moving Average'
SRWMA = 'Square Root Weighted Moving Average'
SW_MA = 'Sine-Weighted Moving Average'
SWMA = '* Symmetrically Weighted Moving Average'
TEMA = 'Triple Exponential Moving Average'
THMA = 'Triple Hull Moving Average'
TREMA = 'Triangular Exponential Moving Average'
TRSMA = 'Triangular Simple Moving Average'
TT3 = 'Tillson T3'
VAMA = 'Volatility Adjusted Moving Average'
VIDYA = 'Variable Index Dynamic Average'
VWAP = '* VWAP'
VWMA = 'Volume-weighted Moving Average'
WMA = 'Weighted Moving Average'
WWMA = 'Welles Wilder Moving Average'
XEMA = 'Optimized Exponential Moving Average'
ZEMA = 'Zero-Lag Exponential Moving Average'
ZSMA = 'Zero-Lag Simple Moving Average'
Bollinger Bands + Keltner Channel Refurbished█ Goals
This is an indicator that brings together Bollinger Bands and Keltner's Channels in one thing.
Both are very similar, so I decided to make a merge of the best features I found out there.
Here there is the possibility of choosing one of these two as needed.
In addition, I added the following resources:
1. Pre-Defined intermediate bands with Fibonacci values;
2. Detachment of the bands in which the price was present;
3. Choice of Moving Average:
"Simple", "Exponential", "Regularized Exponential", "Hull", "Arnaud Legoux", "Weighted Moving Average", "Least Squares Moving Average (Linear Regression)", "Volume Weighted Moving Average", "Smoothed Moving Average", "Median", "VWAP");
4. Statistics: bars count within the bands.
█ Concepts
Keltner Channels vs. Bollinger Bands
"These two indicators are quite similar.
Keltner Channels use ATR to calculate the upper and lower bands while Bollinger Bands use standard deviation instead.
The interpretation of the indicators is similar, although since the calculations are different the two indicators may provide slightly different information or trade signals."
(Investopedia)
Bollinger Bands (BB)
"Bollinger Bands (BB) are a widely popular technical analysis instrument created by John Bollinger in the early 1980’s.
Bollinger Bands consist of a band of three lines which are plotted in relation to security prices.
The line in the middle is usually a Simple Moving Average (SMA) set to a period of 20 days (the type of trend line and period can be changed by the trader; however a 20 day moving average is by far the most popular).
The SMA then serves as a base for the Upper and Lower Bands which are used as a way to measure volatility by observing the relationship between the Bands and price.
Typically the Upper and Lower Bands are set to two standard deviations away from the SMA (The Middle Line); however the number of standard deviations can also be adjusted by the trader."
(TradingView)
Keltner Channels (KC)
"The Keltner Channels (KC) indicator is a banded indicator similar to Bollinger Bands and Moving Average Envelopes.
They consist of an Upper Envelope above a Middle Line as well as a Lower Envelope below the Middle Line.
The Middle Line is a moving average of price over a user-defined time period.
Either a simple moving average or an exponential moving average are typically used. The Upper and Lower Envelopes (user defined) are set a range away from the Middle Line.
This can be a multiple of the daily high/low range, or more commonly a multiple of the Average True Range."
(TradingView)
█ Examples
Bollinger Bands with 200 REMA:
Keltner Channel with 200 REMA:
Bollinger Bands with 55 ALMA:
Keltner Channel with 55 ALMA:
Bollinger Bands with 55 Least Squares Moving Average:
█ Thanks
- TradingView (BB, KC, ATR, MA's)
- everget (Regularized Exponential Moving Average)
- TimeFliesBuy ("Triple Bollinger Bands")
- Rashad ("Fibonacci Bollinger Bands")
- Dicargo_Beam ("Is the Bollinger Bands assumption wrong?")
Trend Thrust Indicator - RafkaThis indicator defines the impact of volume on the volume-weighted moving average, emphasizing trends with greater volume.
What determines a security’s value? Price is the agreement to exchange despite the possible disagreement in value. Price is the conviction, emotion, and volition of investors. It is not a constant but is influenced by information, opinions, and emotions over time. Volume represents this degree of conviction and is the embodiment of information and opinions flowing through investor channels. It is the asymmetry between the volume being forced through supply (offers) and demand (bids) that facilitates price change. Quantifying the extent of asymmetry between price trends and the corresponding volume flows is a primary objective of volume analysis. Volume analysis research reveals that volume often leads price but may also be used to confirm the present price trend.
