Indicator catalog¶

Choose from 244 callable indicators. Each page contains a complete example, parameter meanings, and named outputs.

Start with EMA, RSI, MACD, Bollinger Bands, or ATR. Read indicator usage for registration and validity checks. Use the documentation site's search to find names or IDs, or your reader's Find command when reading Markdown.

Candles and patterns · Cycles · Momentum · Moving averages and prices · Returns and drawdowns · Statistics · Trend · Series utilities · Volatility · Volume

Candles and patterns¶

Indicator	Use
Doji (`cdl_doji`)	Compare the body with the preceding window's range to identify small-bodied candles; a positive value denotes a doji, not a bullish signal.
Engulfing pattern (`cdl_engulfing`)	Identify engulfing relationships between adjacent candle bodies; scores retain direction and strength.
Evening Star (`cdl_eveningstar`)	Identify a three-candle evening-star pattern; confirmation requires the third candle.
Hammer (`cdl_hammer`)	Identify conditions such as a small body and long lower shadow, outputting a pattern score.
Harami (`cdl_harami`)	Identify containment relationships between consecutive bodies; scores retain direction and strength.
Inside bar (`cdl_inside`)	Identify candles whose high-low range lies inside the previous bar; the score sign denotes the current candle's direction.
Marubozu (`cdl_marubozu`)	Identify body candles with very short shadows and output a directional score.
Morning Star (`cdl_morningstar`)	Identify a three-candle morning-star pattern; confirmation requires the third candle.
Shooting Star (`cdl_shootingstar`)	Identify conditions such as a small body and long upper shadow, outputting a pattern score.
Candle z-score (`cdl_z`)	Standardize open, high, low, and close separately to measure each price's deviation from its rolling mean.
Heikin-Ashi (`ha`)	Transform OHLC into smoothed candles; the outputs are not original traded prices.

Cycles¶

Indicator	Use
Detrended synthetic price (`dsp`)	Remove the price trend component to examine the remaining cyclical fluctuations.
Even Better Sine Wave (`ebsw`)	Use a filtered waveform to examine cycle states; the output oscillates within a bounded range.
Hilbert dominant cycle period (`ht_dcperiod`)	Estimate the length of the dominant price cycle to examine changes in its period.
Hilbert dominant cycle phase (`ht_dcphase`)	Estimate the dominant cycle phase for cycle analysis.
Hilbert phasor (`ht_phasor`)	Provide in-phase and quadrature components to describe cycle phase relationships.
Hilbert sine wave (`ht_sine`)	Output sine and leading-sine components to compare cycle phases.
Hilbert trend mode (`ht_trendmode`)	Distinguish trend and cycle market regimes using a state value.
Mesa Sine Wave (`msw`)	Estimate phase over the recent window and generate sine and leading-sine components; period must exceed one.

