Help for package talib

Title:

Interface to 'TA-Lib' for Technical Analysis and Candlestick Patterns

Version:

0.9-0

Description:

Interface to the 'TA-Lib' (Technical Analysis Library) C library, providing access to 150+ indicators (e.g. Average Directional Movement Index (ADX), Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Stochastic Oscillator, Bollinger Bands), candlestick pattern recognition, and rolling-window utilities. Core computations are implemented in C for fast Open-High-Low-Close-Volume (OHLCV) time-series feature engineering and rule-based signal generation, with optional interactive visualization via 'plotly'.

X-schema.org-keywords:

technical analysis, TA-Lib, technical indicators, algorithmic trading, trading signals, quantitative finance, financial time series, OHLCV, candlestick patterns, RSI, MACD, Bollinger Bands, Stochastic, moving averages, plotly

X-schema.org-applicationCategory:

Finance

X-schema.org-applicationSubCategory:

Algorithmic Trading

SystemRequirements:

CMake

License:

BSD_3_clause + file LICENSE

Encoding:

UTF-8

RoxygenNote:

7.3.2

NeedsCompilation:

yes

Suggests:

ggplot2, knitr, plotly, rmarkdown, testthat (≥ 3.0.0)

Config/testthat/edition:

Depends:

R (≥ 3.5)

LazyData:

true

VignetteBuilder:

knitr

URL:

https://serkor1.github.io/ta-lib-R/, https://github.com/serkor1/ta-lib-R, https://ta-lib.org/

BugReports:

https://github.com/serkor1/ta-lib-R/issues

Packaged:

2026-04-18 23:45:39 UTC; serkor

Author:

Serkan Korkmaz

[cre, aut, cph], Mario Fortier [cph] (Copyright holder of the bundled TA-Lib C library (src/ta-lib/))

Maintainer:

Serkan Korkmaz <serkor1@duck.com>

Repository:

CRAN

Date/Publication:

2026-04-21 19:50:02 UTC

Cosmos (ATOM)

Description

Daily OHLCV price data for Cosmos (ATOM), denominated in USDC, covering 2022-01-01 to 2022-12-31. Includes a full bear-market cycle useful for testing candlestick pattern recognition on cryptocurrency data.

Usage

ATOM

Format

A data.frame with 366 rows and 5 columns.

open: Opening price for the trading day.
high: Highest price reached during the trading day.
low: Lowest price reached during the trading day.
close: Closing price for the trading day.
volume: Total trading volume for the day.

Source

Loaded using cryptoQuotes.

Examples

## Load the dataset
data(ATOM, package = "talib")

## Scan for Doji patterns on ATOM
talib::doji(ATOM)

Bitcoin (BTC)

Description

Daily OHLCV price data for Bitcoin (BTC), denominated in USDC, covering 2024-01-01 to 2024-12-31. Useful for testing technical analysis indicators and candlestick pattern recognition on cryptocurrency data.

Usage

BTC

Format

A data.frame with 366 rows and 5 columns.

open: Opening price for the trading day.
high: Highest price reached during the trading day.
low: Lowest price reached during the trading day.
close: Closing price for the trading day.
volume: Total trading volume for the day.

Source

Loaded using cryptoQuotes.

Examples

## Load the dataset
data(BTC, package = "talib")

## Compute RSI on Bitcoin closing prices
talib::relative_strength_index(BTC)

NVIDIA Corporation (NVDA)

Description

Daily OHLCV price data for NVIDIA Corporation (NVDA), covering 2022-01-01 to 2023-12-31. Includes a period of high volatility useful for testing momentum and trend-following indicators.

Usage

NVDA

Format

A matrix with 501 rows and 5 columns.

open: Opening price for the trading day.
high: Highest price reached during the trading day.
low: Lowest price reached during the trading day.
close: Closing price for the trading day.
volume: Total trading volume for the day.

Source

Loaded using quantmod.

Examples

## Load the dataset
data(NVDA, package = "talib")

## Compute MACD on NVDA
talib::moving_average_convergence_divergence(NVDA)

SPDR S&P 500 ETF (SPY)

Description

Daily OHLCV price data for the SPDR S&P 500 ETF (SPY), covering 2023-01-01 to 2024-12-31. A widely used benchmark for U.S. equity market performance and a common test case for technical analysis strategies.

Usage

SPY

Format

A matrix with 501 rows and 5 columns.

open: Opening price for the trading day.
high: Highest price reached during the trading day.
low: Lowest price reached during the trading day.
close: Closing price for the trading day.
volume: Total trading volume for the day.

Source

Loaded using quantmod.

Examples

## Load the dataset
data(SPY, package = "talib")

## Compute Bollinger Bands on SPY
talib::bollinger_bands(SPY)

Abandoned Baby

Description

abandoned_baby() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

Every indicator always emits leading NAs for the initial lookback period - positions where there is not yet enough data to produce a result. This is separate from how NAs already present in the input are handled, which is controlled by the na.bridge argument:

na.bridge = FALSE (default)

The input is passed to the underlying TA-Lib C routine as-is. Because most indicators smooth across time (EMA, RSI, MACD, Bollinger Bands, ...), a single NA in the input typically propagates forward and poisons every subsequent value - it is common for one missing observation to produce an output that is entirely NA from that position onward. This mode is the right choice when you want to see the missing data in the output rather than silently compute around it.

na.bridge = TRUE

NA rows are stripped from the input before the C routine runs; the indicator is computed on the resulting dense series; results are then re-expanded to the original length with NA inserted at every position the input had NA. Output length always matches input length, so the result can be joined back to the source data.frame by row.

Consequence to understand before enabling: bridging causes the indicator to treat non-consecutive observations as consecutive. A 14-period RSI with na.bridge = TRUE over a series containing a month-long gap will compute using 14 observations that span several real-world months as if they were 14 adjacent trading days. For sparse missing values (e.g. a single missing tick) this is harmless; for clustered gaps (e.g. a delisted period, a weekend encoded as NA) the output is correctly aligned by position but economically meaningless across the gap. Inspect gap structure with which(is.na(x)) before enabling on low-quality time series.

Usage

abandoned_baby(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

(logical). A logical of length 1. FALSE by default. When FALSE, input NAs propagate through the TA-Lib C routine (most indicators will fill the remaining output with NA). When TRUE, input NA rows are stripped before computation and re-inserted at the original positions in the output, causing the indicator to treat non-consecutive non-NA observations as if they were adjacent — see the Handling of NA values section above for the consequences.

