PINE LIBRARY
Atualizado TechnicalAnalysis
█ DCAUT TechnicalAnalysis Library
📊 OVERVIEW
DCAUT TechnicalAnalysis is a professional-grade Pine Script technical analysis library designed for traders and quantitative analysts who demand excellence. This library brings together 25+ advanced moving averages and smoothing filters, from classic SMA/EMA to cutting-edge Kalman Filters and adaptive algorithms, all meticulously implemented based on academic research and industry best practices.
🎯 Core Features
🎯 CONCEPTS
Why do we need this technical analysis library?
While TradingView has abundant technical indicator code, there are significant issues:
Inconsistent Code Quality
Low Development Efficiency
Our Solution
🚀 USING THIS LIBRARY
Import Library
Pine Script®
Basic Usage Example
Pine Script®
Advanced Trading System
Pine Script®
📋 FUNCTIONS REFERENCE
sma(source, length)
Calculates the Simple Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Simple Moving Average value.
ema(source, length)
Calculates the Exponential Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Exponential Moving Average value.
wma(source, length)
Calculates the Weighted Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Weighted Moving Average value.
rma(source, length)
Calculates the Rolling Moving Average (SMMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Rolling Moving Average value.
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float): Series of values to process.
alpha (series float): The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
hma(source, length)
Calculates the Hull Moving Average (HMA) of a given data series. HMA reduces lag and improves smoothing by using weighted averages.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Hull Moving Average value.
vwma(source, volumeSource, length)
Calculates the Volume Weighted Moving Average (VWMA) of a given data series. VWMA gives more weight to periods with higher volume.
Parameters:
source (series float): Series of values to process.
volumeSource (series float): Volume series to be used for weighting.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Volume Weighted Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for efficiency calculation.
fastLength (simple int): Fast EMA length. Optional, default is 2.
slowLength (simple int): Slow EMA length. Optional, default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
ama(source, length, fastLength, slowLength)
Calculates the Adaptive Moving Average (AMA) of a given data series. AMA adjusts its smoothing based on price range and volatility.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for adaptation calculation.
fastLength (simple int): Fast smoothing length. Optional, default is 2.
slowLength (simple int): Slow smoothing length. Optional, default is 30.
Returns: (float) The calculated Adaptive Moving Average value.
vidya(source, length, cmoLength)
Calculates the Variable Index Dynamic Average (VIDYA) of a given data series. VIDYA adapts its smoothing factor based on market volatility using CMO.
Parameters:
source (series float): Series of values to process.
length (simple int): Period for EMA calculation.
cmoLength (simple int): Period for CMO volatility calculation. Optional, default is 9.
Returns: (float) The calculated Variable Index Dynamic Average value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
volumeFactor (simple float): Volume factor affecting responsiveness. Optional, default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations.
Parameters:
source (series float): Series of values to process.
processNoise (simple float): Process noise variance (Q). Controls adaptation speed. Optional, default is 0.05.
measurementNoise (simple float): Measurement noise variance (R). Controls smoothing. Optional, default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically.
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals.
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: ([float, float]) Tuple containing [MAMA, FAMA] values.
alma(source, length, offset, sigma)
Calculates the Arnaud Legoux Moving Average (ALMA) of a given data series. ALMA is a Gaussian filter-based moving average that balances responsiveness and smoothness.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
offset (simple float): Phase offset parameter (0-1). Higher values increase responsiveness. Optional, default is 0.85.
sigma (simple float): Standard deviation parameter affecting filter width. Optional, default is 6.0.
Returns: (float) The calculated Arnaud Legoux Moving Average value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for filter calculation.
Returns: (float) The calculated Super Smoother value.
laguerreFilter(source, length, gamma)
Calculates the Laguerre Filter of a given data series. Laguerre Filter uses 6-pole feedback with UltimateSmoother preprocessing.
Parameters:
source (series float): Series of values to process.
length (simple int): Length for UltimateSmoother preprocessing.
gamma (simple float): Feedback coefficient (0-1). Lower values reduce lag. Optional, default is 0.5.
Returns: (float) The calculated Laguerre Filter value.
lsma(source, length, offset)
Calculates the Least Squares Moving Average (LSMA) of a given data series. LSMA uses linear regression to predict trend with reduced lag.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for linear regression calculation.
offset (simple int): Offset for the regression line. Optional, default is 0.
Returns: (float) The calculated Least Squares Moving Average value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for average range calculation.
multiplier (simple float): Multiplier for smooth range. Higher values increase filtering. Optional, default is 2.618.
