Adding indicators:

We add the Bollinger Bands indicator from the TTR package to the strategy. It takes a Simple Moving Average (SMA) and the High Low Close data from each of our stocks, add the indicator to the defined BBands_Strategy and creates a new column in our data alled BollingerBands_Label.

# Add indicators
BBands_Strategy <- add.indicator(
  strategy = BBands_Strategy,
  name = "BBands",
  arguments = list(
    HLC = quote(HLC(mktdata)),
    maType = 'SMA'),
  label = 'BollingerBands_Label')

Adding signals:

• The first signal states that when the close is greater than the upper Bollinger Band, add a value of 1 into a column called Close.gt.LowerBBand. The sigCrossover function only returns the first occurence of the relationship switching from false to true. Replacing with sigComparison here would return all occurences of the relationship being greater than the upper band.

• The second signal states that when the close is less than the lower Bollinger Band add a value of 1 into a column called Close.lt.LowerBBand. Again using the sigCrossover function to only return the first occurence.

• The third signal

The below code creates 3 new columns in our mktdata. dn.BollingerBands_Label, mavg.BollingerBands_Label, up.BollingerBands_Label. Which are the lower, middle and upper Bollinger Band values. It also creates the following: Close.gt.UpperBBand, Close.lt.LowerBBand, Cross.MiddleBBand. Which takes on the values according to whether the `sigCrossover’ has occured.

# Add Signals

BBands_Strategy <- add.signal(
  BBands_Strategy, 
  name = "sigCrossover",
  arguments = list(
    columns = c("Close","up"),
    relationship = "gt"),
  label = "Close.gt.UpperBBand")

BBands_Strategy <- add.signal(
  BBands_Strategy,
  name = "sigCrossover",
  arguments = list(
    columns = c("Close","dn"),
    relationship = "lt"),
  label = "Close.lt.LowerBBand")

BBands_Strategy <- add.signal(
  BBands_Strategy,
  name = "sigCrossover",
  arguments = list(
    columns = c("High","Low","mavg"),
    relationship = "op"),
  label = "Cross.MiddleBBand")

The mktdata (which gets presented after running applyStrategy and updatePortf) would look like:

In row 4 of the below table, the close is 68.76 which is less than the lower Bollinger Band dn.BollingerBands_Label column 68.822 and a 1 is indicated in the Close.lt.LowerBBand column. In row 13 of the same data, there is a 1 in the column Close.gt.UpperBBand. The close is 73.26 and the up.BollingerBands_Label is 72.23 so the close is gt then Upper Bollinger Band.

Add the rules

• When the close is greater than the upper Bollinger Band we go short by a quantity of 100 at the market price
• When the close is lower than the lower Bollinger Band we go long by a quantity of 100 at the market price.
• When the Close crosses the middle Bollinger Band we exit all our positions.

# Add rules

BBands_Strategy <- add.rule(
  BBands_Strategy,
  name = 'ruleSignal',
  arguments = list(
    sigcol = "Close.gt.UpperBBand", 
    sigval = TRUE, 
    orderqty = -100, 
    ordertype = 'market',
    orderside = NULL,
    threshold = NULL),
  type = 'enter')

BBands_Strategy <- add.rule(
  BBands_Strategy, 
  name = 'ruleSignal',
  arguments = list(
    sigcol = "Close.lt.LowerBBand",
    sigval = TRUE,
    orderqty = 100,
    ordertype = 'market',
    orderside = NULL,
    threshold = NULL),
  type = 'enter')

BBands_Strategy <- add.rule(
  BBands_Strategy,
  name = 'ruleSignal',
  arguments = list(
    sigcol = "Cross.MiddleBBand",
    sigval = TRUE,
    orderqty = 'all',
    ordertype = 'market',
    orderside = NULL,
    threshold = NULL),
  type = 'exit')

Apply the strategy and update the portfolio

out <- applyStrategy(
  strategy = BBands_Strategy,
  portfolios = 'BollingerBandsStrat',
  parameters = list( 
    sd = 1.6, # number of standard deviations
    n = 20) # MA periods
  )

updatePortf(Portfolio = 'BollingerBandsStrat', Dates = paste('::',as.Date(Sys.time()),sep=''))

Find the best performance stocks:

tradeStats(portfolio.st) %>% 
  tibble::rownames_to_column("ticker") %>%
  t() %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))

Performance plots and trade statistics

for(sym in symbols){
  chart.Posn(Portfolio = 'BollingerBandsStrat', Symbol = sym)
  plot(add_BBands(on = 1, sd = 1.6, n = 20))
  
  perf <- tradeStats(portfolio.st) %>% 
    tibble::rownames_to_column("ticker") %>% 
    filter(ticker == sym) %>% 
    t() %>% 
    kable() %>%
    kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
}

Zooming in one some of the charts, it’s possible to see when we entered and exited each trade.

chart.Posn(Portfolio = 'BollingerBandsStrat', Symbol = "AMD")

plot(add_BBands(on = 1, sd = 1.6, n = 20))

zoom_Chart('2017-01::2017-03')

chart.Posn(Portfolio = 'BollingerBandsStrat', Symbol = "WMT")

plot(add_BBands(on = 1, sd = 1.6, n = 20))

zoom_Chart('2017-01::2017-03')

chart.Posn(Portfolio = 'BollingerBandsStrat', Symbol = "AMZN")

plot(add_BBands(on = 1, sd = 1.6, n = 20))

zoom_Chart('2017-01::2017-03')