Trend thrust indicator
The trend thrust indicator (TTI), an enhanced version of the volume-weighted moving average convergence/divergence (VW-Macd) indicator, was introduced in Buff Pelz Dormeier's book 'Investing With Volume Analysis'. The TTI uses a volume multiplier in unique ways to exaggerate the impact of volume on volume-weighted moving averages. Like the VW-Macd, the TTI uses volume-weighted moving averages as opposed to exponential moving averages. Volume-weighted averages weigh closing prices proportionally to the volume traded during each time period, so the TTI gives greater emphasis to those price trends with greater volume and less emphasis to time periods with lighter volume. In the February 2001 issue of Stocks & Commodities, I showed that volume-weighted moving averages (Buff averages, or Vwmas) improve responsiveness while increasing reliability of simple moving averages.
Like the Macd and VW-Macd, the TTI calculates a spread by subtracting the short (fast) average from the long (slow) average. This spread combined with a volume multiplier creates the Buff spread
Trend Volume Accumulation R1 by JustUncleLThis simple indicator shows the Accumulated Volume within the current uptrend or downtrend. The uptrend/downtrend is detected by a change in direction of the candles which works very well with Heikin Ashi and Renko charts. Alternatively you can use a Moving average direction to indicate trend direction, which should work on any candle type.
You can select between 11 different types of moving average:
SMA = Simple Moving Average.
EMA = Exponential Moving Average.
WMA = Weighted Moving Average
VWMA = Volume Weighted Moving Average
SMMA = Smoothed Simple Moving Average.
DEMA = Double Exponential Moving Average
TEMA = Triple Exponential Moving Average.
HullMA = Hull Moving Average
SSMA = Ehlers Super Smoother Moving average
ZEMA = Near Zero Lag Exponential Moving Average.
TMA = Triangular (smoothed) Simple Moving Average.
Here is a sample chart using EMA length 6 for trend Direction:
GC Magic(EMA/RMA) V1This is the second script I am posting on TV . This is a Trend based indicator with the option of using it as Exponential Moving Averages or Rsi Moving Averages.The RMA's are giving better signal than the Exponential Moving Averages. The script has the option to select either of them. Works on all time frames. The default options are working good on all time frames.
With the help of Indicator Properties following Options can be changed
a. Type of moving averages for using Guppy method
b. Option to use higher time frame Signal moving average of your choice along with higher time frame
c. Enable or disable to show signal EMA/RMA on chart .
d. Enable or disable to show Guppy EMA/RMA on chart
Indicator Properties:
1. Select to use EMA , Uncheck to use RMA: --> Check to Select EMA based Guppy or Uncheck to use RMA based Guppy
2. Fast EMA/RMA For Cross --> Fast EMA/RMA cross Length
3. Slow EMA/RMA For Cross --> Slow EMA/RMA Length
4. Signal EMA/RMA --> Moving average to use for Signal filters. This moving average will be based on the timeframe u will be selecting below
5. Time interval for Signal EMA/RMA (W, D, ) --> Which time frame moving average you want for the above Signal EMA
6. Do you want to use Signal EMA/RMA for Signals? --> Do you want to use Signal EMA as filter or just the cross of Guppy . Check to use and uncheck for just cross
7. Show Signal EMA on Chart? --> Do you want to display higher timeframe Signal EMA on chart
8. Show Guppy-Slow-Red On Chart? --> Shows/Hides Slow EMA/RMAs
9. Show Guppy-Fast-Green On Chart? --> Shows/Hides Fast EMA/RMAs
Examples:
GbpAud 15m
GbpNzd 1hr
Oil 4hr
AudUSD 1hr
Combined EMA/Smiley & DEM System## 🔷 General Overview
This script creates an advanced technical analysis system for TradingView, combining multiple Exponential Moving Averages (EMAs), Simple Moving Averages (SMAs), dynamic Fibonacci levels, and ATR (Average True Range) analysis. It presents the results clearly through interactive, real-time tables directly on the chart.
---
## 🔹 Indicator Structure
The script consists of two main parts:
### **1. EMA & SMA Combined System with Fibonacci**
- **Purpose:**
Provides visual insights by comparing multiple EMA/SMA periods and identifying significant dynamic price levels using Fibonacci ratios around a calculated "Golden" line.
- **Components:**
- **Moving Averages (MAs)**:
- 20 EMAs (periods from 20 to 400)
- 20 SMAs (also from 20 to 400)
- **Golden Line:**
Calculated as the average of all EMAs and SMAs.
- **Dynamic Fibonacci Levels:**
Key ratios around the Golden line (0.5, 0.618, 0.786, 1.0, 1.272, 1.414, 1.618, 2.0) dynamically adjust based on market conditions.
