TechnicalAnalysis

vor 4 Stunden

█ 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

• 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

vor 1 Stunde

Versionshinweise

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