Returns: ([float, int, float, float]) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals.
Parameters:
source (series float): Series of values to process.
rsiLength (simple int): Number of bars for RSI calculation. Optional, default is 14.
rsiSmooth (simple int): Number of bars for smoothing RSI. Optional, default is 5.
qqeFactor (simple float): QQE factor for volatility band width. Optional, default is 4.236.
Returns: ([float, float]) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: ([float, float]) Tuple containing SSL Up and SSL Down lines.
📚 RELEASE NOTES
v1.0 (2025.09.22)
📄 License: MIT License
👨💻 Developer: DCAUT Team
📊 OVERVIEW
DCAUT TechnicalAnalysis is a professional-grade Pine Script technical analysis library designed for traders and quantitative analysts who demand excellence. This library brings together 25+ advanced moving averages and smoothing filters, from classic SMA/EMA to cutting-edge Kalman Filters and adaptive algorithms, all meticulously implemented based on academic research and industry best practices.
🎯 Core Features
- Academic Precision - All algorithms strictly follow original papers and formulas
- Performance Optimized - Pre-calculated constants and optimized algorithms ensure fast response
- Professional Standards - Unified interface design following TradingView best practices
- Continuous Innovation - Constantly integrating latest technical analysis research
🎯 CONCEPTS
Why do we need this technical analysis library?
While TradingView has abundant technical indicator code, there are significant issues:
Inconsistent Code Quality
- Many public indicators lack optimization and perform poorly
- Algorithm implementations deviate from academic standards
- Code structure is messy, difficult to maintain and extend
- Lacks unified interface design and naming conventions
Low Development Efficiency
- Need to rewrite basic indicator functions every time
- Debugging and testing consume excessive time
- High code duplication, prone to introducing errors
- Lack of professional-grade algorithm implementation references
Our Solution
- Standardized Implementation: Strictly follows academic papers and original formulas
- Performance Optimization: Pre-calculated constants reduce redundant computations
- Unified Interface: Consistent function signatures and naming conventions
- Plug and Play: One-line import, direct usage, dramatically improves development efficiency
- Continuous Maintenance: Professional team maintains code quality and accuracy
🚀 USING THIS LIBRARY
Import Library
//@version=6
import DCAUT/TechnicalAnalysis/1 as dta
indicator("Advanced Technical Analysis", overlay=true)
Basic Usage Example
// Classic moving average combination
ema20 = dta.ema(close, 20)
hma20 = dta.hma(close, 20)
plot(ema20, "EMA20", color.red, 2)
plot(hma20, "HMA20", color.green, 2)
Advanced Trading System
// Adaptive moving average system
kama = dta.kama(close, 20, 2, 30)
hma = dta.hma(close, 20)
// Trend confirmation and entry signals
bullTrend = kama > kama[1] and hma > hma[1]
bearTrend = kama < kama[1] and hma < hma[1]
longSignal = ta.crossover(close, kama) and bullTrend
shortSignal = ta.crossunder(close, kama) and bearTrend
plot(kama, "KAMA", color.blue, 3)
plot(hma, "HMA", color.orange, 2)
plotshape(longSignal, "Buy", shape.triangleup, location.belowbar, color.green)
plotshape(shortSignal, "Sell", shape.triangledown, location.abovebar, color.red)
📋 FUNCTIONS REFERENCE
sma(source, length)
Calculates the Simple Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Simple Moving Average value.
ema(source, length)
Calculates the Exponential Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Exponential Moving Average value.
wma(source, length)
Calculates the Weighted Moving Average of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Weighted Moving Average value.
rma(source, length)
Calculates the Rolling Moving Average (SMMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Rolling Moving Average value.
ewma(source, alpha)
Calculates the Exponentially Weighted Moving Average with dynamic alpha parameter.
Parameters:
source (series float): Series of values to process.
alpha (series float): The smoothing parameter of the filter.
Returns: (float) The exponentially weighted moving average value.
dema(source, length)
Calculates the Double Exponential Moving Average (DEMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Double Exponential Moving Average value.
tema(source, length)
Calculates the Triple Exponential Moving Average (TEMA) of a given data series.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Triple Exponential Moving Average value.
zlema(source, length)
Calculates the Zero-Lag Exponential Moving Average (ZLEMA) of a given data series. This indicator attempts to eliminate the lag inherent in all moving averages.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Zero-Lag Exponential Moving Average value.
hma(source, length)
Calculates the Hull Moving Average (HMA) of a given data series. HMA reduces lag and improves smoothing by using weighted averages.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Hull Moving Average value.
vwma(source, volumeSource, length)
Calculates the Volume Weighted Moving Average (VWMA) of a given data series. VWMA gives more weight to periods with higher volume.
