We can use ggplot2's `scale_shape_manual` to get us shapes that appear close to shading, and we can plot these over the grey heatmap. 
_Note: This was adapted from @Jay's comments in the original blog posting for the calendar heatmap
http://blog.revolutionanalytics.com/2009/11/charting-time-series-as-calendar-heat-maps-in-r.html


    # PACKAGES
    library(ggplot2)
    library(data.table)

    # Transofrm data
    stock.data <- transform(stock.data,
      week = as.POSIXlt(Date)$yday %/% 7 + 1,
      month = as.POSIXlt(Date)$mon + 1,
      wday = factor(as.POSIXlt(Date)$wday, levels=0:6, labels=levels(weekdays(1, abb=FALSE)), ordered=TRUE),
      year = as.POSIXlt(Date)$year + 1900)

    # find when the months change
    #   Not used, but could be 
    stock.data$mchng <- as.logical(c(0, diff(stock.data$month)))

    # we need dummy data for Sunday / Saturday to be included.
    #  These added rows will not be plotted due to their NA values
    dummy <- as.data.frame(stock.data[1:2, ])
    dummy[, -which(names(dummy) %in% c("wday", "year"))] <- NA
    dummy[, "wday"] <- weekdays(2:3, FALSE)
    dummy[, "mchng"] <- TRUE
    rbind(dummy, stock.data) -> stock.data

    # convert the continuous var to a categorical var 
    stock.data$Adj.Disc <- cut(stock.data$Adj.Close, b = 6, labels = F)


    # PLOT
      # Expected warning due to dummy variable with NA's: 
      # Warning message:
      # Removed 2 rows containing missing values (geom_point). 
    ggplot(stock.data) + 
      aes(week, wday, fill=as.factor(Adj.Disc), 
          shape=as.factor(Adj.Disc), color=as.factor(month %% 2)) + 
      geom_tile(linetype=1, size=1.8) + 
      geom_tile(linetype=6, size=0.4, color="white") + 
      scale_color_manual(values=vals) +
      geom_point(aes(alpha=0.2), color="black") + 
      scale_fill_grey(start=0, end=0.9) +  scale_shape_manual(values=c(2, 3, 4, 12, 14, 8)) + 
      theme(legend.position="none")  +  labs(y="Day of the Week") +  facet_wrap(~ year, ncol = 1)