Indicators Reference

Pandas TA Classic includes 192 indicators in the Category system plus 62 CDL patterns accessible via cdl_pattern() (252 unique total — cdl_doji and cdl_inside are counted in both) organized into the following categories:

• Candles (5 wrappers + 62 native CDL patterns) - Category count includes wrapper/accessor indicators (cdl_pattern, cdl_doji, cdl_inside, cdl_z, ha). The 62 pattern names are exposed through cdl_pattern().

• Cycles (8) - Cycle-based and Hilbert Transform indicators

• Momentum (52) - Momentum and oscillator indicators

• Overlap (46) - Moving averages and trend-following indicators

• Performance (3) - Performance and return metrics

• Statistics (14) - Statistical analysis functions

• Trend (26) - Trend identification and direction indicators

• Volatility (18) - Volatility and range-based indicators

• Volume (20) - Volume analysis indicators

• Math (28) - Element-wise math operators and transforms

Note

The category system now uses dynamic discovery - indicators are automatically detected from the package structure, ensuring the list is always up-to-date with available indicators.

Candles (5 Wrappers + 62 Native Patterns)

Candlestick patterns for identifying market sentiment and potential reversals.

The category count is 5 because dynamic discovery tracks callable indicator entries, while the 62 native CDL patterns are selectable names handled by cdl_pattern().

All 62 CDL patterns have native Python implementations. The dispatch order inside cdl_pattern() is: native first → TA-Lib fallback → warning. Because every pattern in ALL_PATTERNS has a native implementation, the TA-Lib branch is never reached in practice. Patterns are accessible via df.ta.cdl_pattern(name=...), or for doji and inside specifically via their dedicated accessor methods.

# All 62 patterns at once (native only, no TA-Lib needed)
df = df.ta.cdl_pattern(name="all")

# Single pattern
df = df.ta.cdl_pattern(name="engulfing")

# Multiple patterns
df = df.ta.cdl_pattern(name=["hammer", "morningstar", "engulfing"])

# Dedicated accessor methods (only these two have them)
result = df.ta.cdl_doji()
result = df.ta.cdl_inside()

Note

Native implementations take priority in cdl_pattern()’s dispatch chain. TA-Lib is only used as a fallback for any pattern that lacks a native implementation — which is none of the 62 patterns in ALL_PATTERNS.

Available patterns:

• 2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers

• abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack

• darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar

• gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave

• hikkake, hikkakemod, homingpigeon, identical3crows, inside, inneck, invertedhammer

• kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow

• mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods

• separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich

• takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods

Note

cdl_doji() and cdl_inside() have dedicated implementations accessible as df.ta.cdl_doji() and df.ta.cdl_inside().

Other candle indicators:

• CDL Pattern Dispatcher: cdl_pattern — unified candlestick pattern accessor (supports 62 pattern names)

• CDL Doji (Dedicated Accessor): cdl_doji — convenience wrapper for doji pattern

• CDL Inside (Dedicated Accessor): cdl_inside — convenience wrapper for inside pattern

• Heikin-Ashi: ha — df.ta.ha() — not a CDL pattern, not valid as cdl_pattern(name=...)

• Z Score: cdl_z — df.ta.cdl_z() — Z-score normalisation of candle bodies, not a CDL pattern

Note

TA-Lib and core indicators: For 34 non-candle indicators (ema, sma, rsi, macd, obv, atr, and others), TA-Lib’s implementation is used by default when TA-Lib is installed, for numerical consistency with TA-Lib-based workflows. Every such indicator accepts a talib=False kwarg to force the native implementation:

# Uses TA-Lib EMA if installed (default behaviour)
ema = df.ta.ema(length=20)

# Force native implementation regardless of TA-Lib
ema = df.ta.ema(length=20, talib=False)

# Indicators with TA-Lib passthrough:
# ad, adosc, apo, aroon, atr, bbands, bop, cci, cmo, dema, dm,
# ema, hlc3, macd, mfi, midpoint, midprice, mom, natr, obv, ppo,
# roc, rsi, sma, stdev, t3, tema, trima, true_range, uo,
# variance, wcp, willr, wma

Cycles (8)

• Detrended Synthetic Price: dsp

• Even Better Sinewave: ebsw

• Hilbert Transform — Dominant Cycle Period: ht_dcperiod

• Hilbert Transform — Dominant Cycle Phase: ht_dcphase

• Hilbert Transform — Phasor Components: ht_phasor (returns InPhase + Quadrature)

• Hilbert Transform — SineWave: ht_sine (returns Sine + LeadSine)

