dmplot

Dereck’s library for plotting financial and time series data as well helper functions for plotting in the style of Dereck.

Installation

You can install the development version of dmplot using:

# install.packages("remotes")
remotes::install_github("dereckmezquita/dmplot")

Financial data

You must provide a function which calculates these indicators and returns a list which can then be plotted.

Get financial data

Shown here kucoin is a private package by myself for interacting with the KuCoin api. You can use any source of financial data as long as you pass the variables to the stat correctly.

ticker <- "BTC/USDT"

dt <- kucoin::get_market_data(
    symbols = ticker,
    from = "2022-11-28 15:29:43 EST", # lubridate::now() - lubridate::days(7),
    to = "2022-12-05 15:29:31 EST",# lubridate::now(),
    frequency = "1 hour"
)

dt
#>        symbol            datetime    open    high     low   close   volume
#>        <char>              <POSc>   <num>   <num>   <num>   <num>    <num>
#>   1: BTC/USDT 2022-11-28 15:00:00 16215.3 16233.6 16126.0 16144.1 327.8979
#>   2: BTC/USDT 2022-11-28 16:00:00 16144.1 16382.6 16000.0 16305.9 837.5801
#>   3: BTC/USDT 2022-11-28 17:00:00 16305.9 16382.0 16195.4 16205.4 507.8351
#>  ---                                                                      
#> 167: BTC/USDT 2022-12-05 13:00:00 17254.5 17282.5 17208.1 17229.7 105.2655
#> 168: BTC/USDT 2022-12-05 14:00:00 17229.8 17241.4 17175.1 17205.2 140.4375
#> 169: BTC/USDT 2022-12-05 15:00:00 17205.1 17205.1 17021.6 17083.0 504.9158
#> 1 variable not shown: [turnover <num>]

Note a demonstration dataset is included in the demo/data/ directory.

Plot financial data

Here I demonstrate how to use the stats for plotting financial data along with the theme functions included in this package:

1. dmplot::stat_candlesticks()
2. dmplot::stat_bollingerbands()
3. dmplot::stat_movingaverages()
4. dmplot::stat_macd()

And the theme functions for styling:

1. dmplot::theme_dereck_dark()
2. dmplot::theme_dereck_light()

## ------
# redifine our function to return a list
ema <- function(x, n, wilder = TRUE) {
    return(as.list(as.data.frame(TTR::EMA(x, n = n, wilder = wilder))))
}

bb <- function(close, n = 2, sd = 2) {
    return(as.list(as.data.frame(TTR::BBands(close, n = n, sd = sd))))
}

# calculate the short and long moving averages
dt[, ema_short := ema(close, n = 10, wilder = TRUE)]
dt[, ema_long := ema(close, n = 50, wilder = TRUE)]

# calculate the bollinger bands
dt[, c("bb_lower", "bb_mavg", "bb_upper", "bb_pct") := bb(close, n = 10, sd = 2)]

## ------
# plot
p <- dt |>
    ggplot2::ggplot(ggplot2::aes(
        x = datetime,
        open = open,
        close = close,
        high = high,
        low = low,
        group = symbol
    )) +
    ## ------------------------------------
    dmplot::stat_candlestick() +
    ## ------------------------------------
    # moving averages
    dmplot::stat_movingaverages(ggplot2::aes(
        short = ema_short,
        long = ema_long
    ), alpha = list(mavg = 0.5)) +
    dmplot::stat_bollingerbands(ggplot2::aes(
        ymin = bb_lower,
        mavg = bb_mavg,
        ymax = bb_upper
    ), colour = list("pink", "cyan", "cyan")) +
    ## ------------------------------------
    ggplot2::scale_x_continuous(n.breaks = 25, labels = \(x) {
        lubridate::floor_date(lubridate::as_datetime(x), "hours")
    }) +
    ggplot2::scale_y_continuous(n.breaks = 25) +
    ggplot2::labs(
        title = ticker,
        x = "Date",
        y = "Price (USD)"
    ) +
    dmplot::theme_dereck_dark() +
    ggplot2::theme(
        axis.text.x = ggplot2::element_text(angle = 75, vjust = 0.925, hjust = 0.975),
        panel.grid.minor = ggplot2::element_blank()
    )

p

Plotting the MACD (moving average convergence divergence) indicator:

macd <- function(x, s = 12, l = 26, k = 9) {
    return(as.list(as.data.frame(TTR::MACD(x, s, l, k))))
}

# calculate the macd
dt[, c("macd", "macd_signal") := macd(close, s = 12, l = 26, k = 9)]
dt[, macd_diff := macd - macd_signal]

na.omit(dt) |>
    ggplot2::ggplot(ggplot2::aes(x = datetime)) +
    ## ------------------------------------
    dmplot::stat_macd(ggplot2::aes(macd = macd, macd_signal = macd_signal, macd_diff = macd_diff)) +
    ## ------------------------------------
    ggplot2::scale_x_continuous(n.breaks = 25, labels = \(x) {
        lubridate::floor_date(lubridate::as_datetime(x), "hours")
    }) +
    ggplot2::scale_y_continuous(n.breaks = 25) +
    ggplot2::labs(
        title = ticker,
        x = "Date",
        y = "Price (USD)"
    ) +
    dmplot::theme_dereck_dark() +
    ggplot2::theme(
        axis.text.x = ggplot2::element_text(angle = 75, vjust = 0.925, hjust = 0.975),
        panel.grid.minor = ggplot2::element_blank()
    )

Now let’s do the same plot in a light theme:

p + dmplot::theme_dereck_light() +
    ggplot2::theme(
        axis.text.x = ggplot2::element_text(angle = 75, vjust = 0.925, hjust = 0.975),
        panel.grid.minor = ggplot2::element_blank()
    )

Gallery