- **Fibonacci Labels:**
Labels are shown next to Fibonacci lines, indicating their numeric value clearly on the chart.
- **Table (Top Right Corner):**
- Displays:
- **Input:** EMA/SMA periods sorted by their current average price levels.
- **AVG:** The average of corresponding EMA & SMA pairs.
- **EMA & SMA Values:** Individual EMA/SMA values clearly marked.
- **Dynamic Highlighting:** Highlights the row whose average (EMA+SMA)/2 is closest to the current price, helping identify immediate price action significance.
- **Sorting Logic:**
Each EMA/SMA pair is dynamically sorted based on their average values. Color coding (red/green) is used:
- **Green:** EMA/SMA pairs with shorter periods when their average is lower.
- **Red:** EMA/SMA pairs with longer periods when their average is lower.
- **Star (⭐):** Represents the "Golden" average clearly.
---
### **2. DEM System (Dynamic EMA/ATR Metrics)**
- **Purpose:**
Provides detailed ATR statistics to assess market volatility clearly and quickly.
- **Components:**
- **Moving Averages:**
- SMA lines: 25, 50, 100, 200.
- **Bollinger Bands:**
- Based on 20-period SMA of highs and standard deviation of lows.
- **ATR Analysis:**
- ATR calculations for multiple periods (1-day, 10, 20, 30, 40, 50).
- **ATR Premium:** Average ATR of all calculated periods, providing an overarching volatility indicator.
- **ATR Table (Bottom Right Corner):**
- Displays clearly structured ATR values and percentages relative to the current close price:
- Columns: **ATR Period**, **Value**, and **% of Close**.
- Rows: Each specific ATR (1D, 10, 20, 30, 40, 50), plus ATR premium.
- The ATR premium is highlighted in yellow to signify its importance clearly.
---
## 🔹 Key Features and Logic Explained
- **Dynamic EMA/SMA Sorting:**
The script computes the average of each EMA/SMA pair and sorts them dynamically on each bar, highlighting their relative importance visually. This allows traders to easily interpret the strength of current support/resistance levels based on moving averages.
- **Closest EMA/SMA Pair to Current Price:**
Calculates the absolute difference between the current price and all EMA/SMA averages, highlighting the closest one for quick reference.
- **Fibonacci Ratios:**
- Dynamically calculated Fibonacci levels based on the "Golden" EMA/SMA average give clear visual guidance for potential targets, supports, and resistances.
- Labels are continuously updated and placed next to levels for clarity.
- **ATR Volatility Analysis:**
- Provides immediate insight into market volatility with absolute and relative (percentage-based) ATR values.
- ATR premium summarizes volatility across multiple timeframes clearly.
---
## 🔹 Practical Use Case:
- Traders can quickly identify support/resistance and critical price zones through EMA/SMA and Fibonacci combinations.
- Useful in assessing immediate volatility, guiding stop-loss and take-profit levels through detailed ATR metrics.
- The dynamic highlighting in tables provides intuitive, real-time decision support for active traders.
---
## 🔹 How to Use this Script:
1. **Adjust EMA & SMA Lengths** from indicator settings if different periods are preferred.
2. **Monitor dynamic Fibonacci levels** around the "Golden" average to identify possible reversal or continuation points.
3. **Check EMA/SMA table:** Rows highlighted indicate immediate significance concerning current market price.
4. **ATR table:** Use volatility metrics for better risk management.
---
## 🔷 Conclusion
This advanced Pine Script indicator efficiently combines multiple EMAs, SMAs, dynamic Fibonacci retracement levels, and volatility analysis using ATR into a comprehensive real-time analytical tool, enhancing traders' decision-making capabilities by providing clear and actionable insights directly on the TradingView chart.
Uptrick: RSI Histogram
1. **Introduction to the RSI and Moving Averages**
2. **Detailed Breakdown of the Uptrick: RSI Histogram**
3. **Calculation and Formula**
4. **Visual Representation**
5. **Customization and User Settings**
6. **Trading Strategies and Applications**
7. **Risk Management**
8. **Case Studies and Examples**
9. **Comparison with Other Indicators**
10. **Advanced Usage and Tips**
---
## 1. Introduction to the RSI and Moving Averages
### **1.1 Relative Strength Index (RSI)**
The Relative Strength Index (RSI) is a momentum oscillator developed by J. Welles Wilder and introduced in his 1978 book "New Concepts in Technical Trading Systems." It is widely used in technical analysis to measure the speed and change of price movements.
