feat(seaborn): implement indicator-bollinger (#3242)

## Implementation: `indicator-bollinger` - seaborn

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

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

**Parent Issue:** #3237

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20795209754)*

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

Author: github-actions[bot]

plots/indicator-bollinger/implementations/seaborn.py

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+"""pyplots.ai
+indicator-bollinger: Bollinger Bands Indicator Chart
+Library: seaborn | Python 3.13
+Quality: pending | Created: 2026-01-07
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Data - Generate realistic stock price data with Bollinger Bands
+np.random.seed(42)
+n_days = 120
+
+# Generate cumulative price movement (random walk with drift)
+dates = pd.date_range(start="2025-07-01", periods=n_days, freq="B")  # Business days
+returns = np.random.normal(0.0005, 0.015, n_days)  # Small positive drift, realistic volatility
+price_base = 150.0
+close = price_base * np.cumprod(1 + returns)
+
+# Calculate Bollinger Bands (20-period SMA, 2 standard deviations)
+window = 20
+df = pd.DataFrame({"date": dates, "close": close})
+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 calculations
+df = df.dropna().reset_index(drop=True)
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+
+# Fill between upper and lower bands
+ax.fill_between(
+    df["date"], df["lower_band"], df["upper_band"], alpha=0.25, color="#306998", label="Bollinger Bands (±2σ)"
+)
+
+# Plot the bands and price using seaborn
+sns.lineplot(data=df, x="date", y="upper_band", ax=ax, color="#306998", linewidth=2, linestyle="-", label="Upper Band")
+sns.lineplot(data=df, x="date", y="lower_band", ax=ax, color="#306998", linewidth=2, linestyle="-", label="Lower Band")
+sns.lineplot(data=df, x="date", y="sma", ax=ax, color="#FFD43B", linewidth=2.5, linestyle="--", label="20-day SMA")
+sns.lineplot(data=df, x="date", y="close", ax=ax, color="#1a1a2e", linewidth=3, label="Close Price")
+
+# Styling
+ax.set_xlabel("Date", fontsize=20)
+ax.set_ylabel("Price ($)", fontsize=20)
+ax.set_title("indicator-bollinger · seaborn · pyplots.ai", fontsize=24)
+ax.tick_params(axis="both", labelsize=16)
+ax.grid(True, alpha=0.3, linestyle="--")
+
+# Legend
+ax.legend(loc="upper left", fontsize=14, framealpha=0.9)
+
+# Format x-axis dates
+fig.autofmt_xdate(rotation=30)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/indicator-bollinger/metadata/seaborn.yaml

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+# Per-library metadata for seaborn implementation of indicator-bollinger
+# Auto-generated by impl-generate.yml
+
+library: seaborn
+specification_id: indicator-bollinger
+created: '2026-01-07T20:18:54Z'
+updated: '2026-01-07T20:18:54Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20795209754
+issue: 3237
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/indicator-bollinger/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/indicator-bollinger/seaborn/plot_thumb.png
+preview_html: null
+quality_score: null
+review:
+  strengths: []
+  weaknesses: []