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: seaborn 0.13.2 | Python 3.13.13
Quality: 88/100 | Updated: 2026-05-20
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.lines import Line2D
# 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" # imprint green — X columns (bullish)
BEAR_COLOR = "#AE3030" # imprint red — O columns (bearish)
sns.set_theme(
style="ticks",
rc={
"figure.facecolor": PAGE_BG,
"axes.facecolor": PAGE_BG,
"axes.edgecolor": INK_SOFT,
"axes.labelcolor": INK,
"text.color": INK,
"xtick.color": INK_SOFT,
"ytick.color": INK_SOFT,
"grid.color": INK,
"grid.alpha": 0.10,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Synthetic biotech stock over 300 trading days
np.random.seed(42)
n_days = 300
base_price = 80.0
returns = np.random.normal(0.001, 0.018, n_days)
returns[40:90] += 0.006
returns[130:180] -= 0.006
returns[230:270] += 0.005
prices = base_price * np.exp(np.cumsum(returns))
close_prices = pd.Series(prices)
# Point and Figure — 3-box reversal, $2 box size
box_size = 2.0
reversal = 3
pnf_columns = []
current_direction = None
current_col_boxes = []
col_index = 0
first_box = round(close_prices.iloc[0] / box_size) * box_size
for price in close_prices:
rounded_price = round(price / box_size) * box_size
if current_direction is None:
current_col_boxes = [first_box]
if rounded_price > first_box:
current_direction = "X"
while current_col_boxes[-1] + box_size <= rounded_price:
current_col_boxes.append(current_col_boxes[-1] + box_size)
elif rounded_price < first_box:
current_direction = "O"
while current_col_boxes[-1] - box_size >= rounded_price:
current_col_boxes.append(current_col_boxes[-1] - box_size)
continue
if current_direction == "X":
top_box = max(current_col_boxes)
if rounded_price >= top_box + box_size:
while current_col_boxes[-1] + box_size <= rounded_price:
current_col_boxes.append(current_col_boxes[-1] + box_size)
elif rounded_price <= top_box - reversal * box_size:
pnf_columns.append((col_index, list(current_col_boxes), "X"))
col_index += 1
start_box = top_box - box_size
current_col_boxes = [start_box]
current_direction = "O"
while current_col_boxes[-1] - box_size >= rounded_price:
current_col_boxes.append(current_col_boxes[-1] - box_size)
else:
bottom_box = min(current_col_boxes)
if rounded_price <= bottom_box - box_size:
while current_col_boxes[-1] - box_size >= rounded_price:
current_col_boxes.append(current_col_boxes[-1] - box_size)
elif rounded_price >= bottom_box + reversal * box_size:
pnf_columns.append((col_index, list(current_col_boxes), "O"))
col_index += 1
start_box = bottom_box + box_size
current_col_boxes = [start_box]
current_direction = "X"
while current_col_boxes[-1] + box_size <= rounded_price:
current_col_boxes.append(current_col_boxes[-1] + box_size)
if current_col_boxes:
pnf_columns.append((col_index, current_col_boxes, current_direction or "X"))
# Build DataFrame with series labels for seaborn hue encoding
plot_rows = []
for col_idx, boxes, direction in pnf_columns:
series = "X — Rising" if direction == "X" else "O — Falling"
for box in boxes:
plot_rows.append({"column": col_idx, "price": box, "series": series})
plot_df = pd.DataFrame(plot_rows)
# 45-degree trend line anchors
o_bottoms = [(ci, min(boxes)) for ci, boxes, d in pnf_columns if d == "O"]
x_tops = [(ci, max(boxes)) for ci, boxes, d in pnf_columns if d == "X"]
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
price_min = plot_df["price"].min() - box_size
price_max = plot_df["price"].max() + box_size
col_max = int(plot_df["column"].max())
# Seaborn hue + style dual-encoding: direction → color (palette) + marker shape
sns.scatterplot(
data=plot_df,
x="column",
y="price",
hue="series",
style="series",
hue_order=["X — Rising", "O — Falling"],
style_order=["X — Rising", "O — Falling"],
palette={"X — Rising": BULL_COLOR, "O — Falling": BEAR_COLOR},
markers={"X — Rising": "X", "O — Falling": "o"},
s=200,
linewidth=2.5,
legend=False,
ax=ax,
)
# 45-degree support trend line — ascending from the lowest O-column bottom
if o_bottoms:
supp_col, supp_price = min(o_bottoms, key=lambda t: t[1])
x_end = min(col_max + 0.5, supp_col + (price_max - supp_price) / box_size)
if x_end > supp_col:
ax.plot(
[supp_col, x_end],
[supp_price, supp_price + (x_end - supp_col) * box_size],
"--",
color=BULL_COLOR,
alpha=0.55,
linewidth=1.5,
zorder=1,
)
ax.annotate(
"Support",
xy=(supp_col, supp_price),
xytext=(4, -12),
textcoords="offset points",
fontsize=7,
color=BULL_COLOR,
alpha=0.85,
)
# 45-degree resistance trend line — descending from the highest X-column top
if x_tops:
res_col, res_price = max(x_tops, key=lambda t: t[1])
x_end = min(col_max + 0.5, res_col + (res_price - price_min) / box_size)
if x_end > res_col:
ax.plot(
[res_col, x_end],
[res_price, res_price - (x_end - res_col) * box_size],
"--",
color=BEAR_COLOR,
alpha=0.55,
linewidth=1.5,
zorder=1,
)
ax.annotate(
"Resistance",
xy=(res_col, res_price),
xytext=(4, 4),
textcoords="offset points",
fontsize=7,
color=BEAR_COLOR,
alpha=0.85,
)
# Style
ax.set_xlabel("Column (Reversals)", fontsize=10, color=INK)
ax.set_ylabel("Price ($)", fontsize=10, color=INK)
ax.set_title("point-and-figure-basic · python · seaborn · anyplot.ai", fontsize=12, fontweight="medium", color=INK)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT)
yticks = np.arange(int(price_min / box_size) * box_size, price_max + box_size, box_size * 2)
ax.set_yticks(yticks)
ax.set_ylim(price_min, price_max)
ax.set_xlim(-0.5, col_max + 0.5)
ax.yaxis.grid(True, alpha=0.10, linewidth=0.8, color=INK)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_color(INK_SOFT)
ax.spines["bottom"].set_color(INK_SOFT)
# Legend: hollow-O convention + trend line entries
legend_handles = [
Line2D([0], [0], marker="X", color=BULL_COLOR, markersize=8, linewidth=0, markeredgewidth=2.5),
Line2D(
[0], [0], marker="o", color=BEAR_COLOR, markersize=8, linewidth=0, markerfacecolor="none", markeredgewidth=2.5
),
Line2D([0], [0], linestyle="--", color=BULL_COLOR, alpha=0.7, linewidth=1.5),
Line2D([0], [0], linestyle="--", color=BEAR_COLOR, alpha=0.7, linewidth=1.5),
]
legend_labels = ["X — Rising", "O — Falling", "Support (45°)", "Resistance (45°)"]
leg = ax.legend(
handles=legend_handles,
labels=legend_labels,
loc="upper left",
fontsize=8,
frameon=True,
facecolor=ELEVATED_BG,
edgecolor=INK_SOFT,
)
for text in leg.get_texts():
text.set_color(INK)
plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, bbox_inches="tight", facecolor=PAGE_BG)
Part of Point and Figure Chart on anyplot.ai.