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLABANDONEDBABY: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::abandoned_baby(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::abandoned_baby
 )
}

Absolute Price Oscillator

Description

absolute_price_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

absolute_price_oscillator() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

absolute_price_oscillator(
  x,
  cols,
  fast = 12,
  slow = 26,
  ma = SMA(n = 9),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

fast

(integer). Period for the fast Moving Average (MA).

slow

(integer). Period for the slow Moving Average (MA).

ma

(list). The type of Moving Average (MA) used for the fast and slow MA. SMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

APO: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::absolute_price_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::absolute_price_oscillator
 )
}

Acceleration Bands

Description

acceleration_bands() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

acceleration_bands(x, cols, n = 20, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

UpperBand: double
MiddleBand: double
LowerBand: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::acceleration_bands(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::acceleration_bands
 )
}

Advance Block

Description

advance_block() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

advance_block(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLADVANCEBLOCK: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::advance_block(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::advance_block
 )
}

Aroon

Description

aroon() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

aroon(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

AroonDown: double
AroonUp: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::aroon(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::aroon
 )
}

Aroon Oscillator

Description

aroon_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

aroon_oscillator(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

AROONOSC: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::aroon_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::aroon_oscillator
 )
}

Average Directional Movement Index

Description

average_directional_movement_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

average_directional_movement_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ADX: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::average_directional_movement_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::average_directional_movement_index
 )
}

Average Directional Movement Index Rating

Description

average_directional_movement_index_rating() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

average_directional_movement_index_rating(
  x,
  cols,
  n = 14,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ADXR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::average_directional_movement_index_rating(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::average_directional_movement_index_rating
 )
}

Average Price

Description

average_price() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

average_price(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

AVGPRICE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::average_price(BTC)

## display the results
utils::tail(output)

Average True Range

Description

average_true_range() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

average_true_range(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ATR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::average_true_range(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::average_true_range
 )
}

Balance of Power

Description

balance_of_power() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

balance_of_power(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

BOP: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::balance_of_power(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::balance_of_power
 )
}

Belt Hold

Description

belt_hold() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

belt_hold(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLBELTHOLD: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::belt_hold(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::belt_hold
 )
}

Bollinger Bands

Description

bollinger_bands() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

bollinger_bands() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

bollinger_bands(
  x,
  cols,
  ma = SMA(n = 5),
  sd = 2,
  sd_down,
  sd_up,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

ma

(list). The type of Moving Average (MA) used for the MiddleBand. SMA by default.

sd

(double). Deviation multiplier for the upper and lower band.

sd_down

(double). Optional. Deviation multiplier for lower band

sd_up

(double). Optional. Deviation multiplier for upper band

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

UpperBand: double
MiddleBand: double
LowerBand: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::bollinger_bands(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::bollinger_bands
 )
}

Break Away

Description

break_away() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

break_away(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLBREAKAWAY: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::break_away(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::break_away
 )
}

Chaikin A/D Line

Description

chaikin_accumulation_distribution_line() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

chaikin_accumulation_distribution_line(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close + volume.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

AD: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::chaikin_accumulation_distribution_line(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::chaikin_accumulation_distribution_line
 )
}

Chaikin A/D Oscillator

Description

chaikin_accumulation_distribution_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

chaikin_accumulation_distribution_oscillator(
  x,
  cols,
  fast = 3,
  slow = 10,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close + volume.

fast

(integer). Period for the fast Moving Average (MA).

slow

(integer). Period for the slow Moving Average (MA).

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ADOSC: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::chaikin_accumulation_distribution_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::chaikin_accumulation_distribution_oscillator
 )
}

Chande Momentum Oscillator

Description

chande_momentum_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

chande_momentum_oscillator() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

chande_momentum_oscillator(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

CMO: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::chande_momentum_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::chande_momentum_oscillator
 )
}

Create an OHLC Chart

Description

chart() creates interactive candlestick or OHLC bar charts from financial price data. It initializes the charting environment so that subsequent calls to indicator() can attach technical indicators as subcharts.

Calling chart() without any arguments resets the charting environment, clearing all stored chart state (main chart, subcharts, and data).

See vignette(topic = "charting", package = "talib") for a comprehensive guide on building multi-panel technical analysis charts.

Usage

chart(x, type = "candlestick", idx = NULL, title, ...)

Arguments

x

An OHLC-V data.frame (or object coercible to one) with columns named open, high, low, close, and optionally volume. Column names are case-sensitive.

type

A character string, either "candlestick" (default) or "ohlc". Candlestick charts use filled/hollow bodies with wicks; OHLC charts use vertical bars with horizontal open/close ticks.

idx

An optional vector with the same length as the number of rows in x. Replaces the default x-axis labels (row names or integer index). Useful for custom date formatting or non-standard index types.

title

An optional character string for the chart title. If omitted, the title is inferred from the variable name passed to x.

...

Additional parameters passed to the backend chart constructor (e.g., plotly::plot_ly()).

Details

chart() acts as the entry point for the package's charting system. It stores the OHLC data and the main price chart internally so that subsequent indicator() calls can attach panels below the price chart without requiring the data to be passed again.

The chart title is automatically inferred from the name of the object passed to x (e.g., chart(BTC) produces the title "BTC"). The title also displays the number of observations and, when available, the date range.

Two rendering backends are supported:

"plotly" (default): Produces interactive HTML charts with hover tooltips, pan/zoom, and built-in drawing tools (lines, rectangles). Requires the plotly package.
"ggplot2": Produces static charts suitable for reports and publications. Requires the ggplot2 package.

Options

The following options() control chart appearance and behavior:

talib.chart.backend [character]: "plotly" by default. Set to "ggplot2" for static charts.
talib.chart.slider [logical]: FALSE by default. If TRUE, a range slider is added below the x-axis for interactive zooming (plotly backend only).
talib.chart.slider.size [numeric]: 0.05 by default. Controls the height of the range slider as a fraction of the total chart height.
talib.chart.legend [logical]: TRUE by default. If FALSE, legends are hidden on all panels.
talib.chart.scale [numeric]: 1 by default. A scaling factor applied to all font sizes. Values greater than 1 increase font size.
talib.chart.main [numeric]: 0.7 by default. The fraction of total chart height allocated to the main price panel when subcharts are present.

Colors are controlled via set_theme(). See set_theme() for available themes and color customization.

