QuanTAlib/docs/indicators.md

Indicator Catalog

QuanTAlib provides technical indicators organized into mathematical families. Understanding these families helps choose the right tool for the analytical problem at hand. Selecting an indicator without understanding its category leads to confusion at best, losses at worst.

Category Reference

Category	What It Measures	Representative Indicators	When to Reach for It
Trends (FIR)	Trend direction via finite impulse response filters	SMA, WMA, ALMA, HMA, LSMA	Trend identification with predictable lag and finite memory. Output depends only on a fixed window of past prices.
Trends (IIR)	Trend direction via infinite impulse response filters	EMA, DEMA, TEMA, JMA, KAMA, MAMA	Trend identification with recursive calculation and theoretically infinite memory. More responsive per unit of smoothness.
Filters	Signal processing filters for noise reduction	Bessel, Butterworth, Super Smoother	Removing noise while preserving trend structure. Designed by engineers, borrowed by traders.
Oscillators	Cyclical movement around a baseline	Stochastic, Fisher, UltOsc, Williams %R	Identifying overbought/oversold conditions and potential reversals. Bounded indicators that oscillate.
Dynamics	Trend strength and structural changes	ADX, Aroon, SuperTrend, Chop	Determining market regime (trending vs ranging) and measuring trend conviction.
Momentum	Speed and magnitude of price changes	ROC, RSI, MACD, CMO	Measuring acceleration or deceleration in price. First derivative territory.
Volatility	Size and variability of price movements	ATR, StdDev, HV, YZV	Position sizing, stop-loss placement, regime identification. How much prices move matters as much as direction.
Volume	Trading activity and price-volume relationships	OBV, VWAP, MFI, CMF	Confirming price movements with participation. Volume validates or contradicts price action.
Channels	Price boundaries and range definitions	Bollinger, Keltner, Donchian	Breakout strategies and range-bound trading. Defining "normal" so abnormal becomes visible.
Statistics	Mathematical relationships between price series	Correlation, Covariance, Beta, Z-Score	Portfolio analysis, pairs trading, statistical arbitrage. Quantitative analysis beyond single instruments.
Numerics	Mathematical transformations and signal processing	Slope, Accel, Normalize, Sigmoid	Custom indicator development and advanced signal processing. Building blocks for novel indicators.
Errors	Measurement accuracy and model fit quality	MAE, RMSE, R², Huber	Model validation and forecast assessment. Quantifying wrongness before production quantifies losses.
Forecasts	Future price prediction and projection	AFIRMA	Projecting price based on historical patterns. Predictions that invite humility.
Cycles	Periodic patterns and dominant frequencies	Hilbert Transform, EBSW, STC	Identifying cyclical market behavior. Markets exhibit cycles; detecting them reliably remains hard.
Reversals	Turning points and stop levels	Pivot Points, PSAR, Chandelier, Swings	Identifying potential trend reversals, computing adaptive stops, and defining support/resistance.

Selection by Experience Level

Beginning technical analysis? Start with Trends (FIR/IIR), Volatility, and Oscillators. SMA teaches moving average fundamentals. ATR teaches volatility measurement. RSI teaches bounded oscillators. These provide foundation for everything else.

Building a trading strategy? Add Volume for confirmation, Channels for breakouts, Dynamics for regime detection. Volume validates price moves. Channels define breakout boundaries. ADX distinguishes trending from ranging markets.

Quantitative development? Filters, Statistics, Numerics, and Errors provide tools for signal processing, model validation, and custom indicator construction. Butterworth filters for noise reduction. Correlation for pairs trading. Error metrics for model selection.

Implemented Indicators

Trends (FIR)

Finite Impulse Response filters. Output depends only on a fixed window of inputs. Always stable. Predictable lag characteristics.