Parameters:
source (series float): Series of values to process.
volumeSource (series float): Volume series to be used for weighting.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Volume Weighted Moving Average value.
tma(source, length)
Calculates the Triangular Moving Average (TMA) of a given data series. TMA is a double-smoothed simple moving average that reduces noise.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Triangular Moving Average value.
frama(source, length)
Calculates the Fractal Adaptive Moving Average (FRAMA) of a given data series. FRAMA adapts its smoothing factor based on fractal geometry to reduce lag.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: (float) The calculated Fractal Adaptive Moving Average value.
kama(source, length, fastLength, slowLength)
Calculates Kaufman's Adaptive Moving Average (KAMA) of a given data series. KAMA adjusts its smoothing based on market efficiency ratio.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for efficiency calculation.
fastLength (simple int): Fast EMA length. Optional, default is 2.
slowLength (simple int): Slow EMA length. Optional, default is 30.
Returns: (float) The calculated Kaufman's Adaptive Moving Average value.
ama(source, length, fastLength, slowLength)
Calculates the Adaptive Moving Average (AMA) of a given data series. AMA adjusts its smoothing based on price range and volatility.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for adaptation calculation.
fastLength (simple int): Fast smoothing length. Optional, default is 2.
slowLength (simple int): Slow smoothing length. Optional, default is 30.
Returns: (float) The calculated Adaptive Moving Average value.
vidya(source, length, cmoLength)
Calculates the Variable Index Dynamic Average (VIDYA) of a given data series. VIDYA adapts its smoothing factor based on market volatility using CMO.
Parameters:
source (series float): Series of values to process.
length (simple int): Period for EMA calculation.
cmoLength (simple int): Period for CMO volatility calculation. Optional, default is 9.
Returns: (float) The calculated Variable Index Dynamic Average value.
mcginleyDynamic(source, length)
Calculates the McGinley Dynamic of a given data series. McGinley Dynamic is an adaptive moving average that adjusts to market speed changes.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for dynamic calculation.
Returns: (float) The calculated McGinley Dynamic value.
t3(source, length, volumeFactor)
Calculates the Tilson Moving Average (T3) of a given data series. T3 is a triple-smoothed exponential moving average with improved lag characteristics.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
volumeFactor (simple float): Volume factor affecting responsiveness. Optional, default is 0.7.
Returns: (float) The calculated Tilson Moving Average value.
ultimateSmoother(source, length)
Calculates the Ultimate Smoother of a given data series. Uses advanced filtering techniques to reduce noise while maintaining responsiveness.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for smoothing calculation.
Returns: (float) The calculated Ultimate Smoother value.
kalmanFilter(source, processNoise, measurementNoise)
Calculates the Kalman Filter of a given data series. Optimal estimation algorithm that estimates true value from noisy observations.
Parameters:
source (series float): Series of values to process.
processNoise (simple float): Process noise variance (Q). Controls adaptation speed. Optional, default is 0.05.
measurementNoise (simple float): Measurement noise variance (R). Controls smoothing. Optional, default is 1.0.
Returns: (float) The calculated Kalman Filter value.
mama(source, fastLimit, slowLimit)
Calculates the Mesa Adaptive Moving Average (MAMA) of a given data series. MAMA uses Hilbert Transform Discriminator to adapt to market cycles dynamically.
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: (float) The calculated Mesa Adaptive Moving Average value.
fama(source, fastLimit, slowLimit)
Calculates the Following Adaptive Moving Average (FAMA) of a given data series. FAMA follows MAMA with reduced responsiveness for crossover signals.
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: (float) The calculated Following Adaptive Moving Average value.
mamaFama(source, fastLimit, slowLimit)
Calculates Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA).
Parameters:
source (series float): Series of values to process.
fastLimit (simple float): Maximum alpha (responsiveness). Optional, default is 0.5.
slowLimit (simple float): Minimum alpha (smoothing). Optional, default is 0.05.