Momentum¶

Indicator	Use
Acceleration Oscillator (`ac`)	Compare momentum oscillator values with their smoothed values to examine momentum's rate of change.
Awesome Oscillator (`ao`)	Compare fast and slow averages of the midpoint price to examine momentum direction and changes.
Absolute Price Oscillator (`apo`)	Compute the absolute difference between fast and slow moving averages, retaining the price scale.
Bias (`bias`)	Measure the proportional deviation of price from its moving average.
Balance of Power (`bop`)	Describe the current candle's price pressure through the relationship between the open-close difference and high-low range.
BRAR sentiment indicators (`brar`)	Measure upward and downward pressure relative to the open and previous close separately.
Commodity Channel Index (`cci`)	Standardize price deviation by mean deviation to measure the distance from the mean.
Chande Forecast Oscillator (`cfo`)	Compare actual price with the regression forecast to examine forecast deviation.
Center of Gravity Oscillator (`cg`)	Use the weighted center of gravity of window prices to describe turning patterns.
Chande Momentum Oscillator (`cmo`)	Compare gains and losses within the window to measure net momentum.
Coppock Curve (`coppock`)	Smooth a combination of rates of change at different periods to examine longer-term momentum.
Connors RSI (`crsi`)	Combine price RSI, consecutive-rise/fall RSI, and rate-of-change rank.
Correlation Trend Indicator (`cti`)	Measure how closely window prices follow a linear trend.
Directional movement (`dm`)	Compute positive and negative directional movement separately; read the two channels individually.
Efficiency ratio (`er`)	Compare net change with cumulative movement to examine directionality and noise along the path.
Elder Ray Index (`eri`)	Compute high and low distances from a moving average to represent bull and bear power separately.
Fisher Transform (`fisher`)	Transform normalized prices and provide a main line and signal line.
Forecast Oscillator (`fosc`)	Compute the relative difference between the actual close and the one-step-ahead regression forecast.
Gator Oscillator (`gator`)	Examine distances between Alligator moving averages, providing upper and lower channels.
Internal Bar Strength (`ibs`)	Measure the close's position within the current high-low range.
Inertia (`inertia`)	Apply linear-regression smoothing to the relative volatility index RVI to examine slower changes.
KDJ stochastic indicator (`kdj`)	Form K, D, and derived J lines from the stochastic position and smoothed values.
Know Sure Thing (`kst`)	Combine and smooth rates of change at different periods, providing a main line and signal line.
Laguerre RSI (`lrsi`)	Construct a relative-strength series with Laguerre filtering; parameters control smoothing.
Moving Average Convergence Divergence (`macd`)	The fast-minus-slow EMA difference forms the main line; the signal smooths that line, and the histogram is their difference.
Extended MACD (`macdext`)	Allow different moving-average types for the fast, slow, and signal lines.
Fixed-period MACD (`macdfix`)	Fix the fast and slow periods at 12 and 26; only the signal period is adjustable.
Momentum difference (`mom`)	Compute the difference between current price and the historical price at the specified interval.
Pretty Good Oscillator (`pgo`)	Measure the distance from price to its simple moving average in units of average true range.
Price Momentum Oscillator (`pmo`)	Recursively smooth price momentum, providing a main line and signal line.
Projection Oscillator (`po`)	Measure price deviation from a linear-regression trend.
Percentage Price Oscillator (`ppo`)	Normalize the fast-minus-slow moving-average difference as a percentage and provide signal and histogram values.