Indicator	Full Name	Notes
ALMA	Arnaud Legoux MA	Gaussian-weighted with offset parameter
BLMA	Blackman Window MA	Spectral leakage reduction
BWMA	Bessel-Weighted MA	Linear phase response
CONV	Convolution MA	Arbitrary kernel support
DWMA	Double Weighted MA	WMA applied twice
GWMA	Gaussian Weighted MA	Normal distribution weights
HAMMA	Hamming Weighted MA	Spectral analysis window
HANMA	Hanning Weighted MA	Cosine-based window
HMA	Hull MA	Reduced lag via WMA differencing
HWMA	Henderson Weighted MA	Henderson curve smoothing
LSMA	Least Squares MA	Linear regression endpoint
PWMA	Pascal Weighted MA	Binomial coefficient weights
SGMA	Savitzky-Golay MA	Polynomial smoothing
SINEMA	Sine-Weighted MA	Sinusoidal weight distribution
SMA	Simple MA	Equal weights, the baseline
TRIMA	Triangular MA	Double-smoothed SMA
WMA	Weighted MA	Linear weight decay

Trends (IIR)

Infinite Impulse Response filters. Output depends on current input and past outputs. Recursive structure. More responsive but requires stability analysis.

Indicator	Full Name	Notes
DECYCLER	Ehlers Decycler	Complementary HP filter subtracting high-frequency noise
DEMA	Double Exponential MA	EMA of EMA with lag compensation
DSMA	Deviation-Scaled MA	Volatility-adaptive smoothing
EMA	Exponential MA	The fundamental IIR filter
FRAMA	Ehlers Fractal Adaptive MA	Dimension-based adaptation
HEMA	Hull Exponential MA	Hull concept with EMA
HTIT	Ehlers Hilbert Instantaneous Trend	Dominant cycle extraction
JMA	Jurik MA	Adaptive, low-lag, proprietary algorithm
KAMA	Kaufman Adaptive MA	Efficiency ratio adaptation
MAMA	Ehlers MESA Adaptive MA	Homodyne discriminator based
MGDI	McGinley Dynamic	Market-speed tracking
MMA	Modified MA	Smoothed EMA variant
QEMA	Quad Exponential MA	Four-stage exponential
REMA	Regularized Exponential MA	Regularization for stability
RGMA	Recursive Gaussian MA	Gaussian approximation
RMA	WildeR MA	Wilder's smoothing (1/n decay)
T3	Tillson T3 MA	Six-stage DEMA variant
TEMA	Triple Exponential MA	Three-stage lag reduction
VAMA	Volatility Adjusted MA	ATR-based adaptation
VIDYA	Variable Index Dynamic	CMO-based adaptation
YZVAMA	Yang-Zhang Vol Adjusted MA	YZ volatility adaptation
ZLDEMA	Zero-Lag Double Exponential MA	Momentum-compensated DEMA
ZLEMA	Zero-Lag Exponential MA	Momentum-compensated EMA
ZLTEMA	Zero-Lag Triple Exponential MA	Momentum-compensated TEMA

Filters

Signal processing filters adapted for financial time series. Designed to separate signal from noise with controlled frequency response.

