feat(seaborn): implement candlestick-volume

plots/candlestick-volume/implementations/seaborn.py

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+""" pyplots.ai
+candlestick-volume: Stock Candlestick Chart with Volume
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 88/100 | Created: 2025-12-31
+"""
+
+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 for consistent aesthetics
+sns.set_style("whitegrid")
+sns.set_context("talk", font_scale=1.2)
+
+# Generate realistic stock data for 60 trading days
+np.random.seed(42)
+n_days = 60
+
+# Start with a base price and generate realistic price movements
+dates = pd.date_range("2024-01-02", periods=n_days, freq="B")  # Business days
+base_price = 150.0
+
+# Generate price series with trends and volatility
+returns = np.random.normal(0.001, 0.02, n_days)  # Daily returns
+prices = base_price * np.cumprod(1 + returns)
+
+# Generate OHLC from the price series
+opens = np.zeros(n_days)
+highs = np.zeros(n_days)
+lows = np.zeros(n_days)
+closes = np.zeros(n_days)
+
+opens[0] = base_price
+for i in range(n_days):
+    if i > 0:
+        opens[i] = closes[i - 1] + np.random.normal(0, 0.5)
+    closes[i] = prices[i]
+    # Ensure more significant price moves for better visibility
+    daily_range = abs(closes[i] - opens[i]) + np.random.uniform(1.0, 3.0)
+    highs[i] = max(opens[i], closes[i]) + np.random.uniform(0.5, daily_range * 0.6)
+    lows[i] = min(opens[i], closes[i]) - np.random.uniform(0.5, daily_range * 0.6)
+
+# Generate volume with some correlation to price movements
+base_volume = 5_000_000
+volume = base_volume + np.random.normal(0, 1_000_000, n_days)
+# Higher volume on bigger price moves
+price_change = np.abs(closes - opens)
+volume = volume + price_change * 500_000
+volume = np.clip(volume, 1_000_000, 15_000_000).astype(int)
+
+# Create DataFrame
+df = pd.DataFrame({"date": dates, "open": opens, "high": highs, "low": lows, "close": closes, "volume": volume})
+
+# Determine bullish vs bearish candles
+df["bullish"] = df["close"] >= df["open"]
+df["day_idx"] = range(len(df))
+
+# Colors for consistent styling
+BULLISH_COLOR = "#306998"  # Python Blue
+BEARISH_COLOR = "#FFD43B"  # Python Yellow
+
+# Create figure with two subplots sharing x-axis (75% price, 25% volume)
+fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(16, 9), height_ratios=[3, 1], sharex=True, gridspec_kw={"hspace": 0.05})
+
+# Set grid below chart elements FIRST before any plotting
+for ax in [ax1, ax2]:
+    ax.set_axisbelow(True)
+
+# === Upper pane: Candlestick chart using seaborn ===
+# Prepare data for seaborn lineplot (wicks) - create long format for high-low lines
+df["wick_min"] = df[["open", "close"]].min(axis=1)
+df["wick_max"] = df[["open", "close"]].max(axis=1)
+df["body_height"] = (df["wick_max"] - df["wick_min"]).clip(lower=0.5)  # Minimum height for visibility
+df["direction"] = df["bullish"].map({True: "Bullish", False: "Bearish"})
+
+# Draw high-low wicks using seaborn lineplot with units parameter
+wick_long = pd.melt(
+    df[["day_idx", "high", "low", "direction"]],
+    id_vars=["day_idx", "direction"],
+    value_vars=["low", "high"],
+    var_name="price_type",
+    value_name="price",
+).sort_values(["day_idx", "price_type"])
+
+sns.lineplot(
+    data=wick_long,
+    x="day_idx",
+    y="price",
+    hue="direction",
+    palette={"Bullish": BULLISH_COLOR, "Bearish": BEARISH_COLOR},
+    linewidth=2,
+    units="day_idx",
+    estimator=None,
+    legend=False,
+    ax=ax1,
+    zorder=2,
+)
+
+# Draw candle bodies with minimum height for visibility
+for _, row in df.iterrows():
+    color = BULLISH_COLOR if row["bullish"] else BEARISH_COLOR
+    body_low = row["wick_min"]
+    body_high = body_low + row["body_height"]
+    ax1.fill_between(
+        [row["day_idx"] - 0.35, row["day_idx"] + 0.35],
+        [body_low] * 2,
+        [body_high] * 2,
+        color=color,
+        alpha=1.0,
+        linewidth=0,
+        zorder=3,
+    )
+
+# Style the price axis
+ax1.set_ylabel("Price ($)", fontsize=20)
+ax1.set_xlabel("")
+ax1.tick_params(axis="both", labelsize=16)
+ax1.set_title("candlestick-volume · seaborn · pyplots.ai", fontsize=24, pad=15)
+
+# Set y-axis range with padding
+price_min = df["low"].min()
+price_max = df["high"].max()
+price_padding = (price_max - price_min) * 0.05
+ax1.set_ylim(price_min - price_padding, price_max + price_padding)
+
+# === Lower pane: Volume bars ===
+# Draw volume bars directly with matplotlib to ensure single bar per day
+# Colors match the candlestick bullish/bearish scheme
+bar_colors = [BULLISH_COLOR if b else BEARISH_COLOR for b in df["bullish"]]
+ax2.bar(df["day_idx"], df["volume"], color=bar_colors, width=0.7, alpha=0.9, zorder=2)
+
+# Style the volume axis with units