Returns: ([float, float]) Tuple containing [MAMA, FAMA] values.
alma(source, length, offset, sigma)
Calculates the Arnaud Legoux Moving Average (ALMA) of a given data series. ALMA is a Gaussian filter-based moving average that balances responsiveness and smoothness.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
offset (simple float): Phase offset parameter (0-1). Higher values increase responsiveness. Optional, default is 0.85.
sigma (simple float): Standard deviation parameter affecting filter width. Optional, default is 6.0.
Returns: (float) The calculated Arnaud Legoux Moving Average value.
superSmoother(source, length)
Calculates the Super Smoother of a given data series. SuperSmoother is a second-order Butterworth filter from aerospace technology.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for filter calculation.
Returns: (float) The calculated Super Smoother value.
laguerreFilter(source, length, gamma)
Calculates the Laguerre Filter of a given data series. Laguerre Filter uses 6-pole feedback with UltimateSmoother preprocessing.
Parameters:
source (series float): Series of values to process.
length (simple int): Length for UltimateSmoother preprocessing.
gamma (simple float): Feedback coefficient (0-1). Lower values reduce lag. Optional, default is 0.5.
Returns: (float) The calculated Laguerre Filter value.
lsma(source, length, offset)
Calculates the Least Squares Moving Average (LSMA) of a given data series. LSMA uses linear regression to predict trend with reduced lag.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for linear regression calculation.
offset (simple int): Offset for the regression line. Optional, default is 0.
Returns: (float) The calculated Least Squares Moving Average value.
rangeFilter(source, length, multiplier)
Calculates the Range Filter of a given data series. Range Filter reduces noise by filtering price movements within a dynamic range.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for average range calculation.
multiplier (simple float): Multiplier for smooth range. Higher values increase filtering. Optional, default is 2.618.
Returns: ([float, int, float, float]) Tuple containing filtered value, trend direction, upper band, and lower band.
qqe(source, rsiLength, rsiSmooth, qqeFactor)
Calculates the Quantitative Qualitative Estimation (QQE) of a given data series. QQE is an improved RSI that reduces noise and provides smoother signals.
Parameters:
source (series float): Series of values to process.
rsiLength (simple int): Number of bars for RSI calculation. Optional, default is 14.
rsiSmooth (simple int): Number of bars for smoothing RSI. Optional, default is 5.
qqeFactor (simple float): QQE factor for volatility band width. Optional, default is 4.236.
Returns: ([float, float]) Tuple containing smoothed RSI and QQE trend line.
sslChannel(source, length)
Calculates the Semaphore Signal Level (SSL) Channel of a given data series. SSL Channel provides clear trend signals using moving averages of high and low prices.
Parameters:
source (series float): Series of values to process.
length (simple int): Number of bars for moving average calculation.
Returns: ([float, float]) Tuple containing SSL Up and SSL Down lines.
📚 RELEASE NOTES
v1.0 (2025.09.22)
- 25+ professional technical analysis functions
- Complete adaptive moving average series
- Advanced signal processing filters
- Performance optimization and constant pre-calculation
- Unified function interface design
📄 License: MIT License
👨💻 Developer: DCAUT Team
Notas de Lançamento
v2.0 (2025.09.23)- ✅ Added universal moving average interface ma()
- ✅ Added customizable MA type ATR function
- ✅ Added customizable MA type MACD function
- ✅ Support for flexible combinations of 21 moving average algorithms
- ✅ Enhanced volatility and trend analysis tools
Biblioteca do Pine
No verdadeiro espirito do TradingView, o autor desse código Pine o publicou como uma biblioteca de código aberto, para que outros programadores Pine da nossa comunidade possam reusa-los. Parabéns ao autor! Você pode usar essa biblioteca privadamente ou em outras publicações de código aberto, mas a reutilização desse código em publicações é regida pelas Regras da Casa.
Aviso legal
As informações e publicações não devem ser e não constituem conselhos ou recomendações financeiras, de investimento, de negociação ou de qualquer outro tipo, fornecidas ou endossadas pela TradingView. Leia mais em Termos de uso.
Biblioteca do Pine
No verdadeiro espirito do TradingView, o autor desse código Pine o publicou como uma biblioteca de código aberto, para que outros programadores Pine da nossa comunidade possam reusa-los. Parabéns ao autor! Você pode usar essa biblioteca privadamente ou em outras publicações de código aberto, mas a reutilização desse código em publicações é regida pelas Regras da Casa.
Aviso legal
As informações e publicações não devem ser e não constituem conselhos ou recomendações financeiras, de investimento, de negociação ou de qualquer outro tipo, fornecidas ou endossadas pela TradingView. Leia mais em Termos de uso.