Psychological Line (`psl`)	Count the fraction of rising periods within the window to examine how frequently gains occur.
Percentage Volume Oscillator (`pvo`)	Compare fast and slow volume moving averages, providing main, signal, and histogram channels.
Quantitative Qualitative Estimation (`qqe`)	Construct dynamic bands and directional states from smoothed RSI; the long and short bands have sparse outputs.
Rank Correlation Index (`rci`)	Compare price ranks with time ranks to examine monotonic price direction within the window.
Relative Momentum Index (`rmi`)	Construct relative strength using momentum over the specified interval rather than only adjacent prices.
Percentage rate of change (`roc`)	Compute price change over the specified interval and express it as a percentage.
Fractional rate of change (`rocp`)	Compute the proportional price change without multiplying by one hundred.
Price ratio (`rocr`)	Compute the ratio of current price to the specified historical price; one denotes no change.
Price ratio on a percentage scale (`rocr100`)	Multiply the price ratio by one hundred; one hundred denotes no change.
Relative Strength Index (`rsi`)	Compare smoothed gains and losses to examine price momentum.
Smoothed relative strength (`rsx`)	Use multistage smoothing to form a relative-strength series; the algorithm differs from ordinary RSI.
Relative Vigor Index (`rvgi`)	Compare open-close changes with high-low ranges, providing main, signal, and difference channels.
Change slope (`slope`)	Compute the slope over the specified interval, optionally expressed as an angle.
Liquidity sweep (`smc_sweep`)	Identify rejection patterns after crossing local high or low levels; this does not observe order-book liquidity.
SMI Ergodic (`smi`)	Form main, signal, and difference channels from doubly smoothed momentum; this differs from stochastic momentum SMI.
Stochastic Momentum Index (`smi_stochastic`)	Smooth the price's distance from the window midpoint and normalize by the range; this differs from SMI Ergodic.
Squeeze (`squeeze`)	Compare Bollinger Bands with Keltner Channels and combine momentum with compression and release states.
Multilevel Squeeze (`squeeze_pro`)	Provide squeeze states and momentum for different channel widths; read state channels individually.
Schaff Trend Cycle (`stc`)	Apply successive stochastic normalization and smoothing to MACD to form cycle readings.
Stochastic Oscillator (`stoch`)	Measure the close's position within the recent high-low range, providing smoothed K and D lines.
Fast Stochastic Oscillator (`stochf`)	Provide a less-smoothed stochastic K line and its D line.
Stochastic RSI (`stochrsi`)	Normalize RSI within a window and provide K and D lines.
TD Sequential count (`td_seq`)	Accumulate upward and downward sequence counts by comparing historical prices; no trading outcome is guaranteed.
Triple-smoothed rate of change (`trix`)	Compute the rate of change after triple exponential smoothing and provide a signal line.
Triple-smoothed histogram (`trixh`)	Output the difference between TRIX and its signal line in addition to both lines.
True Strength Index (`tsi`)	Double-smooth momentum and absolute momentum, providing strength and signal values.
Ultimate Oscillator (`uo`)	Combine buying-pressure-to-true-range relationships across multiple periods to examine momentum.
Volume-weighted MACD (`vwmacd`)	Replace ordinary fast and slow averages with volume-weighted averages, outputting main, signal, and histogram channels.
WaveTrend (`wavetrend`)	Standardize and smooth typical-price deviation, providing a main wave line and signal line.
Williams Percent Range (`willr`)	Measure the close's position relative to the window's highest and lowest prices.

