Doji Star

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

Handling of `NA` values

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

na.bridge = FALSE (default)

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

na.bridge = TRUE

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

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

Usage

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

Arguments

x: An OHLC-V series coercible to data.frame.
cols: (formula). An optional 4-variable formula selecting columns from x via model.frame. Defaults to ~open + high + low + close.
na.bridge: (logical). A logical of length 1. FALSE by default. When FALSE, input NAs propagate through the TA-Lib C routine (most indicators will fill the remaining output with NA). When TRUE, input NA rows are stripped before computation and re-inserted at the original positions in the output, causing the indicator to treat non-consecutive non-NA observations as if they were adjacent — see the Handling of NA values section above for the consequences.
...: Additional parameters passed into model.frame.

Value

An object of same class and length of x:

CDLDOJISTAR: integer

Pattern codes depend on options(talib.normalize):

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

Details

General options for candlestick pattern recognition:

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

Available options and their defaults:

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

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

Author

Serkan Korkmaz

Examples

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

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

## display the results
utils::tail(output)
#>                     CDLDOJISTAR
#> 2024-12-26 01:00:00           0
#> 2024-12-27 01:00:00           0
#> 2024-12-28 01:00:00           0
#> 2024-12-29 01:00:00           0
#> 2024-12-30 01:00:00           0
#> 2024-12-31 01:00:00           0

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

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

Handling of NA values