Indicator	Full Name	Notes
AGC	Ehlers Automatic Gain Control	Ehlers amplitude normalization via peak tracking
ALAGUERRE	Ehlers Adaptive Laguerre Filter	Ehlers variable-alpha from tracking error
BAXTERKING	Baxter-King Band-Pass Filter	Symmetric FIR band-pass for cycle extraction
CFITZ	Christiano-Fitzgerald Filter	Asymmetric full-sample band-pass, random-walk optimal
BESSEL	Bessel Filter	Maximally flat group delay
BILATERAL	Bilateral Filter	Edge-preserving smoothing
BPF	BandPass Filter	Frequency band isolation
BUTTER	Ehlers Butterworth Filter	Maximally flat passband
CHEBY1	Chebyshev Type I	Steeper rolloff with passband ripple
CHEBY2	Chebyshev Type II	Steeper rolloff with stopband ripple
EDCF	Ehlers Distance Coefficient Filter	Nonlinear FIR, distance-weighted smoothing
ELLIPTIC	Elliptic (Cauer) Filter	Sharpest transition, both band ripple
GAUSS	Gaussian Filter	No overshoot, smooth response
HANN	Hann Filter	Raised cosine window filter
HP	Hodrick-Prescott Filter	Trend-cycle decomposition
HPF	Ehlers Highpass Filter	Ehlers high-pass design
KALMAN	Kalman Filter	Optimal recursive estimation
LAGUERRE	Ehlers Laguerre Filter	Ehlers 4-element all-pass cascade
LMS	Least Mean Squares	Widrow-Hoff adaptive FIR filter
RLS	Recursive Least Squares	Faster convergence than LMS
LOESS	LOESS Smoothing	Local polynomial regression
NOTCH	Notch Filter	Single frequency rejection
ONEEURO	One Euro Filter	Speed-adaptive low-pass, adaptive cutoff
ROOFING	Ehlers Roofing Filter	Ehlers HP + SS bandpass cascade
SGF	Savitzky-Golay Filter	Polynomial least-squares fitting
SPBF	Ehlers Super Passband Filter	Ehlers wide-band bandpass with RMS envelope
SSF	Ehlers Super Smoother Filter	Ehlers two-pole design
USF	Ehlers Ultimate Smoother	Ehlers high-fidelity filter
VOSS	Ehlers Voss Predictive Filter	Ehlers BPF + negative group delay predictor
WAVELET	Wavelet Denoising Filter	A trous Haar + MAD soft thresholding
WIENER	Wiener Filter	Minimum mean-square error denoising

Oscillators

Bounded indicators that oscillate around a centerline or between fixed extremes. Useful for mean-reversion signals.

Indicator	Full Name	Notes
AC	Acceleration Oscillator	AO acceleration (2nd derivative)
AO	Awesome Oscillator	Midpoint momentum
APO	Absolute Price Oscillator	EMA difference
BBB	Bollinger %B	Position within Bollinger Bands
BBS	Bollinger Band Squeeze	BB inside KC squeeze detection
CFO	Chande Forecast Oscillator	Forecast error percentage
DECO	Ehlers Decycler Oscillator	Dual HP bandpass cycle isolation
DPO	Detrended Price Oscillator	Displaced SMA trend removal
FISHER	Ehlers Fisher Transform	Gaussian-normalized price reversal
INERTIA	Inertia	Linear regression residual
KDJ	KDJ Indicator	Enhanced Stochastic (J = 3K − 2D)
PGO	Pretty Good Oscillator	ATR-normalized SMA displacement
SMI	Stochastic Momentum Index	Distance from range midpoint (K/D lines)
STOCH	Stochastic Oscillator	Close within N-period H/L range (%K/%D)
STOCHF	Stochastic Fast	Unsmoothed Stochastic (%K/%D, SMA smoothing only)
STOCHRSI	Stochastic RSI	Stochastic applied to RSI (%K/%D)
TRIX	Triple Exponential Average	ROC of triple-smoothed EMA
TTM_WAVE	TTM Wave	Fibonacci-period MACD composite (A/B/C waves)
ULTOSC	Ultimate Oscillator	Multi-timeframe weighted buying pressure
WILLR	Williams %R	Inverse Stochastic (-100 to 0)

Dynamics

Indicators measuring trend strength, regime, and directional movement quality.