State and concurrency

The chart() + indicator() pair follows the same active-target model as base R's plot() / lines(). When chart() is called it stashes a per-pipeline state object in its caller's evaluation frame; subsequent indicator() calls retrieve the state by walking up the call stack.

The practical consequences:

chart() and the subsequent indicator() calls must live in the same enclosing frame - the same REPL session, the same function body, the same renderPlot() / renderPlotly() block, the same local({...}) expression, the same testthat::test_that({...}) block, etc. Splitting them across unrelated helpers is not supported.
Two unrelated function bodies (or two parallel renderPlot() callbacks in a Shiny app, or two future::future() blocks) each get their own frame, so their chart states are isolated by construction - without talib taking any dependency on Shiny, promises, or futures.
Calling chart() with no arguments clears the state in the caller's frame, mirroring a fresh plot() call on a new device.

Value

A chart object whose class depends on the active backend:

"plotly" backend: a plotly object (interactive HTML widget).
"ggplot2" backend: a gg object (static plot).

When called without arguments, returns NULL invisibly.

Author(s)

Serkan Korkmaz

Examples

## charting OHLC data with {talib}
data(BTC, package = "talib")

## candlestick chart (default)
talib::chart(BTC)

## OHLC bar chart
talib::chart(BTC, type = "ohlc")

## chart with a custom title
talib::chart(BTC, title = "Bitcoin / USD")

## reset the charting environment
talib::chart()

Chart Themes

Description

The charting system ships with a set of built-in color themes inspired by chartthemes.com. Each theme controls candle colors, background, text, grid lines, and a 10-color palette (colorway) used to distinguish indicator lines.

Use set_theme() to apply or list themes.

Details

Available Themes

default: A dark theme with cyan and steel-blue tones. Light (⁠#E0FFFF⁠) bullish candles on a near-black (⁠#141414⁠) background with a cool blue (⁠#4682B4⁠) bearish candle. The colorway spans icy blues through teal and soft purple.
hawks_and_doves: A light, grayscale theme with a white background. Candles use shades of gray, making it suitable for print or presentations where color is secondary. The colorway uses muted, accessible tones.
payout: A dark teal theme on a near-black (⁠#1A1A1A⁠) background. Bullish candles are teal (⁠#008080⁠), bearish candles are dark slate (⁠#2F4F4F⁠). The colorway follows the default Plotly palette.
tp_slapped: A bright theme on a light gray (⁠#ECF0F1⁠) background. Red (⁠#E74C3C⁠) bearish and teal (⁠#1ABC9C⁠) bullish candles provide strong visual contrast. The colorway uses vivid, saturated colors.
trust_the_process: A subtle, earth-toned theme on a light gray (⁠#F5F5F5⁠) background. Both bull and bear candles use shades of gray, keeping the focus on indicator lines. The colorway uses muted natural tones.
bloomberg_terminal: A dark theme on a near-black (⁠#0E1017⁠) background with orange (⁠#FF8F40⁠) bullish and neutral-gray (⁠#BBB9B2⁠) bearish candles. Inspired by the Bloomberg Terminal interface. Colorblind-friendly (see below).
limit_up: A dark monochrome theme on a near-black (⁠#121212⁠) background. Candles use only luminance to encode direction (light-gray bullish vs dark-gray bearish). Colorblind-friendly (see below).
bid_n_ask: A light theme on an azure (⁠#F0FFFF⁠) background with steel-blue (⁠#4682B4⁠) bullish and tomato-red (⁠#FF6347⁠) bearish candles. The classic blue-vs-red trading pair. Colorblind-friendly (see below).

Colorblind-Friendly Themes

The following themes encode bull/bear direction in ways that remain distinguishable under the most common color-vision deficiencies. The colorways for bloomberg_terminal, limit_up, and bid_n_ask are derived from the Okabe & Ito (2008) qualitative palette, the de-facto standard for accessible scientific visualization.

limit_up, hawks_and_doves, trust_the_process: Encode direction with luminance only. Safe under deuteranopia, protanopia, tritanopia, and full achromatopsia.
bloomberg_terminal: Orange + neutral gray. Safe under all three CVD types thanks to Okabe-Ito-style hue separation.
default, payout: Cyan/teal + blue or dark slate. Safe under all three CVD types — the color pair sits inside the blue-yellow axis that CVD users perceive normally.
bid_n_ask: Blue + red. Safe under deuteranopia and protanopia (the most common forms, affecting ~8% of males); the pair separates more weakly under tritanopia.

tp_slapped is the only built-in that uses a teal/red pair adjacent to the red-green CVD axis; prefer the themes above when accessibility matters.

Theme Properties

Each theme sets the following color properties:

Candle colors: bearish_body, bearish_wick, bearish_border, bullish_body, bullish_wick, bullish_border
General colors: background_color, foreground_color (axes and borders), text_color
Grid and reference: gridcolor, threshold_color (horizontal reference lines such as overbought/oversold levels)
Colorway: A vector of 10 colors cycled through for indicator lines and legends

Any of these properties can be individually overridden via the ... argument to set_theme().

References

Okabe, M. & Ito, K. (2008). Color Universal Design (CUD): How to make figures and presentations that are friendly to colorblind people. https://jfly.uni-koeln.de/color/

Examples

## list available themes
talib::set_theme()

## apply a theme by name
talib::set_theme("payout")

## apply a theme with custom overrides
talib::set_theme(
  "hawks_and_doves",
  background_color = "#FAFAFA"
)

## override individual properties
## without switching theme
talib::set_theme(
  bearish_body = "#FF4444",
  bullish_body = "#44FF44"
)

## reset to default theme
talib::set_theme("default")

Closing Marubozu

Description

closing_marubozu() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

closing_marubozu(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLCLOSINGMARUBOZU: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::closing_marubozu(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::closing_marubozu
 )
}

Commodity Channel Index

Description

commodity_channel_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

commodity_channel_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

CCI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::commodity_channel_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::commodity_channel_index
 )
}

Concealing Baby Swallow

Description

concealing_baby_swallow() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

concealing_baby_swallow(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLCONCEALBABYSWALL: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::concealing_baby_swallow(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::concealing_baby_swallow
 )
}

Counter Attack

Description

counter_attack() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

counter_attack(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLCOUNTERATTACK: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::counter_attack(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::counter_attack
 )
}

Dark Cloud Cover

Description

dark_cloud_cover() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

dark_cloud_cover(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLDARKCLOUDCOVER: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::dark_cloud_cover(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::dark_cloud_cover
 )
}