• Hilbert Transform — Trend vs Cycle Mode: ht_trendmode

• Mesa Sine Wave: msw (returns MSW_SINE + MSW_LEAD; period-based DFT cycle detector)

Momentum (52)

Momentum and oscillator indicators for measuring the speed of price changes:

• Awesome Oscillator: ao

• Absolute Price Oscillator: apo

• Bias: bias

• Balance of Power: bop

• BRAR: brar

• Commodity Channel Index: cci

• Chande Forecast Oscillator: cfo

• Center of Gravity: cg

• Chande Momentum Oscillator: cmo

• Coppock Curve: coppock

• Correlation Trend Indicator: cti (wrapper for ta.linreg(series, r=True))

• Directional Movement: dm

• Efficiency Ratio: er

• Elder Ray Index: eri

• Fisher Transform: fisher

• Forecast Oscillator: fosc

• Inertia: inertia

• KDJ: kdj

• KST Oscillator: kst

• Linear Regression RSI: lrsi

• Moving Average Convergence Divergence: macd

• MACD Extended: macdext (MACD with controllable MA type per line; MA types: 0=SMA, 1=EMA, 2=WMA, 3=DEMA, 4=TEMA, 5=TRIMA, 6=KAMA, 7=MAMA, 8=T3)

• MACD Fixed: macdfix (MACD with fixed 12/26 periods; only signal period is configurable; uses TA-Lib MACDFIX when available)

• Momentum: mom

• Pretty Good Oscillator: pgo

• Projection Oscillator: po

• Percentage Price Oscillator: ppo

• Psychological Line: psl

• Percentage Volume Oscillator: pvo

• Quantitative Qualitative Estimation: qqe (returns QQE, QQEs, QQEl, QQEb_l, QQEb_s, QQEd)

• Rate of Change: roc

• Rate of Change Percentage: rocp

• Rate of Change Ratio: rocr

• Rate of Change Ratio * 100: rocr100

• Relative Strength Index: rsi

• Relative Strength Xtra: rsx

• Relative Vigor Index: rvgi

• Schaff Trend Cycle: stc

• Slope: slope

• SMI Ergodic: smi

• Squeeze: squeeze (Default is John Carter’s. Enable Lazybear’s with lazybear=True)

• Squeeze Pro: squeeze_pro

• Stochastic Oscillator: stoch

• Stochastic Fast: stochf

• Stochastic RSI: stochrsi

• TD Sequential: td_seq (Excluded from df.ta.strategy())

• Trix: trix

• TRIX Histogram: trixh

• True strength index: tsi

• Ultimate Oscillator: uo

• Volume Weighted MACD: vwmacd

• Williams %R: willr

Overlap (46)

Moving averages and trend-following indicators:

• Arnaud Legoux Moving Average: alma

• Average Price (OHLC/4): avgprice (arithmetic mean of open, high, low, close; equivalent to TA-Lib AVGPRICE and tulipy avgprice)

• Double Exponential Moving Average: dema

• Exponential Moving Average: ema

• Fibonacci’s Weighted Moving Average: fwma

• Gann High-Low Activator: hilo

• High-Low Average: hl2

• High-Low-Close Average: hlc3 (Commonly known as ‘Typical Price’)

• Hull Exponential Moving Average: hma

• Hilbert Transform Instantaneous Trendline: ht_trendline

• Holt-Winter Moving Average: hwma

• Ichimoku Kinkō Hyō: ichimoku (Returns two DataFrames. lookahead=False drops the Chikou Span Column)

• Jurik Moving Average: jma

• Kaufman’s Adaptive Moving Average: kama

• Linear Regression: linreg

• Linear Regression Angle: linregangle (angle in degrees of the linear regression slope)

• Linear Regression Intercept: linregintercept (y-intercept of the linear regression line)

• Linear Regression Slope: linregslope (slope of the linear regression line)

• Moving Average: ma (Generic moving average selector)

• MESA Adaptive Moving Average: mama (returns MAMA + FAMA)

• Moving Average with Variable Period: mavp

• Madrid Moving Average Ribbon: mmar

• Median Price (H+L)/2: medprice (arithmetic mean of high and low; equivalent to TA-Lib MEDPRICE and tulipy medprice)

• McGinley Dynamic: mcgd

• Midpoint: midpoint

• Midprice: midprice

• Open-High-Low-Close Average: ohlc4

• Pascal’s Weighted Moving Average: pwma

• Rainbow Moving Average: rainbow

• WildeR’s Moving Average: rma

• Sine Weighted Moving Average: sinwma

• Simple Moving Average: sma

• Ehler’s Super Smoother Filter: ssf

• Supertrend: supertrend

• Symmetric Weighted Moving Average: swma

• T3 Moving Average: t3

• Triple Exponential Moving Average: tema

• Time Series Forecast: tsf

• Triangular Moving Average: trima

• Typical Price (H+L+C)/3: typprice (arithmetic mean of high, low, close; equivalent to TA-Lib TYPPRICE and tulipy typprice)

