Exponential Moving Average (EMA) Indicator Chart — Seaborn

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 Seaborn

Python source (Seaborn)

""" anyplot.ai
indicator-ema: Exponential Moving Average (EMA) Indicator Chart
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-19
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns


# 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 — positions 1, 2, 3
OKABE = ["#009E73", "#C475FD", "#4467A3"]

sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "grid.color": INK,
        "grid.alpha": 0.10,
        "legend.facecolor": ELEVATED_BG,
        "legend.edgecolor": INK_SOFT,
    },
)

# Data — $200 starting price with higher volatility to diverge from sibling impls
np.random.seed(42)
n_days = 150
dates = pd.date_range(start="2024-03-01", periods=n_days, freq="B")
returns = np.random.normal(0.001, 0.025, n_days)
price = 200 * np.cumprod(1 + returns)

price_series = pd.Series(price)
ema_12 = price_series.ewm(span=12, adjust=False).mean().values
ema_26 = price_series.ewm(span=26, adjust=False).mean().values

df = pd.DataFrame({"date": dates, "Close Price": price, "EMA 12": ema_12, "EMA 26": ema_26})

# Crossover detection before melting
cross_up = (df["EMA 12"].shift(1) < df["EMA 26"].shift(1)) & (df["EMA 12"] > df["EMA 26"])
cross_down = (df["EMA 12"].shift(1) > df["EMA 26"].shift(1)) & (df["EMA 12"] < df["EMA 26"])

# Long format for idiomatic seaborn hue grouping
df_long = df.melt(
    id_vars=["date"], value_vars=["Close Price", "EMA 12", "EMA 26"], var_name="series", value_name="price"
)

# Plot
fig, ax = plt.subplots(figsize=(16, 9), facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)

palette = {"Close Price": OKABE[0], "EMA 12": OKABE[1], "EMA 26": OKABE[2]}

sns.lineplot(
    data=df_long,
    x="date",
    y="price",
    hue="series",
    hue_order=["Close Price", "EMA 12", "EMA 26"],
    palette=palette,
    ax=ax,
)

# Set linewidths: price thicker, EMAs thinner
for line, lw in zip(ax.get_lines(), [3.5, 2.5, 2.5], strict=False):
    line.set_linewidth(lw)

# Crossover markers — green up triangle (bullish), vermillion down triangle (bearish)
ax.scatter(
    df.loc[cross_up, "date"],
    df.loc[cross_up, "EMA 12"],
    color=OKABE[0],
    s=250,
    zorder=5,
    marker="^",
    edgecolors=PAGE_BG,
    linewidth=1.0,
)
ax.scatter(
    df.loc[cross_down, "date"],
    df.loc[cross_down, "EMA 12"],
    color=OKABE[1],
    s=250,
    zorder=5,
    marker="v",
    edgecolors=PAGE_BG,
    linewidth=1.0,
)

# Style
ax.set_title("indicator-ema · python · seaborn · anyplot.ai", fontsize=24, fontweight="bold", pad=20, color=INK)
ax.set_xlabel("Date", fontsize=20, color=INK)
ax.set_ylabel("Price (USD)", fontsize=20, color=INK)
ax.tick_params(axis="both", labelsize=16, colors=INK_SOFT)

fig.autofmt_xdate(rotation=30)

# Legend — clean single entry per series from hue grouping
legend = ax.legend(fontsize=16, loc="upper left")
legend.get_frame().set_facecolor(ELEVATED_BG)
legend.get_frame().set_edgecolor(INK_SOFT)
for text in legend.get_texts():
    text.set_color(INK)

# Grid — y-axis only, subtle
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8)
ax.set_axisbelow(True)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=300, bbox_inches="tight", facecolor=PAGE_BG)

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

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