Moving averages and prices¶

Indicator	Use
Alligator moving averages (`alligator`)	Output jaw, teeth, and lips lines with different smoothing periods; no plotting displacement is applied.
Arnaud Legoux Moving Average (`alma`)	Smooth price with an offsettable Gaussian weight distribution to control smoothing and response. floor=True rounds the weight center down and normalizes stably; the fixed backend weight direction is retained, without promising numerical parity with Pine.
Condition-anchored VWAP (`anchored_vwap`)	Explicit 0/1 anchors reset before including the current observation; values are invalid before the first anchor, missing source or volume is skipped, zero volume retains an existing positive-weight mean, and negative volume is rejected.
Condition-anchored VWAP bands (`anchored_vwap_bands`)	The center matches condition-anchored VWAP; outer bands use the volume-weighted population standard deviation. Accumulation or output overflow raises an error.
Average OHLC price (`avgprice`)	Compute the arithmetic mean of open, high, low, and close.
Double Exponential Moving Average (`dema`)	Combine first- and second-level EMAs to reduce lag introduced by ordinary smoothing.
Exponential Moving Average (`ema`)	Give recent prices greater weight to smooth price and examine trends; the default seed is a simple average.
Fractal Adaptive Moving Average (`frama`)	Adjust smoothing speed according to the window's fractal characteristics.
Fibonacci Weighted Moving Average (`fwma`)	Smooth window prices using Fibonacci weights.
Gann High Low Activator (`hilo`)	Combine high and low moving averages to form levels and direction outputs that switch with the trend.
High-low midpoint (`hl2`)	Compute the mean of high and low.
Typical price (`hlc3`)	Compute the mean of high, low, and close.
Hull Moving Average (`hma`)	Combine weighted averages of different window lengths to smooth price and reduce lag.
Hilbert trendline (`ht_trendline`)	Use cycle estimation to form a smoothed trendline.
Holt-Winters Moving Average (`hwma`)	Smooth prices using recursively updated components such as level and trend.
Ichimoku (`ichimoku`)	Provide conversion, base, and same-index cloud lines; no future extension or lagging line is output.
Jurik-style Moving Average (`jma`)	Examine price trends through adaptive smoothing; this implementation does not promise exact equivalence with other platforms.
Kaufman Adaptive Moving Average (`kama`)	Use the efficiency ratio to adjust smoothing speed and distinguish directional change from noise.
Linear regression value (`linreg`)	Fit a rolling linear trend and read its regression value. offset selects position length-1-offset on the current window's fitted line, permitting negative extrapolation; it does not shift the result series, and length=1 remains invalid.
Linear regression angle (`linregangle`)	Convert the linear-regression slope to an angle.
Linear regression intercept (`linregintercept`)	Output the intercept of the rolling linear fit.
Linear regression slope (`linregslope`)	Output the per-step slope of the rolling linear fit.
Selectable moving average (`ma`)	Compute a smoothed value using the specified moving-average type; available types are constrained by the parameter domain.
Moving-average envelopes (`ma_envelopes`)	Set upper and lower bands at a fixed proportion on either side of the moving average.
Mesa Adaptive Moving Average (`mama`)	Adjust smoothing speed according to cycle-phase changes and provide a following moving average.
Variable-period Moving Average (`mavp`)	Use a caller-supplied per-bar periods series to vary the moving-average window within the permitted range.
McGinley Dynamic (`mcgd`)	Adjust recursive smoothing according to the distance between price and the dynamic line.
Median price (`medprice`)	Compute the high-low mean for subsequent price transformations and smoothing.
Window midpoint (`midpoint`)	Compute the midpoint of the maximum and minimum within a window of one input series.
Window high-low midpoint (`midprice`)	Compute the midpoint of the highest high and lowest low within the window.
Madrid moving-average ribbon (`mmar`)	Compute moving averages at different periods simultaneously to examine their ordering and spacing.
OHLC average (`ohlc4`)	Compute the average of the four OHLC prices for use as a composite indicator input.
Pascal Weighted Moving Average (`pwma`)	Smooth the window using Pascal triangle weights.
Rainbow moving-average ribbon (`rainbow`)	Repeatedly average the previous smoothing stage to output multiple moving-average layers.
Wilder Moving Average (`rma`)	Use a Wilder smoothing coefficient related to window length.
Sine Weighted Moving Average (`sinwma`)	Smooth window prices using sine weights.
Simple Moving Average (`sma`)	Compute the arithmetic mean of recent window prices with equal weight for each sample.
Super Smoother Filter (`ssf`)	Use recursive filtering with the specified pole count to reduce high-frequency price fluctuations.
Supertrend (`supertrend`)	Construct trend-following levels from average true range, providing direction and long/short levels.
Symmetric Weighted Moving Average (`swma`)	Smooth the series with symmetric triangular weights; window length is configurable.
T3 Moving Average (`t3`)	Combine multistage EMAs with a volume factor to form a smoothed trendline.
Triple Exponential Moving Average (`tema`)	Combine first-, second-, and third-level EMAs to reduce smoothing lag.
Triangular Moving Average (`trima`)	Smooth prices using triangular weights.
Time Series Forecast (`tsf`)	Compute a one-step-ahead forecast from window linear regression without reading future prices.
Typical price (`typprice`)	Average high, low, and close into a single channel.
Variable Index Dynamic Average (`vidya`)	Adjust the EMA smoothing coefficient using relative momentum.
Volume Weighted Average Price (`vwap`)	Accumulate price and volume within groups defined by the datetime index; exchange sessions are not inferred automatically.
Volume Weighted Moving Average (`vwma`)	Weight prices by volume within a rolling window.
Weighted Close Price (`wcp`)	Compute the weighted average of high, low, and twice the close.
Linear Weighted Moving Average (`wma`)	Apply linear weights to window prices.
Zero-lag Moving Average (`zlma`)	Apply lag compensation before smoothing to examine a faster-responding trend value.

Returns and drawdowns¶

Indicator	Use
Drawdown series (`drawdown`)	Compute price drawdown relative to historical peaks, outputting channels at different scales.
Log return (`log_return`)	Compute the logarithm of price ratios, optionally cumulatively.
Fractional return (`percent_return`)	Compute relative price changes, optionally cumulatively; the output definition determines the precise scale.

