A Simple Moving Average (SMA) chart displays price or value data with one or more SMA overlays, typically showing short, medium, and long-term periods (e.g., 20, 50, 200-day). Each SMA line smooths out price fluctuations by averaging the last N data points, revealing underlying trends at different time scales. This multi-period overlay is fundamental to technical analysis, helping traders identify trend direction, support/resistance levels, and potential crossover signals.
#' anyplot.ai
#' indicator-sma: Simple Moving Average (SMA) Indicator Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 86/100 | Created: 2026-05-19
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
library(ragg)
set.seed(42)
# Theme tokens
THEME <- Sys.getenv("ANYPLOT_THEME", "light")
PAGE_BG <- if (THEME == "light") "#FAF8F1" else "#1A1A17"
ELEVATED_BG <- if (THEME == "light") "#FFFDF6" else "#242420"
INK <- if (THEME == "light") "#1A1A17" else "#F0EFE8"
INK_SOFT <- if (THEME == "light") "#4A4A44" else "#B8B7B0"
IMPRINT <- c("#009E73", "#C475FD", "#4467A3", "#BD8233",
"#AE3030", "#2ABCCD", "#954477")
# Data: 500 trading days of simulated stock prices
n_days <- 700L
all_dates <- seq(as.Date("2022-01-03"), by = "day", length.out = n_days)
trading_dates <- all_dates[!weekdays(all_dates) %in% c("Saturday", "Sunday")]
trading_dates <- trading_dates[seq_len(500L)]
daily_returns <- rnorm(500L, mean = 0.0004, sd = 0.016)
close_price <- 148.0 * cumprod(1 + daily_returns)
# Rolling SMA helper
sma <- function(x, k) {
out <- rep(NA_real_, length(x))
for (i in seq(k, length(x))) {
out[i] <- mean(x[(i - k + 1L):i])
}
out
}
df <- data.frame(
date = trading_dates,
Price = close_price,
`SMA 20` = sma(close_price, 20L),
`SMA 50` = sma(close_price, 50L),
`SMA 200` = sma(close_price, 200L),
check.names = FALSE
)
df_long <- tidyr::pivot_longer(df, cols = -date, names_to = "series", values_to = "value")
df_long$series <- factor(df_long$series, levels = c("Price", "SMA 20", "SMA 50", "SMA 200"))
series_colors <- setNames(IMPRINT[1:4], c("Price", "SMA 20", "SMA 50", "SMA 200"))
series_widths <- c("Price" = 1.4, "SMA 20" = 1.0, "SMA 50" = 1.0, "SMA 200" = 1.0)
# Plot
p <- ggplot(df_long, aes(x = date, y = value, color = series, linewidth = series)) +
geom_line(na.rm = TRUE) +
scale_color_manual(values = series_colors) +
scale_linewidth_manual(values = series_widths) +
scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
scale_y_continuous(labels = scales::dollar_format(prefix = "$", accuracy = 1)) +
guides(linewidth = "none") +
labs(
title = "Daily Close Price · indicator-sma · r · ggplot2 · anyplot.ai",
x = "Date",
y = "Price (USD)",
color = NULL
) +
theme_minimal(base_size = 14) +
theme(
plot.background = element_rect(fill = PAGE_BG, color = PAGE_BG),
panel.background = element_rect(fill = PAGE_BG, color = NA),
panel.grid.major.y = element_line(color = INK_SOFT, linewidth = 0.25),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
axis.title = element_text(color = INK, size = 20),
axis.text = element_text(color = INK_SOFT, size = 16),
axis.text.x = element_text(angle = 30, hjust = 1),
axis.line.x = element_line(color = INK_SOFT, linewidth = 0.4),
axis.line.y = element_line(color = INK_SOFT, linewidth = 0.4),
plot.title = element_text(color = INK, size = 22, face = "bold"),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT, linewidth = 0.4),
legend.text = element_text(color = INK_SOFT, size = 16),
legend.key = element_rect(fill = NA),
legend.position = "top",
legend.direction = "horizontal"
)
# Save
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 16,
height = 9,
units = "in",
dpi = 300
)
Part of Simple Moving Average (SMA) Indicator Chart on anyplot.ai.