A MACD (Moving Average Convergence Divergence) chart displaying three components: the MACD line, signal line, and histogram. The MACD line represents the difference between 12-day and 26-day exponential moving averages, while the signal line is a 9-day EMA of the MACD. The histogram visualizes the difference between these two lines. This is an essential momentum oscillator for technical analysis, helping traders identify trend direction, momentum strength, and potential buy/sell signals through line crossovers.
""" anyplot.ai
indicator-macd: MACD Technical Indicator Chart
Library: letsplot 4.9.0 | Python 3.13.13
Quality: 93/100 | Updated: 2026-05-16
"""
import os
import numpy as np
import pandas as pd
from lets_plot import *
LetsPlot.setup_html()
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
# Okabe-Ito palette
BRAND = "#009E73" # First series (green)
SECONDARY = "#BD8233" # imprint ochre — signal line (categorical contrast)
# Generate realistic stock price data with momentum
np.random.seed(42)
n_days = 120
# Simulate stock prices with trend and volatility (more realistic)
dates = pd.date_range(start="2024-01-01", periods=n_days + 35, freq="B")
returns = np.random.normal(0.0005, 0.015, n_days + 35)
# Add trending behavior
trend = np.sin(np.linspace(0, 4 * np.pi, n_days + 35)) * 0.012
returns = returns + trend
prices = 100 * np.cumprod(1 + returns)
# Calculate EMAs using pandas ewm
ema_12 = pd.Series(prices).ewm(span=12, adjust=False).mean().values
ema_26 = pd.Series(prices).ewm(span=26, adjust=False).mean().values
# Calculate MACD components
macd_line = ema_12 - ema_26
signal_line = pd.Series(macd_line).ewm(span=9, adjust=False).mean().values
histogram = macd_line - signal_line
# Use the last n_days (after warmup period)
start_idx = 35
df = pd.DataFrame(
{
"date": dates[start_idx:],
"macd": macd_line[start_idx:],
"signal": signal_line[start_idx:],
"histogram": histogram[start_idx:],
}
)
# Convert dates to numeric for plotting
df["day_num"] = range(len(df))
df["hist_color"] = np.where(df["histogram"] >= 0, "Positive", "Negative")
# Create separate dataframes for lines
df_lines = pd.melt(
df[["day_num", "macd", "signal"]],
id_vars=["day_num"],
value_vars=["macd", "signal"],
var_name="line_type",
value_name="value",
)
df_lines["line_type"] = df_lines["line_type"].map({"macd": "MACD Line", "signal": "Signal Line"})
# Create the MACD chart
plot = (
ggplot()
+ geom_bar(
data=df, mapping=aes(x="day_num", y="histogram", fill="hist_color"), stat="identity", width=0.8, alpha=0.8
)
+ geom_hline(yintercept=0, color=INK_SOFT, size=0.8, linetype="dashed")
+ geom_line(data=df_lines, mapping=aes(x="day_num", y="value", color="line_type"), size=1.5)
+ scale_fill_manual(values={"Positive": "#2ABCCD", "Negative": "#AE3030"}, name="Histogram") # imprint red for negative bars
+ scale_color_manual(values={"MACD Line": BRAND, "Signal Line": SECONDARY}, name="Lines")
+ labs(x="Trading Day", y="MACD Value", title="indicator-macd · letsplot · anyplot.ai")
+ theme_minimal()
+ theme(
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major_y=element_line(color=INK_SOFT, size=0.3),
panel_grid_minor=element_blank(),
axis_title=element_text(size=20, color=INK),
axis_text=element_text(size=16, color=INK_SOFT),
axis_line=element_line(color=INK_SOFT),
plot_title=element_text(size=24, color=INK),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(size=16, color=INK_SOFT),
legend_title=element_text(size=18, color=INK),
legend_position="right",
)
+ ggsize(1600, 900)
)
# Save as PNG (scale=3 gives 4800x2700)
ggsave(plot, f"plot-{THEME}.png", path=".", scale=3)
# Save interactive HTML version
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of MACD Technical Indicator Chart on anyplot.ai.