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: plotnine 0.15.4 | Python 3.13.13
Quality: 87/100 | Updated: 2026-05-27
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_line,
element_rect,
element_text,
geom_line,
geom_point,
geom_segment,
geom_smooth,
geom_text,
geom_vline,
ggplot,
guides,
labs,
scale_color_manual,
scale_shape_manual,
scale_size_manual,
scale_x_datetime,
theme,
theme_minimal,
)
# 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"
# Imprint palette — canonical order
IMPRINT_PALETTE = ["#009E73", "#C475FD", "#4467A3", "#BD8233", "#AE3030", "#2ABCCD", "#954477", "#99B314"]
color_map = {
"Earnings": IMPRINT_PALETTE[0], # #009E73 brand green
"Dividend": IMPRINT_PALETTE[1], # #C475FD lavender
"News": IMPRINT_PALETTE[2], # #4467A3 blue
"Split": IMPRINT_PALETTE[3], # #BD8233 ochre
}
shape_map = {"Earnings": "s", "Dividend": "D", "News": "^", "Split": "o"}
# Earnings get larger markers — primary price catalyst deserves visual emphasis
size_map = {"Earnings": 5, "Dividend": 3, "News": 3, "Split": 3}
# Data
np.random.seed(42)
n_days = 180
dates = pd.date_range("2024-01-02", periods=n_days, freq="B")
returns = np.random.normal(0.0005, 0.018, n_days)
price = 150 * np.cumprod(1 + returns)
df_price = pd.DataFrame({"date": dates, "close": price})
# 2:1 split adjustment: halve prices from the day after the split effective date
split_date = pd.Timestamp("2024-05-20")
df_price.loc[df_price["date"] > split_date, "close"] /= 2
events = pd.DataFrame(
{
"event_date": pd.to_datetime(
[
"2024-01-25",
"2024-02-15",
"2024-04-02",
"2024-04-25",
"2024-05-20",
"2024-06-10",
"2024-07-25",
"2024-08-15",
]
),
"event_type": ["Earnings", "Dividend", "News", "Earnings", "Split", "Dividend", "Earnings", "News"],
"event_label": [
"Q4 Beat",
"Div $0.50",
"New Product",
"Q1 Miss",
"2:1 Split",
"Div $0.55",
"Q2 Beat",
"Partnership",
],
}
)
events["matched_date"] = events["event_date"].apply(
lambda x: df_price.loc[(df_price["date"] - x).abs().idxmin(), "date"]
)
events["price_at_event"] = events["matched_date"].apply(
lambda x: df_price.loc[df_price["date"] == x, "close"].values[0]
)
max_price = df_price["close"].max()
min_price = df_price["close"].min()
price_range = max_price - min_price
# Alternate flags above/below price to avoid overlap
flag_offsets = []
for i, (_, row) in enumerate(events.iterrows()):
if i % 2 == 0:
flag_offsets.append(row["price_at_event"] + price_range * 0.15 + (i % 3) * price_range * 0.08)
else:
flag_offsets.append(row["price_at_event"] - price_range * 0.15 - (i % 3) * price_range * 0.08)
events["flag_y"] = flag_offsets
# Separate earnings labels (larger) from secondary event labels for size hierarchy
events_earnings = events[events["event_type"] == "Earnings"]
events_other = events[events["event_type"] != "Earnings"]
# Plot
plot = (
ggplot()
+ geom_line(data=df_price, mapping=aes(x="date", y="close"), color=INK_SOFT, size=1.0, alpha=0.9)
+ geom_smooth(
data=df_price,
mapping=aes(x="date", y="close"),
method="lowess",
color=IMPRINT_PALETTE[0],
fill=IMPRINT_PALETTE[0],
size=0.7,
alpha=0.1,
)
+ geom_vline(
data=events, mapping=aes(xintercept="matched_date"), linetype="dashed", color=INK_SOFT, alpha=0.35, size=0.4
)
+ geom_segment(
data=events,
mapping=aes(x="matched_date", xend="matched_date", y="price_at_event", yend="flag_y", color="event_type"),
size=0.7,
)
+ geom_point(
data=events,
mapping=aes(x="matched_date", y="flag_y", color="event_type", shape="event_type", size="event_type"),
fill=ELEVATED_BG,
stroke=1.5,
)
+ geom_text(
data=events_other,
mapping=aes(x="matched_date", y="flag_y", label="event_label", color="event_type"),
size=3.5,
ha="center",
va="bottom",
nudge_y=price_range * 0.03,
fontweight="bold",
)
+ geom_text(
data=events_earnings,
mapping=aes(x="matched_date", y="flag_y", label="event_label", color="event_type"),
size=4,
ha="center",
va="bottom",
nudge_y=price_range * 0.03,
fontweight="bold",
)
+ scale_color_manual(values=color_map, name="Event Type")
+ scale_shape_manual(values=shape_map, name="Event Type")
+ scale_size_manual(values=size_map)
+ guides(size="none")
+ scale_x_datetime(date_labels="%b %Y", date_breaks="1 month")
+ labs(title="stock-event-flags · python · plotnine · anyplot.ai", x="Date", y="Stock Price ($)")
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK, size=0.3, alpha=0.15),
panel_grid_minor=element_blank(),
axis_title=element_text(color=INK, size=10),
axis_text=element_text(color=INK_SOFT, size=8),
axis_text_x=element_text(rotation=45, ha="right", color=INK_SOFT, size=8),
axis_line=element_line(color=INK_SOFT),
plot_title=element_text(color=INK, size=12, weight="bold"),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
legend_text=element_text(color=INK_SOFT, size=8),
legend_title=element_text(color=INK, size=10, weight="bold"),
legend_position="right",
)
)
# Save
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Stock Chart with Event Flags on anyplot.ai.