Statistics¶

Indicator	Use
Rolling beta (`beta`)	Compare joint changes of price and a benchmark series; the caller must supply a benchmark on the same calendar.
Rolling correlation (`correl`)	Measure linear correlation between two price series on the same calendar.
Rolling entropy (`entropy`)	Compute information content from the relative distribution within the window to examine distribution changes.
Rolling kurtosis (`kurtosis`)	Measure tail and peak characteristics of the window distribution.
Mean Absolute Deviation (`mad`)	Compute the mean absolute deviation of window samples from their mean.
Mean Deviation (`md`)	Compute the mean absolute deviation of window samples from their mean; the current implementation calls MAD.
Rolling median (`median`)	Read the middle of the window distribution to reduce the influence of extreme values.
Mode of finite observations (`mode`)	Return the mode of the most recent length finite observations; ties choose the smallest, non-finite values are skipped, and missing rows retain the result after the window fills.
Rolling quantile (`quantile`)	Read the window statistic at the quantile specified by the parameter.
Rolling skewness (`skew`)	Measure asymmetry of the window distribution.
Standard error of the mean (`stderr`)	Estimate uncertainty of the mean using sample standard deviation and window length.
Rolling standard deviation (`stdev`)	Measure dispersion of window prices; the parameter determines degrees of freedom.
Rolling variance (`variance`)	Compute window dispersion on a squared scale.
Rolling z-score (`zscore`)	Standardize price deviation using the mean and standard deviation.

Trend¶

Indicator	Use
Average Directional Index (`adx`)	Smooth directional-movement strength and provide positive and negative direction channels; strength itself does not denote direction.
Average Directional Index Rating (`adxr`)	Combine current and lagged ADX to further smooth trend strength.
Archer Moving Average Trend (`amat`)	Compare changes in fast and slow moving averages to output bullish and bearish trend states.
Aroon (`aroon`)	Measure the time since recent highs and lows, outputting up, down, and difference channels.
ATR trailing stop (`atr_stop`)	Recursively compute stop levels from price and ATR and output directional states.
Choppiness Index (`chop`)	Combine cumulative true range with the price range to distinguish trending and ranging characteristics.
Chande Kroll Stop (`cksp`)	Form long and short reference stop levels from price extremes and volatility.
Central Pivot Range (`cpr`)	Compute the pivot and support/resistance using only the previous input bar; no cross-timeframe aggregation is automatic.
Signal decay (`decay`)	Propagate historical signals forward using the selected decay mode.
Decreasing test (`decreasing`)	Test whether input decreases over the specified period, optionally requiring consecutive decreases.
Detrended Price Oscillator (`dpo`)	Remove the moving-average trend; the current mode is fixed as non-centered and without lookahead.
Directional Index (`dx`)	Compute trend strength from the relative difference between positive and negative directional movement.
Exponential decay (`edecay`)	Recursively decay historical series values while remaining constrained by the current input.
Increasing test (`increasing`)	Test whether input increases over the specified period, optionally requiring consecutive increases.
Long trend phase (`long_run`)	Identify bullish phases from rising and falling relationships of fast and slow series.
Negative directional movement (`minus_dm`)	Smooth downward directional movement; it is not yet normalized into the negative directional index.
Confirmed period pivots (`pivot_points`)	Compute support and resistance from the previous complete period marked by explicit anchor values; Woodie additionally uses the new period's open. developing=True outputs cumulative levels for the current anchored interval; the first anchor bar is immediately available, invalid OHLC contaminates values until the next anchor, and Woodie forbids developing.
Confirmed pivot high (`pivothigh`)	Output the high, original position, and confirmation position after the right window completes; do not backfill the original high.
Confirmed pivot low (`pivotlow`)	Output the low, original position, and confirmation position after the right window completes; do not backfill the original low.
Positive directional movement (`plus_dm`)	Smooth upward directional movement; it is not yet normalized into the positive directional index.
Price Maximizer stop (`pmax`)	Combine moving averages and true range to generate dynamic trend reference levels.
Parabolic SAR (`psar`)	Recursively track trends and reversals, providing long/short levels, acceleration factors, and reversal flags.
Qstick (`qstick`)	Smooth the close-minus-open difference to examine the average candle-body direction.
Extended Parabolic SAR (`sarext`)	Control acceleration parameters separately for long and short phases.
Short trend phase (`short_run`)	Identify bearish phases from directional changes of fast and slow series.
Trend events (`tsignals`)	Convert input trend states into trading changes, entry events, and exit events.
TTM Trend (`ttm_trend`)	Compare the close with recent average price levels to provide trend direction.
Vertical Horizontal Filter (`vhf`)	Compare the price range with cumulative changes to examine concentration of directional movement.
Volatility trailing stop (`volatility_stop`)	Recursively compute stops from volatility and direction; output starts on the bar after the first reversal, without historical backfilling.
Vortex Indicator (`vortex`)	Measure positive and negative trend movement separately.
Threshold-crossing events (`xsignals`)	Convert input signal threshold crossings into trend, entry, and exit events.