Directional Movement Index

Description

directional_movement_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

directional_movement_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

DX: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::directional_movement_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::directional_movement_index
 )
}

Doji

Description

doji() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

doji(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLDOJI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::doji(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::doji
 )
}

Doji Star

Description

doji_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

doji_star(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLDOJISTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::doji_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::doji_star
 )
}

Hilbert Transform - Dominant Cycle Period

Description

dominant_cycle_period() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

dominant_cycle_period() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

dominant_cycle_period(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

HT_DCPERIOD: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::dominant_cycle_period(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::dominant_cycle_period
 )
}

Hilbert Transform - Dominant Cycle Phase

Description

dominant_cycle_phase() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

dominant_cycle_phase() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

dominant_cycle_phase(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

HT_DCPHASE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::dominant_cycle_phase(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::dominant_cycle_phase
 )
}

Double Exponential Moving Average

Description

double_exponential_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

double_exponential_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

double_exponential_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', double_exponential_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

DEMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::double_exponential_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::double_exponential_moving_average
 )
}

Dragonfly Doji

Description

dragonfly_doji() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

dragonfly_doji(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLDRAGONFLYDOJI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::dragonfly_doji(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::dragonfly_doji
 )
}

Engulfing

Description

engulfing() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

engulfing(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLENGULFING: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::engulfing(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::engulfing
 )
}

Evening Doji Star

Description

evening_doji_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

evening_doji_star(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLEVENINGDOJISTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::evening_doji_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::evening_doji_star
 )
}

Evening Star

Description

evening_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

evening_star(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLEVENINGSTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::evening_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::evening_star
 )
}

Exponential Moving Average

Description

exponential_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

exponential_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

exponential_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', exponential_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

EMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::exponential_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::exponential_moving_average
 )
}

Moving Average Convergence Divergence (Extended)

Description

extended_moving_average_convergence_divergence() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

extended_moving_average_convergence_divergence() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

extended_moving_average_convergence_divergence(
  x,
  cols,
  fast = EMA(n = 12),
  slow = EMA(n = 26),
  signal = EMA(n = 9),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

fast

(list). Period and Moving Average (MA) type for the fast MA. EMA by default.

slow

(list). Period and Moving Average (MA) type for the slow MA. EMA by default.

signal

(list). Period and Moving Average (MA) type for the signal MA. EMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MACD: double
MACDSignal: double
MACDHist: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::extended_moving_average_convergence_divergence(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::extended_moving_average_convergence_divergence
 )
}

Parabolic Stop and Reverse (SAR) - Extended

Description

extended_parabolic_stop_and_reverse() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

extended_parabolic_stop_and_reverse(
  x,
  cols,
  init = 0,
  offset = 0,
  init_long = 0.02,
  long = 0.02,
  max_long = 0.2,
  init_short = 0.02,
  short = 0.02,
  max_short = 0.2,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

init

(double). Start value and direction. 0 for Auto, >0 for Long, <0 for Short.

offset

(double). Offset added/removed to initial stop on short/long reversal.

init_long

(double). Acceleration factor initial value for the Long direction.

long

(double). Acceleration factor for the Long direction.

max_long

(double). Acceleration factor maximum value for the Long direction.

init_short

(double). Acceleration factor initial value for the Short direction.

short

(double). Acceleration factor for the Short direction.

max_short

(double). Acceleration factor maximum value for the Short direction.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

SAREXT: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::extended_parabolic_stop_and_reverse(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::extended_parabolic_stop_and_reverse
 )
}

Fast Stochastic

Description

fast_stochastic() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

fast_stochastic(x, cols, fastk = 5, fastd = SMA(n = 3), na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

fastk

(integer). Period for the fast-k line.

fastd

(list). Period and Moving Average (MA) type for the fast-d line. SMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

FastK: double
FastD: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::fast_stochastic(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::fast_stochastic
 )
}

Moving Average Convergence Divergence (Fixed)

Description

fixed_moving_average_convergence_divergence() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

fixed_moving_average_convergence_divergence() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

fixed_moving_average_convergence_divergence(
  x,
  cols,
  signal = 9,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

signal

(integer). Period for the signal Moving Average (MA).

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MACD: double
MACDSignal: double
MACDHist: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::fixed_moving_average_convergence_divergence(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::fixed_moving_average_convergence_divergence
 )
}

Up/Down-gap side-by-side white lines

Description

gaps_side_white() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

gaps_side_white(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLGAPSIDESIDEWHITE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::gaps_side_white(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::gaps_side_white
 )
}

Gravestone Doji

Description

gravestone_doji() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

gravestone_doji(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLGRAVESTONEDOJI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::gravestone_doji(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::gravestone_doji
 )
}

Hammer

Description

hammer() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

hammer(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHAMMER: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::hammer(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::hammer
 )
}

Hanging Man

Description

hanging_man() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

hanging_man(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHANGINGMAN: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::hanging_man(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::hanging_man
 )
}

Harami

Description

harami() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

harami(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHARAMI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::harami(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::harami
 )
}

Harami Cross

Description

harami_cross() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

harami_cross(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHARAMICROSS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::harami_cross(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::harami_cross
 )
}

High Wave

Description

high_wave() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

high_wave(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHIGHWAVE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::high_wave(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::high_wave
 )
}

Hikkake

Description

hikakke() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

hikakke(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHIKKAKE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::hikakke(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::hikakke
 )
}

Hikkake Modified

Description

hikakke_mod() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

hikakke_mod(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHIKKAKEMOD: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::hikakke_mod(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::hikakke_mod
 )
}

Homing Pigeon

Description

homing_pigeon() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

homing_pigeon(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLHOMINGPIGEON: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::homing_pigeon(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::homing_pigeon
 )
}

In Neck

Description

in_neck() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

in_neck(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLINNECK: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::in_neck(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::in_neck
 )
}

Add Technical Indicators to a Chart

Description

indicator() attaches one or more technical indicators to an existing chart(), or renders an indicator as a standalone chart. Each indicator is displayed in its own subchart panel below the main price chart.

If chart() has not been called beforehand, indicator() creates a standalone indicator chart — in this case, data must be provided explicitly.

See vignette(topic = "charting", package = "talib") for a comprehensive guide.

Usage

indicator(FUN, ...)

Arguments

FUN

An indicator function or an indicator call. In single indicator mode, pass the bare function (e.g., RSI); arguments for the indicator go in .... In multi-indicator mode, pass a call with parentheses (e.g., RSI(n = 14)); additional indicator calls go in ....

