Exponential Moving Average (EMA) Indicator Chart — plotnine

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.

Exponential Moving Average (EMA) Indicator Chart rendered with plotnine

Python source (plotnine)

""" anyplot.ai
indicator-ema: Exponential Moving Average (EMA) Indicator Chart
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 84/100 | Updated: 2026-05-19
"""

import numpy as np
import pandas as pd
from plotnine import (
    aes,
    element_rect,
    element_text,
    geom_line,
    geom_point,
    ggplot,
    labs,
    scale_color_manual,
    scale_x_datetime,
    theme,
    theme_minimal,
)


# Data - Generate synthetic stock price data with EMAs
np.random.seed(42)

# Generate 120 trading days
n_days = 120
dates = pd.date_range(start="2024-01-02", periods=n_days, freq="B")

# Generate realistic stock price using random walk with drift
initial_price = 150.0
returns = np.random.normal(0.0008, 0.018, n_days)
price = initial_price * np.cumprod(1 + returns)

# Calculate EMAs using pandas ewm
ema_12 = pd.Series(price).ewm(span=12, adjust=False).mean().values
ema_26 = pd.Series(price).ewm(span=26, adjust=False).mean().values

# Create long-format DataFrame for plotnine
df_price = pd.DataFrame({"date": dates, "value": price, "series": "Close Price"})
df_ema12 = pd.DataFrame({"date": dates, "value": ema_12, "series": "EMA-12"})
df_ema26 = pd.DataFrame({"date": dates, "value": ema_26, "series": "EMA-26"})
df = pd.concat([df_price, df_ema12, df_ema26], ignore_index=True)

# Define colors - Python Blue for price, distinct colors for EMAs
colors = {"Close Price": "#306998", "EMA-12": "#E24A33", "EMA-26": "#FFD43B"}

# Find crossover points (where EMA-12 crosses EMA-26)
crossover_indices = []
for i in range(1, len(ema_12)):
    if (ema_12[i - 1] <= ema_26[i - 1] and ema_12[i] > ema_26[i]) or (
        ema_12[i - 1] >= ema_26[i - 1] and ema_12[i] < ema_26[i]
    ):
        crossover_indices.append(i)

df_crossovers = pd.DataFrame({"date": dates[crossover_indices], "value": ema_12[crossover_indices]})

# Plot
plot = (
    ggplot(df, aes(x="date", y="value", color="series"))
    + geom_line(data=df[df["series"] == "Close Price"], size=1.8, alpha=0.9)
    + geom_line(data=df[df["series"] == "EMA-12"], size=1.2, alpha=0.85)
    + geom_line(data=df[df["series"] == "EMA-26"], size=1.2, alpha=0.85)
    + geom_point(df_crossovers, aes(x="date", y="value"), color="#2CA02C", size=5, alpha=1.0, inherit_aes=False)
    + scale_color_manual(values=colors)
    + scale_x_datetime(date_breaks="1 month", date_labels="%b %Y")
    + labs(title="indicator-ema \u00b7 plotnine \u00b7 pyplots.ai", x="Date", y="Price ($)", color="Series")
    + theme_minimal()
    + theme(
        figure_size=(16, 9),
        plot_title=element_text(size=24, weight="bold"),
        axis_title=element_text(size=20),
        axis_text=element_text(size=16),
        axis_text_x=element_text(angle=45, ha="right"),
        legend_title=element_text(size=18),
        legend_text=element_text(size=16),
        legend_position="right",
        legend_background=element_rect(fill="white", alpha=0.8),
    )
)

# Save
plot.save("plot.png", dpi=300, verbose=False)

Part of Exponential Moving Average (EMA) Indicator Chart on anyplot.ai.

Exponential Moving Average (EMA) Indicator Chart — plotnine

Python source (plotnine)

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