Package 'TTR' reference manual

Title:	Technical Trading Rules
Description:	A collection of over 50 technical indicators for creating technical trading rules. The package also provides fast implementations of common rolling-window functions, and several volatility calculations.
Authors:	Joshua Ulrich [cre, aut], Ethan B. Smith [ctb]
Maintainer:	Joshua Ulrich <[email protected]>
License:	GPL (>= 2)
Version:	0.24.4
Built:	2024-09-10 03:53:42 UTC
Source:	https://github.com/joshuaulrich/ttr

Split and dividend adjustment ratios

Description

Create split and dividend adjustment ratio vectors.

Usage

adjRatios(splits, dividends, close)
adjRatios(splits, dividends, close)

Arguments

`splits`	Split series that is coercible to xts.
`dividends`	Dividend series that is coercible to xts.
`close`	Close price series that is coercible to xts.

Details

If only splits is provided, the resulting object will only have as many observations as splits.
If splits and close are provided, the resulting object will have as many observations as max(NROW(splits), NROW(close)).
close is required if dividends is provided.

Value

A xts object containing the columns:

Split: The split adjustment ratio.
Div: The dividend adjustment ratio.

Author(s)

Joshua Ulrich

Welles Wilder's Directional Movement Index

Description

Directional Movement Index; developed by J. Welles Wilder.

Usage

ADX(HLC, n = 14, maType, ...)
ADX(HLC, n = 14, maType, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`n`	Number of periods to use for DX calculation (not ADX calculation).
`maType`	A function or a string naming the function to be called.
`...`	Other arguments to be passed to the `maType` function.

Details

The DIp/DIn (positive/negative) is the percentage of the true range that is up/down.

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the columns:

DIp: The positive Direction Index.
DIn: The negative Direction Index.
DX: The Direction Index.
ADX: The Average Direction Index (trend strength).

Note

A buy/sell signal is generated when the +/-DI crosses up over the -/+DI, when the DX/ADX signals a strong trend. A high/low DX signals a strong/weak trend. DX is usually smoothed with a moving average (i.e. the ADX).

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/DI.htm
https://www.fmlabs.com/reference/DX.htm
https://www.fmlabs.com/reference/ADX.htm
https://www.fmlabs.com/reference/ADXR.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=49
https://www.linnsoft.com/techind/directional-indicator-diplus-diminus
https://www.linnsoft.com/techind/adx-avg-directional-movement
https://www.linnsoft.com/techind/adxr-avg-directional-movement-rating
https://school.stockcharts.com/doku.php?id=technical_indicators:average_directional_index_adx

Examples


 data(ttrc)
 dmi.adx <- ADX(ttrc[,c("High","Low","Close")])

data(ttrc)
 dmi.adx <- ADX(ttrc[,c("High","Low","Close")])

Aroon

Description

The Aroon indicator attempts to identify starting trends. The indicator consists of up and down lines, which measure how long it has been since the highest high/lowest low has occurred in the last n periods. Developed by Tushar Chande in 1995.

Usage

aroon(HL, n = 20)
aroon(HL, n = 20)

Arguments

`HL`	Object that is coercible to xts or matrix and contains either a High-Low price series, or a Close price series.
`n`	Number of periods to use in the calculation.

Details

Aroon up (down) is the elapsed time, expressed as a percentage, between today and the highest (lowest) price in the last n periods. If today's price is a new high (low) Aroon up (down) will be 100. Each subsequent period without another new high (low) causes Aroon up (down) to decrease by (1 / n) x 100.

Value

A object of the same class as HL or a matrix (if try.xts fails) containing the columns:

aroonUp: The Aroon up indicator.
aroonDn: The Aroon down indicator.
oscillator: The Aroon oscillator (aroonUp - aroonDn).

Note

If High-Low prices are given, the function calculates the max/min using the high/low prices. Otherwise the function calculates the max/min of the single series.

Up (down) trends are indicated when the aroonUp(Dn) is between 70 and 100. Strong trends are indicated when when the aroonUp(Dn) is above 70 while the aroonDn(Up) is below 30. Also, crossovers may be useful.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/Aroon.htm
https://www.fmlabs.com/reference/AroonOscillator.htm
https://www.linnsoft.com/techind/aroon-arn
https://school.stockcharts.com/doku.php?id=technical_indicators:aroon

Examples


 ## Get Data and Indicator ##
 data(ttrc)
 trend <- aroon( ttrc[,c("High", "Low")], n=20 )

## Get Data and Indicator ##
 data(ttrc)
 trend <- aroon( ttrc[,c("High", "Low")], n=20 )

Bollinger Bands

Description

Bollinger Bands are a way to compare a security's volatility and price levels over a period of time. Developed by John Bollinger.

Usage

BBands(HLC, n = 20, maType, sd = 2, ...)
BBands(HLC, n = 20, maType, sd = 2, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`sd`	The number of standard deviations to use.
`...`	Other arguments to be passed to the `maType` function.

Details

Bollinger Bands consist of three lines:

The middle band is generally a 20-period SMA of the typical price ([high + low + close]/3). The upper and lower bands are sd standard deviations (generally 2) above and below the MA.

The middle band is usually calculated using the typical price, but if a univariate series (e.g. Close, Weighted Close, Median Price, etc.) is provided, it will be used instead.

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the columns:

dn: The lower Bollinger Band.
mavg: The middle Moving Average (see notes).
up: The upper Bollinger Band.
pctB: The %B calculation.

Note

Using any moving average other than SMA will result in inconsistencies between the moving average calculation and the standard deviation calculation. Since, by definition, a rolling standard deviation uses a simple moving average.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/Bollinger.htm
https://www.fmlabs.com/reference/BollingerWidth.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=36
https://www.linnsoft.com/techind/bollinger-bands
https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_bands
https://school.stockcharts.com/doku.php?id=technical_indicators:bollinger_band_width

Examples


 ## The examples below show the differences between using a
 ## High-Low-Close series, and just a close series when
 ## calculating Bollinger Bands.
 data(ttrc)
 bbands.HLC <- BBands( ttrc[,c("High","Low","Close")] )
 bbands.close <- BBands( ttrc[,"Close"] )
## The examples below show the differences between using a
 ## High-Low-Close series, and just a close series when
 ## calculating Bollinger Bands.
 data(ttrc)
 bbands.HLC <- BBands( ttrc[,c("High","Low","Close")] )
 bbands.close <- BBands( ttrc[,"Close"] )

Commodity Channel Index

Description

The Commodity Channel Index (CCI) attempts to identify starting and ending trends.

Usage

CCI(HLC, n = 20, maType, c = 0.015, ...)
CCI(HLC, n = 20, maType, c = 0.015, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`c`	Constant to apply to the mean deviation.
`...`	Other arguments to be passed to the `maType` function.

Details

CCI relates the current price and the average of price over n periods. The CCI usually falls in a channel of -100 to 100. A basic CCI trading system is: Buy (sell) if CCI rises above 100 (falls below -100) and sell (buy) when it falls below 100 (rises above -100).

CCI is usually calculated using the typical price, but if a univariate series (e.g. Close, Weighted Close, Median Price, etc.) is provided, it will be used instead.

Value

A object of the same class as HLC or a vector (if try.xts fails) containing the CCI values.

Note

If HLC is a High-Low-Close matrix, then typical price will be calculated. If HLC is a vector, then those values will be used instead of the typical price.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/CCI.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=42
https://www.linnsoft.com/techind/cci-commodity-channel-index
https://school.stockcharts.com/doku.php?id=technical_indicators:commodity_channel_index_cci

Examples


 data(ttrc)
 cci <- CCI(ttrc[,c("High","Low","Close")])

data(ttrc)
 cci <- CCI(ttrc[,c("High","Low","Close")])

Chaikin Accumulation / Distribution

Description

The Chaikin Accumulation / Distribution (AD) line is a measure of the money flowing into or out of a security. It is similar to On Balance Volume (OBV). Developed by Marc Chaikin.

Usage

chaikinAD(HLC, volume)
chaikinAD(HLC, volume)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`volume`	Vector or matrix of volume observations corresponding to the `HLC` object.

Details

The AD line is similar to OBV; the difference is that OBV sums volume multiplied by +/- 1 if the close is higher/lower than the previous close, while the AD line multiplies volume by the close location value (CLV).

Value

A object of the same class as HLC and volume or a vector (if try.xts fails) containing the accumulation / distribution values.

Note

The Accumulation/Distribution Line is interpreted by looking for a divergence in the direction of the indicator relative to price.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/AccumDist.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=27
https://www.linnsoft.com/techind/accumulation-distribution
https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line

Examples


 data(ttrc)
 ad <- chaikinAD(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

data(ttrc)
 ad <- chaikinAD(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

Chaikin Volatility

Description

Chaikin Volatility measures the rate of change of the security's trading range. Developed by Marc Chaikin.

Usage

chaikinVolatility(HL, n = 10, maType, ...)
chaikinVolatility(HL, n = 10, maType, ...)

Arguments

`HL`	Object that is coercible to xts or matrix and contains High-Low prices.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`...`	Other arguments to be passed to the `maType` function.

Details

The Chaikin Volatility indicator defines volatility as an increase in the difference between the high and low.

Value

A object of the same class as HL or a vector (if try.xts fails) containing the Chaikin Volatility values.

Note

A rapid increase in Chaikin Volatility indicates an approaching bottom. A slow decrease in Chaikin Volatility indicates an approaching top.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/ChaikinVolatility.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=120

Examples


 data(ttrc)
 volatility <- chaikinVolatility(ttrc[,c("High","Low")])

data(ttrc)
 volatility <- chaikinVolatility(ttrc[,c("High","Low")])

Close Location Value

Description

The Close Location Value (CLV) relates the day's close to its trading range.

Usage

CLV(HLC)
CLV(HLC)

Arguments

HLC

Object that is coercible to xts or matrix and contains High-Low-Close prices.

Details

The CLV will fall in a range of -1 to +1. If the CLV is +/-1, the close is at the high/low; if the CLV is 0, the close is directly between the high and low.

Value

A object of the same class as HLC or a vector (if try.xts fails) containing the Close Location Values of a High-Low-Close price series.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://school.stockcharts.com/doku.php?id=technical_indicators:accumulation_distribution_line

Examples


 data(ttrc)
 clv <- CLV(ttrc[,c("High","Low","Close")])

data(ttrc)
 clv <- CLV(ttrc[,c("High","Low","Close")])

Chaikin Money Flow

Description

Chaikin Money Flow compares total volume over the last n time periods to total volume times the Close Location Value (CLV) over the last n time periods. Developed by Marc Chaikin.

Usage

CMF(HLC, volume, n = 20)
CMF(HLC, volume, n = 20)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`volume`	Vector or matrix of volume observations corresponding to the `HLC` object.
`n`	Number of periods to use.

Details

Chaikin Money Flow is calculated by taking dividing the sum of the Chaikin Accumulation / Distribution line over the past n periods by the sum of volume over the past n periods.

Value

A object of the same class as HLC and volume or a vector (if try.xts fails) containing the Chaikin Money Flow values.

Note

When Chaikin Money Flow is above/below +/- 0.25 it is a bullish/bearish signal. If Chaikin Money Flow remains below zero while the price is rising, it indicates a probable reversal.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/ChaikinMoneyFlow.htm
https://www.linnsoft.com/techind/chaikin-money-flow-cmf
https://school.stockcharts.com/doku.php?id=technical_indicators:chaikin_money_flow_cmf

Examples


 data(ttrc)
 cmf <- CMF(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

data(ttrc)
 cmf <- CMF(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

Chande Momentum Oscillator

Description

The Chande Momentum Oscillator (CMO) is a modified RSI. Developed by Tushar S. Chande.

Usage

CMO(x, n = 14)
CMO(x, n = 14)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`n`	Number of periods to use.

Details

The CMO divides the total movement by the net movement ([up - down] / [up + down]), where RSI divides the upward movement by the net movement (up / [up + down]).

Value

A object of the same class as x or a vector (if try.xts fails) containing Chande Momentum Oscillator values.

Note

There are several ways to interpret the CMO:

Values over/under +/- 50 indicate overbought/oversold conditions.
High CMO values indicate strong trends.
When the CMO crosses above/below a moving average of the CMO, it is a buy/sell signal.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/CMO.htm

Examples


 data(ttrc)
 cmo <- CMO(ttrc[,"Close"])

data(ttrc)
 cmo <- CMO(ttrc[,"Close"])

Ehler's Correlation Trend Indicator

Description

Ehler's Correlation Trend Indicator (CTI) measures the Spearman correlation of the price with the ideal trend line: a straight line with increasing slope.

Usage

CTI(price, n = 20, slope = 1)
CTI(price, n = 20, slope = 1)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	Number of periods to use.
`slope`	Slope of desired trend.

Details

The CTI measures the Spearman correlation between the price and the ideal trend line with slope of slope, over the past n days.

See URL in references section for further details.

Value

A object of the same class as price or a matrix (if try.xts fails) with the column:

cti: The Correlation Trend Indicator.

Note

Positive/negative CTI values signal positive/negative correlation with the desired trend line slope. A simple strategy could be long when the CTI is positive and, short when it is negative.

Author(s)

Ethan Smith, Joshua Ulrich

References

John Ehlers, Correlation Trend Indicator, Stocks & Commodities May-2020 The following site(s) were used to code/document this indicator:
https://financial-hacker.com/petra-on-programming-a-unique-trend-indicator/

Examples


data(ttrc)
cti <- CTI(ttrc[,"Close"], n = 20)

data(ttrc)
cti <- CTI(ttrc[,"Close"], n = 20)

Donchian Channel

Description

Donchian Channels were created by Richard Donchian and were used to generate buy and sell signals for the Turtle Trading system.

Usage

DonchianChannel(HL, n = 10, include.lag = FALSE)
DonchianChannel(HL, n = 10, include.lag = FALSE)

Arguments

`HL`	Object that is coercible to xts or matrix and contains High-Low prices.
`n`	Number of periods for moving average.
`include.lag`	Should values be lagged so that today's prices are not included in the calculation? See Note.

Details

Donchian Channels consist of two (sometimes three) lines:

The top line is the highest high of the past n periods. The bottom line is the lowest low of the past n periods. The middle line is the average of the top and bottom lines.

Value

A object of the same class as HL or a matrix (if try.xts fails) containing the columns:

high: The highest high series.
mid: The average of high and low.
low: The lowest low series.

Note

The default of include.lag=FALSE makes DonchainChannel consistent with other TTR functions, in that it includes the current period in the calculation.

The default is different than the original calculation, which would calculate the indicator using periods t-1 through t-n. Setting include.lag=TRUE will return the result of the original calculation.

The default of this argument may change in the future.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.linnsoft.com/techind/donchian-channels

Examples


 data(ttrc)
 dc <- DonchianChannel( ttrc[,c("High","Low")] )

data(ttrc)
 dc <- DonchianChannel( ttrc[,c("High","Low")] )

De-Trended Price Oscillator

Description

The Detrended Price Oscillator (DPO) removes the trend in prices - or other series - by subtracting a moving average of the price from the price.

Usage

DPO(x, n = 10, maType, shift = n/2 + 1, percent = FALSE, ...)
DPO(x, n = 10, maType, shift = n/2 + 1, percent = FALSE, ...)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`shift`	The number of periods to shift the moving average.
`percent`	logical; if `TRUE`, the percentage difference between the slow and fast moving averages is returned, otherwise the difference between the respective averages is returned.
`...`	Other arguments to be passed to the `maType` function.

Details

The Detrended Price shows cycles and overbought / oversold conditions.

Value

A object of the same class as x or a vector (if try.xts fails) containing the DPO values.

Warning

The detrended price oscillator removes the trend in the series by centering the moving average. Centering the moving average causes it to include future data. Therefore, even though this indicator looks like a classic oscillator, it should not be used for trading rule signals.

Note

DPO does not extend to the last date because it is based on a displaced moving average. The calculation shifts the results shift periods, so the last shift periods will be zero.
As stated above, the DPO can be used on any univariate series, not just price.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://school.stockcharts.com/doku.php?id=technical_indicators:detrended_price_osci

Examples


 data(ttrc)
 priceDPO <- DPO(ttrc[,"Close"])
 volumeDPO <- DPO(ttrc[,"Volume"])

data(ttrc)
 priceDPO <- DPO(ttrc[,"Close"])
 volumeDPO <- DPO(ttrc[,"Volume"])

DV Intermediate Oscillator

Description

The DV Intermediate oscillator (DVI) is a very smooth momentum oscillator that can also be used as a trend indicator. Created by David Varadi.

Usage

DVI(
  price,
  n = 252,
  wts = c(0.8, 0.2),
  smooth = 3,
  magnitude = c(5, 100, 5),
  stretch = c(10, 100, 2),
  exact.multiplier = 1
)
DVI(
  price,
  n = 252,
  wts = c(0.8, 0.2),
  smooth = 3,
  magnitude = c(5, 100, 5),
  stretch = c(10, 100, 2),
  exact.multiplier = 1
)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	Number of periods for the percent rank.
`wts`	The weight given to the smoothed returns (magnitude) component and the up/down days (stretch) component, respectively.
`smooth`	The number of periods to smooth price.
`magnitude`	A set of 3 periods used to smooth magnitude.
`stretch`	A set of 3 periods used to smooth stretch.
`exact.multiplier`	The weight applied to identical values in the window. See `runPercentRank`.

Details

The DVI combines smoothed returns over different time windows and the relative number of up versus down days (stretch) over different time windows.

Value

A object of the same class as price or a vector (if try.xts fails) containing the DVI values.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://cssanalytics.wordpress.com/2009/12/13/what-is-the-dvi/
https://marketsci.wordpress.com/2010/07/27/css-analytics%E2%80%99-dvi-indicator-revealed/

Examples


 data(ttrc)
 dvi <- DVI(ttrc[,"Close"])

data(ttrc)
 dvi <- DVI(ttrc[,"Close"])

Arms' Ease of Movement Value

Description

Arms' Ease of Movement Value (EMV) emphasizes days where the security moves easily and minimizes days where the security does not move easily. Developed by Richard W. Arms, Jr.

Usage

EMV(HL, volume, n = 9, maType, vol.divisor = 10000, ...)
EMV(HL, volume, n = 9, maType, vol.divisor = 10000, ...)

Arguments

`HL`	Object that is coercible to xts or matrix and contains High-Low prices.
`volume`	Vector or matrix of volume observations corresponding to the `HL` object.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`vol.divisor`	An increment to make the results larger and easier to work with.
`...`	Other arguments to be passed to the `maType` function.

Details

The EMV is calculated by dividing the midpoint ([high + low]/2) move by the 'Box Ratio' (volume divided by the high minus low).

Value

A object of the same class as HL and volume or a matrix (if try.xts fails) containing the columns:

emv: The ease of movement values.
maEMV: The smoothed (as specified by ma) ease of movement values.

Note

A buy/sell signal is generated when the EMV crosses above/below zero. When the EMV hovers around zero, there are small price movements and/or high volume, and the price is not moving easily.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/ArmsEMV.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=51
https://www.linnsoft.com/techind/arms-ease-movement

Examples


 data(ttrc)
 emv <- EMV(ttrc[,c("High","Low")], ttrc[,"Volume"])

data(ttrc)
 emv <- EMV(ttrc[,c("High","Low")], ttrc[,"Volume"])

Guppy Multiple Moving Averages

Description

Calculate the Guppy Multiple Moving Average of a series.

Usage

GMMA(
  x,
  short = c(3, 5, 8, 10, 12, 15),
  long = c(30, 35, 40, 45, 50, 60),
  maType
)
GMMA(
  x,
  short = c(3, 5, 8, 10, 12, 15),
  long = c(30, 35, 40, 45, 50, 60),
  maType
)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`short`	Vector of short-term periods.
`long`	Vector of long-term periods.
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.

Details

The Guppy Multiple Moving Average signals a changing trend when the short and long groups of moving averages intersect. An up/down trend exists when the short/long-term moving averages are greater than the long/short-term averages.

Value

A object of the same class as x or price or a vector (if try.xts fails) containing the Guppy Multiple Moving Average.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.investopedia.com/terms/g/guppy-multiple-moving-average.asp

Examples


 data(ttrc)
 gmma <- GMMA(ttrc[,"Close"])

data(ttrc)
 gmma <- GMMA(ttrc[,"Close"])

Keltner Channels

Description

Keltner Channels are volatility-based envelopes set above and below a moving average. This indicator is similar to Bollinger Bands, but Keltner Channels use the Average True Range (ATR) to set channel distance.

Usage

keltnerChannels(HLC, n = 20, maType, atr = 2, ...)
keltnerChannels(HLC, n = 20, maType, atr = 2, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`atr`	The number of average true range distances to apply.
`...`	Other arguments to be passed to the maType function.

Details

Keltner Channels are a trend following indicator, and can also be used to identify overbought and oversold levels when there is no trend.

Chester Keltner is credited with the original version of Keltner Channels in his 1960 book. Linda Bradford Raschke introduced the newer version of Keltner Channels in the 1980s.

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the columns:

SMA: Simple moving average.
EMA: Exponential moving average.

`dn`	The lower Keltner Channel.
`mavg`	The middle moving average.
`up`	The upper Keltner Channel.

Details

Keltner Channels consist of three lines: The middle band is generally a 20-period EMA of the typical price ([high + low + close]/3). The upper and lower bands are multiples of average true range (usually 2) above and below the MA.

The middle band is usually calculated using the typical price, but if a univariate series (e.g. Close, Weighted Close, Median Price, etc.) is provided, it will be used instead.

Author(s)

Nick Procyk, Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://school.stockcharts.com/doku.php?id=technical_indicators:keltner_channels
https://www.linnsoft.com/techind/keltner-channels-keltu-keltd
https://www.investopedia.com/terms/k/keltnerchannel.asp

Examples


data(ttrc)
kc <- keltnerChannels(ttrc[,c("High","Low","Close")])

data(ttrc)
kc <- keltnerChannels(ttrc[,c("High","Low","Close")])

Know Sure Thing

Description

The Know Sure Thing (KST) is a smooth, summed, rate of change indicator. Developed by Martin Pring.

Usage

KST(
  price,
  n = c(10, 10, 10, 15),
  nROC = c(10, 15, 20, 30),
  nSig = 9,
  maType,
  wts = 1:NROW(n),
  ...
)
KST(
  price,
  n = c(10, 10, 10, 15),
  nROC = c(10, 15, 20, 30),
  nSig = 9,
  maType,
  wts = 1:NROW(n),
  ...
)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	A vector of the number of periods to use in the MA calculations.
`nROC`	A vector of the number of periods to use in the ROC calculations.
`nSig`	The number of periods to use for the KST signal line.
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.
`wts`	A vector the same length as `n`, of the weight for each period (need not sum to one).
`...`	Other arguments to be passed to the `maType` function in case (1) above.

Details

For each day (week, month, etc.), the KST calculates the ROC over several periods. Those ROCs are smoothed using the given moving averages, then multiplied by their respective weighting values. The resulting values are summed for each day (month, week, etc.).

Value

A object of the same class as price or a vector (if try.xts fails) containing the Know Sure Thing values.

Note

The KST indicates bullish/bearish momentum as it crosses above/below its moving average. Because the KST tends to lead price action, look for trend confirmation in the price.

The default arguments are for the daily KST. There is also the Long-Term KST, with arguments: n=c(9, 12, 18, 24) - where the periods are months, not days - and the moving average periods are 6, 6, 6, and 9 months, respectively.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://web.archive.org/web/20110715112957/http://www.pring.com/movieweb/daily_kst.htm
https://web.archive.org/web/20100101162707/http://www.pring.com/movieweb/KST_MCM.htm

Examples


 data(ttrc)
 kst <- KST(ttrc[,"Close"])

 kst4MA <- KST(ttrc[,"Close"],
   maType=list(list(SMA),list(EMA),list(DEMA),list(WMA)))

data(ttrc)
 kst <- KST(ttrc[,"Close"])

 kst4MA <- KST(ttrc[,"Close"],
   maType=list(list(SMA),list(EMA),list(DEMA),list(WMA)))

Miscellaneous Tools

Description

Various functions that may be useful in designing technical trading rules.

Usage

lags(x, n = 1)

growth(price, signals, ...)

naCheck(x, n = 0)
lags(x, n = 1)

growth(price, signals, ...)

naCheck(x, n = 0)

Arguments

`x`	Object that is coercible to xts or matrix.
`n`	Number of periods to use.
`price`	Price series that is coercible to xts or matrix.
`signals`	Signals to use (defaults to vector of ones). Use '0' for no position, '1' for long position, and '-1' for short position.
`...`	Further arguments to be passed from or to other methods.

Details

growth calculates the growth of an investment using given prices and signals.

lags calculates the lags of a given series.

Value

growth returns a vector of the growth of the investment.

lags returns a matrix of lagged values of the original vector.

Note

In growth you can specify the number of periods and type of compounding to use when calculating returns of the price series via the '\dots' argument.

Author(s)

Joshua Ulrich

MACD Oscillator

Description

The MACD was developed by Gerald Appel and is probably the most popular price oscillator. The MACD function documented in this page compares a fast moving average (MA) of a series with a slow MA of the same series. It can be used as a generic oscillator for any univariate series, not only price.

Usage

MACD(x, nFast = 12, nSlow = 26, nSig = 9, maType, percent = TRUE, ...)
MACD(x, nFast = 12, nSlow = 26, nSig = 9, maType, percent = TRUE, ...)

Arguments

`x`	Object that is coercible to xts or matrix; usually price, but can be volume, etc.
`nFast`	Number of periods for fast moving average.
`nSlow`	Number of periods for slow moving average.
`nSig`	Number of periods for signal moving average.
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.
`percent`	logical; if `TRUE`, the percentage difference between the fast and slow moving averages is returned, otherwise the difference between the respective averages is returned.
`...`	Other arguments to be passed to the `maType` function in case (1) above.

Details

The MACD function either subtracts the fast MA from the slow MA, or finds the rate of change between the fast MA and the slow MA.

Value

A object of the same class as x or a matrix (if try.xts fails) containing the columns:

macd: The price (volume, etc.) oscillator.
signal: The oscillator signal line (a moving average of the oscillator).

Note

The MACD is a special case of the general oscillator applied to price. The MACD can be used as a general oscillator applied to any series. Time periods for the MACD are often given as 26 and 12, but the original formula used exponential constants of 0.075 and 0.15, which are closer to 25.6667 and 12.3333 periods.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
Moving Average Convergence/Divergence (MACD):
https://www.fmlabs.com/reference/MACD.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=66
https://www.linnsoft.com/techind/macd
https://school.stockcharts.com/doku.php?id=technical_indicators:moving_average_convergence_divergence_macd

Price Oscillator:
https://www.fmlabs.com/reference/PriceOscillator.htm
https://www.fmlabs.com/reference/PriceOscillatorPct.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=94
https://school.stockcharts.com/doku.php?id=technical_indicators:price_oscillators_ppo

Volume Oscillator:
https://www.fmlabs.com/reference/PVO.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=122

Examples


 data(ttrc)

 macd  <- MACD( ttrc[,"Close"], 12, 26, 9, maType="EMA" )
 macd2 <- MACD( ttrc[,"Close"], 12, 26, 9,
          maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

data(ttrc)

 macd  <- MACD( ttrc[,"Close"], 12, 26, 9, maType="EMA" )
 macd2 <- MACD( ttrc[,"Close"], 12, 26, 9,
          maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

Money Flow Index

Description

The MFI is a ratio of positive and negative money flow over time.

Usage

MFI(HLC, volume, n = 14)
MFI(HLC, volume, n = 14)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`volume`	Vector or matrix of volume observations corresponding to `HLC` object.
`n`	Number of periods to use.

Details

Money Flow (MF) is the product of price and volume. Positive/negative MF occur when today's price is higher/lower than yesterday's price. The MFI is calculated by dividing positive MF by negative MF for the past n periods. It is then scaled between 0 and 100.

MFI is usually calculated using the typical price, but if a univariate series (e.g. Close, Weighted Close, Median Price, etc.) is provided, it will be used instead.

Value

A object of the same class as HLC and volume or a vector (if try.xts fails) containing the MFI values.

Note

Divergence between MFI and price can be indicative of a reversal. In addition, values above/below 80/20 indicate market tops/bottoms.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/default.htm?url=MoneyFlowIndex.htm
https://www.linnsoft.com/techind/money-flow-index-mfi
https://school.stockcharts.com/doku.php?id=technical_indicators:money_flow_index_mfi

Examples


 data(ttrc)
 mfi <- MFI(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

data(ttrc)
 mfi <- MFI(ttrc[,c("High","Low","Close")], ttrc[,"Volume"])

On Balance Volume (OBV)

Description

On Balance Volume (OBV) is a measure of the money flowing into or out of a security. It is similar to Chaikin Accumulation / Distribution.

Usage

OBV(price, volume)
OBV(price, volume)

Arguments

`price`	Price series that is coercible to xts or matrix.
`volume`	Volume series that is coercible to xts or matrix, that corresponds to price object.

Details

OBV is calculated by adding (subtracting) each day's volume to a running cumulative total when the security's price closes higher (lower).

Value

A object of the same class as price and volume or a vector (if try.xts fails) containing the OBV values.

Note

OBV is usually compared with the price chart of the underlying security to look for divergences/confirmation.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/OBV.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=82
https://www.linnsoft.com/techind/balance-open-interest
https://school.stockcharts.com/doku.php?id=technical_indicators:on_balance_volume_obv

Examples


 data(ttrc)
 obv <- OBV(ttrc[,"Close"], ttrc[,"Volume"])

data(ttrc)
 obv <- OBV(ttrc[,"Close"], ttrc[,"Volume"])

Construct (optionally further smoothed and centered ) volatility bands around prices

Description

John Bollinger's famous adaptive volatility bands most often use the typical price of an HLC series, or may be calculated on a univariate price series (see BBands).

Usage

PBands(
  prices,
  n = 20,
  maType = "SMA",
  sd = 2,
  ...,
  fastn = 2,
  centered = FALSE,
  lavg = FALSE
)
PBands(
  prices,
  n = 20,
  maType = "SMA",
  sd = 2,
  ...,
  fastn = 2,
  centered = FALSE,
  lavg = FALSE
)

Arguments

`prices`	A univariate series of prices.
`n`	Number of periods to average over.
`maType`	A function or a string naming the function to be called.
`sd`	The number of standard deviations to use.
`...`	any other pass-thru parameters, usually for function named by `maType`.
`fastn`	Number of periods to use for smoothing higher-frequency 'noise'.
`centered`	Whether to center the bands around a series adjusted for high frequency noise, default `FALSE`.
`lavg`	Whether to use a longer `(n*2)` smoothing period for centering, default `FALSE`.

Details

This function applies a second moving average denoted by fastn to filter out higher-frequency noise, making the bands somewhat more stable to temporary fluctuations and spikes.

If centered is TRUE, the function also further smoothes and centers the bands around a centerline adjusted to remove this higher frequency noise. If lavg is also TRUE, the smoothing applied for the middle band (but not the volatility bands) is doubled to further smooth the price-response function.

If you have multiple different price series in prices, and want to use this function, call this functions using lapply(prices,PBands,...).

Value

A object of the same class as prices or a matrix (if try.xts fails) containing the columns:

dn: The lower price volatility Band.
center: The smoothed centerline (see details).
up: The upper price volatility Band.

Author(s)

Brian G. Peterson

Examples


   data(ttrc)
   pbands.close <- PBands( ttrc[,"Close"] )

data(ttrc)
   pbands.close <- PBands( ttrc[,"Close"] )

Rate of Change / Momentum

Description

Calculate the (rate of) change of a series over n periods.

Usage

ROC(x, n = 1, type = c("continuous", "discrete"), na.pad = TRUE)

momentum(x, n = 1, na.pad = TRUE)
ROC(x, n = 1, type = c("continuous", "discrete"), na.pad = TRUE)

momentum(x, n = 1, na.pad = TRUE)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`n`	Number of periods to use.
`type`	Compounding type; either `"continuous"` (the default) or `"discrete"`.
`na.pad`	Should periods prior to `n` be appended? Default is `TRUE`.

Details

The ROC indicator provides the percentage difference of a series over two observations, while the momentum indicator simply provides the difference.

Value

A object of the same class as x or a vector (if try.xts fails) containing the rate-of-change (or return) values for ROC or a vector containing the differenced price series for momentum.

Author(s)

Joshua Ulrich

Examples


 data(ttrc)
 roc <- ROC(ttrc[,"Close"])
 mom <- momentum(ttrc[,"Close"])
data(ttrc)
 roc <- ROC(ttrc[,"Close"])
 mom <- momentum(ttrc[,"Close"])

Analysis of Running/Rolling/Moving Windows

Description

Various functions to analyze data over a moving window of periods.

Usage

rollSFM(Ra, Rb, n = 60)
rollSFM(Ra, Rb, n = 60)

Arguments

`Ra`	Object coercible to xts or matrix, containing the excess return for an individual security
`Rb`	Object coercible to xts or matrix, containing the market / benchmark return
`n`	Number of periods to use in the window

Value

A object of the same class as Ra (and Rb?) or a vector (if try.xts fails).

rollSFM: returns single-factor model parameters and R-squared over a n-period moving window.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator: https://en.wikipedia.org/wiki/Simple_linear_regression

Relative Strength Index

Description

The Relative Strength Index (RSI) calculates a ratio of the recent upward price movements to the absolute price movement. Developed by J. Welles Wilder.

Usage

RSI(price, n = 14, maType, ...)
RSI(price, n = 14, maType, ...)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	Number of periods for moving averages.
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.
`...`	Other arguments to be passed to the `maType` function in case (1) above.

Details

The RSI calculation is RSI = 100 - 100 / ( 1 + RS ), where RS is the smoothed ratio of 'average' gains over 'average' losses. The 'averages' aren't true averages, since they're divided by the value of n and not the number of periods in which there are gains/losses.

Value

A object of the same class as price or a vector (if try.xts fails) containing the RSI values.

Note

The RSI is usually interpreted as an overbought/oversold (over 70 / below 30) indicator. Divergence with price may also be useful. For example, if price is making new highs/lows, but RSI is not, it could indicate a reversal.

You can calculate a stochastic RSI by using the function stoch on RSI values.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
Relative Strength Index:
https://www.fmlabs.com/reference/RSI.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=100
https://www.linnsoft.com/techind/relative-strength-index-rsi
https://school.stockcharts.com/doku.php?id=technical_indicators:relative_strength_index_rsi

Stochastic RSI:
https://www.fmlabs.com/reference/StochRSI.htm
https://school.stockcharts.com/doku.php?id=technical_indicators:stochrsi

Examples


 data(ttrc)
 price <- ttrc[,"Close"]

 # Default case
 rsi <- RSI(price)

 # Case of one 'maType' for both MAs
 rsiMA1 <- RSI(price, n=14, maType="WMA", wts=ttrc[,"Volume"])

 # Case of two different 'maType's for both MAs
 rsiMA2 <- RSI(price, n=14, maType=list(maUp=list(EMA),maDown=list(WMA)))


data(ttrc)
 price <- ttrc[,"Close"]

 # Default case
 rsi <- RSI(price)

 # Case of one 'maType' for both MAs
 rsiMA1 <- RSI(price, n=14, maType="WMA", wts=ttrc[,"Volume"])

 # Case of two different 'maType's for both MAs
 rsiMA2 <- RSI(price, n=14, maType=list(maUp=list(EMA),maDown=list(WMA)))

Percent Rank over a Moving Window

Description

This function computes a running/rolling percentage rank.

Usage

runPercentRank(x, n = 260, cumulative = FALSE, exact.multiplier = 0.5)
runPercentRank(x, n = 260, cumulative = FALSE, exact.multiplier = 0.5)

Arguments

`x`	Object coercible to xts or matrix.
`n`	Number of periods to use in the window or, if `cumulative=TRUE`, the number of observations to use before the first result is returned. Must be between 1 and `nrow(x)`, inclusive.
`cumulative`	Logical, use from-inception calculation?
`exact.multiplier`	The weight applied to identical values in the window. Must be between 0 and 1, inclusive. See details.

Details

The computation for a percentage rank can vary depending on the weight given to values in the window that are equal to the value being ranked. This weight can be set using the exact.multiplier argument which defaults to 0.5.

exact.multiplier = 0 scores equal values in the lookback window as always being greater than the value being ranked. exact.multiplier = 1 scores equal values as being below the value being ranked. Any multiplier between 0 and 1 counts that proportion of the equal values as being below the value being ranked.

The value of exact.multiplier has the most impact when the window is relatively small or when the number of discrete values in the window is small. For non-repeating values, changing exact.multiplier = 0 to exact.multiplier = 1 for a window of size N will shift the resulting percentile rankings by 1/N. It is equivalent to changing the question from, "how many values are < the value" to "how many values are <= the value".

Value

A object of percent ranks over a n-period moving window of the same class as x and y or a vector (if try.xts fails).

Note

This computation is different from the one used in Microsoft Excel's PERCENTRANK formula. Excel's computation is rather strange and gives inconsistent results as it uses interpolation to rank values that are not found within the lookback window.

Author(s)

Charlie Friedemann

References

The following site(s) were used to code/document this indicator:
https://en.wikipedia.org/wiki/Percentile_rank

Analysis of Running/Rolling/Moving Windows

Description

Various functions to analyze data over a moving window of periods.

Usage

runSum(x, n = 10, cumulative = FALSE)

runMin(x, n = 10, cumulative = FALSE)

runMax(x, n = 10, cumulative = FALSE)

runMean(x, n = 10, cumulative = FALSE)

runMedian(x, n = 10, non.unique = "mean", cumulative = FALSE)

runCov(x, y, n = 10, use = "all.obs", sample = TRUE, cumulative = FALSE)

runCor(x, y, n = 10, use = "all.obs", sample = TRUE, cumulative = FALSE)

runVar(x, y = NULL, n = 10, sample = TRUE, cumulative = FALSE)

runSD(x, n = 10, sample = TRUE, cumulative = FALSE)

runMAD(
  x,
  n = 10,
  center = NULL,
  stat = "median",
  constant = 1.4826,
  non.unique = "mean",
  cumulative = FALSE
)

wilderSum(x, n = 10)
runSum(x, n = 10, cumulative = FALSE)

runMin(x, n = 10, cumulative = FALSE)

runMax(x, n = 10, cumulative = FALSE)

runMean(x, n = 10, cumulative = FALSE)

runMedian(x, n = 10, non.unique = "mean", cumulative = FALSE)

runCov(x, y, n = 10, use = "all.obs", sample = TRUE, cumulative = FALSE)

runCor(x, y, n = 10, use = "all.obs", sample = TRUE, cumulative = FALSE)

runVar(x, y = NULL, n = 10, sample = TRUE, cumulative = FALSE)

runSD(x, n = 10, sample = TRUE, cumulative = FALSE)

runMAD(
  x,
  n = 10,
  center = NULL,
  stat = "median",
  constant = 1.4826,
  non.unique = "mean",
  cumulative = FALSE
)

wilderSum(x, n = 10)

Arguments

`x`	Object coercible to xts or matrix.
`n`	Number of periods to use in the window or, if `cumulative=TRUE`, the number of observations to use before the first result is returned. Must be between 1 and `nrow(x)`, inclusive.
`cumulative`	Logical, use from-inception calculation?
`non.unique`	One of 'mean', 'max', or 'min'; which compute their respective statistics for the two middle values of even-sized samples.
`y`	Object coercible to xts or matrix.
`use`	Only `"all.obs"` currently implemented.
`sample`	Logical, sample covariance if `TRUE` (denominator of `n-1`)
`center`	The values to use as the measure of central tendency, around which to calculate deviations. The default (`NULL`) uses the median.
`stat`	Statistic to calculate, one of 'median' or 'mean' (e.g. median absolute deviation or mean absolute deviation, respectively.)
`constant`	Scale factor applied to approximate the standard deviation.

Value

A object of the same class as x and y or a vector (if try.xts fails).

runSum: returns sums over a n-period moving window.
runMin: returns minimums over a n-period moving window.
runMax: returns maximums over a n-period moving window.
runMean: returns means over a n-period moving window.
runMedian: returns medians over a n-period moving window.
runCov: returns covariances over a n-period moving window.
runCor: returns correlations over a n-period moving window.
runVar: returns variances over a n-period moving window.
runSD: returns standard deviations over a n-period moving window.
runMAD: returns median/mean absolute deviations over a n-period moving window.
wilderSum: retuns a Welles Wilder style weighted sum over a n-period moving window.

Author(s)

Joshua Ulrich

Parabolic Stop-and-Reverse

Description

The Parabolic Stop-and-Reverse calculates a trailing stop. Developed by J. Welles Wilder.

Usage

SAR(HL, accel = c(0.02, 0.2))
SAR(HL, accel = c(0.02, 0.2))

Arguments

`HL`	Object that is coercible to xts or matrix and contains High-Low prices.
`accel`	accel[1]: Acceleration factor. accel[2]: Maximum acceleration factor.

Details

The calculation for the SAR is quite complex. See the URLs in the references section for calculation notes.

The SAR assumes that you are always in the market, and calculates the Stop And Reverse point when you would close a long position and open a short position or vice versa.

Value

A object of the same class as HL or a vector (if try.xts fails) containing the Parabolic Stop and Reverse values.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.linnsoft.com/techind/parabolic-sar-sar
https://www.fmlabs.com/reference/SAR.htm
https://school.stockcharts.com/doku.php?id=technical_indicators:parabolic_sar
https://www.metastock.com/Customer/Resources/TAAZ/?p=87

Examples


 data(ttrc)
 sar <- SAR(ttrc[,c("High","Low")])

data(ttrc)
 sar <- SAR(ttrc[,c("High","Low")])

Moving Averages

Description

Calculate various moving averages (MA) of a series.

Usage

SMA(x, n = 10, ...)

EMA(x, n = 10, wilder = FALSE, ratio = NULL, ...)

DEMA(x, n = 10, v = 1, wilder = FALSE, ratio = NULL)

WMA(x, n = 10, wts = 1:n, ...)

EVWMA(price, volume, n = 10, ...)

ZLEMA(x, n = 10, ratio = NULL, ...)

VWAP(price, volume, n = 10, ...)

HMA(x, n = 20, ...)

ALMA(x, n = 9, offset = 0.85, sigma = 6, ...)
SMA(x, n = 10, ...)

EMA(x, n = 10, wilder = FALSE, ratio = NULL, ...)

DEMA(x, n = 10, v = 1, wilder = FALSE, ratio = NULL)

WMA(x, n = 10, wts = 1:n, ...)

EVWMA(price, volume, n = 10, ...)

ZLEMA(x, n = 10, ratio = NULL, ...)

VWAP(price, volume, n = 10, ...)

HMA(x, n = 20, ...)

ALMA(x, n = 9, offset = 0.85, sigma = 6, ...)

Arguments

`x`	Price, volume, etc. series that is coercible to xts or matrix.
`n`	Number of periods to average over. Must be between 1 and `nrow(x)`, inclusive.
`...`	any other passthrough parameters
`wilder`	logical; if `TRUE`, a Welles Wilder type EMA will be calculated; see notes.
`ratio`	A smoothing/decay ratio. `ratio` overrides `wilder` in `EMA`.
`v`	The 'volume factor' (a number in [0,1]). See Notes.
`wts`	Vector of weights. Length of `wts` vector must equal the length of `x`, or `n` (the default).
`price`	Price series that is coercible to xts or matrix.
`volume`	Volume series that is coercible to xts or matrix, that corresponds to price series, or a constant. See Notes.
`offset`	Percentile at which the center of the distribution should occur.
`sigma`	Standard deviation of the distribution.

Details

SMA calculates the arithmetic mean of the series over the past n observations.

EMA calculates an exponentially-weighted mean, giving more weight to recent observations. See Warning section below.

WMA is similar to an EMA, but with linear weighting if the length of wts is equal to n. If the length of wts is equal to the length of x, the WMA will use the values of wts as weights.

DEMA is calculated as: DEMA = (1 + v) * EMA(x,n) - EMA(EMA(x,n),n) * v (with the corresponding wilder and ratio arguments).

EVWMA uses volume to define the period of the MA.

ZLEMA is similar to an EMA, as it gives more weight to recent observations, but attempts to remove lag by subtracting data prior to (n-1)/2 periods (default) to minimize the cumulative effect.

VWMA and VWAP calculate the volume-weighted moving average price.

HMA a WMA of the difference of two other WMAs, making it very reponsive.

ALMA inspired by Gaussian filters. Tends to put less weight on most recent observations, reducing tendency to overshoot.

Value

A object of the same class as x or price or a vector (if try.xts fails) containing the columns:

SMA: Simple moving average.
EMA: Exponential moving average.
WMA: Weighted moving average.
DEMA: Double-exponential moving average.
EVWMA: Elastic, volume-weighted moving average.
ZLEMA: Zero lag exponential moving average.
VWMA: Volume-weighed moving average (same as VWAP).
VWAP: Volume-weighed average price (same as VWMA).
VWA: Variable-length moving average.
HMA: Hull moving average.
ALMA: Arnaud Legoux moving average.

Warning

Some indicators (e.g. EMA, DEMA, EVWMA, etc.) are calculated using the indicators' own previous values, and are therefore unstable in the short-term. As the indicator receives more data, its output becomes more stable. See example below.

Note

For EMA, wilder=FALSE (the default) uses an exponential smoothing ratio of 2/(n+1), while wilder=TRUE uses Welles Wilder's exponential smoothing ratio of 1/n. The EMA result is initialized with the n-period sample average at period n. The exponential decay is applied from that point forward.

Since WMA can accept a weight vector of length equal to the length of x or of length n, it can be used as a regular weighted moving average (in the case wts=1:n) or as a moving average weighted by volume, another indicator, etc.

Since DEMA allows adjusting v, it is technically Tim Tillson's generalized DEMA (GD). When v=1 (the default), the result is the standard DEMA. When v=0, the result is a regular EMA. All other values of v return the GD result. This function can be used to calculate Tillson's T3 indicator (see example below). Thanks to John Gavin for suggesting the generalization.

For EVWMA, if volume is a series, n should be chosen so the sum of the volume for n periods approximates the total number of outstanding shares for the security being averaged. If volume is a constant, it should represent the total number of outstanding shares for the security being averaged.

Author(s)

Joshua Ulrich, Ivan Popivanov (HMA, ALMA)

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/ExpMA.htm
https://www.fmlabs.com/reference/WeightedMA.htm
https://www.fmlabs.com/reference/DEMA.htm
https://www.fmlabs.com/reference/T3.htm
https://www.linnsoft.com/techind/evwma-elastic-volume-weighted-moving-average
https://www.fmlabs.com/reference/ZeroLagExpMA.htm
https://www.fmlabs.com/reference/VIDYA.htm
https://www.traderslog.com/hullmovingaverage
https://web.archive.org/web/20180222085959/http://arnaudlegoux.com/

Examples


 data(ttrc)
 ema.20 <-   EMA(ttrc[,"Close"], 20)
 sma.20 <-   SMA(ttrc[,"Close"], 20)
 dema.20 <-  DEMA(ttrc[,"Close"], 20)
 evwma.20 <- EVWMA(ttrc[,"Close"], ttrc[,"Volume"], 20)
 zlema.20 <- ZLEMA(ttrc[,"Close"], 20)
 alma <- ALMA(ttrc[,"Close"])
 hma <- HMA(ttrc[,"Close"])

 ## Example of Tim Tillson's T3 indicator
 T3 <- function(x, n=10, v=1) DEMA(DEMA(DEMA(x,n,v),n,v),n,v)
 t3 <- T3(ttrc[,"Close"])

 ## Example of short-term instability of EMA
 ## (and other indicators mentioned above)
 x <- rnorm(100)
 tail( EMA(x[90:100],10), 1 )
 tail( EMA(x[70:100],10), 1 )
 tail( EMA(x[50:100],10), 1 )
 tail( EMA(x[30:100],10), 1 )
 tail( EMA(x[10:100],10), 1 )
 tail( EMA(x[ 1:100],10), 1 )

data(ttrc)
 ema.20 <-   EMA(ttrc[,"Close"], 20)
 sma.20 <-   SMA(ttrc[,"Close"], 20)
 dema.20 <-  DEMA(ttrc[,"Close"], 20)
 evwma.20 <- EVWMA(ttrc[,"Close"], ttrc[,"Volume"], 20)
 zlema.20 <- ZLEMA(ttrc[,"Close"], 20)
 alma <- ALMA(ttrc[,"Close"])
 hma <- HMA(ttrc[,"Close"])

 ## Example of Tim Tillson's T3 indicator
 T3 <- function(x, n=10, v=1) DEMA(DEMA(DEMA(x,n,v),n,v),n,v)
 t3 <- T3(ttrc[,"Close"])

 ## Example of short-term instability of EMA
 ## (and other indicators mentioned above)
 x <- rnorm(100)
 tail( EMA(x[90:100],10), 1 )
 tail( EMA(x[70:100],10), 1 )
 tail( EMA(x[50:100],10), 1 )
 tail( EMA(x[30:100],10), 1 )
 tail( EMA(x[10:100],10), 1 )
 tail( EMA(x[ 1:100],10), 1 )

Signal to Noise Ratio

Description

The n-day SNR for a given market is calculated by taking the absolute price change over an n-day period and dividing it by the average n-day volatility.

Usage

SNR(HLC, n, ...)
SNR(HLC, n, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`n`	Number of periods for moving average.
`...`	Other arguments to be passed to `ATR`.

Details

$SNR_n = \frac{|C_t - C_{t-n}|}{ATR_n}$

Using average true range as the volatility measure captures more of the intraday and overnight volatility in a way that a measurement of Close-to-Close price change does not.

The interpretation is then relatively intuitive: an SNR value of five indicates that the market has moved five times the volatility (average true range) over the given look-back period.

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the signal to noise ratio.

Author(s)

Peter Carl

References

Skeggs, James and Hill, Alex (2015). Back in Black Part 2: The Opportunity Set for Trend Following.

Stochastic Oscillator / Stochastic Momentum Index

Description

The stochastic oscillator is a momentum indicator that relates the location of each day's close relative to the high/low range over the past n periods. Developed by George C. Lane in the late 1950s. The SMI relates the close to the midpoint of the high/low range. Developed by William Blau in 1993.

Usage

stoch(
  HLC,
  nFastK = 14,
  nFastD = 3,
  nSlowD = 3,
  maType,
  bounded = TRUE,
  smooth = 1,
  ...
)

SMI(HLC, n = 13, nFast = 2, nSlow = 25, nSig = 9, maType, bounded = TRUE, ...)
stoch(
  HLC,
  nFastK = 14,
  nFastD = 3,
  nSlowD = 3,
  maType,
  bounded = TRUE,
  smooth = 1,
  ...
)

SMI(HLC, n = 13, nFast = 2, nSlow = 25, nSig = 9, maType, bounded = TRUE, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`nFastK`	Number of periods for fast %K (i.e. the number of past periods to use).
`nFastD`	Number of periods for fast %D (i.e. the number smoothing periods to apply to fast %K).
`nSlowD`	Number of periods for slow %D (i.e. the number smoothing periods to apply to fast %D).
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.
`bounded`	Logical, should current period's values be used in the calculation?
`smooth`	Number of internal smoothing periods to be applied before calculating FastK. See Details.
`...`	Other arguments to be passed to the `maType` function in case (1) above.
`n`	Number of periods to use.
`nFast`	Number of periods for initial smoothing.
`nSlow`	Number of periods for double smoothing.
`nSig`	Number of periods for signal line.

Details

If a High-Low-Close series is provided, the indicator is calculated using the high/low values. If a vector is provided, the calculation only uses that series. This allows stochastics to be calculated for: (1) series that have no HLC definition (e.g. foreign exchange), and (2) stochastic indicators (e.g. stochastic RSI - see examples).

The smooth argument is the number of periods of internal smoothing to apply to the differences in the high-low-close range before calculating Fast K. Thanks to Stanley Neo for the suggestion.

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the columns:

fastK: Stochastic Fast %K
fastD: Stochastic Fast %D
slowD: Stochastic Slow %D
SMI: Stochastic Momentum Index
signal: Stochastic Momentum Index signal line

Note

The calculation for William's %R is similar to that of stochastics' fast %K.

The value for fast %K will be 0.5 whenever the highest high and lowest low are the same over the last n periods.

The stochastic oscillator and SMI calculate relative value of the close versus the high/low range and the midpoint of the high/low range, respectively.

The stochastic oscillator and the stochastic momentum index are interpreted similarly. Readings below 20 (above 80) are considered oversold (overbought). However, readings below 20 (above 80) are not necessarily bearish (bullish). Lane believed some of the best sell (buy) signals occurred when the oscillator moved from overbought (oversold) back below 80 (above 20).

For the stochastic oscillator, buy (sell) signals can also be given when %K crosses above (below) %D. Crossover signals are quite frequent however, which may result in whipsaws.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document these indicators:
Stochastic Oscillator:
https://www.fmlabs.com/reference/StochasticOscillator.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=106
https://www.linnsoft.com/techind/stochastics
https://school.stockcharts.com/doku.php?id=technical_indicators:stochastic_oscillator_fast_slow_and_full

SMI:
https://www.fmlabs.com/reference/default.htm?url=SMI.htm

Examples


 data(ttrc)
 stochOSC <- stoch(ttrc[,c("High","Low","Close")])
 stochWPR <- WPR(ttrc[,c("High","Low","Close")])

 plot(tail(stochOSC[,"fastK"], 100), type="l",
     main="Fast %K and Williams %R", ylab="",
     ylim=range(cbind(stochOSC, stochWPR), na.rm=TRUE) )
 lines(tail(stochWPR, 100), col="blue")
 lines(tail(1-stochWPR, 100), col="red", lty="dashed")

 stoch2MA <- stoch( ttrc[,c("High","Low","Close")],
     maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

 SMI3MA <- SMI(ttrc[,c("High","Low","Close")],
     maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

 stochRSI <- stoch( RSI(ttrc[,"Close"]) )
data(ttrc)
 stochOSC <- stoch(ttrc[,c("High","Low","Close")])
 stochWPR <- WPR(ttrc[,c("High","Low","Close")])

 plot(tail(stochOSC[,"fastK"], 100), type="l",
     main="Fast %K and Williams %R", ylab="",
     ylim=range(cbind(stochOSC, stochWPR), na.rm=TRUE) )
 lines(tail(stochWPR, 100), col="blue")
 lines(tail(1-stochWPR, 100), col="red", lty="dashed")

 stoch2MA <- stoch( ttrc[,c("High","Low","Close")],
     maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

 SMI3MA <- SMI(ttrc[,c("High","Low","Close")],
     maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA)) )

 stochRSI <- stoch( RSI(ttrc[,"Close"]) )

Fetch Internet Data

Description

Get investment data from the internet.

Usage

stockSymbols(
  exchange = c("AMEX", "NASDAQ", "NYSE", "ARCA", "BATS", "IEX"),
  sort.by = c("Exchange", "Symbol"),
  quiet = FALSE
)

getYahooData(
  symbol,
  start,
  end,
  freq = "daily",
  type = "price",
  adjust = TRUE,
  quiet = FALSE
)
stockSymbols(
  exchange = c("AMEX", "NASDAQ", "NYSE", "ARCA", "BATS", "IEX"),
  sort.by = c("Exchange", "Symbol"),
  quiet = FALSE
)

getYahooData(
  symbol,
  start,
  end,
  freq = "daily",
  type = "price",
  adjust = TRUE,
  quiet = FALSE
)

Arguments

`exchange`	Character vector of exchange names on which desired instrument symbols are traded.
`sort.by`	Character vector of columns by which returned data will be sorted. Must be one or more of `"Name"`, `"Symbol"`, `"Market.Cap"`, or `"Exchange"`.
`quiet`	Logical; if `TRUE`, status messages will be printed to the console.
`symbol`	Yahoo! Finance instrument symbol.
`start`	Numeric; first date of desired data, in YYYYMMDD format. Default is first date of series.
`end`	Numeric; last date of desired data, in YYYYMMDD format. Default is last date of series.
`freq`	Desired data frequency. One of `"daily"`, `"weekly"`, `"monthly"`.
`type`	Type of data to return. One of `"price"`, or `"split"`. `type="split"` will return both split and dividend data.
`adjust`	Logical; if `TRUE`, the Open, High, Low, and Close prices will be adjusted for dividends and splits, and Volume will be adjusted for dividends.

Details

getYahooData fetches individual stock data from the Yahoo! Finance website. It also adjusts price for splits and dividends, and volume for splits. See the Warning section, and note that it is deprecated in favor of getSymbols in the quantmod package.

stockSymbols fetches instrument symbols from the nasdaq.com website, and adjusts the symbols to be compatible with the Yahoo! Finance website.

Value

getYahooData returns an xts object containing the columns:

stockSymbols returns a character vector containing all the listed symbols for the given exchanges.

Date: Trade date, in CCYYMMDD format.
Open: Open price.
High: High price.
Low: Low price.
Close: Close price.
Volume: Volume.

Warning

As of TTR 0.23-2, getYahooData has been patched to work with changes to Yahoo Finance, which also included the following changes to the raw data:

The adjusted close column appears to no longer include dividend adjustments
The open, high, and low columns are adjusted for splits, and
The raw data may contain missing values.
The raw data may contain errors.

As of TTR 0.24.2, stockSymbols began using data from NASDAQ's FTP site because the data from the original site is no longer available. This new file does not contain data for the columns: LastSale, MarketCap, IPOyear, Sector, and Industry. All the columns still appear in the results,#' but all the values in the columns are set to NA.

Note

The symbols returned by stockSymbols may not be in the format necessary to retrieve data using getYahooData.

getYahooData has only been tested on daily data. It isn't known if the function correctly adjusts data for any other frequency.

Author(s)

Joshua Ulrich

References

Examples


 ### Note: you must have a working internet
 ### connection for these examples to work!
 if (interactive()) {
   ge <- getYahooData("GE", 19990404, 20050607, adjust = FALSE)

   nyse.symbols <- stockSymbols("NYSE")
 }

### Note: you must have a working internet
 ### connection for these examples to work!
 if (interactive()) {
   ge <- getYahooData("GE", 19990404, 20050607, adjust = FALSE)

   nyse.symbols <- stockSymbols("NYSE")
 }

Trend Detection Index

Description

The Trend Detection Index (TDI) attempts to identify starting and ending trends. Developed by M. H. Pee.

Usage

TDI(price, n = 20, multiple = 2)
TDI(price, n = 20, multiple = 2)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	Number of periods to use.
`multiple`	Multiple used to calculate (2).

Details

The TDI is the (1) absolute value of the n-day sum of the n-day momentum, minus the quantity of (2) multiple*n-day sum of the absolute value of the n-day momentum, minus (3) n-day sum of the absolute value of the n-day momentum.

I.e. TDI = (1) - [ (2) - (3) ]

The direction indicator is the sum of the n-day momentum over the last n days.

See URL in references section for further details.

Value

A object of the same class as price or a matrix (if try.xts fails) containing the columns:

tdi: The Trend Detection Index.
di: The Direction Indicator.

Note

Positive/negative TDI values signal a trend/consolidation. A positive/ negative direction indicator signals a up/down trend. I.e. buy if the TDI and the direction indicator are positive, and sell if the TDI is positive while the direction indicator is negative.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.linnsoft.com/techind/trend-detection-index-tdi

Examples


 data(ttrc)
 tdi <- TDI(ttrc[,"Close"], n=30)

data(ttrc)
 tdi <- TDI(ttrc[,"Close"], n=30)

True Range / Average True Range

Description

True range (TR) is a measure of volatility of a High-Low-Close series; average true range (ATR) is a Welles Wilder's style moving average of the TR. Developed by J. Welles Wilder in 1978.

Usage

TR(HLC)

ATR(HLC, n = 14, maType, ...)
TR(HLC)

ATR(HLC, n = 14, maType, ...)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`n`	Number of periods for moving average.
`maType`	A function or a string naming the function to be called.
`...`	Other arguments to be passed to the `maType` function.

Details

TR incorporates yesterday's close in the calculation (high minus low). E.g. if yesterday's close was higher than today's high, then the TR would equal yesterday's close minus today's low.

The ATR is a component of the Welles Wilder Directional Movement Index (DX, ADX).

Value

A object of the same class as HLC or a matrix (if try.xts fails) containing the columns:

tr: The true range of the series.
atr: The average (as specified by ma) true range of the series.
trueHigh: The true high of the series.
trueLow: The true low of the series.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/TR.htm
https://www.fmlabs.com/reference/ATR.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=35
https://www.linnsoft.com/techind/true-range-tr
https://school.stockcharts.com/doku.php?id=technical_indicators:average_true_range_atr

Examples


data(ttrc)
tr <- TR(ttrc[,c("High","Low","Close")])
atr <- ATR(ttrc[,c("High","Low","Close")], n=14)

data(ttrc)
tr <- TR(ttrc[,c("High","Low","Close")])
atr <- ATR(ttrc[,c("High","Low","Close")], n=14)

Triple Smoothed Exponential Oscillator

Description

The TRIX indicator calculates the rate of change of a triple exponential moving average. Developed by Jack K. Hutson.

Usage

TRIX(price, n = 20, nSig = 9, maType, percent = TRUE, ...)
TRIX(price, n = 20, nSig = 9, maType, percent = TRUE, ...)

Arguments

`price`	Price series that is coercible to xts or matrix.
`n`	Number of periods for moving average.
`nSig`	Number of periods for signal line moving average.
`maType`	Either: A function or a string naming the function to be called. A list with the first component like (1) above, and additional parameters specified as named components. See Examples.
`percent`	logical; if `TRUE`, the rate of change is calculated using the `ROC` function, otherwise the `momentum` function is used.
`...`	Other arguments to be passed to the `maType` function in case (1) above.

Details

The TRIX is calculated as follows:
3MA = MA( MA( MA(price) ) )
trix = 100 * [ 3MA(t) / 3MA(t-1) - 1 ]

Value

A object of the same class as price or a vector (if try.xts fails) containing the TRIX values.

Note

Buy/sell signals are generated when the TRIX crosses above/below zero. A nine-period EMA of the TRIX is used as a default signal line. Buy/sell signals are generated when the TRIX crosses above/below the signal line and is also above/below zero.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/default.htm?url=TRIX.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=114
https://www.linnsoft.com/techind/trix-triple-smoothed-exponential-oscillator
https://school.stockcharts.com/doku.php?id=technical_indicators:trix

Examples


 data(ttrc)
 trix  <- TRIX(ttrc[,"Close"])
 trix4 <- TRIX(ttrc[,"Close"],
 maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA), list(DEMA)))

data(ttrc)
 trix  <- TRIX(ttrc[,"Close"])
 trix4 <- TRIX(ttrc[,"Close"],
 maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA), list(DEMA)))

Functions to create Technical Trading Rules (TTR)

Description

This package contains many of the most popular technical analysis functions, as well as functions to retrieve U.S. stock symbols, and data from Yahoo Finance.

Details

Users will probably be most interested in the following functions:
ADX
BBands
changes
MovingAverages
MACD
RSI
runFun
stoch
VWAP
WebData

Author(s)

Joshua Ulrich

Maintainer: Joshua Ulrich

References

The following sites were used to code/document this package:
https://www.fmlabs.com/reference/default.htm
https://www.metastock.com/Customer/Resources/TAAZ/
https://www.linnsoft.com/indicators
https://school.stockcharts.com/doku.php?id=technical_indicators

Examples


 data(ttrc)

 # Bollinger Bands
 bbands <- BBands( ttrc[,c("High","Low","Close")] )

 # Directional Movement Index
 adx <- ADX(ttrc[,c("High","Low","Close")])

 # Moving Averages
 ema <- EMA(ttrc[,"Close"], n=20)
 sma <- SMA(ttrc[,"Close"], n=20)

 # MACD
 macd <- MACD( ttrc[,"Close"] )

 # RSI
 rsi <- RSI(ttrc[,"Close"])

 # Stochastics
 stochOsc <- stoch(ttrc[,c("High","Low","Close")])

 ### Note: you must have a working internet connection
 ### for the examples below to work!
 if (interactive()) {
   # Fetch U.S. symbols from the internet
   nyseSymbols <- stockSymbols("NYSE")

   # Fetch Yahoo! Finance data from the internet
   ge <- getYahooData("GE", 19990404, 20050607, adjust = FALSE)
 }

data(ttrc)

 # Bollinger Bands
 bbands <- BBands( ttrc[,c("High","Low","Close")] )

 # Directional Movement Index
 adx <- ADX(ttrc[,c("High","Low","Close")])

 # Moving Averages
 ema <- EMA(ttrc[,"Close"], n=20)
 sma <- SMA(ttrc[,"Close"], n=20)

 # MACD
 macd <- MACD( ttrc[,"Close"] )

 # RSI
 rsi <- RSI(ttrc[,"Close"])

 # Stochastics
 stochOsc <- stoch(ttrc[,c("High","Low","Close")])

 ### Note: you must have a working internet connection
 ### for the examples below to work!
 if (interactive()) {
   # Fetch U.S. symbols from the internet
   nyseSymbols <- stockSymbols("NYSE")

   # Fetch Yahoo! Finance data from the internet
   ge <- getYahooData("GE", 19990404, 20050607, adjust = FALSE)
 }

Technical Trading Rule Composite data

Description

Historical Open, High, Low, Close, and Volume data for the periods January 2, 1985 to December 31, 2006. Randomly generated.

Format

The format is:

Date:	Class 'Date'	5480 5481 5482 5485 5486 ...
Open:	num	3.18 3.09 3.11 3.09 3.10 ...
High:	num	3.18 3.15 3.12 3.12 3.12 ...
Low:	num	3.08 3.09 3.08 3.07 3.08 ...
Close:	num	3.08 3.11 3.09 3.10 3.11 ...
Volume:	num	1870906 3099506 2274157 2086758 2166348 ...

Details

These data do not represent an actual security. They are provided so examples do not necessitate an internet connection.

Source

Randomly generated.

Examples


 data(ttrc)
 plot(tail(ttrc[,"Close"],100), type="l")
data(ttrc)
 plot(tail(ttrc[,"Close"],100), type="l")

The Ultimate Oscillator

Description

The Ultimate Oscillator is a momentum oscillator designed to capture momentum across three different time frames.

Usage

ultimateOscillator(HLC, n = c(7, 14, 28), wts = c(4, 2, 1))
ultimateOscillator(HLC, n = c(7, 14, 28), wts = c(4, 2, 1))

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices.
`n`	A vector of the number of periods to use for each average calculation.
`wts`	The weights applied to each average.

Details

Created by Larry Williams in 1976.

Author(s)

Ivan Popivanov

References

The following site(s) were used to code/document this indicator:
https://school.stockcharts.com/doku.php?id=technical_indicators:ultimate_oscillator

Examples


data(ttrc)
ult.osc <- ultimateOscillator(ttrc[,c("High","Low","Close")])

data(ttrc)
ult.osc <- ultimateOscillator(ttrc[,c("High","Low","Close")])

Vertical Horizontal Filter

Description

The Vertical Horizontal Filter (VHF) attempts to identify starting and ending trends. Developed by Adam White.

Usage

VHF(price, n = 28)
VHF(price, n = 28)

Arguments

`price`	Object that is coercible to xts or matrix and contains a Close price series, or a High-Low-Close price series.
`n`	Number of periods to use.

Details

The VHF is calculated by subtracting the n-period lowest low from the n-period highest high and dividing that result by the n-period rolling sum of the close price changes.

Value

A object of the same class as price or a vector (if try.xts fails) containing the VHF values.

Note

If Close prices are given, the function calculates the max/min using only those prices (the default). If HLC prices are given, the function calculates the max/min using the high/low prices (added for flexibility).

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.metastock.com/Customer/Resources/TAAZ/?p=119

Examples


 data(ttrc)
 vhf.close <- VHF(ttrc[,"Close"])
 vhf.hilow <- VHF(ttrc[,c("High","Low","Close")])

data(ttrc)
 vhf.close <- VHF(ttrc[,"Close"])
 vhf.hilow <- VHF(ttrc[,c("High","Low","Close")])

Volatility

Description

Selected volatility estimators/indicators; various authors.

Usage

volatility(OHLC, n = 10, calc = "close", N = 260, mean0 = FALSE, ...)
volatility(OHLC, n = 10, calc = "close", N = 260, mean0 = FALSE, ...)

Arguments

`OHLC`	Object that is coercible to xts or matrix and contains Open-High-Low-Close prices (or only Close prices, if `calc="close"`).
`n`	Number of periods for the volatility estimate.
`calc`	The calculation (type) of estimator to use.
`N`	Number of periods per year.
`mean0`	Use a mean of 0 rather than the sample mean.
`...`	Arguments to be passed to/from other methods.

Details

Close-to-Close Volatility (calc="close")

$\sigma_{cl} = \sqrt{\frac{N}{n-2} \sum_{i=1}^{n-1}(r_i-\bar{r})^2}$

$where\;\; r_i = \log \left(\frac{C_i}{C_{i-1}}\right)$

$and\;\; \bar{r} = \frac{r_1+r_2+\ldots +r_{n-1}}{n-1}$
OHLC Volatility: Garman and Klass (calc="garman.klass")
The Garman and Klass estimator for estimating historical volatility assumes Brownian motion with zero drift and no opening jumps (i.e. the opening = close of the previous period). This estimator is 7.4 times more efficient than the close-to-close estimator.

$\sigma = \sqrt{ \frac{N}{n} \sum \left[ \textstyle\frac{1}{2}\displaystyle \left( \log \frac{H_i}{L_i} \right)^2 - (2\log 2-1) \left( \log \frac{C_i}{O_i} \right)^2 \right] }$
High-Low Volatility: Parkinson (calc="parkinson")
The Parkinson formula for estimating the historical volatility of an underlying based on high and low prices.

$\sigma = \sqrt{ \frac{N}{4 n \times \log 2} \sum_{i=1}^{n} \left(\log \frac{H_i}{L_i}\right)^2}$
OHLC Volatility: Rogers and Satchell (calc="rogers.satchell")
The Roger and Satchell historical volatility estimator allows for non-zero drift, but assumed no opening jump.

$\sigma = \sqrt{ \textstyle\frac{N}{n} \sum \left[ \log \textstyle\frac{H_i}{C_i} \times \log \textstyle\frac{H_i}{O_i} + \log \textstyle\frac{L_i}{C_i} \times \log \textstyle\frac{L_i}{O_i} \right] }$
OHLC Volatility: Garman and Klass - Yang and Zhang (calc="gk.yz")
This estimator is a modified version of the Garman and Klass estimator that allows for opening gaps.

$\sigma = \sqrt{ \textstyle\frac{N}{n} \sum \left[ \left( \log \textstyle\frac{O_i}{C_{i-1}} \right)^2 + \textstyle\frac{1}{2}\displaystyle \left( \log \textstyle\frac{H_i}{L_i} \right)^2 - (2 \times \log 2-1) \left( \log \textstyle\frac{C_i}{O_i} \right)^2 \right] }$
OHLC Volatility: Yang and Zhang (calc="yang.zhang")
The Yang and Zhang historical volatility estimator has minimum estimation error, and is independent of drift and opening gaps. It can be interpreted as a weighted average of the Rogers and Satchell estimator, the close-open volatility, and the open-close volatility.

Users may override the default values of $\alpha$ (1.34 by default) or $k$ used in the calculation by specifying alpha or k in ..., respectively. Specifying k will cause alpha to be ignored, if both are provided.

$\sigma^2 = \sigma_o^2 + k\sigma_c^2 + (1-k)\sigma_{rs}^2$

$\sigma_o^2 =\textstyle \frac{N}{n-1} \sum \left( \log \frac{O_i}{C_{i-1}}-\mu_o \right)^2$

$\mu_o=\textstyle \frac{1}{n} \sum \log \frac{O_i}{C_{i-1}}$

$\sigma_c^2 =\textstyle \frac{N}{n-1} \sum \left( \log \frac{C_i}{O_i}-\mu_c \right)^2$

$\mu_c=\textstyle \frac{1}{n} \sum \log \frac{C_i}{O_i}$

$\sigma_{rs}^2 = \textstyle\frac{N}{n} \sum \left( \log \textstyle\frac{H_i}{C_i} \times \log \textstyle\frac{H_i}{O_i} + \log \textstyle\frac{L_i}{C_i} \times \log \textstyle\frac{L_i}{O_i} \right)$

$k=\frac{\alpha-1}{alpha+\frac{n+1}{n-1}}$

Value

A object of the same class as OHLC or a vector (if try.xts fails) containing the chosen volatility estimator values.

Author(s)

Joshua Ulrich

References

The following sites were used to code/document these indicators. All were created by Thijs van den Berg under the GNU Free Documentation License and were retrieved on 2008-04-20. The original links are dead, but can be accessed via internet archives.

Close-to-Close Volatility (calc="close"):
https://web.archive.org/web/20100421083157/http://www.sitmo.com/eq/172

OHLC Volatility: Garman Klass (calc="garman.klass"):
https://web.archive.org/web/20100326172550/http://www.sitmo.com/eq/402

High-Low Volatility: Parkinson (calc="parkinson"):
https://web.archive.org/web/20100328195855/http://www.sitmo.com/eq/173

OHLC Volatility: Rogers Satchell (calc="rogers.satchell"):
https://web.archive.org/web/20091002233833/http://www.sitmo.com/eq/414

OHLC Volatility: Garman Klass - Yang Zhang (calc="gk.yz"):
https://web.archive.org/web/20100326215050/http://www.sitmo.com/eq/409

OHLC Volatility: Yang Zhang (calc="yang.zhang"):
https://web.archive.org/web/20100326215050/http://www.sitmo.com/eq/409

Examples


 data(ttrc)
 ohlc <- ttrc[,c("Open","High","Low","Close")]
 vClose <- volatility(ohlc, calc="close")
 vClose0 <- volatility(ohlc, calc="close", mean0=TRUE)
 vGK <- volatility(ohlc, calc="garman")
 vParkinson <- volatility(ohlc, calc="parkinson")
 vRS <- volatility(ohlc, calc="rogers")

data(ttrc)
 ohlc <- ttrc[,c("Open","High","Low","Close")]
 vClose <- volatility(ohlc, calc="close")
 vClose0 <- volatility(ohlc, calc="close", mean0=TRUE)
 vGK <- volatility(ohlc, calc="garman")
 vParkinson <- volatility(ohlc, calc="parkinson")
 vRS <- volatility(ohlc, calc="rogers")

Williams Accumulation / Distribution

Description

The Williams Accumulation / Distribution (AD) line is a measure of market momentum. Developed by Larry Williams.

Usage

williamsAD(HLC)
williamsAD(HLC)

Arguments

HLC

Object that is coercible to xts or matrix and contains High-Low-Close prices.

Details

The Williams AD line differs from OBV and chaikinAD in that it doesn't take volume into account.

Value

A object of the same class as HLC or a vector (if try.xts fails) containing the accumulation / distribution values.

Note

The Accumulation/Distribution Line is interpreted by looking for a divergence in the direction of the indicator relative to price.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/WilliamsAD.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=125

Examples


 data(ttrc)
 ad <- williamsAD(ttrc[,c("High","Low","Close")])

data(ttrc)
 ad <- williamsAD(ttrc[,c("High","Low","Close")])

William's %R

Description

William's % R.

Usage

WPR(HLC, n = 14)
WPR(HLC, n = 14)

Arguments

`HLC`	Object that is coercible to xts or matrix and contains High-Low-Close prices. If only a univariate series is given, it will be used. See details.
`n`	Number of periods to use.

Details

If an High-Low-Close series is provided, the indicator is calculated using the high/low values. If a vector is provided, the calculation only uses that series.

Value

A object of the same class as HLC or a vector (if try.xts fails) containing the William's %R values.

Note

The William's %R calculation is similar to stochastics' fast %K.

The value for William's %R will be 0.5 whenever the highest high and lowest low are the same over the last n periods.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/WilliamsR.htm
https://www.metastock.com/Customer/Resources/TAAZ/?p=126
https://www.linnsoft.com/techind/williams-r-wpr
https://school.stockcharts.com/doku.php?id=technical_indicators:williams_r

Examples


 data(ttrc)
 stochOsc <- stoch(ttrc[,c("High","Low","Close")])
 stochWPR<- WPR(ttrc[,c("High","Low","Close")])

 plot(tail(stochOsc[,"fastK"], 100), type="l",
     main="Fast %K and Williams %R", ylab="",
     ylim=range(cbind(stochOsc, stochWPR), na.rm=TRUE) )
 lines(tail(stochWPR, 100), col="blue")
 lines(tail(1-stochWPR, 100), col="red", lty="dashed")

data(ttrc)
 stochOsc <- stoch(ttrc[,c("High","Low","Close")])
 stochWPR<- WPR(ttrc[,c("High","Low","Close")])

 plot(tail(stochOsc[,"fastK"], 100), type="l",
     main="Fast %K and Williams %R", ylab="",
     ylim=range(cbind(stochOsc, stochWPR), na.rm=TRUE) )
 lines(tail(stochWPR, 100), col="blue")
 lines(tail(1-stochWPR, 100), col="red", lty="dashed")

Zig Zag

Description

Zig Zag higlights trends by removing price changes smaller than change and interpolating lines between the extreme points.

Usage

ZigZag(HL, change = 10, percent = TRUE, retrace = FALSE, lastExtreme = TRUE)
ZigZag(HL, change = 10, percent = TRUE, retrace = FALSE, lastExtreme = TRUE)

Arguments

`HL`	Object that is coercible to xts or matrix and contains either a High-Low price series, or a Close price series.
`change`	Minimum price movement, either in dollars or percent (see `percent`).
`percent`	Use percentage or dollar change?
`retrace`	Is `change` a retracement of the previous move, or an absolute change from peak to trough?
`lastExtreme`	If the extreme price is the same over multiple periods, should the extreme price be the first or last observation?

Details

The Zig Zag is non-predictive. The purpose of the Zig Zag is filter noise and make chart patterns clearer. It's more a visual tool than an indicator.

Value

A object of the same class as HL or a vector (if try.xts fails) containing the Zig Zag indicator.

Warning

The last value of the ZigZag indicator is unstable (i.e. unknown) until the turning point actually occurs. Therefore this indicator isn't well-suited for use for systematic trading strategies.

Note

If High-Low prices are given, the function calculates the max/min using the high/low prices. Otherwise the function calculates the max/min of the single series.

Author(s)

Joshua Ulrich

References

The following site(s) were used to code/document this indicator:
https://www.fmlabs.com/reference/default.htm?url=ZigZag.htm
https://www.linnsoft.com/techind/zig-zag-indicator-zig-zzo
https://www.linnsoft.com/techind/zig-zag-oscillator-indicator-zzo
https://www.metastock.com/Customer/Resources/TAAZ/?p=127
https://school.stockcharts.com/doku.php?id=technical_indicators:zigzag

Examples


 ## Get Data and Indicator ##
 data(ttrc)
 zz <- ZigZag( ttrc[,c("High", "Low")], change=20 )

## Get Data and Indicator ##
 data(ttrc)
 zz <- ZigZag( ttrc[,c("High", "Low")], change=20 )

Package 'TTR'

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Chaikin Volatility

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Examples

Close Location Value

Description

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Arguments

Details