Indicator	Full Name	Notes
ADX	Average Directional Index	Trend strength 0-100
ADXR	ADX Rating	Smoothed ADX
ALLIGATOR	Williams Alligator	Three displaced SMAs for trend detection
AMAT	Archer MA Trends	MA-based trend detection
AROON	Aroon	High/low recency
AROONOSC	Aroon Oscillator	Aroon Up minus Down
CHOP	Choppiness Index	ATR sum vs range; trending vs choppy
DMX	Jurik DMX	Enhanced directional movement
DX	Directional Movement Index	Raw directional strength
HT_TRENDMODE	Ehlers Hilbert Transform Trend vs Cycle Mode	Cycle vs trend regime detection
ICHIMOKU	Ichimoku Cloud	Multi-component trend system
IMI	Intraday Momentum Index	Candlestick-based momentum
IMPULSE	Elder Impulse System	EMA + MACD-H trend/momentum fusion
QSTICK	Qstick	Average close-open difference
SUPER	SuperTrend	ATR-based trend bands
TTM_SQUEEZE	TTM Squeeze	BB inside KC squeeze with momentum
TTM_TREND	TTM Trend	Bar coloring by close vs midline
VORTEX	Vortex Indicator	Uptrend/downtrend movement comparison

Momentum

Rate of change and velocity measurements. First derivatives of price.

Indicator	Full Name	Notes
BOP	Balance of Power	Close position in range
CCI	Commodity Channel Index	Mean deviation normalized
CFB	Composite Fractal Behavior	Jurik fractal momentum
CMO	Chande Momentum Oscillator	Up/down ratio oscillator
MACD	Moving Average Convergence Divergence	EMA crossover system
MOM	Momentum	Raw price difference over N periods
PMO	Price Momentum Oscillator	Double-smoothed ROC
PPO	Percentage Price Oscillator	Percentage EMA difference
PRS	Price Relative Strength	Dual-input ratio comparison
ROC	Rate of Change	Absolute price change over N periods
ROCP	Rate of Change Percentage	Percentage price change over N periods
ROCR	Rate of Change Ratio	Price ratio over N periods
RSI	Relative Strength Index	Bounded 0-100 momentum
RSX	Jurik RSX	Smoothed RSI variant
TSI	True Strength Index	Double-smoothed momentum oscillator
VEL	Jurik Velocity	Adaptive velocity

Volatility

Measures of price variability and range. Essential for position sizing and stop placement.

Indicator	Full Name	Notes
ADR	Average Daily Range	Simple range averaging
ATR	Average True Range	Gap-adjusted range
ATRN	ATR Normalized	ATR scaled to [0,1]
ATRP	ATR Percent	Percentage-based ATR
BBW	Bollinger Band Width	Band width as percentage of middle band
BBWN	BB Width Normalized	Band width normalized to [0,1]
BBWP	BB Width Percentile	Band width historical percentile
CCV	Close-to-Close Volatility	Log-return standard deviation
CV	Coefficient of Variation	StdDev / Mean ratio
CVI	Chaikin Volatility	EMA change of H-L range
EWMA	EWMA Volatility	Exponentially weighted variance
GKV	Garman-Klass Volatility	OHLC-based efficiency estimator
HLV	High-Low Volatility	Parkinson range-based estimator
HV	Historical Volatility	Annualized log-return StdDev
JVOLTY	Jurik Volatility	Adaptive volatility measure
JVOLTYN	Jurik Volatility Normalized	Jurik volatility scaled to [0,100]
MASSI	Mass Index	EMA ratio of H-L range
NATR	Normalized ATR	ATR as percentage of close
RSV	Rogers-Satchell Volatility	Drift-independent OHLC estimator
RV	Realized Volatility	Sum of squared returns
RVI	Relative Volatility Index	RSI applied to StdDev
TR	True Range	Max(H-L, H-prevC, prevC-L)
UI	Ulcer Index	Downside deviation from highs
VOV	Volatility of Volatility	Second-order volatility
VR	Volatility Ratio	ATR-relative true range
YZV	Yang-Zhang Volatility	Optimal OHLC estimator

Volume

Price-volume relationships and accumulation/distribution measurements.

