feat(bokeh): implement point-and-figure-basic (#3861)

## Implementation: `point-and-figure-basic` - bokeh

Implements the **bokeh** version of `point-and-figure-basic`.

**File:** `plots/point-and-figure-basic/implementations/bokeh.py`

**Parent Issue:** #3755

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21047207311)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/point-and-figure-basic/implementations/bokeh.py

@@ -0,0 +1,214 @@
+""" pyplots.ai
+point-and-figure-basic: Point and Figure Chart
+Library: bokeh 3.8.2 | Python 3.13.11
+Quality: 91/100 | Created: 2026-01-15
+"""
+
+import numpy as np
+import pandas as pd
+from bokeh.io import export_png, save
+from bokeh.models import ColumnDataSource
+from bokeh.plotting import figure
+
+
+# Generate synthetic stock price data
+np.random.seed(42)
+n_days = 300
+
+# Start price and generate realistic daily returns
+start_price = 100
+daily_returns = np.random.normal(0.0005, 0.015, n_days)
+
+# Add some trending periods
+daily_returns[50:80] += 0.003  # Uptrend
+daily_returns[100:140] -= 0.004  # Downtrend
+daily_returns[180:220] += 0.0035  # Uptrend
+daily_returns[240:280] -= 0.003  # Downtrend
+
+close_prices = start_price * np.cumprod(1 + daily_returns)
+
+# Generate high/low based on close
+volatility = np.abs(np.random.normal(0, 0.01, n_days))
+high_prices = close_prices * (1 + volatility)
+low_prices = close_prices * (1 - volatility)
+
+dates = pd.date_range("2024-01-01", periods=n_days, freq="D")
+
+df = pd.DataFrame({"date": dates, "high": high_prices, "low": low_prices, "close": close_prices})
+
+# Point and Figure calculation
+box_size = 2.0  # Each box represents $2
+reversal = 3  # 3-box reversal
+
+# Calculate P&F columns
+columns = []
+current_direction = None  # 'X' for up, 'O' for down
+current_column_start = None
+current_column_end = None
+
+# Initialize with first price
+first_price = df["close"].iloc[0]
+box_start = np.floor(first_price / box_size) * box_size
+
+for _i, row in df.iterrows():
+    price = row["close"]
+    box_price = np.floor(price / box_size) * box_size
+
+    if current_direction is None:
+        # Initialize first direction based on next movement
+        current_column_start = box_start
+        current_column_end = box_start
+        # Wait for significant move
+        if price >= box_start + box_size:
+            current_direction = "X"
+            current_column_end = box_price
+        elif price <= box_start - box_size:
+            current_direction = "O"
+            current_column_end = box_price
+    else:
+        if current_direction == "X":
+            # In uptrend
+            if box_price >= current_column_end + box_size:
+                # Continue up
+                current_column_end = box_price
+            elif box_price <= current_column_end - reversal * box_size:
+                # Reversal down - save current column and start new O column
+                columns.append({"type": "X", "start": current_column_start, "end": current_column_end})
+                current_direction = "O"
+                current_column_start = current_column_end - box_size
+                current_column_end = box_price
+        else:
+            # In downtrend
+            if box_price <= current_column_end - box_size:
+                # Continue down
+                current_column_end = box_price
+            elif box_price >= current_column_end + reversal * box_size:
+                # Reversal up - save current column and start new X column
+                columns.append({"type": "O", "start": current_column_start, "end": current_column_end})
+                current_direction = "X"
+                current_column_start = current_column_end + box_size
+                current_column_end = box_price
+
+# Save the last column
+if current_direction is not None:
+    columns.append({"type": current_direction, "start": current_column_start, "end": current_column_end})
