An Exponential Moving Average (EMA) overlay chart displays price data with one or more EMA lines that give greater weight to recent prices, making them more responsive to new information than simple moving averages. The EMA calculation applies an exponential weighting factor that decreases with each older data point, allowing traders to identify trends faster. This technical indicator is fundamental in trading for spotting trend direction, dynamic support/resistance levels, and crossover signals.
#' anyplot.ai
#' indicator-ema: Exponential Moving Average (EMA) Indicator Chart
#' Library: ggplot2 3.5.1 | R 4.4.1
#' Quality: 92/100 | Created: 2026-05-19
library(ggplot2)
library(scales)
library(tidyr)
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(
"Price" = "#009E73",
"EMA (12)" = "#C475FD",
"EMA (26)" = "#4467A3"
)
# Data
n_days <- 180
dates <- seq.Date(as.Date("2024-01-02"), by = "day", length.out = n_days)
close <- 150 * cumprod(1 + rnorm(n_days, mean = 0.0005, sd = 0.018))
# Inline EMA via Reduce — no helper function needed
k12 <- 2 / (12 + 1)
k26 <- 2 / (26 + 1)
ema12 <- Reduce(function(e, x) x * k12 + e * (1 - k12), close, accumulate = TRUE)
ema26 <- Reduce(function(e, x) x * k26 + e * (1 - k26), close, accumulate = TRUE)
df <- data.frame(date = dates, close = close, ema12 = ema12, ema26 = ema26)
# Detect EMA crossovers (sign change in ema12 - ema26)
diff_ema <- df$ema12 - df$ema26
cross_idx <- which(diff(sign(diff_ema)) != 0) + 1
crossovers <- df[cross_idx, , drop = FALSE]
# Long form for idiomatic ggplot2 multi-series mapping
df_long <- pivot_longer(df, cols = c(close, ema12, ema26),
names_to = "series", values_to = "price")
df_long$series <- factor(df_long$series,
levels = c("close", "ema12", "ema26"),
labels = c("Price", "EMA (12)", "EMA (26)"))
# Plot
p <- ggplot(df_long, aes(x = date, y = price, color = series)) +
geom_vline(data = crossovers, aes(xintercept = date),
color = INK_SOFT, linetype = "dashed",
linewidth = 0.4, alpha = 0.7) +
geom_line(aes(linewidth = series, alpha = series)) +
geom_point(data = crossovers, aes(x = date, y = ema12),
shape = 21, size = 4, fill = PAGE_BG,
color = IMPRINT[["EMA (12)"]], stroke = 1.5,
inherit.aes = FALSE) +
scale_color_manual(name = NULL, values = IMPRINT,
breaks = c("Price", "EMA (12)", "EMA (26)")) +
scale_linewidth_manual(
values = c("Price" = 1.5, "EMA (12)" = 1.1, "EMA (26)" = 1.1),
guide = "none") +
scale_alpha_manual(
values = c("Price" = 0.80, "EMA (12)" = 1.0, "EMA (26)" = 1.0),
guide = "none") +
scale_x_date(date_labels = "%b %Y", date_breaks = "2 months") +
scale_y_continuous(labels = dollar_format()) +
labs(
title = "Tech Stock EMA · indicator-ema · r · ggplot2 · anyplot.ai",
x = "Date",
y = "Closing Price (USD)"
) +
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 = 0.3),
panel.grid.major.x = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank(),
axis.title = element_text(color = INK, size = 20),
axis.text = element_text(color = INK_SOFT, size = 16),
axis.line = element_line(color = INK_SOFT, linewidth = 0.5),
plot.title = element_text(color = INK, size = 24, face = "bold"),
legend.background = element_rect(fill = ELEVATED_BG, color = INK_SOFT),
legend.text = element_text(color = INK_SOFT, size = 16),
legend.key = element_rect(fill = ELEVATED_BG, color = NA),
legend.position = "bottom",
plot.margin = margin(20, 20, 20, 20, unit = "pt")
)
ggsave(
filename = sprintf("plot-%s.png", THEME),
plot = p,
device = ragg::agg_png,
width = 16,
height = 9,
units = "in",
dpi = 300
)
Part of Exponential Moving Average (EMA) Indicator Chart on anyplot.ai.