**Purpose of RSI:**
- **Identify Overbought/Oversold Conditions:** RSI values range from 0 to 100. Traditionally, values above 70 are considered overbought, while values below 30 are considered oversold. These thresholds help traders identify potential reversal points in the market.
- **Trend Strength Measurement:** RSI also indicates the strength of a trend. High RSI values suggest strong bullish momentum, while low values indicate bearish momentum.
**Calculation of RSI:**
1. **Calculate the Average Gain and Loss:** Over a specified period (e.g., 14 days), calculate the average gain and loss.
2. **Compute the Relative Strength (RS):** RS is the ratio of average gain to average loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS))
### **1.2 Moving Averages (MA)**
Moving Averages are used to smooth out price data and identify trends by filtering out short-term fluctuations. Two common types are:
**Simple Moving Average (SMA):** The average of prices over a specified number of periods.
**Exponential Moving Average (EMA):** A type of moving average that gives more weight to recent prices, making it more responsive to recent price changes.
**Smoothed Moving Average (SMA):** Used to reduce the impact of volatility and provide a clearer view of the underlying trend. The RMA, or Running Moving Average, used in the USH script is similar to an EMA but based on the average of RSI values.
## 2. Detailed Breakdown of the Uptrick: RSI Histogram
### **2.1 Indicator Overview**
The Uptrick: RSI Histogram (USH) is a technical analysis tool that combines the RSI with a moving average to create a histogram that reflects momentum and trend strength.
**Key Components:**
- **RSI Calculation:** Determines the relative strength of price movements.
- **Moving Average Application:** Smooths the RSI values to provide a clearer trend indication.
- **Histogram Plotting:** Visualizes the deviation of the smoothed RSI from a neutral level.
### **2.2 Indicator Purpose**
The primary purpose of the USH is to provide a clear visual representation of the market's momentum and trend strength. It helps traders identify:
- **Bullish and Bearish Trends:** By showing how far the smoothed RSI is from the neutral 50 level.
- **Potential Reversal Points:** By highlighting changes in momentum.
### **2.3 Indicator Design**
**RSI Moving Average (RSI MA):** The RSI MA is a smoothed version of the RSI, calculated using a running moving average. This smooths out short-term fluctuations and provides a clearer indication of the underlying trend.
**Histogram Calculation:**
- **Neutral Level:** The histogram is plotted relative to the neutral level of 50. This level represents a balanced market where neither bulls nor bears have dominance.
- **Histogram Values:** The histogram bars show the difference between the RSI MA and the neutral level. Positive values indicate bullish momentum, while negative values indicate bearish momentum.
## 3. Calculation and Formula
### **3.1 RSI Calculation**
The RSI calculation involves:
1. **Average Gain and Loss:** Calculated over the specified length (e.g., 14 periods).
2. **Relative Strength (RS):** RS = Average Gain / Average Loss.
3. **RSI Formula:** RSI = 100 - (100 / (1 + RS)).
### **3.2 Moving Average Calculation**
For the USH indicator, the RSI is smoothed using a running moving average (RMA). The RMA formula is similar to that of the EMA but is based on averaging RSI values over the specified length.
### **3.3 Histogram Calculation**
The histogram value is calculated as:
- **Histogram Value = RSI MA - 50**
**Plotting the Histogram:**
- **Positive Histogram Values:** Indicate that the RSI MA is above the neutral level, suggesting bullish momentum.
- **Negative Histogram Values:** Indicate that the RSI MA is below the neutral level, suggesting bearish momentum.
## 4. Visual Representation
### **4.1 Histogram Bars**
The histogram is plotted as bars on the chart:
- **Bullish Bars:** Colored green when the RSI MA is above 50.
- **Bearish Bars:** Colored red when the RSI MA is below 50.
### **4.2 Customization Options**
Traders can customize:
- **RSI Length:** Adjust the length of the RSI calculation to match their trading style.
- **Bull and Bear Colors:** Choose colors for histogram bars to enhance visual clarity.
### **4.3 Interpretation**
**Bullish Signal:** A histogram bar that moves from red to green indicates a potential shift to a bullish trend.
**Bearish Signal:** A histogram bar that moves from green to red indicates a potential shift to a bearish trend.
## 5. Customization and User Settings
### **5.1 Adjusting RSI Length**
The length parameter determines the number of periods over which the RSI is calculated and smoothed. Shorter lengths make the RSI more sensitive to price changes, while longer lengths provide a smoother view of trends.
### **5.2 Color Settings**
Traders can adjust:
- **Bull Color:** Color of histogram bars indicating bullish momentum.
- **Bear Color:** Color of histogram bars indicating bearish momentum.