Series utilities¶

Indicator	Use
Bars since condition (`barssince`)	Count bars since the most recent true condition; no valid result exists before the first occurrence.
Interval change (`change`)	Compute the difference between the current input and the value at the specified historical interval.
Bidirectional crossover series (`cross`)	Mark crossings in either direction between the previous and current bars, returning a historical series.
Crossover series (`crossover`)	Mark a crossover when the current left value exceeds the right and the previous left value was less than or equal to the right; return a historical series.
Crossunder series (`crossunder`)	Mark a crossunder when the current left value is below the right and the previous left value was greater than or equal to the right; return a historical series.
Cumulative sum (`cum`)	Accumulate inputs in historical order; missing positions have no valid output, and subsequent valid values continue the prior total.
Below preceding window test (`falling`)	Test whether the current value is strictly below the minimum of the preceding length bars; consecutive falls are not required.
Window highest value (`highest`)	Read the maximum valid value within the recent fixed window.
Highest-value position (`highestbars`)	Return the non-positive bar offset of the window maximum relative to the current bar; ties choose the rightmost position.
Window lowest value (`lowest`)	Read the minimum valid value within the recent fixed window.
Lowest-value position (`lowestbars`)	Return the non-positive bar offset of the window minimum relative to the current bar; ties choose the rightmost position.
Interpolated percentile (`percentile_linear_interpolation`)	Compute the specified window percentile using linear interpolation.
Nearest-rank percentile (`percentile_nearest_rank`)	Read the specified window percentile using the nearest-rank rule.
Percentile rank (`percentrank`)	Measure the current input's relative rank within the window.
Window range (`range`)	Compute the difference between the window maximum and minimum.
Above preceding window test (`rising`)	Test whether the current value strictly exceeds the maximum of the preceding length bars; consecutive rises are not required.
Condition count (`rolling_count`)	Count how often the condition holds within the window.
Rolling sum (`rolling_sum`)	Compute the input sum within the recent fixed window.
Running maximum (`running_max`)	Recursively record the largest value observed in history.
Running minimum (`running_min`)	Recursively record the smallest value observed in history.
Historical shift (`shift`)	Read input from the specified number of bars ago; future shifts are unavailable.
Condition-event value (`valuewhen`)	Return the input at the specified historical occurrence of a true condition; unconfirmed events are not written back into the past.

