Moving Average Convergence Divergence (Extended)

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

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

Handling of -values

Leading NAs are always produced for the initial lookback period where insufficient data is available. If the input itself contains NAs they are passed through to the underlying C routine, which can cause the entire output to be filled with NAs. Set na.ignore = TRUE to strip NAs before calculation and re-insert them at their original positions in the output.

Usage

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

Arguments

x

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

cols

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

fast

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

slow

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

signal

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

na.ignore

(logical). A logical of length 1. FALSE by default. If TRUE, NAs in the input are stripped before calculation and re-inserted at their original positions in the output.

...

Additional parameters passed into model.frame

Value

An object of same class and length of x:

MACD

double

MACDSignal

double

MACDHist

double

See also

Other Momentum Indicator: absolute_price_oscillator(), aroon(), aroon_oscillator(), average_directional_movement_index(), average_directional_movement_index_rating(), balance_of_power(), chande_momentum_oscillator(), commodity_channel_index(), directional_movement_index(), fast_stochastic(), fixed_moving_average_convergence_divergence(), intraday_movement_index(), minus_directional_indicator(), minus_directional_movement(), momentum(), money_flow_index(), moving_average_convergence_divergence(), percentage_price_oscillator(), plus_directional_indicator(), plus_directional_movement(), rate_of_change(), ratio_of_change(), relative_strength_index(), stochastic(), stochastic_relative_strength_index(), triple_exponential_average(), ultimate_oscillator(), williams_oscillator()

Author

Serkan Korkmaz

Examples

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

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

## display the results
utils::tail(output)
#>                           MACD MACDSignal   MACDHist
#> 2024-12-26 01:00:00  608.68287  1590.9032  -982.2204
#> 2024-12-27 01:00:00  243.07106  1321.3368 -1078.2657
#> 2024-12-28 01:00:00   29.87501  1063.0444 -1033.1694
#> 2024-12-29 01:00:00 -261.68536   798.0985 -1059.7838
#> 2024-12-30 01:00:00 -561.79956   526.1189 -1087.9184
#> 2024-12-31 01:00:00 -729.69370   274.9564 -1004.6501

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

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