+ax2.set_ylabel("Volume (M shares)", fontsize=20)
+ax2.set_xlabel("Date", fontsize=20)
+ax2.tick_params(axis="both", labelsize=16)
+
+# Format y-axis for volume (millions)
+ax2.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"{x / 1e6:.1f}M"))
+
+# === Aligned grid lines across both panes ===
+# Use consistent grid styling (grid already set below via set_axisbelow earlier)
+for ax in [ax1, ax2]:
+    ax.grid(True, axis="both", alpha=0.3, linestyle="--", linewidth=0.8)
+
+# Configure x-axis with date labels at regular intervals
+n_ticks = 6
+tick_positions = np.linspace(0, len(df) - 1, n_ticks, dtype=int)
+tick_labels = [df.iloc[i]["date"].strftime("%b %d") for i in tick_positions]
+ax2.set_xticks(tick_positions)
+ax2.set_xticklabels(tick_labels, rotation=45, ha="right")
+
+# Add legend to price pane
+legend_elements = [
+    Patch(facecolor=BULLISH_COLOR, label="Bullish (Close ≥ Open)"),
+    Patch(facecolor=BEARISH_COLOR, label="Bearish (Close < Open)"),
+]
+ax1.legend(handles=legend_elements, loc="upper left", fontsize=14, framealpha=0.9)
+
+# === Add crosshair cursor spanning both panes ===
+# Draw static crosshair lines at a representative position to show the feature
+# Using a position at approximately 2/3 of the chart for good visibility
+crosshair_idx = int(len(df) * 0.65)
+crosshair_price = (df.iloc[crosshair_idx]["high"] + df.iloc[crosshair_idx]["low"]) / 2
+crosshair_volume = df.iloc[crosshair_idx]["volume"]
+
+# Vertical crosshair line spanning both panes - more prominent styling
+crosshair_color = "#E63946"  # Red for high visibility
+for ax in [ax1, ax2]:
+    ax.axvline(x=crosshair_idx, color=crosshair_color, linestyle="--", linewidth=2, alpha=0.8, zorder=5)
+
+# Horizontal crosshair lines in both panes for precise reading
+ax1.axhline(y=crosshair_price, color=crosshair_color, linestyle="--", linewidth=2, alpha=0.8, zorder=5)
+ax2.axhline(y=crosshair_volume, color=crosshair_color, linestyle="--", linewidth=2, alpha=0.8, zorder=5)
+
+# Add crosshair label annotation for price pane with arrow
+ax1.annotate(
+    f"${crosshair_price:.2f}",
+    xy=(crosshair_idx, crosshair_price),
+    xytext=(crosshair_idx + 5, crosshair_price + (price_max - price_min) * 0.08),
+    fontsize=14,
+    fontweight="bold",
+    color=crosshair_color,
+    va="center",
+    arrowprops={"arrowstyle": "->", "color": crosshair_color, "lw": 1.5},
+    bbox={"boxstyle": "round,pad=0.4", "facecolor": "white", "edgecolor": crosshair_color, "alpha": 0.95},
+)
+
+# Add crosshair label annotation for volume pane with arrow
+ax2.annotate(
+    f"{crosshair_volume / 1e6:.1f}M shares",
+    xy=(crosshair_idx, crosshair_volume),
+    xytext=(crosshair_idx + 5, crosshair_volume * 1.15),
+    fontsize=14,
+    fontweight="bold",
+    color=crosshair_color,
+    va="center",
+    arrowprops={"arrowstyle": "->", "color": crosshair_color, "lw": 1.5},
+    bbox={"boxstyle": "round,pad=0.4", "facecolor": "white", "edgecolor": crosshair_color, "alpha": 0.95},
+)
+
+# Add date label at crosshair position
+crosshair_date = df.iloc[crosshair_idx]["date"].strftime("%b %d, %Y")
+ax2.annotate(
+    crosshair_date,
+    xy=(crosshair_idx, 0),
+    xytext=(crosshair_idx, -ax2.get_ylim()[1] * 0.15),
+    fontsize=12,
+    fontweight="bold",
+    color=crosshair_color,
+    ha="center",
+    va="top",
+    bbox={"boxstyle": "round,pad=0.3", "facecolor": "white", "edgecolor": crosshair_color, "alpha": 0.95},
+    annotation_clip=False,
+)
+
+# Adjust layout and save
+fig.subplots_adjust(left=0.08, right=0.95, top=0.92, bottom=0.12, hspace=0.05)
+plt.savefig("plot.png", dpi=300, bbox_inches="tight")

plots/candlestick-volume/metadata/seaborn.yaml

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+library: seaborn
+specification_id: candlestick-volume
+created: '2025-12-31T13:52:19Z'
+updated: '2025-12-31T14:44:44Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620312762
+issue: 3068
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/candlestick-volume/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/candlestick-volume/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 88
+review:
+  strengths:
+  - Excellent dual-pane layout with proper 75/25 height ratio as specified
+  - Good color scheme using Python blue/yellow that is colorblind accessible
+  - Clean seaborn styling with whitegrid and talk context provides professional appearance
+  - Realistic stock data generation with price-volume correlation
+  - Well-formatted axis labels with units and proper tick formatting for millions
+  weaknesses:
+  - Crosshair feature appears in code but is not visible in the rendered output image
+  - Legend placement in upper left may overlap with price data in some scenarios
+  - Volume axis label shows just Volume instead of Volume (M shares) as coded