**Customization Benefits:**
- **Visual Clarity:** Traders can choose colors that stand out against their chart’s background.
- **Personal Preference:** Adjust settings to match individual trading styles and preferences.
## 6. Trading Strategies and Applications
### **6.1 Trend Following**
**Identifying Entry Points:**
- **Bullish Entry:** When the histogram changes from red to green, it signals a potential entry point for long positions.
- **Bearish Entry:** When the histogram changes from green to red, it signals a potential entry point for short positions.
**Trend Confirmation:** The histogram helps confirm the strength of a trend. Strong, consistent green bars indicate robust bullish momentum, while strong, consistent red bars indicate robust bearish momentum.
### **6.2 Swing Trading**
**Momentum Analysis:**
- **Entry Signals:** Look for significant shifts in the histogram to time entries. A shift from bearish to bullish (red to green) indicates potential for upward movement.
- **Exit Signals:** A shift from bullish to bearish (green to red) suggests a potential weakening of the trend, signaling an exit or reversal point.
### **6.3 Range Trading**
**Market Conditions:**
- **Consolidation:** The histogram close to zero suggests a range-bound market. Traders can use this information to identify support and resistance levels.
- **Breakout Potential:** A significant move away from the neutral level may indicate a potential breakout from the range.
### **6.4 Risk Management**
**Stop-Loss Placement:**
- **Bullish Positions:** Place stop-loss orders below recent support levels when the histogram is green.
- **Bearish Positions:** Place stop-loss orders above recent resistance levels when the histogram is red.
**Position Sizing:** Adjust position sizes based on the strength of the histogram signals. Strong trends (indicated by larger histogram bars) may warrant larger positions, while weaker signals suggest smaller positions.
## 7. Risk Management
### **7.1 Importance of Risk Management**
Effective risk management is crucial for long-term trading success. It involves protecting capital, managing losses, and optimizing trade setups.
### **7.2 Using USH for Risk Management**
**Stop-Loss and Take-Profit Levels:**
- **Stop-Loss Orders:** Use the histogram to set stop-loss levels based on trend strength. For instance, place stops below support levels in bullish trends and above resistance levels in bearish trends.
- **Take-Profit Targets:** Adjust take-profit levels based on histogram changes. For example, lock in profits as the histogram starts to shift from green to red.
**Position Sizing:**
- **Trend Strength:** Scale position sizes based on the strength of histogram signals. Larger histogram bars indicate stronger trends, which may justify larger positions.
- **Volatility:** Consider market volatility and adjust position sizes to mitigate risk.
## 8. Case Studies and Examples
### **8.1 Example 1: Bullish Trend**
**Scenario:** A trader notices a transition from red to green histogram bars.
**Analysis:**
- **Entry Point:** The transition indicates a potential bullish trend. The trader decides to enter a long position.
- **Stop-Loss:** Set stop-loss below recent support levels.
- **Take-Profit:** Consider taking profits as the histogram moves back towards zero or turns red.
**Outcome:** The bullish trend continues, and the histogram remains green, providing a profitable trade setup.
### **8.2 Example 2: Bearish Trend**
**Scenario:** A trader observes a transition from green to red histogram bars.
**Analysis:**
- **Entry Point:** The transition suggests a potential
bearish trend. The trader decides to enter a short position.
- **Stop-Loss:** Set stop-loss above recent resistance levels.
- **Take-Profit:** Consider taking profits as the histogram approaches zero or shifts to green.
**Outcome:** The bearish trend continues, and the histogram remains red, resulting in a successful trade.
## 9. Comparison with Other Indicators
### **9.1 RSI vs. USH**
**RSI:** Measures momentum and identifies overbought/oversold conditions.
**USH:** Builds on RSI by incorporating a moving average and histogram to provide a clearer view of trend strength and momentum.
### **9.2 RSI vs. MACD**
**MACD (Moving Average Convergence Divergence):** A trend-following momentum indicator that uses moving averages to identify changes in trend direction.
**Comparison:**
- **USH:** Provides a smoothed RSI perspective and visual histogram for trend strength.
- **MACD:** Offers signals based on the convergence and divergence of moving averages.
### **9.3 RSI vs. Stochastic Oscillator**
**Stochastic Oscillator:** Measures the level of the closing price relative to the high-low range over a specified period.
**Comparison:**
- **USH:** Focuses on smoothed RSI values and histogram representation.
- **Stochastic Oscillator:** Provides overbought/oversold signals and potential reversals based on price levels.
## 10. Advanced Usage and Tips
### **10.1 Combining Indicators**
**Multi-Indicator Strategies:** Combine the USH with other technical indicators (e.g., Moving Averages, Bollinger Bands) for a comprehensive trading strategy.
