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: plotnine 0.15.4 | Python 3.13.13
Quality: 83/100 | Updated: 2026-05-20
"""
import os
import sys
# Work around filename shadowing the plotnine library
sys.path.pop(0)
import numpy as np
import pandas as pd
from plotnine import (
aes,
coord_fixed,
element_blank,
element_line,
element_rect,
element_text,
geom_point,
geom_segment,
geom_text,
ggplot,
guide_legend,
guides,
labs,
scale_color_manual,
scale_y_continuous,
theme,
theme_minimal,
)
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"
X_COLOR = "#009E73" # imprint green - rising columns
O_COLOR = "#AE3030" # imprint red - falling columns
# Data
np.random.seed(42)
n_days = 300
returns = np.random.normal(0.001, 0.02, n_days)
returns[50:100] += 0.005
returns[120:160] -= 0.008
returns[180:250] += 0.004
price = 100 * np.cumprod(1 + returns)
close = price
box_size = 2.0
reversal = 3
# Build Point and Figure data
pf_data = []
current_direction = None
current_column = 0
start_box = int(np.floor(close[0] / box_size))
current_high_box = start_box
current_low_box = start_box
for i in range(1, len(close)):
current_box = int(np.floor(close[i] / box_size))
if current_direction is None:
if current_box > current_high_box:
current_direction = "X"
for b in range(current_low_box, current_box + 1):
pf_data.append((current_column, b * box_size, "X"))
current_high_box = current_box
elif current_box < current_low_box:
current_direction = "O"
for b in range(current_box, current_high_box + 1):
pf_data.append((current_column, b * box_size, "O"))
current_low_box = current_box
elif current_direction == "X":
if current_box > current_high_box:
for b in range(current_high_box + 1, current_box + 1):
pf_data.append((current_column, b * box_size, "X"))
current_high_box = current_box
elif current_box <= current_high_box - reversal:
current_column += 1
current_direction = "O"
current_low_box = current_box
for b in range(current_box, current_high_box):
pf_data.append((current_column, b * box_size, "O"))
elif current_direction == "O":
if current_box < current_low_box:
for b in range(current_box, current_low_box):
pf_data.append((current_column, b * box_size, "O"))
current_low_box = current_box
elif current_box >= current_low_box + reversal:
current_column += 1
current_direction = "X"
current_high_box = current_box
for b in range(current_low_box + 1, current_box + 1):
pf_data.append((current_column, b * box_size, "X"))
df = pd.DataFrame(pf_data, columns=["column", "price", "symbol"])
# Use full label text as the color aesthetic value to avoid plotnine legend prefix bug
df["direction"] = pd.Categorical(
df["symbol"].map({"X": "Rising (X)", "O": "Falling (O)"}), categories=["Rising (X)", "Falling (O)"]
)
# 45-degree support and resistance trend lines (one box per column)
min_idx = df["price"].idxmin()
max_idx = df["price"].idxmax()
max_col = float(df["column"].max())
support_col = float(df.loc[min_idx, "column"])
support_price = float(df.loc[min_idx, "price"])
resist_col = float(df.loc[max_idx, "column"])
resist_price = float(df.loc[max_idx, "price"])
trend_lines = pd.DataFrame(
{
"x": [support_col, resist_col],
"y": [support_price, resist_price],
"xend": [max_col, max_col],
"yend": [support_price + (max_col - support_col) * box_size, resist_price - (max_col - resist_col) * box_size],
}
)
# Plot
plot = (
ggplot(df, aes(x="column", y="price"))
+ geom_segment(
data=trend_lines,
mapping=aes(x="x", y="y", xend="xend", yend="yend"),
color=INK_SOFT,
size=0.6,
linetype="dashed",
alpha=0.7,
inherit_aes=False,
)
+ geom_text(mapping=aes(color="direction", label="symbol"), size=8, fontweight="bold", show_legend=False)
+ geom_point(mapping=aes(color="direction"), size=0.01, alpha=0.01)
+ coord_fixed(ratio=box_size)
+ scale_color_manual(values={"Rising (X)": X_COLOR, "Falling (O)": O_COLOR}, name="Direction")
+ guides(color=guide_legend(override_aes={"size": 3, "alpha": 1}))
+ scale_y_continuous(
breaks=np.arange(
int(df["price"].min() / box_size) * box_size, int(df["price"].max() / box_size + 2) * box_size, box_size * 2
)
)
+ labs(x="Column (Reversals)", y="Price Level ($)", title="point-and-figure-basic · python · plotnine · anyplot.ai")
+ theme_minimal()
+ theme(
figure_size=(6, 6),
text=element_text(size=7, color=INK),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
plot_title=element_text(size=12, color=INK),
legend_text=element_text(size=8, color=INK_SOFT),
legend_title=element_text(size=9, color=INK),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
legend_background=element_rect(fill=ELEVATED_BG, size=0),
panel_border=element_blank(),
axis_line=element_line(color=INK_SOFT, size=0.5),
panel_grid_major_x=element_blank(),
panel_grid_minor=element_blank(),
panel_grid_major_y=element_line(color=INK, size=0.3, alpha=0.10),
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=6, height=6, units="in")
Part of Point and Figure Chart on anyplot.ai.