Indicator	Full Name	Notes
ADL	Accumulation/Distribution Line	Volume-weighted close position
ADOSC	Chaikin A/D Oscillator	ADL momentum (fast EMA - slow EMA)
AOBV	Archer On-Balance Volume	OBV with signal line
CMF	Chaikin Money Flow	Volume-weighted close position over period
EFI	Elder's Force Index	Price change × volume
EOM	Ease of Movement	Price movement per unit volume
III	Intraday Intensity Index	Close position within H-L × volume
KVO	Klinger Volume Oscillator	Trend-volume force oscillator
MFI	Money Flow Index	Volume-weighted RSI
NVI	Negative Volume Index	Cumulative on low-volume days
OBV	On Balance Volume	Cumulative signed volume
PVD	Price Volume Divergence	Price-volume correlation divergence
PVI	Positive Volume Index	Cumulative on high-volume days
PVO	Percentage Volume Oscillator	Percentage volume MA difference
PVR	Price Volume Rank	Categorical price-volume classification
PVT	Price Volume Trend	ROC-weighted cumulative volume
TVI	Trade Volume Index	Tick-direction cumulative volume
TWAP	Time Weighted Average Price	Time-equal-weighted price average
VA	Volume Accumulation	Cumulative volume by close position
VF	Volume Force	EMA-smoothed price-volume force
VO	Volume Oscillator	Short vs long volume MA difference
VROC	Volume Rate of Change	Volume change over lookback period
VWAD	Volume Weighted A/D	Close-position cumulative volume
VWAP	Volume Weighted Average Price	Price × volume / total volume
VWMA	Volume Weighted MA	Volume-weighted moving average
WAD	Williams A/D	True range-based accumulation

Channels

Price envelope and boundary indicators for breakout and mean-reversion strategies.

Indicator	Full Name	Notes
ABBER	Aberration Bands	Statistical deviation bands
ACCBANDS	Acceleration Bands	Volatility-adjusted envelope
APCHANNEL	Andrews' Pitchfork	Three-line channel from pivot points
APZ	Adaptive Price Zone	EMA-based volatility zone
ATRBANDS	ATR Bands	ATR-based envelope around price
BBANDS	Bollinger Bands	SMA ± StdDev bands
DCHANNEL	Donchian Channels	Highest-high / lowest-low breakout bands
DECAYCHANNEL	Decay Min-Max Channel	Exponential decay toward midpoint
FCB	Fractal Chaos Bands	Williams fractal-based support/resistance
JBANDS	Jurik Adaptive Bands	Snap-to-extreme, decay-to-price volatility bands
KCHANNEL	Keltner Channel	EMA with ATR bands; smoother than Bollinger
MAENV	Moving Average Envelope	Fixed percentage bands around selectable MA
MMCHANNEL	Min-Max Channel	Rolling highest high / lowest low
PCHANNEL	Price Channel	Highest high / lowest low with midline
REGCHANNEL	Regression Channel	Linear regression with StdDev bands
SDCHANNEL	Standard Deviation Channel	MA with standard deviation bands
STARCHANNEL	Stoller Average Range Channel	SMA with ATR bands
STBANDS	Super Trend Bands	ATR-based SuperTrend envelope
TTM_LRC	TTM Linear Regression Channel	John Carter's regression channel
UBANDS	Ehlers Ultimate Bands	Ehlers bandpass-based bands
UCHANNEL	Ehlers Ultimate Channel	Ehlers smoothed channel
VWAPBANDS	VWAP Bands	VWAP with StdDev bands
VWAPSD	VWAP StdDev Bands	VWAP with standard deviation envelopes

Statistics

Mathematical and statistical computations on price series.

