R/support_resistant.R

In nguyenngocbinh/bml_stocks: Provide simple forecast on stocks price

#' @title Detect support and resistance point
#' @author Nguyen Ngoc Binh \email{nguyenngocbinhneu@@gmail.com}
#' @export detect_support_resistance
#' @param timeSeries timeSeries a univariate numeric vector of (evenly-sampled) price observation. If you use OHLC prices, you may separately fit support and resistance levels to, respectively, lows and highs.
#' @param tolerance Virtually no support or resistance level is perfectly horizontal. Thus we allow some tolerance (in percentage of max(timeSeries)-min(timeSeries)) for the difference of the first and the last extreme. The default value of 0.01 maybe too restrictive, so you may try 0.02 and 0.03
#' @param nChunks Number of chunks to split the timeSeries. At each chunk the maximum and minimum will be detected. If one does not split the time series in chunks but just detects the first largest extreme, second largest extreme, etc, these extreme values will likely be too close to each other. Generally, it is difficult to recommend the "optimal" number of chunks but 10 seems to be a good trade-off if the time series contains at least 200 observations.
#' @param nPoints How many extremes to consider by fitting support or resistance level. It is commonly believed that a level should fit to at least three extremes (and it is really hard to fit more than three, esp. if tolerance is low). Thus the default value of 3 is really not bad.
#' @param plotChartWhether to plot the results or not.
#' @examples
#' quantmod::getSymbols("AAPL",src='yahoo')
#' df <- data.frame(Date=index(AAPL),coredata(AAPL))
#' detect_support_resistance(df[,5])
#' 
detect_support_resistance <-
  function(timeSeries,
           tolerance = 0.01,
           nChunks = 10,
           nPoints = 3,
           plotChart = TRUE) {
    
    #detect maximums and minimums
    N = length(timeSeries)
    stp = floor(N / nChunks)
    minz = array(0.0, dim = nChunks)
    whichMinz = array(0, dim = nChunks)
    maxz = array(0.0, dim = nChunks)
    whichMaxz = array(0, dim = nChunks)
    
    for (j in 1:(nChunks - 1)){
      #left and right elements of each chunk
      lft = (j - 1) * stp + 1  
      rght = j * stp
      whichMinz[j] = which.min(timeSeries[lft:rght]) + lft
      minz[j] = min(timeSeries[lft:rght])
      whichMaxz[j] = which.max(timeSeries[lft:rght]) + lft
      maxz[j] = max(timeSeries[lft:rght])
    }
    
    #last chunk
    lft = j * stp + 1  #left and right elements of each chunk
    rght = N
    whichMinz[nChunks] = which.min(timeSeries[lft:rght]) + lft
    minz[nChunks] = min(timeSeries[lft:rght])
    whichMaxz[nChunks] = which.max(timeSeries[lft:rght]) + lft
    maxz[nChunks] = max(timeSeries[lft:rght])
    
    result = list()
    result[["minima"]] = NULL
    result[["minimaAt"]] = NULL
    result[["maxima"]] = NULL
    result[["maximaAt"]] = NULL
    
    span = tolerance * (max(maxz) - min(minz))
    rang = order(minz)[1:nPoints]
    
    if ((minz[rang[nPoints]] - minz[rang[1]]) <= span){
      result[["minima"]] = minz[rang[1:nPoints]]
      result[["minimaAt"]] = whichMinz[rang[1:nPoints]]
    }
    
    rang = order(maxz, decreasing = TRUE)[1:nPoints]
    
    if ((maxz[rang[1]] - maxz[rang[nPoints]]) <= span){ 
      result[["maxima"]] = maxz[rang[1:nPoints]]
      result[["maximaAt"]] = whichMaxz[rang[1:nPoints]]
    }
    
    if (plotChart){
      ts.plot(timeSeries)
      points(whichMinz, minz, col = "red")
      points(whichMaxz, maxz, col = "blue")
      if (!is.null(result[["minima"]])  &&
          !is.null(result[["minimaAt"]]))
        abline(lm(result[["minima"]] ~  result[["minimaAt"]]))
      if (!is.null(result[["maxima"]])  &&
          !is.null(result[["maximaAt"]]))
        abline(lm(result[["maxima"]] ~  result[["maximaAt"]]))
    }
    
    return(result)
  }

x <- detectSupportResistance(df[,5], 0.1)