**Confirmation Signals:** Use the USH to confirm signals from other indicators. For instance, a bullish histogram combined with a moving average crossover may provide a stronger buy signal.
### **10.2 Customization Tips**
**Adjust RSI Length:** Experiment with different RSI lengths to match various market conditions and trading styles.
**Color Preferences:** Choose histogram colors that enhance visibility and align with personal preferences.
### **10.3 Continuous Learning**
**Backtesting:** Regularly backtest the USH with historical data to refine strategies and improve accuracy.
**Education:** Stay updated with trading education and adapt strategies based on market changes and personal experiences.
Moving Average Confluence [ST]Moving Average Confluence
Description in English:
This indicator uses multiple moving averages (SMA, EMA, WMA) with different periods to identify confluence points that can indicate support or resistance zones.
Detailed Explanation:
Configuration:
SMA Length: This input defines the period for the Simple Moving Average (SMA). The default value is 50.
EMA Length: This input defines the period for the Exponential Moving Average (EMA). The default value is 50.
WMA Length: This input defines the period for the Weighted Moving Average (WMA). The default value is 50.
Confluence Threshold: This input defines the maximum allowable difference between the moving averages to consider them in confluence. The default value is 0.01.
Calculation of Moving Averages:
SMA: Calculated as the simple arithmetic mean of the closing prices over the specified period.
EMA: Calculated by giving more weight to recent prices.
WMA: Calculated by weighting the closing prices based on their age.
Identification of Confluence:
Confluence is identified when the differences between SMA, EMA, and WMA are all within the specified threshold. This can indicate potential support or resistance zones.
Plotting:
The SMA, EMA, and WMA are plotted with different colors for easy identification.
Confluence points are marked with yellow labels on the chart.
Indicator Benefits:
Support and Resistance Identification: Helps traders identify potential support and resistance zones through the confluence of different moving averages.
Visual Cues: Provides clear visual signals for confluence points, aiding in making informed trading decisions.
Customizable Parameters: Allows traders to adjust the periods of the moving averages and the confluence threshold to suit different trading strategies and market conditions.
Justification of Component Combination:
Combining multiple types of moving averages (SMA, EMA, WMA) provides a comprehensive view of market trends. Identifying confluence points where these averages are close together can indicate strong support or resistance levels.
How Components Work Together:
The script calculates the SMA, EMA, and WMA for the specified periods.
It then checks if the differences between these moving averages are within the specified threshold.
When a confluence is detected, it is marked on the chart with a yellow label, providing a clear visual signal to the trader.
Título: Confluência de Médias Móveis
Descrição em Português:
Este indicador utiliza várias médias móveis (SMA, EMA, WMA) com diferentes períodos para identificar pontos de confluência que podem indicar zonas de suporte ou resistência.
Explicação Detalhada:
Configuração:
Comprimento da SMA: Este parâmetro define o período para a Média Móvel Simples (SMA). O valor padrão é 50.
Comprimento da EMA: Este parâmetro define o período para a Média Móvel Exponencial (EMA). O valor padrão é 50.
Comprimento da WMA: Este parâmetro define o período para a Média Móvel Ponderada (WMA). O valor padrão é 50.
Limite de Confluência: Este parâmetro define a diferença máxima permitida entre as médias móveis para considerá-las em confluência. O valor padrão é 0.01.
Cálculo das Médias Móveis:
SMA: Calculada como a média aritmética simples dos preços de fechamento ao longo do período especificado.
EMA: Calculada atribuindo mais peso aos preços mais recentes.
WMA: Calculada ponderando os preços de fechamento com base em sua idade.
Identificação de Confluência:
A confluência é identificada quando as diferenças entre SMA, EMA e WMA estão todas dentro do limite especificado. Isso pode indicar potenciais zonas de suporte ou resistência.
Plotagem:
A SMA, EMA e WMA são plotadas com cores diferentes para fácil identificação.
Pontos de confluência são marcados com etiquetas amarelas no gráfico.
Benefícios do Indicador:
Identificação de Suporte e Resistência: Ajuda os traders a identificar potenciais zonas de suporte e resistência através da confluência de diferentes médias móveis.
Sinais Visuais Claros: Fornece sinais visuais claros para pontos de confluência, auxiliando na tomada de decisões informadas.
Parâmetros Personalizáveis: Permite que os traders ajustem os períodos das médias móveis e o limite de confluência para se adequar a diferentes estratégias de negociação e condições de mercado.
