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: matplotlib 3.10.9 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-20
"""
import os
import sys
# Remove the script's directory from sys.path to prevent shadowing the installed matplotlib package
if sys.path and sys.path[0] and "implementations" in sys.path[0]:
sys.path.pop(0)
import matplotlib.patches as mpatches
import matplotlib.patheffects as pe
import matplotlib.pyplot as plt
import numpy as np
# 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 semantic anchors: green for rising (X), red for falling (O)
X_COLOR = "#009E73"
O_COLOR = "#AE3030"
# Data: synthetic stock price with upward drift
np.random.seed(42)
n_days = 300
close = 100.0 * np.cumprod(1 + np.random.normal(0.001, 0.02, n_days))
# P&F parameters
box_size = 2.0
reversal = 3
# Build P&F columns
columns = []
current_col = None
current_price = None
for price in close:
if current_col is None:
current_col = {
"type": "X",
"start": np.floor(price / box_size) * box_size,
"end": np.floor(price / box_size) * box_size,
}
current_price = current_col["end"]
continue
if current_col["type"] == "X":
new_high = np.floor(price / box_size) * box_size
if new_high > current_col["end"]:
current_col["end"] = new_high
current_price = new_high
elif price <= current_price - reversal * box_size:
columns.append(current_col.copy())
current_col = {
"type": "O",
"start": current_col["end"] - box_size,
"end": np.ceil(price / box_size) * box_size,
}
current_price = current_col["end"]
else:
new_low = np.ceil(price / box_size) * box_size
if new_low < current_col["end"]:
current_col["end"] = new_low
current_price = new_low
elif price >= current_price + reversal * box_size:
columns.append(current_col.copy())
current_col = {
"type": "X",
"start": current_col["end"] + box_size,
"end": np.floor(price / box_size) * box_size,
}
current_price = current_col["end"]
if current_col is not None:
columns.append(current_col)
n_cols = len(columns)
all_prices = [p for col in columns for p in [col["start"], col["end"]]]
y_min = min(all_prices) - 2 * box_size
y_max = max(all_prices) + 2 * box_size
# Find the most significant buy signal: X column exceeding prior X column peak by most boxes
prev_x_high = None
best_breakout = None
for i, col in enumerate(columns):
if col["type"] == "X":
col_high = max(col["start"], col["end"])
if prev_x_high is not None and col_high > prev_x_high:
excess = col_high - prev_x_high
if best_breakout is None or excess > best_breakout[3]:
best_breakout = (i, prev_x_high, col_high, excess)
prev_x_high = col_high
# Trend line anchors
# Support: from overall lowest low, rising 45° (one box per column to the right)
all_lows = [(i, min(col["start"], col["end"])) for i, col in enumerate(columns)]
lowest_col_idx, lowest_price = min(all_lows, key=lambda x: x[1])
# Resistance: from top of first column, falling 45° (one box per column to the right)
first_col_high = max(columns[0]["start"], columns[0]["end"])
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
# Draw X and O symbols using patheffects for crisp separation between dense symbols
symbol_stroke = [pe.withStroke(linewidth=2, foreground=PAGE_BG)]
for col_idx, col in enumerate(columns):
lo = min(col["start"], col["end"])
hi = max(col["start"], col["end"])
char = "X" if col["type"] == "X" else "O"
color = X_COLOR if col["type"] == "X" else O_COLOR
for box_price in np.arange(lo, hi + box_size / 2, box_size):
ax.text(
col_idx,
box_price,
char,
fontsize=8,
fontweight="bold",
ha="center",
va="center",
color=color,
path_effects=symbol_stroke,
)
# Support trend line: 45° upward from the overall lowest low
support_x = np.array([0, n_cols - 1])
support_y = np.array(
[lowest_price - lowest_col_idx * box_size, lowest_price + (n_cols - 1 - lowest_col_idx) * box_size]
)
ax.plot(support_x, support_y, color=X_COLOR, linewidth=0.8, linestyle="--", alpha=0.5, zorder=0)
# Resistance trend line: 45° downward from the top of the first column
resistance_x = np.array([0, n_cols - 1])
resistance_y = np.array([first_col_high, first_col_high - (n_cols - 1) * box_size])
ax.plot(resistance_x, resistance_y, color=O_COLOR, linewidth=0.8, linestyle="--", alpha=0.5, zorder=0)
# Axes configuration
ax.set_xlim(-0.5, n_cols - 0.5)
ax.set_ylim(y_min, y_max)
# Major ticks at $10 intervals (labeled + gridlines); minor ticks at $2 box intervals (tick marks only)
major_ticks = np.arange(np.floor(y_min / 10) * 10, np.ceil(y_max / 10) * 10 + 10, 10)
minor_ticks = np.arange(
np.floor(y_min / box_size) * box_size, np.ceil(y_max / box_size) * box_size + box_size, box_size
)
ax.set_yticks(major_ticks)
ax.set_yticks(minor_ticks, minor=True)
ax.tick_params(axis="y", which="major", labelsize=8, colors=INK_SOFT, length=4)
ax.tick_params(axis="y", which="minor", length=2, colors=INK_SOFT, labelsize=0)
ax.tick_params(axis="x", labelsize=8, colors=INK_SOFT)
# Style
ax.yaxis.grid(True, which="major", alpha=0.15, linewidth=0.6, color=INK)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for s in ("left", "bottom"):
ax.spines[s].set_color(INK_SOFT)
ax.set_xlabel("Column (Reversal Number)", fontsize=10, color=INK)
ax.set_ylabel("Price ($)", fontsize=10, color=INK)
ax.set_title("point-and-figure-basic · python · matplotlib · anyplot.ai", fontsize=12, fontweight="medium", color=INK)
# Annotate the most prominent buy signal — X column breakout above prior X peak (narrative focal point)
if best_breakout is not None:
b_col, _, b_col_high, _ = best_breakout
text_offset = 2.5 if b_col < n_cols * 0.65 else -3.5
ax.annotate(
"Buy Signal\n(Column Breakout)",
xy=(b_col, b_col_high),
xytext=(b_col + text_offset, b_col_high + 4),
fontsize=7,
fontweight="bold",
color=X_COLOR,
ha="center",
arrowprops={"arrowstyle": "->", "color": X_COLOR, "lw": 0.8},
bbox={"facecolor": ELEVATED_BG, "edgecolor": X_COLOR, "alpha": 0.9, "boxstyle": "round,pad=0.3"},
)
# Legend using color patches (avoids marker/text mismatch)
x_patch = mpatches.Patch(facecolor=X_COLOR, label="Rising (X)", edgecolor=PAGE_BG)
o_patch = mpatches.Patch(facecolor=O_COLOR, label="Falling (O)", edgecolor=PAGE_BG)
leg = ax.legend(handles=[x_patch, o_patch], fontsize=8, loc="upper left")
leg.get_frame().set_facecolor(ELEVATED_BG)
leg.get_frame().set_edgecolor(INK_SOFT)
plt.setp(leg.get_texts(), color=INK_SOFT)
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.