A Kagi chart is a Japanese charting technique that displays price movements using vertical lines of varying thickness. Unlike time-based charts, Kagi charts change direction only when price moves by a significant amount (the reversal threshold), effectively filtering out market noise. Thick lines (yang) indicate uptrends when price exceeds previous highs, while thin lines (yin) show downtrends when price falls below previous lows, making trend identification intuitive.
""" anyplot.ai
kagi-basic: Basic Kagi Chart
Library: altair 6.1.0 | Python 3.13.13
Quality: 92/100 | Updated: 2026-05-17
"""
import os
from pathlib import Path
import altair as alt
import numpy as np
import pandas as pd
# Get script directory for saving outputs
SCRIPT_DIR = Path(__file__).parent
# 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
YANG_COLOR = "#009E73" # Bluish green (brand)
YIN_COLOR = "#AE3030" # imprint red — bearish
# Generate realistic stock price data (simulated random walk)
np.random.seed(42)
n_days = 250
dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
returns = np.random.normal(0.0005, 0.02, n_days)
prices = 100 * np.cumprod(1 + returns)
# Kagi chart algorithm
# Reversal threshold: 4% as specified
reversal_pct = 0.04
# Build Kagi line segments
segments = []
current_direction = None # 1 = up, -1 = down
current_high = prices[0]
current_low = prices[0]
current_x = 0
last_y = prices[0]
# Track yang/yin state (yang = thick/bullish, yin = thin/bearish)
yang = True # Start as yang
last_shoulder = prices[0] # Last yang high
last_waist = prices[0] # Last yin low
for i in range(1, len(prices)):
price = prices[i]
if current_direction is None:
# Initialize direction
if price >= current_high * (1 + reversal_pct):
current_direction = 1
segments.append({"x": current_x, "y": last_y, "x2": current_x, "y2": price, "yang": yang})
current_high = price
last_y = price
elif price <= current_low * (1 - reversal_pct):
current_direction = -1
segments.append({"x": current_x, "y": last_y, "x2": current_x, "y2": price, "yang": yang})
current_low = price
last_y = price
elif current_direction == 1: # Currently going up
if price > current_high:
# Extend upward - update last segment
if segments:
segments[-1]["y2"] = price
current_high = price
last_y = price
elif price <= current_high * (1 - reversal_pct):
# Reversal down
# Check if we break below last waist (transition to yin)
if price < last_waist:
yang = False
last_waist = price
# Draw horizontal connector and vertical down
current_x += 1
segments.append({"x": current_x - 1, "y": last_y, "x2": current_x, "y2": last_y, "yang": yang})
last_shoulder = last_y # Record shoulder
segments.append({"x": current_x, "y": last_y, "x2": current_x, "y2": price, "yang": yang})
current_low = price
current_direction = -1
last_y = price
else: # current_direction == -1, going down
if price < current_low:
# Extend downward
if segments:
segments[-1]["y2"] = price
current_low = price
last_y = price
elif price >= current_low * (1 + reversal_pct):
# Reversal up
# Check if we break above last shoulder (transition to yang)
if price > last_shoulder:
yang = True
last_shoulder = price
# Draw horizontal connector and vertical up
current_x += 1
segments.append({"x": current_x - 1, "y": last_y, "x2": current_x, "y2": last_y, "yang": yang})
last_waist = last_y # Record waist
segments.append({"x": current_x, "y": last_y, "x2": current_x, "y2": price, "yang": yang})
current_high = price
current_direction = 1
last_y = price
# Create DataFrame for segments
df_segments = pd.DataFrame(segments)
df_segments["color"] = df_segments["yang"].map({True: YANG_COLOR, False: YIN_COLOR})
df_segments["thickness"] = df_segments["yang"].map({True: 6, False: 2})
df_segments["type"] = df_segments["yang"].map({True: "Yang (Bullish)", False: "Yin (Bearish)"})
# Create Altair chart using mark_rule for line segments
chart = (
alt.Chart(df_segments)
.mark_rule()
.encode(
x=alt.X("x:Q", title="Line Index"),
x2="x2:Q",
y=alt.Y("y:Q", title="Price ($)", scale=alt.Scale(zero=False)),
y2="y2:Q",
color=alt.Color(
"type:N",
scale=alt.Scale(domain=["Yang (Bullish)", "Yin (Bearish)"], range=[YANG_COLOR, YIN_COLOR]),
legend=alt.Legend(title="Trend"),
),
strokeWidth=alt.StrokeWidth("yang:N", scale=alt.Scale(domain=[True, False], range=[6, 2]), legend=None),
)
.properties(width=1600, height=900, title="kagi-basic · altair · anyplot.ai", background=PAGE_BG)
.configure_view(fill=PAGE_BG, stroke=INK_SOFT)
.configure_title(fontSize=28, anchor="middle", color=INK)
.configure_axis(
domainColor=INK_SOFT,
tickColor=INK_SOFT,
gridColor=INK,
gridOpacity=0.10,
labelColor=INK_SOFT,
labelFontSize=18,
titleColor=INK,
titleFontSize=22,
)
.configure_legend(
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
labelColor=INK_SOFT,
labelFontSize=16,
titleColor=INK,
titleFontSize=18,
)
)
# Save as PNG and HTML with theme-suffixed filenames
chart.save(str(SCRIPT_DIR / f"plot-{THEME}.png"), scale_factor=3.0)
chart.save(str(SCRIPT_DIR / f"plot-{THEME}.html"))
Part of Basic Kagi Chart on anyplot.ai.