Justificação da Combinação de Componentes:
Combinar vários tipos de médias móveis (SMA, EMA, WMA) fornece uma visão abrangente das tendências do mercado. Identificar pontos de confluência onde essas médias estão próximas pode indicar níveis fortes de suporte ou resistência.
Como os Componentes Funcionam Juntos:
O script calcula a SMA, EMA e WMA para os períodos especificados.
Em seguida, verifica se as diferenças entre essas médias móveis estão dentro do limite especificado.
Quando uma confluência é detectada, ela é marcada no gráfico com uma etiqueta amarela, fornecendo um sinal visual claro para o trader.
Goertzel Adaptive JMA T3Hello Fellas,
The Goertzel Adaptive JMA T3 is a powerful indicator that combines my own created Goertzel adaptive length with Jurik and T3 Moving Averages. The primary intention of the indicator is to demonstrate the new adaptive length algorithm by applying it on bleeding-edge MAs.
It is useable like any moving average, and the new Goertzel adaptive length algorithm can be used to make own indicators Goertzel adaptive.
Used Adaptive Length Algorithms
Normalized Goertzel Power: This uses the normalized power of the Goertzel algorithm to compute an adaptive length without the special operations, like detrending, Ehlers uses for his DFT adaptive length.
Ehlers Mod: This uses the Goertzel algorithm instead of the DFT, originally used by Ehlers, to compute a modified version of his original approach, which sticks as close as possible to the original approach.
Scoring System
The scoring system determines if bars are red or green and collects them.
Then, it goes through all collected red and green bars and checks how big they are and if they are above or below the selected MA. It is positive when green bars are under MA or when red bars are above MA.
Then, it accumulates the size for all positive green bars and for all positive red bars. The same happens for negative green and red bars.
Finally, it calculates the score by ((positiveGreenBars + positiveRedBars) / (negativeGreenBars + negativeRedBars)) * 100 with the scale 0–100.
Signals
Is the price above MA? -> bullish market
Is the price below MA? -> bearish market
Usage
Adjust the settings to reach the highest score, and enjoy an outstanding adaptive MA.
It should be useable on all timeframes. It is recommended to use the indicator on the timeframe where you can get the highest score.
Now, follows a bunch of knowledge for people who don't know about the concepts used here.
T3
The T3 moving average, short for "Tim Tillson's Triple Exponential Moving Average," is a technical indicator used in financial markets and technical analysis to smooth out price data over a specific period. It was developed by Tim Tillson, a software project manager at Hewlett-Packard, with expertise in Mathematics and Computer Science.
The T3 moving average is an enhancement of the traditional Exponential Moving Average (EMA) and aims to overcome some of its limitations. The primary goal of the T3 moving average is to provide a smoother representation of price trends while minimizing lag compared to other moving averages like Simple Moving Average (SMA), Weighted Moving Average (WMA), or EMA.
To compute the T3 moving average, it involves a triple smoothing process using exponential moving averages. Here's how it works:
Calculate the first exponential moving average (EMA1) of the price data over a specific period 'n.'
Calculate the second exponential moving average (EMA2) of EMA1 using the same period 'n.'
Calculate the third exponential moving average (EMA3) of EMA2 using the same period 'n.'
The formula for the T3 moving average is as follows:
T3 = 3 * (EMA1) - 3 * (EMA2) + (EMA3)
By applying this triple smoothing process, the T3 moving average is intended to offer reduced noise and improved responsiveness to price trends. It achieves this by incorporating multiple time frames of the exponential moving averages, resulting in a more accurate representation of the underlying price action.
JMA
The Jurik Moving Average (JMA) is a technical indicator used in trading to predict price direction. Developed by Mark Jurik, it’s a type of weighted moving average that gives more weight to recent market data rather than past historical data.
JMA is known for its superior noise elimination. It’s a causal, nonlinear, and adaptive filter, meaning it responds to changes in price action without introducing unnecessary lag. This makes JMA a world-class moving average that tracks and smooths price charts or any market-related time series with surprising agility.
In comparison to other moving averages, such as the Exponential Moving Average (EMA), JMA is known to track fast price movement more accurately. This allows traders to apply their strategies to a more accurate picture of price action.
Goertzel Algorithm
The Goertzel algorithm is a technique in digital signal processing (DSP) for efficient evaluation of individual terms of the Discrete Fourier Transform (DFT). It's particularly useful when you need to compute a small number of selected frequency components. Unlike direct DFT calculations, the Goertzel algorithm applies a single real-valued coefficient at each iteration, using real-valued arithmetic for real-valued input sequences. This makes it more numerically efficient when computing a small number of selected frequency components¹.
