update(candlestick-basic): seaborn — comprehensive quality review (#4390)

## Summary

Updated **seaborn** implementation for **candlestick-basic**.

**Changes:** Comprehensive quality review — improved visual design, data
quality, code patterns, and library-specific features.

## Test Plan

- [x] Preview images uploaded to GCS staging
- [x] Implementation file passes ruff format/check
- [x] Metadata YAML updated with current versions
- [ ] Automated review triggered

---
Generated with [Claude Code](https://claude.com/claude-code) `/update`
command

---------

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: MarkusNeusinger

plots/candlestick-basic/implementations/seaborn.py

@@ -1,111 +1,152 @@
 """ pyplots.ai
 candlestick-basic: Basic Candlestick Chart
-Library: seaborn 0.13.2 | Python 3.13.11
-Quality: 90/100 | Created: 2025-12-23
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 92/100 | Updated: 2026-02-24
 """
 
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
 import seaborn as sns
-from matplotlib.patches import Patch
-
-
-# Set seaborn style
-sns.set_theme(style="whitegrid")
+from matplotlib.collections import PatchCollection
+from matplotlib.lines import Line2D
+from matplotlib.patches import Patch, Rectangle
+
+
+# Seaborn theme and context
+sns.set_theme(
+    style="whitegrid",
+    rc={
+        "axes.facecolor": "#f8f9fa",
+        "figure.facecolor": "white",
+        "grid.color": "#dee2e6",
+        "text.color": "#212529",
+        "axes.labelcolor": "#495057",
+        "xtick.color": "#495057",
+        "ytick.color": "#495057",
+    },
+)
 sns.set_context("talk", font_scale=1.2)
 
-# Data - 30 days of simulated stock OHLC data
+# Color palette via seaborn — blue/red scheme per spec, colorblind-safe
+candle_palette = sns.color_palette(["#306998", "#c0392b"])
+up_fill, down_fill = candle_palette[0], candle_palette[1]
+up_edge = sns.dark_palette(candle_palette[0], n_colors=4)[2]
+down_edge = sns.dark_palette(candle_palette[1], n_colors=4)[2]
+ma_palette = sns.color_palette(["#e67e22", "#8e44ad"])
+
+# Data — 30 trading days with rally then selloff pattern
 np.random.seed(42)
 n_days = 30
-dates = pd.date_range("2024-01-02", periods=n_days, freq="B")  # Business days
-
-# Generate realistic OHLC data
-price = 150.0
+dates = pd.date_range("2024-01-02", periods=n_days, freq="B")
+
+price = 145.0
+drift = np.concatenate(
+    [
+        np.linspace(0.4, 0.8, 12),  # Uptrend phase
+        np.linspace(-0.1, -0.6, 18),  # Reversal and selloff
+    ]
+)
 opens, highs, lows, closes = [], [], [], []
-
-for _ in range(n_days):
-    # Daily volatility
-    change = np.random.randn() * 3
-    daily_range = abs(np.random.randn()) * 2 + 1
-
+for i in range(n_days):
+    change = drift[i] + np.random.randn() * 2.5
+    volatility = abs(np.random.randn()) * 1.5 + 0.8
     open_price = price
     close_price = price + change
-
-    # High and low include the open/close range plus some extra
-    high_price = max(open_price, close_price) + abs(np.random.randn()) * daily_range
-    low_price = min(open_price, close_price) - abs(np.random.randn()) * daily_range
-
+    high_price = max(open_price, close_price) + abs(np.random.randn()) * volatility
+    low_price = min(open_price, close_price) - abs(np.random.randn()) * volatility
     opens.append(open_price)
     highs.append(high_price)
     lows.append(low_price)
     closes.append(close_price)
-
     price = close_price
 
 df = pd.DataFrame({"date": dates, "open": opens, "high": highs, "low": lows, "close": closes})
-
-# Determine bullish (up) vs bearish (down) days - use colorblind-safe colors
 df["bullish"] = df["close"] >= df["open"]
-# Colorblind-safe: blue for up, orange for down
-color_up = "#1f77b4"  # Blue for bullish
-color_down = "#ff7f0e"  # Orange for bearish
-df["color"] = df["bullish"].map({True: color_up, False: color_down})
-df["x"] = range(len(df))
+df["x"] = range(n_days)
 
-# Create plot
+# Moving averages for trend storytelling
+df["5-Day MA"] = df["close"].rolling(window=5).mean()
+df["10-Day MA"] = df["close"].rolling(window=10).mean()
+
+# Plot
 fig, ax = plt.subplots(figsize=(16, 9))
 
-# Draw candlesticks manually with seaborn styling applied
-body_width = 0.6
+# Wicks
+wick_colors = [up_edge if b else down_edge for b in df["bullish"]]
+ax.vlines(df["x"], df["low"], df["high"], colors=wick_colors, linewidth=1.5)
 