• Variable Index Dynamic Average: vidya

• Volume Weighted Average Price: vwap (Requires the DataFrame index to be a DatetimeIndex)

• Volume Weighted Moving Average: vwma

• Weighted Closing Price: wcp

• Weighted Moving Average: wma

• Zero Lag Moving Average: zlma

Performance (3)

Performance and return metrics. Use parameter cumulative=True for cumulative results:

• Draw Down: drawdown

• Log Return: log_return

• Percent Return: percent_return

Statistics (14)

Statistical analysis functions:

• Beta: beta (asset volatility relative to a benchmark series)

• Pearson Correlation Coefficient: correl

• Entropy: entropy

• Kurtosis: kurtosis

• Mean Absolute Deviation: mad

• Mean Deviation: md (equivalent to tulipy md; rolling mean absolute deviation from mean)

• Median: median

• Quantile: quantile

• Skew: skew

• Standard Deviation: stdev

• Standard Error: stderr

• Think or Swim Standard Deviation All: tos_stdevall

• Variance: variance

• Z Score: zscore

Trend (26)

Trend identification and direction indicators:

• Average Directional Movement Index: adx (Also includes dmp and dmn)

• Average Directional Movement Index Rating: adxr

• Archer Moving Averages Trends: amat

• Aroon & Aroon Oscillator: aroon

• Choppiness Index: chop

• Chande Kroll Stop: cksp

• Central Pivot Range: cpr / cpr_option (4 pivot methods: standard, camarilla, fibonacci, woodie)

• Decay: decay (Formally: linear_decay)

• Decreasing: decreasing

• Detrended Price Oscillator: dpo (Set lookahead=False to disable centering)

• Directional Index: dx

• Exponential Decay: edecay (multiplicative exponential decay; equivalent to tulipy edecay)

• Increasing: increasing

• Long Run: long_run

• Minus Directional Movement: minus_dm (raw Wilder-smoothed −DM before ATR normalisation; uses TA-Lib MINUS_DM by default)

• Parabolic Stop and Reverse: psar (pass talib=True for exact TA-Lib SAR output)

• Plus Directional Movement: plus_dm (raw Wilder-smoothed +DM before ATR normalisation; uses TA-Lib PLUS_DM by default)

• Price Max: pmax

• Q Stick: qstick

• Parabolic SAR Extended: sarext

• Short Run: short_run

• Trend Signals: tsignals

• TTM Trend: ttm_trend

• Vertical Horizontal Filter: vhf

• Vortex: vortex

• Cross Signals: xsignals

Volatility (18)

Volatility and range-based indicators:

• Aberration: aberration

• Acceleration Bands: accbands

• Annualised Volatility: avolume (rolling annualised log-return standard deviation; length * sqrt(252)-scaled)

• Average True Range: atr

• Bollinger Bands: bbands

• Chandelier Exit: ce

• Chaikins Volatility: cvi

• Donchian Channel: donchian

• Historical Volatility: hvol (Annualized; annualization=252 by default)

• Holt-Winter Channel: hwc

• Keltner Channel: kc

• Mass Index: massi

• Normalized Average True Range: natr

• Price Distance: pdist

• Relative Volatility Index: rvi

• Elder’s Thermometer: thermo

• True Range: true_range

• Ulcer Index: ui

Volume (20)

Volume analysis indicators:

• Accumulation/Distribution Index: ad

• Accumulation/Distribution Oscillator: adosc

• Archer On-Balance Volume: aobv

• Chaikin Money Flow: cmf

• Elder’s Force Index: efi

• Ease of Movement: eom

• Ease of Movement (EMV): emv (equivalent to tulipy emv; uses divisor=10000 for scale; rolling-averaged variant with length parameter)

• Klinger Volume Oscillator: kvo

• Market Facilitation Index: marketfi

• Money Flow Index: mfi

• Negative Volume Index: nvi

• On-Balance Volume: obv

• Positive Volume Index: pvi

• Price-Volume: pvol

• Price Volume Rank: pvr

• Price Volume Trend: pvt

• Volume Flow Indicator: vfi

• Volume Oscillator: vosc

• Volume Profile: vp

• Williams Accumulation/Distribution: wad

Math (28)

Element-wise arithmetic operators, rolling aggregation, and mathematical transforms. All functions are available via df.ta.<name>().