Indicator	Full Name	Notes
ACF	Autocorrelation Function	Lagged self-correlation
BETA	Beta Coefficient	Systematic risk measure
BIAS	Bias	Percentage deviation from SMA
CMA	Cumulative Moving Average	Expanding window average
COINTEGRATION	Cointegration	Engle-Granger two-step with ADF test
CORRELATION	Pearson Correlation	Linear relationship [-1, +1]
COVARIANCE	Covariance	Joint variability measure
ENTROPY	Shannon Entropy	Information content via histogram binning
GEOMEAN	Geometric Mean	Rolling geometric mean via log-sum
GRANGER	Granger Causality	F-statistic testing if X predicts Y
HARMEAN	Harmonic Mean	Rolling harmonic mean via reciprocal-sum
HURST	Hurst Exponent	Long-range dependence via R/S analysis
IQR	Interquartile Range	Robust dispersion (Q3 - Q1)
JB	Jarque-Bera Test	Normality test (skewness + kurtosis)
KENDALL	Kendall Tau-a	Rank-based ordinal association
KURTOSIS	Kurtosis	Fourth-moment tail heaviness
LINREG	Linear Regression	Best-fit line via least squares
MEDIAN	Rolling Median	50th percentile
MODE	Mode	Most frequent value in window
PACF	Partial Autocorrelation	Direct correlation at lag k
PERCENTILE	Percentile	Value at given percentile rank
QUANTILE	Quantile	Value at given quantile (0-1)
SKEW	Skewness	Distribution asymmetry
SPEARMAN	Spearman Rank Correlation	Monotonic association [-1, +1]
STDDEV	Standard Deviation	Dispersion measure
SUM	Rolling Sum	Windowed sum
THEIL	Theil T Index	Information-theoretic inequality
VARIANCE	Variance	Squared deviation
ZSCORE	Z-Score	Standard deviations from rolling mean
ZTEST	Z-Test	One-sample t-statistic

Forecasts

Predictive indicators and extrapolation methods.

Indicator	Full Name	Notes
AFIRMA	Adaptive FIR MA	Predictive FIR filter

Cycles

Periodic pattern detection and dominant frequency extraction. Markets exhibit cycles; detecting them reliably remains challenging.

Indicator	Full Name	Notes
CG	Ehlers Center of Gravity	Ehlers cycle measurement
DSP	Ehlers Detrended Synthetic Price	Cycle-isolated price component
EACP	Ehlers Autocorrelation Periodogram	Ehlers dominant cycle detection
EBSW	Ehlers Even Better Sinewave	Ehlers improved cycle indicator
HOMOD	Ehlers Homodyne Discriminator	Dominant cycle period tracking
HT_DCPERIOD	Ehlers HT Dominant Cycle Period	Hilbert Transform period estimation
HT_DCPHASE	Ehlers HT Dominant Cycle Phase	Hilbert Transform phase angle
HT_PHASOR	Ehlers HT Phasor Components	In-phase and quadrature components
HT_SINE	Ehlers HT SineWave	Dominant cycle phase with lead signal
LUNAR	Lunar Phase	Moon phase cycle
SINE	Ehlers Sine Wave	Periodic sine oscillation
SOLAR	Solar Activity Cycle	Solar activity periodicity
SSFDSP	Ehlers SSF Detrended Synthetic Price	Dual Super Smoother oscillator
STC	Schaff Trend Cycle	MACD-based cycle oscillator

Numerics

Mathematical transformations and derivative indicators. Building blocks for analysis.

Indicator	Full Name	Notes
ACCEL	Acceleration (2nd Derivative)	Change in slope
CHANGE	Percentage Change	Relative price movement
EXPTRANS	Exponential Transform	e^x for log-space reversal
HIGHEST	Rolling Maximum	O(1) via monotonic deque
JERK	Jerk (3rd Derivative)	Change in acceleration
LINEARTRANS	Linear Transform	y = ax + b scaling
LOGTRANS	Logarithmic Transform	Natural log for percentage analysis
LOWEST	Rolling Minimum	O(1) via monotonic deque
MIDPOINT	Rolling Midpoint	(Highest + Lowest) / 2
NORMALIZE	Min-Max Normalization	Scale to [0,1] via rolling min/max
RELU	Rectified Linear Unit	max(0, x) activation
SIGMOID	Logistic Function	1/(1+e^-x) bounded [0,1]
SLOPE	Slope (1st Derivative)	Rate of change; velocity
SQRTTRANS	Square Root Transform	√x variance-to-StdDev conversion
STANDARDIZE	Z-Score Normalization	(x - mean) / StdDev scaling

Errors

Error metrics and loss functions for model evaluation, forecast assessment, and strategy validation. Quantifying wrongness before production quantifies losses.