+# Candle bodies via PatchCollection
+body_width = 0.6
+rects, fcolors, ecolors = [], [], []
 for _, row in df.iterrows():
-    x = row["x"]
-    color = row["color"]
-
-    # Draw wick (high-low line) using seaborn's lineplot via dataframe
-    wick_data = pd.DataFrame({"x": [x, x], "price": [row["low"], row["high"]]})
-    sns.lineplot(data=wick_data, x="x", y="price", ax=ax, color=color, linewidth=2, legend=False)
-
-    # Draw candle body using matplotlib Rectangle for precise control
-    body_bottom = min(row["open"], row["close"])
-    body_height = abs(row["close"] - row["open"])
-    # Ensure minimum body height for doji candles
-    if body_height < 0.2:
-        body_height = 0.2
-        body_bottom = (row["open"] + row["close"]) / 2 - 0.1
-
-    rect = plt.Rectangle(
-        (x - body_width / 2, body_bottom), body_width, body_height, facecolor=color, edgecolor=color, linewidth=1
-    )
-    ax.add_patch(rect)
-
-# Set x-axis to show dates
-tick_positions = range(0, n_days, 5)  # Show every 5th date
-tick_labels = [df["date"].iloc[i].strftime("%b %d") for i in tick_positions]
-ax.set_xticks(tick_positions)
-ax.set_xticklabels(tick_labels, rotation=0)
-
-# Style the plot
+    bottom = min(row["open"], row["close"])
+    height = max(abs(row["close"] - row["open"]), 0.15)
+    if abs(row["close"] - row["open"]) < 0.15:
+        bottom = (row["open"] + row["close"]) / 2 - 0.075
+    rects.append(Rectangle((row["x"] - body_width / 2, bottom), body_width, height))
+    fcolors.append(up_fill if row["bullish"] else down_fill)
+    ecolors.append(up_edge if row["bullish"] else down_edge)
+
+bodies = PatchCollection(rects, facecolors=fcolors, edgecolors=ecolors, linewidths=0.8)
+ax.add_collection(bodies)
+
+# Moving average overlays using seaborn lineplot
+ma_long = df[["x", "5-Day MA", "10-Day MA"]].melt(id_vars="x", var_name="Moving Average", value_name="Price").dropna()
+sns.lineplot(
+    data=ma_long,
+    x="x",
+    y="Price",
+    hue="Moving Average",
+    palette={"5-Day MA": ma_palette[0], "10-Day MA": ma_palette[1]},
+    linewidth=2.2,
+    alpha=0.85,
+    ax=ax,
+    legend=False,
+)
+
+# Peak annotation for data storytelling
+peak_idx = df["close"].idxmax()
+peak_row = df.loc[peak_idx]
+ax.annotate(
+    f"Peak ${peak_row['close']:.0f}",
+    xy=(peak_row["x"], peak_row["high"]),
+    xytext=(peak_row["x"] + 3, peak_row["high"] + 2.0),
+    fontsize=12,
+    fontweight="bold",
+    color="#495057",
+    arrowprops={"arrowstyle": "->", "color": "#adb5bd", "lw": 1.5},
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "white", "edgecolor": "#dee2e6", "alpha": 0.9},
+)
+
+# X-axis date labels
+tick_idx = range(0, n_days, 5)
+ax.set_xticks(list(tick_idx))
+ax.set_xticklabels([dates[i].strftime("%b %d") for i in tick_idx])
+
+# Style
 ax.set_xlabel("Date", fontsize=20)
 ax.set_ylabel("Price ($)", fontsize=20)
-ax.set_title("candlestick-basic · seaborn · pyplots.ai", fontsize=24)
-ax.tick_params(axis="both", labelsize=16)
-
-# Add legend with colorblind-safe colors - positioned outside data area
-legend_elements = [Patch(facecolor=color_up, label="Bullish (Up)"), Patch(facecolor=color_down, label="Bearish (Down)")]
-ax.legend(handles=legend_elements, fontsize=14, loc="upper right", bbox_to_anchor=(0.99, 0.99))
-
-# Subtle grid
-ax.grid(True, alpha=0.3, linestyle="--", axis="y")
+ax.set_title("candlestick-basic \u00b7 seaborn \u00b7 pyplots.ai", fontsize=24, fontweight="medium", pad=16)
+ax.tick_params(axis="both", labelsize=16, length=0)
+sns.despine(ax=ax)
+ax.yaxis.grid(True, alpha=0.15, linewidth=0.8)
+ax.xaxis.grid(False)
 ax.set_axisbelow(True)
 
-# Set axis limits
-ax.set_xlim(-0.5, n_days - 0.5)
-y_min = df["low"].min()
-y_max = df["high"].max()
-y_padding = (y_max - y_min) * 0.1
-ax.set_ylim(y_min - y_padding, y_max + y_padding)
+# Combined legend
+legend_handles = [
+    Patch(facecolor=up_fill, edgecolor=up_edge, label="Bullish"),
+    Patch(facecolor=down_fill, edgecolor=down_edge, label="Bearish"),
+    Line2D([0], [0], color=ma_palette[0], linewidth=2.2, label="5-Day MA"),
+    Line2D([0], [0], color=ma_palette[1], linewidth=2.2, label="10-Day MA"),
+]
+ax.legend(handles=legend_handles, fontsize=14, loc="upper left", framealpha=0.9, edgecolor="#dee2e6")
+
+# Axis limits
+ax.set_xlim(-0.8, n_days - 0.2)
+y_pad = (df["high"].max() - df["low"].min()) * 0.08
+ax.set_ylim(df["low"].min() - y_pad, df["high"].max() + y_pad * 2.5)
 
 plt.tight_layout()
 plt.savefig("plot.png", dpi=300, bbox_inches="tight")