...

In single indicator mode: arguments passed to FUN (e.g., n = 14, data = BTC). In multi-indicator mode: additional indicator calls to merge onto the same panel (e.g., RSI(n = 21), MACD()).

Details

indicator() operates in two modes depending on how FUN is passed:

Single Indicator Mode

Pass a bare function name (without parentheses) and its arguments via ...:

chart(BTC)
indicator(RSI, n = 14)

Multi-Indicator Mode

Pass one or more indicator calls (with parentheses) to merge them onto a single subchart panel:

chart(BTC)
indicator(RSI(n = 10), RSI(n = 14), RSI(n = 21))

Each indicator retains its own arguments. Different indicator types can be freely combined on the same panel:

indicator(RSI(n = 14), MACD())

Multi-indicator mode requires an existing chart() — it cannot be used standalone.

Standalone Mode

When no chart() has been called, a standalone indicator chart is created. The data argument is required in this case:

indicator(RSI, data = BTC, n = 14)

The chart title is automatically derived from the indicator function name, converting snake_case to Title Case.

Panel Layout

When subcharts are present, the main price panel occupies 70% of the total height by default (configurable via options(talib.chart.main = ...)), and the remaining space is divided equally among subchart panels.

Value

A chart object whose class depends on the active backend:

"plotly" backend: a plotly object containing the assembled multi-panel chart.
"ggplot2" backend: a talib_chart object (when combined with chart()) or a gg object (standalone).

Author(s)

Serkan Korkmaz

Examples

## indicator charts with {talib}
data(BTC, package = "talib")

## standalone indicator chart
## (no prior chart() call needed)
talib::indicator(
  talib::RSI,
  data = BTC
)

## attach an indicator to a price chart
talib::chart(BTC)
talib::indicator(talib::RSI, n = 14)

## multiple indicators on the same panel
talib::chart(BTC)
talib::indicator(
  talib::RSI(n = 10),
  talib::RSI(n = 14),
  talib::RSI(n = 21)
)

## reset chart state
talib::chart()

Intraday Movement Index

Description

intraday_movement_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

intraday_movement_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~open + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

IMI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::intraday_movement_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::intraday_movement_index
 )
}

Inverted Hammer

Description

inverted_hammer() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

inverted_hammer(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLINVERTEDHAMMER: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::inverted_hammer(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::inverted_hammer
 )
}

Kaufman Adaptive Moving Average

Description

kaufman_adaptive_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

kaufman_adaptive_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

kaufman_adaptive_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', kaufman_adaptive_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

KAMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::kaufman_adaptive_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::kaufman_adaptive_moving_average
 )
}

Kicking

Description

kicking() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

kicking(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLKICKING: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::kicking(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::kicking
 )
}

Kicking Baby Length

Description

kicking_baby_length() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

kicking_baby_length(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLKICKINGBYLENGTH: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::kicking_baby_length(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::kicking_baby_length
 )
}

Ladder Bottom

Description

ladder_bottom() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

ladder_bottom(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLLADDERBOTTOM: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::ladder_bottom(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::ladder_bottom
 )
}

Long Legged Doji

Description

long_legged_doji() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

long_legged_doji(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLLONGLEGGEDDOJI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::long_legged_doji(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::long_legged_doji
 )
}

Long Line

Description

long_line() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

long_line(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLLONGLINE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::long_line(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::long_line
 )
}

Marubozu

Description

marubozu() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

marubozu(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLMARUBOZU: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::marubozu(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::marubozu
 )
}

Mat Hold

Description

mat_hold() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

mat_hold(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLMATHOLD: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::mat_hold(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::mat_hold
 )
}

Matching Low

Description

matching_low() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

matching_low(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLMATCHINGLOW: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::matching_low(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::matching_low
 )
}

Median Price

Description

median_price() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

median_price(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MEDPRICE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::median_price(BTC)

## display the results
utils::tail(output)

MESA Adaptive Moving Average

Description

mesa_adaptive_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

mesa_adaptive_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

mesa_adaptive_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', mesa_adaptive_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

MAMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::mesa_adaptive_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::mesa_adaptive_moving_average
 )
}

Midpoint Price

Description

midpoint_price() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

midpoint_price(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MIDPRICE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::midpoint_price(BTC)

## display the results
utils::tail(output)

Minus Directional Indicator

Description

minus_directional_indicator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

minus_directional_indicator(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MINUS_DI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::minus_directional_indicator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::minus_directional_indicator
 )
}

Minus Directional Movement

Description

minus_directional_movement() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

minus_directional_movement(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MINUS_DM: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::minus_directional_movement(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::minus_directional_movement
 )
}

Momentum

Description

momentum() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

momentum() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

momentum(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MOM: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::momentum(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::momentum
 )
}

Money Flow Index

Description

money_flow_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

money_flow_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close + volume.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MFI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::money_flow_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::money_flow_index
 )
}

Morning Doji Star

Description

morning_doji_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

morning_doji_star(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLMORNINGDOJISTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::morning_doji_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::morning_doji_star
 )
}

Morning Star

Description

morning_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

morning_star(x, cols, eps = 0, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

eps

(double). Penetration threshold for candlestick pattern recognition, expressed as a fraction of the candle body. A double of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLMORNINGSTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::morning_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::morning_star
 )
}

Moving Average Convergence Divergence

Description

moving_average_convergence_divergence() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

moving_average_convergence_divergence() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

moving_average_convergence_divergence(
  x,
  cols,
  fast = 12,
  slow = 26,
  signal = 9,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

fast

(integer). Period for the fast Moving Average (MA).

slow

(integer). Period for the slow Moving Average (MA).

signal

(integer). Period for the signal Moving Average (MA).