Volatility¶

Indicator	Use
Aberration volatility channel (`aberration`)	Construct upper and lower channels around smoothed prices and output true range.
Acceleration Bands (`accbands`)	Adjust upper and lower envelopes using the price range, then smooth them.
Average intrabar range (`adr`)	Average the input bars' high-low ranges; non-daily input is not automatically converted to daily data.
Average True Range (`atr`)	Smooth true ranges including gap effects to measure the scale of volatility.
Annualized historical volatility (`avolume`)	Scale the population standard deviation of log returns by a fixed annualization factor; this is not a volume indicator.
Bollinger Bands (`bbands`)	Construct upper and lower bands around a moving average using standard deviation, providing bandwidth and relative position.
Chandelier Exit (`ce`)	Form long and short exit reference levels from price extremes and ATR.
Chaikin Volatility (`cvi`)	Examine the rate of change of the smoothed high-low range.
Donchian Channel (`donchian`)	Read the rolling highest high and lowest low and provide the channel midpoint.
Historical volatility (`hvol`)	Compute annualized standard deviation of window log returns.
Holt-Winters Channel (`hwc`)	Construct upper and lower channels around Holt-Winters smoothed values.
Keltner Channel (`kc`)	Construct channels around a moving average using price range or true range. use_true_range=False uses smoothed H-L ranges; it does not change the price moving average or ATR, and all parameters still follow the fixed backend.
Mass Index (`massi`)	Examine range expansion through a smoothed ratio of high-low ranges.
Normalized Average True Range (`natr`)	Normalize ATR by the price scale to compare relative volatility.
Price distance (`pdist`)	Combine OHLC movement distances to examine current-bar price activity.
Relative Volatility Index (`rvi`)	Accumulate volatility according to rising and falling directions; this differs from relative vigor RVGI.
STARC Bands (`starc`)	Use a moving average as the center and ATR multiples for upper and lower bands.
Elder Thermometer (`thermo`)	Examine the intensity of high and low price changes, providing temperature, smoothing, and threshold states.
True Range (`true_range`)	Take the maximum of the high-low range and gap ranges relative to the previous close. With handle_na=True, a missing previous close falls back to H-L and the current Close may be missing; non-finite or inverted H/L is invalid. The default retains the previous output.
Ulcer Index (`ui`)	Measure downside volatility using mean squared drawdown, assigning greater weight to larger drawdowns.

Volume¶

Indicator	Use
Accumulation/Distribution Line (`ad`)	Allocate volume according to the close's position within the high-low range and accumulate the result.
Chaikin Accumulation/Distribution Oscillator (`adosc`)	Compute the difference between fast and slow smoothed accumulation/distribution values.
Archer On-Balance Volume (`aobv`)	Add moving averages, range extremes, and trend states to OBV.
Chaikin Money Flow (`cmf`)	Combine the close's range position with volume to measure the window's money-flow tendency.
Elder Force Index (`efi`)	Combine price change and volume to measure the force of price movement.
Raw Ease of Movement (`emv`)	Measure the relationship between price-range movement and volume without subsequent smoothing.
Smoothed Ease of Movement (`eom`)	Smooth the relationship between price movement and volume to examine relative ease of movement.
Intraday Intensity Index (`iii`)	Combine the close's range position with volume to describe intraday pressure.
Klinger Volume Oscillator (`kvo`)	Combine price and volume changes, providing a main line and signal line.
Market Facilitation Index (`marketfi`)	Measure the high-low range per unit of volume; this differs from the money-flow MFI.
Money Flow Index (`mfi`)	Combine typical price and volume to compare positive and negative money flow.
Negative Volume Index (`nvi`)	Update the index by price ratios when volume falls; missing values preserve internal state.
On-Balance Volume (`obv`)	Accumulate volume according to whether prices rise or fall.
Positive Volume Index (`pvi`)	Update the index by price ratios when volume rises; missing values preserve internal state.
Price-volume product (`pvol`)	Compute the product of price and volume with optional directional handling.
Price Volume Rank (`pvr`)	Assign categorical ranks to combinations of rising/falling price and volume.
Price Volume Trend (`pvt`)	Weight volume by the price rate of change and accumulate it.
Relative volume (`rvol`)	Compare current volume with recent average volume.
Volume Flow Indicator (`vfi`)	Combine a price-change threshold with volume to examine money-flow direction.
Volume Oscillator (`vosc`)	Normalize the fast-minus-slow volume moving-average difference by the slow average.
Williams Accumulation/Distribution (`wad`)	Accumulate directional pressure from price changes relative to the previous close.
Williams Variable Accumulation/Distribution (`wvad`)	Weight volume by the open-close change relative to the high-low range.

Unavailable entries¶

These names cannot be called; their pages explain the boundary.

ta.cdl_pattern: Pattern recognition is deferred within the approved scope; scores, warmup, confirmation delay, and native dispatch require separate approval before exposure.
ta.tos_stdevall: Apply polyfit and standard deviation to the entire selected sample; historical outputs depend on later samples and require a causal definition before exposure.
ta.vp: Volume Profile bins output by price, with width rows; it is not a market-data time series.