A Point and Figure (P&F) chart is a price-action focused visualization that uses columns of X's (rising prices) and O's (falling prices) to display significant price movements while filtering out time and minor fluctuations. Unlike traditional time-based charts, P&F charts only plot a new symbol when price moves by a defined box size, and only start a new column when price reverses by a specified number of boxes (typically 3). This makes it ideal for identifying clear trends, support/resistance levels, and generating trading signals.
""" anyplot.ai
point-and-figure-basic: Point and Figure Chart
Library: altair 6.1.0 | Python 3.13.13
Quality: 90/100 | Updated: 2026-05-20
"""
import os
import sys
# Prevent the script's own directory from shadowing the 'altair' package
sys.path = [p for p in sys.path if not p.endswith("/python")]
import altair as alt
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"
BULL_COLOR = "#009E73" # Okabe-Ito position 1 — X columns (bullish)
BEAR_COLOR = "#AE3030" # imprint red — O columns (bearish)
# Data
np.random.seed(42)
n_days = 300
dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
returns = np.random.normal(0.0005, 0.02, n_days)
close = 100 * np.cumprod(1 + returns)
# Point and Figure parameters
box_size = 2.0 # $2 per box
reversal = 3 # 3-box reversal
# Build P&F columns
pf_data = []
col = 0
direction = None
current_price = close[0]
col_start = round(current_price / box_size) * box_size
for i in range(1, len(close)):
price = close[i]
if direction is None:
if price >= col_start + box_size:
direction = "X"
boxes_up = int((price - col_start) / box_size)
for b in range(boxes_up + 1):
pf_data.append({"column": col, "price": col_start + b * box_size, "symbol": "X", "dir": "bullish"})
current_price = col_start + boxes_up * box_size
elif price <= col_start - box_size:
direction = "O"
boxes_down = int((col_start - price) / box_size)
for b in range(boxes_down + 1):
pf_data.append({"column": col, "price": col_start - b * box_size, "symbol": "O", "dir": "bearish"})
current_price = col_start - boxes_down * box_size
elif direction == "X":
if price >= current_price + box_size:
boxes_up = int((price - current_price) / box_size)
for b in range(1, boxes_up + 1):
pf_data.append({"column": col, "price": current_price + b * box_size, "symbol": "X", "dir": "bullish"})
current_price += boxes_up * box_size
elif price <= current_price - reversal * box_size:
col += 1
boxes_down = int((current_price - price) / box_size)
new_start = current_price - box_size
for b in range(boxes_down):
pf_data.append({"column": col, "price": new_start - b * box_size, "symbol": "O", "dir": "bearish"})
current_price = new_start - (boxes_down - 1) * box_size
direction = "O"
else:
if price <= current_price - box_size:
boxes_down = int((current_price - price) / box_size)
for b in range(1, boxes_down + 1):
pf_data.append({"column": col, "price": current_price - b * box_size, "symbol": "O", "dir": "bearish"})
current_price -= boxes_down * box_size
elif price >= current_price + reversal * box_size:
col += 1
boxes_up = int((price - current_price) / box_size)
new_start = current_price + box_size
for b in range(boxes_up):
pf_data.append({"column": col, "price": new_start + b * box_size, "symbol": "X", "dir": "bullish"})
current_price = new_start + (boxes_up - 1) * box_size
direction = "X"
pf_df = pd.DataFrame(pf_data)
max_col = int(pf_df["column"].max())
# 45-degree support line: ascends from lowest O price
o_df = pf_df[pf_df["symbol"] == "O"]
sup_col = int(o_df.loc[o_df["price"].idxmin(), "column"])
sup_price = float(o_df["price"].min())
support_df = pd.DataFrame(
{
"column": list(range(sup_col, max_col + 1)),
"price": [sup_price + (c - sup_col) * box_size for c in range(sup_col, max_col + 1)],
}
)
# 45-degree resistance line: descends from highest X price
x_df = pf_df[pf_df["symbol"] == "X"]
res_col = int(x_df.loc[x_df["price"].idxmax(), "column"])
res_price = float(x_df["price"].max())
resist_df = pd.DataFrame(
{
"column": list(range(res_col, max_col + 1)),
"price": [res_price - (c - res_col) * box_size for c in range(res_col, max_col + 1)],
}
)
# Plot
TITLE = "point-and-figure-basic · python · altair · anyplot.ai"
SUBTITLE = f"Box Size: ${box_size:.0f} | Reversal: {reversal} boxes"
pf_marks = (
alt.Chart(pf_df)
.mark_text(fontSize=18, fontWeight="bold")
.encode(
x=alt.X("column:O", title="Column (Reversals)", axis=alt.Axis(labelFontSize=10, titleFontSize=12)),
y=alt.Y(
"price:Q",
title="Price ($)",
scale=alt.Scale(zero=False),
axis=alt.Axis(labelFontSize=10, titleFontSize=12, format="$.0f"),
),
text="symbol:N",
color=alt.Color(
"dir:N",
scale=alt.Scale(domain=["bullish", "bearish"], range=[BULL_COLOR, BEAR_COLOR]),
legend=alt.Legend(title="Direction"),
),
tooltip=[
alt.Tooltip("column:O", title="Column"),
alt.Tooltip("price:Q", title="Price", format="$.2f"),
alt.Tooltip("symbol:N", title="Symbol"),
],
)
)
support_layer = (
alt.Chart(support_df)
.mark_line(strokeDash=[5, 3], strokeWidth=1.5, color="#4467A3", opacity=0.7)
.encode(x="column:O", y="price:Q")
)
resist_layer = (
alt.Chart(resist_df)
.mark_line(strokeDash=[5, 3], strokeWidth=1.5, color="#BD8233", opacity=0.7)
.encode(x="column:O", y="price:Q")
)
chart = (
alt.layer(pf_marks, support_layer, resist_layer)
.properties(
width=800,
height=450,
background=PAGE_BG,
title=alt.Title(TITLE, fontSize=16, subtitle=SUBTITLE, subtitleFontSize=11),
)
.interactive()
.configure_view(fill=PAGE_BG, stroke=INK_SOFT)
.configure_axis(
domainColor=INK_SOFT, tickColor=INK_SOFT, gridColor=INK, gridOpacity=0.12, labelColor=INK_SOFT, titleColor=INK
)
.configure_title(color=INK, subtitleColor=INK_SOFT)
.configure_legend(
labelFontSize=10,
titleFontSize=12,
fillColor=ELEVATED_BG,
strokeColor=INK_SOFT,
labelColor=INK_SOFT,
titleColor=INK,
)
)
# Save
chart.save(f"plot-{THEME}.png", scale_factor=4.0)
chart.save(f"plot-{THEME}.html")
Part of Point and Figure Chart on anyplot.ai.