Element-wise binary operators:

• Add: add — element-wise addition of two series

• Subtract: sub — element-wise subtraction of two series

• Multiply: mult — element-wise multiplication of two series

• Divide: div — element-wise division of two series

Rolling aggregation operators:

• Rolling Maximum: rolling_max — rolling maximum over a window

• Rolling Minimum: rolling_min — rolling minimum over a window

• Rolling Sum: rolling_sum — rolling sum over a window

Mathematical transforms (wrapping NumPy / SciPy math, TA-Lib MATH TRANSFORM and MATH OPERATORS group, and tulipy equivalents):

• Inverse Sine: asin

• Inverse Cosine: acos

• Inverse Tangent: atan

• Ceiling: ceil

• Cosine: cos

• Hyperbolic Cosine: cosh

• Exponential: exp

• Floor: floor

• Natural Logarithm: ln

• Logarithm Base 10: log10

• Sine: sin

• Hyperbolic Sine: sinh

• Square Root: sqrt

• Tangent: tan

• Hyperbolic Tangent: tanh

Utility / signal functions (accessible directly or via df.ta):

• Crossover: crossover (returns Boolean Series that is True on the bar where a crosses above b)

• Crossany: crossany (returns Boolean Series that is True on any bar where a and b cross in either direction)

• Lag: lag (returns a Series offset by n periods; equivalent to tulipy lag)

Compatibility Matrix

The full per-indicator compatibility table (Native / TA-Lib / tulipy) is maintained here:

Indicator Compatibility Matrix

Legend: yes = indicator has corresponding implementation in that library by direct name or maintained alias mapping.