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

MACD: double
MACDSignal: double
MACDHist: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::moving_average_convergence_divergence(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::moving_average_convergence_divergence
 )
}

Normalized Average True Range

Description

normalized_average_true_range() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

normalized_average_true_range(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

NATR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::normalized_average_true_range(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::normalized_average_true_range
 )
}

On-Balance Volume

Description

on_balance_volume() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

on_balance_volume(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~close + volume.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

OBV: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::on_balance_volume(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::on_balance_volume
 )
}

On-Neck

Description

on_neck() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

on_neck(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLONNECK: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::on_neck(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::on_neck
 )
}

Parabolic Stop and Reverse (SAR)

Description

parabolic_stop_and_reverse() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

parabolic_stop_and_reverse(
  x,
  cols,
  acceleration = 0.02,
  maximum = 0.2,
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

acceleration

(double). Acceleration factor used up to the maximum value.

maximum

(double). Acceleration factor maximum value.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

SAR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::parabolic_stop_and_reverse(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::parabolic_stop_and_reverse
 )
}

Percentage Price Oscillator

Description

percentage_price_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

percentage_price_oscillator() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

percentage_price_oscillator(
  x,
  cols,
  fast = 12,
  slow = 26,
  ma = SMA(n = 9),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

fast

(integer). Period for the fast Moving Average (MA).

slow

(integer). Period for the slow Moving Average (MA).

ma

(list). The type of Moving Average (MA) used for the fast and slow MA. SMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

PPO: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::percentage_price_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::percentage_price_oscillator
 )
}

Hilbert Transform - Phasor Components

Description

phasor_components() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

phasor_components() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

phasor_components(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

InPhase: double
Quadrature: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::phasor_components(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::phasor_components
 )
}

Piercing

Description

piercing() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

piercing(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLPIERCING: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::piercing(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::piercing
 )
}

Plus Directional Indicator

Description

plus_directional_indicator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

plus_directional_indicator(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

PLUS_DI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::plus_directional_indicator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::plus_directional_indicator
 )
}

Plus Directional Movement

Description

plus_directional_movement() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

plus_directional_movement(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 2-variable formula selecting columns from x via model.frame. Defaults to ~high + low.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

PLUS_DM: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::plus_directional_movement(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::plus_directional_movement
 )
}

Rate of Change

Description

rate_of_change() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

rate_of_change() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

rate_of_change(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ROC: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::rate_of_change(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::rate_of_change
 )
}

Ratio of Change

Description

ratio_of_change() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

ratio_of_change() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

ratio_of_change(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ROCR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::ratio_of_change(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::ratio_of_change
 )
}

Relative Strength Index

Description

relative_strength_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

relative_strength_index() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

relative_strength_index(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

RSI: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::relative_strength_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::relative_strength_index
 )
}

Rickshaw Man

Description

rickshaw_man() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

rickshaw_man(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLRICKSHAWMAN: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::rickshaw_man(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::rickshaw_man
 )
}

Rising/Falling Three Methods

Description

rise_fall_3_methods() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

rise_fall_3_methods(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLRISEFALL3METHODS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::rise_fall_3_methods(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::rise_fall_3_methods
 )
}

Rolling Beta

Description

rolling_beta() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_beta(x, y, n = 5, na.bridge = FALSE)

Arguments

x, y

((double), (double)). A pair of double vectors of equal length.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

(logical). A logical of length 1. FALSE by default. When FALSE, input NAs propagate through the TA-Lib C routine (the rolling computation typically fills the remaining output with NA). When TRUE, input NA rows are stripped before computation and re-inserted at the original positions in the output, causing the statistic to treat non-consecutive non-NA observations as if they were adjacent - see the Handling of NA values section above for the consequences.

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the rolling statistic
## between Open and Close
output <- talib::rolling_beta(x = BTC[[1]], y = BTC[[4]])

## display the results
utils::tail(output)

Rolling Correlation

Description

rolling_correlation() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_correlation(x, y, n = 30, na.bridge = FALSE)

Arguments

x, y

((double), (double)). A pair of double vectors of equal length.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the rolling statistic
## between Open and Close
output <- talib::rolling_correlation(x = BTC[[1]], y = BTC[[4]])

## display the results
utils::tail(output)

Rolling Max

Description

rolling_max() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_max(x, n = 30, na.bridge = FALSE)

Arguments

x

(double). A double vector.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## Open
output <- talib::rolling_max(x = BTC[[1]])

## display the results
utils::tail(output)

Rolling Min

Description

rolling_min() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_min(x, n = 30, na.bridge = FALSE)

Arguments

x

(double). A double vector.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## Open
output <- talib::rolling_min(x = BTC[[1]])

## display the results
utils::tail(output)

Rolling Standard Deviation

Description

rolling_standard_deviation() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_standard_deviation(x, n = 5, k = 1, na.bridge = FALSE)

Arguments

x

(double). A double vector.

n

(integer). Lookback period (window size). A positive integer of length 1.

k

(double). Multiplier for the standard deviation.

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## Open
output <- talib::rolling_standard_deviation(x = BTC[[1]])

## display the results
utils::tail(output)

Rolling Sum

Description

rolling_sum() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_sum(x, n = 30, na.bridge = FALSE)

Arguments

x

(double). A double vector.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## Open
output <- talib::rolling_sum(x = BTC[[1]])

## display the results
utils::tail(output)

Rolling Standard Deviation

Description

rolling_variance() is a generic S3 function that preserves the input class: double vector in, double vector out.

Handling of `NA` values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs, the behaviour depends on na.bridge:

na.bridge = FALSE (default): NAs propagate through the TA-Lib C routine. Because rolling statistics smooth across time, a single NA in the input typically poisons every subsequent value.
na.bridge = TRUE: Input NAs are stripped, the statistic is computed on the dense series, and NAs are re-inserted at the original positions. Output length matches input length, but the computation treats non-consecutive observations as if they were adjacent - fine for sparse missing values, misleading across clustered gaps.

Usage

rolling_variance(x, n = 5, k = 1, na.bridge = FALSE)

Arguments

x

(double). A double vector.

n

(integer). Lookback period (window size). A positive integer of length 1.

k

multiplier

na.bridge

Value

A double vector with the same length of x

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## Open
output <- talib::rolling_variance(x = BTC[[1]])

## display the results
utils::tail(output)

Separating Lines

Description

separating_lines() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

separating_lines(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSEPARATINGLINES: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::separating_lines(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::separating_lines
 )
}

Set or List Chart Themes

Description

Apply a named color theme to the charting system, override individual theme properties, or list all available theme names. The themes are inspired by chartthemes.com.

Theme changes take effect immediately and apply to all subsequent chart() and indicator() calls.

Usage

set_theme(name, ...)

Arguments

name

An optional character string matching one of the available theme names. Partial matching is supported. If omitted (and no ... overrides are given), returns the available theme names instead.

...

Named color overrides applied after the base theme. Valid names include any theme property: bearish_body, bearish_wick, bearish_border, bullish_body, bullish_wick, bullish_border, background_color, foreground_color, text_color, gridcolor, threshold_color, and colorway (a character vector of up to 10 colors).

