feat(altair): implement indicator-bollinger (#3248)

## Implementation: `indicator-bollinger` - altair

Implements the **altair** version of `indicator-bollinger`.

**File:** `plots/indicator-bollinger/implementations/altair.py`

**Parent Issue:** #3237

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20795214151)*

---------

Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com>

Author: github-actions[bot]

plots/indicator-bollinger/implementations/altair.py

@@ -0,0 +1,78 @@
+"""pyplots.ai
+indicator-bollinger: Bollinger Bands Indicator Chart
+Library: altair | Python 3.13
+Quality: pending | Created: 2025-01-07
+"""
+
+import altair as alt
+import numpy as np
+import pandas as pd
+
+
+# Data - Generate realistic stock price data with Bollinger Bands
+np.random.seed(42)
+
+# Generate 120 trading days of price data
+n_periods = 120
+dates = pd.date_range("2024-01-02", periods=n_periods, freq="B")  # Business days
+
+# Generate realistic price movement (random walk with drift)
+returns = np.random.normal(0.001, 0.02, n_periods)  # Small positive drift, 2% daily volatility
+price_base = 150.0
+close_prices = price_base * np.cumprod(1 + returns)
+
+# Calculate Bollinger Bands (20-period SMA, 2 standard deviations)
+window = 20
+df = pd.DataFrame({"date": dates, "close": close_prices})
+df["sma"] = df["close"].rolling(window=window).mean()
+df["std"] = df["close"].rolling(window=window).std()
+df["upper_band"] = df["sma"] + 2 * df["std"]
+df["lower_band"] = df["sma"] - 2 * df["std"]
+
+# Drop NaN values from rolling calculation
+df = df.dropna().reset_index(drop=True)
+
+# Create base chart
+base = alt.Chart(df).encode(x=alt.X("date:T", title="Date", axis=alt.Axis(format="%b %Y", labelAngle=-45)))
+
+# Upper band line
+upper_line = base.mark_line(strokeWidth=2, color="#306998", opacity=0.7).encode(
+    y=alt.Y("upper_band:Q", title="Price ($)", scale=alt.Scale(zero=False))
+)
+
+# Lower band line
+lower_line = base.mark_line(strokeWidth=2, color="#306998", opacity=0.7).encode(
+    y=alt.Y("lower_band:Q", scale=alt.Scale(zero=False))
+)
+
+# Band fill area (between upper and lower bands)
+band_area = (
+    alt.Chart(df)
+    .mark_area(opacity=0.15, color="#306998")
+    .encode(x=alt.X("date:T"), y=alt.Y("upper_band:Q", scale=alt.Scale(zero=False)), y2="lower_band:Q")
+)
+
+# Middle band (SMA) - dashed line
+sma_line = base.mark_line(strokeWidth=2.5, strokeDash=[8, 4], color="#306998", opacity=0.9).encode(
+    y=alt.Y("sma:Q", scale=alt.Scale(zero=False))
+)
+
+# Close price line - prominent
+price_line = base.mark_line(strokeWidth=3, color="#FFD43B").encode(y=alt.Y("close:Q", scale=alt.Scale(zero=False)))
+
+# Combine all layers
+chart = (
+    alt.layer(band_area, upper_line, lower_line, sma_line, price_line)
+    .properties(
+        width=1600,
+        height=900,
+        title=alt.Title(text="indicator-bollinger · altair · pyplots.ai", fontSize=28, anchor="middle"),
+    )
+    .configure_axis(labelFontSize=18, titleFontSize=22, gridOpacity=0.3)
+    .configure_view(strokeWidth=0)
+    .configure_legend(titleFontSize=18, labelFontSize=16)
+)
+
+# Save as PNG and HTML
+chart.save("plot.png", scale_factor=3.0)
+chart.save("plot.html")

plots/indicator-bollinger/metadata/altair.yaml

@@ -0,0 +1,19 @@
+# Per-library metadata for altair implementation of indicator-bollinger
+# Auto-generated by impl-generate.yml
+
+library: altair
+specification_id: indicator-bollinger
+created: '2026-01-07T20:19:32Z'
+updated: '2026-01-07T20:19:32Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20795214151
+issue: 3237
+python_version: 3.13.11
+library_version: 6.0.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/indicator-bollinger/altair/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/indicator-bollinger/altair/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/indicator-bollinger/altair/plot.html
+quality_score: null
+review:
+  strengths: []
+  weaknesses: []