pandas-ta-classic indicator support vs TA-Lib and tulipy

Indicator	Category	Native	TA-Lib	tulipy
ao	momentum	yes	no	yes
apo	momentum	yes	yes	yes
bias	momentum	yes	no	no
bop	momentum	yes	yes	yes
brar	momentum	yes	no	no
cci	momentum	yes	yes	yes
cfo	momentum	yes	no	no
cg	momentum	yes	no	no
cmo	momentum	yes	yes	yes
coppock	momentum	yes	no	no
cti	momentum	yes	no	no
dm	momentum	yes	yes	yes
er	momentum	yes	no	no
eri	momentum	yes	no	no
fisher	momentum	yes	no	yes
fosc	momentum	yes	no	yes
inertia	momentum	yes	no	no
kdj	momentum	yes	no	no
kst	momentum	yes	no	no
lrsi	momentum	yes	no	no
macd	momentum	yes	yes	yes
macdext	momentum	yes	yes	no
macdfix	momentum	yes	yes	no
mom	momentum	yes	yes	yes
pgo	momentum	yes	no	no
po	momentum	yes	no	no
ppo	momentum	yes	yes	yes
psl	momentum	yes	no	no
pvo	momentum	yes	no	no
qqe	momentum	yes	no	no
roc	momentum	yes	yes	yes
rocp	momentum	yes	yes	no
rocr	momentum	yes	yes	yes
rocr100	momentum	yes	yes	no
rsi	momentum	yes	yes	yes
rsx	momentum	yes	no	no
rvgi	momentum	yes	no	no
slope	momentum	yes	no	no
smi	momentum	yes	no	no
squeeze	momentum	yes	no	no
squeeze_pro	momentum	yes	no	no
stc	momentum	yes	no	no
stoch	momentum	yes	yes	yes
stochf	momentum	yes	yes	no
stochrsi	momentum	yes	yes	yes
td_seq	momentum	yes	no	no
trix	momentum	yes	yes	yes
trixh	momentum	yes	no	no
tsi	momentum	yes	no	no
uo	momentum	yes	no	no
vwmacd	momentum	yes	no	no
willr	momentum	yes	yes	yes
aberration	volatility	yes	no	no
accbands	volatility	yes	no	no
atr	volatility	yes	yes	yes
avolume	volatility	yes	no	no
bbands	volatility	yes	yes	yes
ce	volatility	yes	no	no
cvi	volatility	yes	no	yes
donchian	volatility	yes	no	no
hvol	volatility	yes	no	no
hwc	volatility	yes	no	no
kc	volatility	yes	no	no
massi	volatility	yes	no	no
natr	volatility	yes	yes	yes
pdist	volatility	yes	no	no
rvi	volatility	yes	no	no
thermo	volatility	yes	no	no
true_range	volatility	yes	no	no
ui	volatility	yes	no	no
beta	statistics	yes	yes	no
correl	statistics	yes	yes	no
entropy	statistics	yes	no	no
kurtosis	statistics	yes	no	no
mad	statistics	yes	no	no
md	statistics	yes	no	yes
median	statistics	yes	no	no
quantile	statistics	yes	no	no
skew	statistics	yes	no	no
stderr	statistics	yes	no	yes
stdev	statistics	yes	yes	yes
tos_stdevall	statistics	yes	no	no
variance	statistics	yes	yes	yes
zscore	statistics	yes	no	no
alma	overlap	yes	no	no
avgprice	overlap	yes	yes	yes
dema	overlap	yes	yes	yes
ema	overlap	yes	yes	yes
fwma	overlap	yes	no	no
hilo	overlap	yes	no	no
hl2	overlap	yes	yes	yes
hlc3	overlap	yes	yes	yes
hma	overlap	yes	no	yes
ht_trendline	overlap	yes	yes	no
hwma	overlap	yes	no	no
ichimoku	overlap	yes	no	no
jma	overlap	yes	no	no
kama	overlap	yes	yes	yes
linreg	overlap	yes	no	yes
linregangle	overlap	yes	yes	no
linregintercept	overlap	yes	yes	yes
linregslope	overlap	yes	yes	yes
ma	overlap	yes	yes	no
mama	overlap	yes	yes	no
mavp	overlap	yes	yes	no
mcgd	overlap	yes	no	no
medprice	overlap	yes	yes	yes
midpoint	overlap	yes	yes	no
midprice	overlap	yes	yes	no
mmar	overlap	yes	no	no
ohlc4	overlap	yes	yes	yes
pwma	overlap	yes	no	no
rainbow	overlap	yes	no	no
rma	overlap	yes	no	yes
sinwma	overlap	yes	no	no
sma	overlap	yes	yes	yes
ssf	overlap	yes	no	no
supertrend	overlap	yes	no	no
swma	overlap	yes	no	no
t3	overlap	yes	yes	no
tema	overlap	yes	yes	yes
trima	overlap	yes	yes	yes
tsf	overlap	yes	yes	yes
typprice	overlap	yes	yes	yes
vidya	overlap	yes	no	yes
vwap	overlap	yes	no	no
vwma	overlap	yes	no	yes
wcp	overlap	yes	no	yes
wma	overlap	yes	yes	yes
zlma	overlap	yes	no	yes
drawdown	performance	yes	no	no
log_return	performance	yes	no	no
percent_return	performance	yes	no	no
dsp	cycles	yes	no	no
ebsw	cycles	yes	no	no
ht_dcperiod	cycles	yes	yes	no
ht_dcphase	cycles	yes	yes	no
ht_phasor	cycles	yes	yes	no
ht_sine	cycles	yes	yes	no
ht_trendmode	cycles	yes	yes	no
msw	cycles	yes	no	yes
adx	trend	yes	yes	yes
adxr	trend	yes	yes	yes
amat	trend	yes	no	no
aroon	trend	yes	yes	yes
chop	trend	yes	no	no
cksp	trend	yes	no	no
cpr	trend	yes	no	no
decay	trend	yes	no	yes
decreasing	trend	yes	no	no
dpo	trend	yes	no	yes
dx	trend	yes	yes	yes
edecay	trend	yes	no	yes
increasing	trend	yes	no	no
long_run	trend	yes	no	no
minus_dm	trend	yes	yes	no
plus_dm	trend	yes	yes	no
pmax	trend	yes	no	no
psar	trend	yes	no	yes
qstick	trend	yes	no	yes
sarext	trend	yes	yes	no
short_run	trend	yes	no	no
tsignals	trend	yes	no	no
ttm_trend	trend	yes	no	no
vhf	trend	yes	no	yes
vortex	trend	yes	no	no
xsignals	trend	yes	no	no
cdl_doji	candles	yes	no	no
cdl_inside	candles	yes	no	no
cdl_pattern	candles	yes	no	no
cdl_z	candles	yes	no	no
ha	candles	yes	no	no
ad	volume	yes	yes	yes
adosc	volume	yes	yes	yes
aobv	volume	yes	no	no
cmf	volume	yes	no	no
efi	volume	yes	no	no
emv	volume	yes	no	yes
eom	volume	yes	no	no
kvo	volume	yes	no	yes
marketfi	volume	yes	no	yes
mfi	volume	yes	yes	yes
nvi	volume	yes	no	yes
obv	volume	yes	yes	yes
pvi	volume	yes	no	yes
pvol	volume	yes	no	no
pvr	volume	yes	no	no
pvt	volume	yes	no	no
vfi	volume	yes	no	no
vosc	volume	yes	no	yes
vp	volume	yes	no	no
wad	volume	yes	no	yes