Details

set_theme supports three usage patterns:

set_theme(): Returns a character vector of available theme names.
set_theme("payout"): Applies the named theme.
set_theme$payout: Applies the named theme via $ syntax (supports tab-completion in interactive sessions).

Themes can be combined with individual color overrides. When both name and ... are provided, the base theme is applied first, then the overrides are applied on top:

# Apply "payout" but with a custom background
set_theme("payout", background_color = "#000000")

It is also possible to override individual properties without selecting a theme:

# Change only the bearish candle color
set_theme(bearish_body = "#FF0000")

See chart_themes for a full description of all available themes and their color properties.

Value

When called without arguments, a character vector of available theme names. Otherwise, invisibly returns NULL; the theme is applied as a side effect to the internal chart-variables state.

Examples

## list available themes
talib::set_theme()

## apply a theme by name
talib::set_theme("payout")

## apply a theme with custom overrides
talib::set_theme(
  "hawks_and_doves",
  background_color = "#FAFAFA"
)

## override individual properties
## without switching theme
talib::set_theme(
  bearish_body = "#FF4444",
  bullish_body = "#44FF44"
)

## reset to default theme
talib::set_theme("default")

Shooting Star

Description

shooting_star() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

shooting_star(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSHOOTINGSTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::shooting_star(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::shooting_star
 )
}

Short Line Candle

Description

short_line() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

short_line(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSHORTLINE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::short_line(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::short_line
 )
}

Simple Moving Average

Description

simple_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

simple_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

simple_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', simple_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

SMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::simple_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::simple_moving_average
 )
}

Hilbert Transform - SineWave

Description

sine_wave() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

sine_wave() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

sine_wave(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

Sine: double
LeadSine: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::sine_wave(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::sine_wave
 )
}

Spinning Top

Description

spinning_top() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

spinning_top(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSPINNINGTOP: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::spinning_top(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::spinning_top
 )
}

Stalled Pattern

Description

stalled_pattern() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

stalled_pattern(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSTALLEDPATTERN: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::stalled_pattern(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::stalled_pattern
 )
}

Stick Sandwich

Description

stick_sandwich() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

stick_sandwich(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLSTICKSANDWICH: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::stick_sandwich(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::stick_sandwich
 )
}

Stochastic

Description

stochastic() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

stochastic(
  x,
  cols,
  fastk = 5,
  slowk = SMA(n = 3),
  slowd = SMA(n = 3),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

fastk

(integer). Period for the fast-k line.

slowk

(list). Period and Moving Average (MA) type for the slow-k line. SMA by default.

slowd

(list). Period and Moving Average (MA) type for the slow-d line. SMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

SlowK: double
SlowD: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::stochastic(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::stochastic
 )
}

Stochastic Relative Strength Index

Description

stochastic_relative_strength_index() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

stochastic_relative_strength_index(
  x,
  cols,
  n = 14,
  n_rsi = 14,
  fastk = 5,
  fastd = SMA(n = 3),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

n_rsi

(integer). Period for the relative_strength_index.

fastk

(integer). Period for the fast-k line.

fastd

(list). Period and Moving Average (MA) type for the fast-d line. SMA by default.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

FastK: double
FastD: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::stochastic_relative_strength_index(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::stochastic_relative_strength_index
 )
}

Triple Exponential Moving Average (T3)

Description

t3_exponential_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

t3_exponential_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

t3_exponential_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', t3_exponential_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

T3: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::t3_exponential_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::t3_exponential_moving_average
 )
}

Takuri

Description

takuri() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

takuri(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLTAKURI: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::takuri(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::takuri
 )
}

Tasuki Gap

Description

tasuki_gap() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

tasuki_gap(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLTASUKIGAP: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::tasuki_gap(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::tasuki_gap
 )
}

Three Black Crows

Description

three_black_crows() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_black_crows(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3BLACKCROWS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_black_crows(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_black_crows
 )
}

Identical Three Crows

Description

three_identical_crows() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_identical_crows(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLIDENTICAL3CROWS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_identical_crows(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_identical_crows
 )
}

Three Inside

Description

three_inside() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_inside(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3INSIDE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_inside(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_inside
 )
}

Three-Line Strike

Description

three_line_strike() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_line_strike(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3LINESTRIKE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_line_strike(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_line_strike
 )
}

Three Outside

Description

three_outside() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_outside(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3OUTSIDE: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_outside(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_outside
 )
}

Three Stars in the South

Description

three_stars_in_the_south() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_stars_in_the_south(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3STARSINSOUTH: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_stars_in_the_south(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_stars_in_the_south
 )
}

Three White Soldiers

Description

three_white_soldiers() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

three_white_soldiers(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL3WHITESOLDIERS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::three_white_soldiers(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::three_white_soldiers
 )
}

Thrusting

Description

thrusting() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

thrusting(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLTHRUSTING: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::thrusting(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::thrusting
 )
}

Trading Volume

Description

trading_volume() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

trading_volume(
  x,
  cols,
  ma = list(SMA(n = 7), SMA(n = 15)),
  na.bridge = FALSE,
  ...
)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~volume + open + close.

ma

A list of MA specifications.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

VOLUME: double
SMA7: double
SMA15: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::trading_volume(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::trading_volume
 )
}

Hilbert Transform - Trend vs Cycle Mode

Description

trend_cycle_mode() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

trend_cycle_mode() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

trend_cycle_mode(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

HT_TRENDMODE: integer

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::trend_cycle_mode(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::trend_cycle_mode
 )
}

Hilbert Transform - Instantaneous Trendline

Description

trendline() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

trendline() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

trendline(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

HT_TRENDLINE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::trendline(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::trendline
 )
}

Triangular Moving Average

Description

triangular_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

triangular_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

triangular_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', triangular_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

TRIMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::triangular_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::triangular_moving_average
 )
}

Triple Exponential Average

Description

triple_exponential_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

triple_exponential_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

triple_exponential_average(x, cols, n = 30, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

TRIX: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::triple_exponential_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::triple_exponential_average
 )
}

Triple Exponential Moving Average

Description

triple_exponential_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

triple_exponential_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

triple_exponential_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', triple_exponential_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

TEMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::triple_exponential_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::triple_exponential_moving_average
 )
}

Tristar

Description

tristar() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

tristar(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLTRISTAR: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::tristar(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::tristar
 )
}

True Range

Description

true_range() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

true_range(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

TRANGE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::true_range(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::true_range
 )
}