Indicator	Full Name	Notes
HUBER	Huber Loss	Quadratic for small errors, linear for large
LOGCOSH	Log-Cosh Loss	Smooth Huber approximation
MAAPE	Mean Arctangent APE	Bounded percentage error
MAE	Mean Absolute Error	Average absolute deviation
MAPD	Mean Absolute % Deviation	Percentage deviation from mean
MAPE	Mean Absolute % Error	Percentage prediction error
MASE	Mean Absolute Scaled Error	Scale-independent accuracy
MDAE	Median Absolute Error	Robust central error
MDAPE	Median Absolute % Error	Robust percentage error
ME	Mean Error	Bias direction indicator
MPE	Mean Percentage Error	Percentage bias measure
MRAE	Mean Relative Absolute Error	Benchmark-relative error
MSE	Mean Squared Error	Variance of residuals
MSLE	Mean Squared Log Error	Ratio-sensitive error
PSEUDOHUBER	Pseudo-Huber Loss	Differentiable Huber approximation
QUANTILELOSS	Quantile Loss	Asymmetric pinball loss
RAE	Relative Absolute Error	MAE relative to baseline
RMSE	Root Mean Squared Error	Standard error magnitude
RMSLE	Root Mean Squared Log Error	Ratio-sensitive RMSE
RSE	Relative Squared Error	MSE relative to baseline
RSQUARED	R² (Coefficient of Determination)	Explained variance fraction
SMAPE	Symmetric MAPE	Symmetric percentage error
THEILU	Theil's U Statistic	Forecast accuracy relative to naive
TUKEY	Tukey Biweight Loss	Robust regression loss
WMAPE	Weighted MAPE	Volume-weighted percentage error
WRMSE	Weighted RMSE	Observation-weighted RMSE

Reversals

Reversal indicators identify potential turning points, compute adaptive stop levels, and define support/resistance zones. Where trend indicators tell you what is happening, reversal indicators warn you when it might stop.

Indicator	Full Name	Notes
CHANDELIER	Chandelier Exit	ATR-based trailing stops from HH/LL; dual ExitLong/ExitShort
CKSTOP	Chande Kroll Stop	ATR-based adaptive trailing stops; dual StopLong/StopShort levels
FRACTALS	Williams Fractals	Five-bar pattern detecting local highs/lows; dual UpFractal/DownFractal
PIVOT	Classic Pivot Points	Floor trader pivots: 7 levels (PP, R1-R3, S1-S3) from previous bar's HLC
PIVOTCAM	Camarilla Pivot Points	Close-centric pivots: 9 levels (PP, R1-R4, S1-S4); R3/S3 mean-reversion zones
PIVOTDEM	DeMark Pivot Points	Conditional pivots: 3 levels (PP, R1, S1); weights OHLC by bar direction
PIVOTEXT	Extended Traditional Pivots	Extended pivots: 11 levels (PP, R1-R5, S1-S5); classic formula with R4/R5/S4/S5
PIVOTFIB	Fibonacci Pivot Points	Fibonacci pivots: 7 levels (PP, R1-R3, S1-S3); ratios 0.382/0.618/1.000 applied to range
PIVOTWOOD	Woodie's Pivot Points	Close-weighted pivots: 7 levels (PP, R1-R3, S1-S3); PP = (H+L+2C)/4 biased toward close
PSAR	Parabolic Stop And Reverse	Accelerating trailing stop; SAR dots flip on reversal; Welles Wilder (1978)
SWINGS	Swing High/Low Detection	Configurable-lookback pattern detector; dual SwingHigh/SwingLow with persistent levels
TTM_SCALPER	TTM Scalper Alert	3-bar pivot high/low detection for scalping entries; John Carter