feat(letsplot): implement candlestick-volume

plots/candlestick-volume/implementations/python/letsplot.py

@@ -1,51 +1,52 @@
-""" pyplots.ai
+""" anyplot.ai
 candlestick-volume: Stock Candlestick Chart with Volume
-Library: letsplot 4.8.2 | Python 3.13.11
-Quality: 91/100 | Created: 2025-12-31
+Library: letsplot 4.9.0 | Python 3.13.13
+Quality: 90/100 | Updated: 2026-05-16
 """
 
+import os
+
 import numpy as np
 import pandas as pd
 from lets_plot import *
 
 
 LetsPlot.setup_html()
 
-
-# Format volume as human-readable (e.g., 5.0M instead of 5000000)
-def format_volume(val):
-    if val >= 1_000_000:
-        return f"{val / 1_000_000:.1f}M"
-    elif val >= 1_000:
-        return f"{val / 1_000:.0f}K"
-    return str(int(val))
-
+THEME = os.getenv("ANYPLOT_THEME", "light")
+PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
+ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
+INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
+INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
 
 # Data - 60 trading days of synthetic stock data
 np.random.seed(42)
 n_days = 60
 dates = pd.date_range("2024-01-02", periods=n_days, freq="B")
 
-# Generate realistic price movement with trend and volatility
-returns = np.random.normal(0.001, 0.02, n_days)
+# Generate realistic price movement with more dramatic reversals
+returns = np.random.normal(0.001, 0.025, n_days)
+returns[15] = -0.08  # Dramatic drop
+returns[35] = 0.06  # