Type: | Package |
Title: | Technical Trading Rules |
Version: | 0.24.4 |
Imports: | xts (≥ 0.10-0), zoo, curl |
LinkingTo: | xts |
Enhances: | quantmod |
Suggests: | RUnit |
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. |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
URL: | https://github.com/joshuaulrich/TTR |
BugReports: | https://github.com/joshuaulrich/TTR/issues |
NeedsCompilation: | yes |
Packaged: | 2023-11-28 00:12:35 UTC; josh |
Author: | Joshua Ulrich [cre, aut], Ethan B. Smith [ctb] |
Maintainer: | Joshua Ulrich <josh.m.ulrich@gmail.com> |
Repository: | CRAN |
Date/Publication: | 2023-11-28 05:20:21 UTC |
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)
}
Welles Wilder's Directional Movement Index
Description
Directional Movement Index; developed by J. Welles Wilder.
Usage
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. The DX calculation uses
ATR
. See aroon
, CCI
,
TDI
, VHF
, GMMA
for other indicators
that measure trend direction/strength.
Examples
data(ttrc)
dmi.adx <- ADX(ttrc[,c("High","Low","Close")])
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, ...)
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section.
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"] )
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, ...)
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See aroon
,
ADX
, TDI
, VHF
, GMMA
for other indicators that measure trend direction/strength.
Examples
data(ttrc)
cci <- CCI(ttrc[,c("High","Low","Close")])
Close Location Value
Description
The Close Location Value (CLV) relates the day's close to its trading range.
Usage
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
See Also
See chaikinAD
, which uses CLV.
Examples
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)
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
|
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
See Also
Examples
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)
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
See Also
See RSI
.
Examples
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)
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/
See Also
See aroon
, CCI
, ADX
,
VHF
, GMMA
, TDI
for other
indicators that measure trend direction/strength.
Examples
data(ttrc)
cti <- CTI(ttrc[,"Close"], n = 20)
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, ...)
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 |
... |
Other arguments to be passed to the |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See MACD
for a general
oscillator.
Examples
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
)
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 |
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"])
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)
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
andlow
.- 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
See Also
See BBands
.
Examples
data(ttrc)
dc <- DonchianChannel( ttrc[,c("High","Low")] )
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, ...)
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
|
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section.
Examples
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
)
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:
|
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
See Also
See aroon
, CCI
, ADX
,
VHF
, TDI
for other indicators that measure trend
direction/strength.
Examples
data(ttrc)
gmma <- GMMA(ttrc[,"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),
...
)
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:
|
wts |
A vector the same length as |
... |
Other arguments to be passed to the |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See ROC
for the
rate-of-change function. See MACD
for a generic oscillator.
Examples
data(ttrc)
kst <- KST(ttrc[,"Close"])
kst4MA <- KST(ttrc[,"Close"],
maType=list(list(SMA),list(EMA),list(DEMA),list(WMA)))
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, ...)
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:
|
percent |
logical; if |
... |
Other arguments to be passed to the |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section.
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)) )
Money Flow Index
Description
The MFI is a ratio of positive and negative money flow over time.
Usage
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
|
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
See Also
Examples
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)
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
See Also
See chaikinAD
.
Examples
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
)
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
|
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 |
lavg |
Whether to use a longer |
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
See Also
Examples
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)
Arguments
x |
Price, volume, etc. series that is coercible to xts or matrix. |
n |
Number of periods to use. |
type |
Compounding type; either |
na.pad |
Should periods prior to |
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"])
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, ...)
Arguments
price |
Price series that is coercible to xts or matrix. |
n |
Number of periods for moving averages. |
maType |
Either:
|
... |
Other arguments to be passed to the |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See CMO
for a variation on
RSI.
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)))
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))
Arguments
HL |
Object that is coercible to xts or matrix and contains High-Low prices. |
accel |
accel[1]: 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
See Also
See ATR
and ADX
, which were also
developed by Welles Wilder.
Examples
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, ...)
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
|
... |
any other passthrough parameters |
wilder |
logical; if |
ratio |
A smoothing/decay ratio. |
v |
The 'volume factor' (a number in [0,1]). See Notes. |
wts |
Vector of weights. Length of |
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/
See Also
See wilderSum
, which is used in calculating a Welles
Wilder type MA.
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 )
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, ...)
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 |
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.
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)
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
See Also
See aroon
, CCI
, ADX
,
VHF
, GMMA
for other indicators that measure trend
direction/strength.
Examples
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, ...)
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See DX
, which uses true
range. See chaikinVolatility
for another volatility measure.
Examples
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, ...)
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:
|
percent |
logical; if |
... |
Other arguments to be passed to the |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section.
Examples
data(ttrc)
trix <- TRIX(ttrc[,"Close"])
trix4 <- TRIX(ttrc[,"Close"],
maType=list(list(SMA), list(EMA, wilder=TRUE), list(SMA), list(DEMA)))
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)
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
See Also
See aroon
, CCI
, ADX
,
TDI
, GMMA
for other indicators that measure trend
direction/strength.
Examples
data(ttrc)
vhf.close <- VHF(ttrc[,"Close"])
vhf.hilow <- VHF(ttrc[,c("High","Low","Close")])
William's %R
Description
William's % R.
Usage
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
See Also
See stoch
.
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")
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)
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 |
Use percentage or dollar change? |
retrace |
Is |
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 )
Split and dividend adjustment ratios
Description
Create split and dividend adjustment ratio vectors.
Usage
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 assplits
.If
splits
andclose
are provided, the resulting object will have as many observations asmax(NROW(splits), NROW(close))
.-
close
is required ifdividends
is provided.
Value
A xts object containing the columns:
- Split
The split adjustment ratio.
- Div
The dividend adjustment ratio.
Author(s)
Joshua Ulrich
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)
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
See Also
See CCI
, ADX
, TDI
,
VHF
, GMMA
for other indicators that measure trend
direction/strength.
Examples
## Get Data and Indicator ##
data(ttrc)
trend <- aroon( ttrc[,c("High", "Low")], n=20 )
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)
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
|
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
See Also
Examples
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, ...)
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See TR
for another
volatility measure.
Examples
data(ttrc)
volatility <- chaikinVolatility(ttrc[,c("High","Low")])
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, ...)
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
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section.
Examples
data(ttrc)
kc <- keltnerChannels(ttrc[,c("High","Low","Close")])
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)
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
Analysis of Running/Rolling/Moving Windows
Description
Various functions to analyze data over a moving window of periods.
Usage
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
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)
Arguments
x |
Object coercible to xts or matrix. |
n |
Number of periods to use in the window or, if
|
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)
Arguments
x |
Object coercible to xts or matrix. |
n |
Number of periods to use in the window or, if
|
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 |
sample |
Logical, sample covariance if |
center |
The values to use as the measure of central tendency, around
which to calculate deviations. The default ( |
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
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, ...)
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:
|
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 |
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
See Also
See EMA
, SMA
, etc. for moving average
options; and note Warning section. See WPR
to compare it's
results to fast %K.
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"]) )
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
)
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 |
quiet |
Logical; if |
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 |
type |
Type of data to return. One of |
adjust |
Logical; if |
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")
}
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")
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))
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")])
Volatility
Description
Selected volatility estimators/indicators; various authors.
Usage
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 |
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) ork
used in the calculation by specifyingalpha
ork
in...
, respectively. Specifyingk
will causealpha
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
See Also
See TR
and chaikinVolatility
for other
volatility measures.
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")
Williams Accumulation / Distribution
Description
The Williams Accumulation / Distribution (AD) line is a measure of market momentum. Developed by Larry Williams.
Usage
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
See Also
Examples
data(ttrc)
ad <- williamsAD(ttrc[,c("High","Low","Close")])