MESA Adaptive Moving Average

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

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

Handling of -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

mesa_adaptive_moving_average(x, cols, n = 10, 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.

n

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

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:

MAMA

double

Details

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

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

See also

Other Overlap Study: acceleration_bands(), bollinger_bands(), double_exponential_moving_average(), exponential_moving_average(), extended_parabolic_stop_and_reverse(), kaufman_adaptive_moving_average(), parabolic_stop_and_reverse(), simple_moving_average(), t3_exponential_moving_average(), trendline(), triangular_moving_average(), triple_exponential_moving_average(), weighted_moving_average()

Author

Serkan Korkmaz

Examples

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

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

## display the results
utils::tail(output)
#>                         MAMA
#> 2024-12-26 01:00:00 98504.62
#> 2024-12-27 01:00:00 98287.78
#> 2024-12-28 01:00:00 98129.43
#> 2024-12-29 01:00:00 97901.16
#> 2024-12-30 01:00:00 95264.58
#> 2024-12-31 01:00:00 95170.89

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

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