+
+# Prepare data for plotting
+x_data = []
+o_data = []
+
+for col_idx, col in enumerate(columns):
+    if col["type"] == "X":
+        start = min(col["start"], col["end"])
+        end = max(col["start"], col["end"])
+        boxes = np.arange(start, end + box_size / 2, box_size)
+        for box in boxes:
+            x_data.append({"col": col_idx, "price": box})
+    else:
+        start = max(col["start"], col["end"])
+        end = min(col["start"], col["end"])
+        boxes = np.arange(end, start + box_size / 2, box_size)
+        for box in boxes:
+            o_data.append({"col": col_idx, "price": box})
+
+# Create figure
+p = figure(
+    width=4800,
+    height=2700,
+    title="point-and-figure-basic · bokeh · pyplots.ai",
+    x_axis_label="Column (Reversal)",
+    y_axis_label="Price ($)",
+)
+
+# Style settings - scaled for 4800x2700 canvas
+p.title.text_font_size = "48pt"
+p.xaxis.axis_label_text_font_size = "36pt"
+p.yaxis.axis_label_text_font_size = "36pt"
+p.xaxis.major_label_text_font_size = "28pt"
+p.yaxis.major_label_text_font_size = "28pt"
+
+# Background and grid
+p.background_fill_color = "#fafafa"
+p.grid.grid_line_alpha = 0.3
+p.grid.grid_line_dash = "dashed"
+p.xgrid.grid_line_color = "#cccccc"
+p.ygrid.grid_line_color = "#cccccc"
+
+# Plot X markers (bullish - green)
+if x_data:
+    x_source = ColumnDataSource(data={"col": [d["col"] for d in x_data], "price": [d["price"] for d in x_data]})
+    p.text(
+        x="col",
+        y="price",
+        text={"value": "X"},
+        source=x_source,
+        text_font_size="48pt",
+        text_color="#2E7D32",
+        text_align="center",
+        text_baseline="middle",
+        text_font_style="bold",
+    )
+
+# Plot O markers (bearish - red)
+if o_data:
+    o_source = ColumnDataSource(data={"col": [d["col"] for d in o_data], "price": [d["price"] for d in o_data]})
+    p.text(
+        x="col",
+        y="price",
+        text={"value": "O"},
+        source=o_source,
+        text_font_size="48pt",
+        text_color="#C62828",
+        text_align="center",
+        text_baseline="middle",
+        text_font_style="bold",
+    )
+
+# Add support trend line (45-degree ascending from low)
+all_prices = [d["price"] for d in x_data] + [d["price"] for d in o_data]
+if all_prices:
+    min_price = min(all_prices)
+    max_price = max(all_prices)
+
+    # Find lowest point for support line
+    support_start_col = 0
+    support_start_price = min_price - box_size
+    support_end_col = len(columns) - 1
+    support_end_price = support_start_price + (support_end_col - support_start_col) * box_size
+
+    if support_end_price <= max_price + 2 * box_size:
+        p.line(
+            x=[support_start_col, support_end_col],
+            y=[support_start_price, support_end_price],
+            line_width=5,
+            line_color="#306998",
+            line_dash="solid",
+            legend_label="Support Trend",
+        )
+
+    # Find highest point for resistance line
+    resistance_start_col = 0
+    resistance_start_price = max_price + box_size
+    resistance_end_col = len(columns) - 1
+    resistance_end_price = resistance_start_price - (resistance_end_col - resistance_start_col) * box_size
+
+    if resistance_end_price >= min_price - 2 * box_size:
+        p.line(
+            x=[resistance_start_col, resistance_end_col],
+            y=[resistance_start_price, resistance_end_price],
+            line_width=5,
+            line_color="#FFD43B",
+            line_dash="solid",
+            legend_label="Resistance Trend",
+        )
+
+# Legend styling
+p.legend.location = "top_left"
+p.legend.label_text_font_size = "28pt"
+p.legend.background_fill_alpha = 0.8
+p.legend.glyph_height = 30
+p.legend.glyph_width = 30
+
+# Save as PNG and HTML (interactive)
+export_png(p, filename="plot.png")
+save(p, filename="plot.html")