A stock price chart with flag-style markers annotating significant events such as earnings releases, dividends, stock splits, or news events. Unlike simple line annotations, flags are positioned above or below the price data with connector lines and styled icons that distinguish event types. This visualization is standard in financial trading platforms, enabling investors to correlate price movements with corporate actions and market events at a glance.
""" anyplot.ai
stock-event-flags: Stock Chart with Event Flags
Library: matplotlib 3.10.9 | Python 3.13.13
Quality: 89/100 | Updated: 2026-05-27
"""
import os
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# 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"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
# Imprint palette — canonical order; first series is always brand green
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
BRAND = IMPRINT_PALETTE[0]
# Data
np.random.seed(42)
dates = pd.date_range("2025-01-01", periods=180, freq="B")
initial_price = 150.0
returns = np.random.normal(0.0005, 0.02, size=180)
prices = initial_price * np.cumprod(1 + returns)
close = prices
high = close * (1 + np.abs(np.random.normal(0, 0.01, size=180)))
low = close * (1 - np.abs(np.random.normal(0, 0.01, size=180)))
open_price = (close + np.random.normal(0, 1, size=180)).clip(low, high)
df = pd.DataFrame({"date": dates, "open": open_price, "high": high, "low": low, "close": close})
events = [
{"date": "2025-01-28", "type": "earnings", "label": "Q4"},
{"date": "2025-02-14", "type": "dividend", "label": "0.50"},
{"date": "2025-03-10", "type": "news", "label": "Launch"},
{"date": "2025-04-22", "type": "earnings", "label": "Q1"},
{"date": "2025-05-08", "type": "split", "label": "2:1"},
{"date": "2025-05-20", "type": "dividend", "label": "0.50"},
{"date": "2025-06-25", "type": "news", "label": "Interview"},
{"date": "2025-07-28", "type": "earnings", "label": "Q2"},
{"date": "2025-08-20", "type": "dividend", "label": "0.55"},
]
events_df = pd.DataFrame(events)
events_df["date"] = pd.to_datetime(events_df["date"])
# Event colors — palette positions 2-5 (position 1 used by price line)
event_colors = {
"earnings": IMPRINT_PALETTE[2], # #4467A3 blue — financial analytics
"dividend": IMPRINT_PALETTE[3], # #BD8233 ochre — value/commodity
"split": IMPRINT_PALETTE[1], # #C475FD lavender — corporate action
"news": IMPRINT_PALETTE[4], # #AE3030 red — alert semantic fit
}
event_markers = {"earnings": "E", "dividend": "D", "split": "S", "news": "!"}
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Price line — first series, brand green
(price_line,) = ax.plot(df["date"], df["close"], color=BRAND, linewidth=2.5, label="Close Price", zorder=2)
# Light fill under price line
ax.fill_between(df["date"], df["close"].min() * 0.95, df["close"], alpha=0.08, color=BRAND, zorder=1)
price_min = df["close"].min()
price_max = df["close"].max()
price_range = price_max - price_min
# Event flags with alternating heights to avoid overlap
for idx, event in events_df.iterrows():
event_date = event["date"]
event_type = event["type"]
event_label = event["label"]
price_row = df.loc[df["date"] == event_date, "close"]
if len(price_row) == 0:
nearest_idx = np.abs(df["date"] - event_date).argmin()
price_at_date = df.iloc[nearest_idx]["close"]
else:
price_at_date = price_row.values[0]
color = event_colors.get(event_type, INK_SOFT)
marker_text = event_markers.get(event_type, "?")
height_level = idx % 3
flag_y = price_max + price_range * (0.12 + height_level * 0.12)
# Connector line from price point up to flag
ax.plot(
[event_date, event_date],
[price_at_date, flag_y],
color=color,
linestyle="--",
linewidth=1.2,
alpha=0.7,
zorder=3,
)
# Marker dot at price level
ax.scatter([event_date], [price_at_date], color=color, s=80, zorder=4, edgecolors=PAGE_BG, linewidth=1.0)
# Flag annotation box
ax.annotate(
f"{marker_text} {event_label}",
xy=(event_date, flag_y),
fontsize=8,
fontweight="bold",
color="white",
ha="center",
va="center",
bbox={
"boxstyle": "round,pad=0.3",
"facecolor": color,
"edgecolor": ELEVATED_BG,
"linewidth": 1.5,
"alpha": 0.92,
},
zorder=10,
)
# Legend — price line + all four event types
legend_handles = [price_line] + [mpatches.Patch(color=c, label=et.capitalize()) for et, c in event_colors.items()]
legend_labels = ["Close Price"] + [et.capitalize() for et in event_colors]
leg = ax.legend(handles=legend_handles, labels=legend_labels, loc="lower right", fontsize=8, framealpha=0.9)
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)
# Style
title = "stock-event-flags · python · matplotlib · anyplot.ai"
ax.set_title(title, fontsize=12, fontweight="medium", color=INK)
ax.set_xlabel("Date", fontsize=10, color=INK)
ax.set_ylabel("Price (USD)", fontsize=10, color=INK)
ax.tick_params(axis="both", labelsize=8, labelcolor=INK_SOFT, color=INK_SOFT)
ax.tick_params(which="both", length=0)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.8, color=INK)
ax.set_ylim(price_min * 0.95, price_max + price_range * 0.55)
plt.setp(ax.get_xticklabels(), rotation=30, ha="right")
fig.subplots_adjust(left=0.09, right=0.97, top=0.91, bottom=0.15)
# Save
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Stock Chart with Event Flags on anyplot.ai.