Two Crows

Description

two_crows() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

two_crows(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDL2CROWS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::two_crows(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::two_crows
 )
}

Typical Price

Description

typical_price() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

typical_price(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

TYPPRICE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::typical_price(BTC)

## display the results
utils::tail(output)

Ultimate Oscillator

Description

ultimate_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

ultimate_oscillator(x, cols, n = c(7, 14, 28), na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

ULTOSC: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::ultimate_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::ultimate_oscillator
 )
}

Unique Three River

Description

unique_3_river() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

unique_3_river(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLUNIQUE3RIVER: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::unique_3_river(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::unique_3_river
 )
}

Upside Gap Two Crows

Description

upside_gap_2_crows() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

upside_gap_2_crows(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLUPSIDEGAP2CROWS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::upside_gap_2_crows(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::upside_gap_2_crows
 )
}

Weighted Close Price

Description

weighted_close_price() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

weighted_close_price(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

WCLPRICE: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::weighted_close_price(BTC)

## display the results
utils::tail(output)

Weighted Moving Average

Description

weighted_moving_average() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

weighted_moving_average() also accepts a double vector, in which case the indicator is calculated directly without column selection. When the result has a single column it is simplified to a double vector; otherwise the full n by k matrix is returned.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

weighted_moving_average(x, cols, n = 10, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame. Alternatively, x may also be supplied as a double vector.

cols

(formula). An optional 1-variable formula selecting columns from x via model.frame. Defaults to ~close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Details

When passed without 'x', weighted_moving_average functions as an 'Moving Average'-specification which is used in, for example, stochastic when constructing the smoothing lines.

When called without 'x' it will return a named list which is used for the indicators that supports various Moving Average specifications.

Value

An object of same class and length of x:

WMA: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::weighted_moving_average(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::weighted_moving_average
 )
}

Williams %R

Description

williams_oscillator() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

williams_oscillator(x, cols, n = 14, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 3-variable formula selecting columns from x via model.frame. Defaults to ~high + low + close.

n

(integer). Lookback period (window size). A positive integer of length 1.

na.bridge

...

Additional parameters passed into model.frame.

Value

An object of same class and length of x:

WILLR: double

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::williams_oscillator(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::williams_oscillator
 )
}

Upside/Downside Gap Three Methods

Description

xside_gap_3_methods() is a generic S3 function that preserves the input class: data.frame in, data.frame out; matrix in, matrix out.

Handling of `NA` values

na.bridge = FALSE (default)

na.bridge = TRUE

Usage

xside_gap_3_methods(x, cols, na.bridge = FALSE, ...)

Arguments

x

An OHLC-V series coercible to data.frame.

cols

(formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.

na.bridge

...

Additional parameters passed into model.frame.

Details

General options for candlestick pattern recognition:

N: integer. Controls the number of candles to consider when identifying patterns.
alpha: double. A sensitivity parameter when identifying patterns.

Available options and their defaults:

BodyLong: (N = 10, alpha = 1.0). Real body is long when it's longer than the average of the 10 previous candles' real body.
BodyVeryLong: (N = 10, alpha = 3.0). Real body is very long when it's longer than 3 times the average of the 10 previous candles' real body.
BodyShort: (N = 10, alpha = 1.0). Real body is short when it's shorter than the average of the 10 previous candles' real bodies.
BodyDoji: (N = 10, alpha = 0.1). Real body is like doji's body when it's shorter than 10% the average of the 10 previous candles' high-low range.
ShadowLong: (N = 0, alpha = 1.0). Shadow is long when it's longer than the real body.
ShadowVeryLong: (N = 0, alpha = 2.0). Shadow is very long when it's longer than 2 times the real body.
ShadowShort: (N = 0, alpha = 1.0). Shadow is short when it's shorter than half the average of the 10 previous candles' sum of shadows.
ShadowVeryShort: (N = 10, alpha = 0.1). Shadow is very short when it's shorter than 10% the average of the 10 previous candles' high-low range.
Near: (N = 5, alpha = 0.2). When measuring distance between parts of candles or width of gaps "near" means "<=20% of the average of the 5 previous candles high low range."
Far: (N = 5, alpha = 0.6). When measuring distance between parts of candles or width of gaps "far" means ">= 60% of the average of the 5 previous candles high-low range."
Equal: (N = 5, alpha = 0.05). When measuring distance between parts of candles or width of gaps "equal" means "<= 5% of the average of the 5 previous candles high-low range."

The options can be modified by running options(talib.BodyLong.N = 5, talib.BodyLong.alpha = 0.2). See vignette("candlestick") for more details.

Value

An object of same class and length of x:

CDLXSIDEGAP3METHODS: integer

Pattern codes depend on options(talib.normalize):

If TRUE: 1 = identified pattern; -1 = identified bearish pattern.
If FALSE: 100 = identified pattern; -100 = identified bearish pattern.
0 = no pattern.

Author(s)

Serkan Korkmaz

Examples

## load Bitcoin (BTC)
## series
data(BTC, package = "talib")

## calculate the indicator
## for Bitcoin (BTC)
output <- talib::xside_gap_3_methods(BTC)

## display the results
utils::tail(output)

## visualize the indicator
## with talib::chart()
##
## see ?talib::chart or ?talib::indicator
## for more details
{
 ## chart OHLC-V
 ## series with talib::chart()
 talib::chart(BTC)

 ## chart indicator
 ## with default values
 talib::indicator(
     talib::xside_gap_3_methods
 )
}

Cosmos (ATOM)

Description

Usage

Format

Source

Examples

Bitcoin (BTC)

Description

Usage

Format

Source

Examples

NVIDIA Corporation (NVDA)

Description

Usage

Format

Source

Examples

SPDR S&P 500 ETF (SPY)

Description

Usage

Format

Source

Examples

Abandoned Baby

Description

Handling of NA values

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Absolute Price Oscillator

Description

Handling of NA values

Usage

Arguments

Value

Author(s)

See Also

Examples

Acceleration Bands

Description

Handling of NA values

Usage

Arguments

Value

Author(s)

See Also

Examples

Advance Block

Description

Handling of NA values

Usage

Arguments

Details

Value

Author(s)

See Also

Examples

Aroon

Description

Handling of NA values

Usage

Arguments

Value

Author(s)

See Also

Examples

Aroon Oscillator

Description

Handling of NA values

Usage

Arguments

Value

Author(s)

See Also

Examples

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values

Handling of `NA` values