Discrete Fourier Transform
The Discrete Fourier Transform (DFT) is a mathematical technique used in signal processing to convert a finite sequence of equally-spaced samples of a function into a same-length sequence of equally-spaced samples of the discrete-time Fourier transform (DTFT), which is a complex-valued function of frequency . The DFT provides a frequency domain representation of the original input sequence .
Usage of DFT/Goertzel In Adaptive Length Algorithms
Adaptive length algorithms are automated trading systems that can dynamically adjust their parameters in response to real-time market data. This adaptability enables them to optimize their trading strategies as market conditions fluctuate. Both the Goertzel algorithm and DFT can be used in these algorithms to analyze market data and detect cycles or patterns, which can then be used to adjust the parameters of the trading strategy.
The Goertzel algorithm is more efficient than the DFT when you need to compute a small number of selected frequency components. However, for covering a full spectrum, the Goertzel algorithm has a higher order of complexity than fast Fourier transform (FFT) algorithms.
I hope this can help you somehow.
Thanks for reading, and keep it up.
Best regards,
simwai
---
Credits to:
@ClassicScott
@yatrader2
@cheatcountry
@loxx
Trend_Trader_WMA (Momentum)<---> Caution! This is first test version of indicator. I am ready to get more ideas+feedback to develop it more. <--->
The "Momentum_Trader_WMA" indicator is a versatile technical analysis tool designed to help traders identify potential trend changes and momentum shifts in the market. It combines multiple indicators and moving averages to provide a comprehensive view of price action and momentum.
Key Features:
Weighted Moving Averages (WMAs): The indicator calculates two different WMAs with user-defined lengths, providing a smoothed representation of price data.
Average True Range (ATR) Bands: ATR is used to calculate dynamic bands around the WMA Average. These bands can help traders gauge market volatility and potential breakout points. The color of the ATR bands can be seen as an early signal of trends or the continuation of current trends.
Commodity Channel Index (CCI): CCI is a momentum oscillator that measures the relative strength of price changes. The indicator calculates CCI values based on a user-defined period.
Exponential Moving Average (EMA) of CCI: An EMA of CCI is plotted to help identify trends and momentum shifts.
Color-Coded Bands: The ATR bands change colors based on CCI conditions, providing visual cues for potential trading opportunities. When ATR bands transition from narrow (indicating low volatility) to wide (indicating increased volatility), it can be seen as an early signal of a potential trend change or the continuation of the current trend.
Buy and Sell Signals: The indicator generates buy and sell signals based on crossovers of WMAs and CCI thresholds, making it easier for traders to identify entry and exit points.
Customizable Moving Averages: Traders can enable or disable different moving averages (e.g., SMA, EMA, WMA, RMA, VWMA, HMA) with various periods and colors to adapt the indicator to their trading preferences.
CCI Dot Alerts: Dots are displayed at the bottom of the chart based on CCI values, helping traders spot extreme CCI conditions.
How to Use:
Trend Identification: The WMAs and ATR bands can help identify the current trend direction and its strength. When the WMAs are in an uptrend (green) and the ATR bands widen, it may indicate a strong bullish trend. Conversely, when the WMAs are in a downtrend (red) and the ATR bands narrow, it may suggest a weakening bearish trend.
Momentum Confirmation: The CCI and its EMA provide insights into market momentum. Look for CCI crossovers above 100 for potential bullish momentum and below -100 for potential bearish momentum.
Buy and Sell Signals: Pay attention to the buy and sell signals generated by the indicator. Buy when the WMAs cross over and CCI crosses above 100. Sell when the WMAs cross under and CCI crosses below -100.
ATR Bands as Early Signals: The color changes in the ATR bands can be seen as early signals of trends or the continuation of current trends. Wide ATR bands may indicate increased volatility and potential trend changes, while narrow ATR bands suggest reduced volatility and potential trend continuation.
Moving Averages: Customize the indicator by enabling or disabling specific moving averages according to your preferred trading strategy.
CCI Dots: Use the CCI dots to identify extreme CCI conditions, which may indicate overbought or oversold market conditions.
PS:
Recommended to use Indicator with price action conecpts(eg. support and resistance) as they play important role in any market.
Buy and sell signals are not really accurate. I would personally look for trend shift in WMA middle line and confirmation from CCI dots at bottom. For example. If middle line turns green and within recent 3-4 candles (or next 3-4 candles) dots tunrns green also, that means momentum has been rised in the direction of bulls.
pls, take s/r concepts first when working. I am thinking to add more precise buy sell signal method to make it easier to trade.
Good luck with your trades :)
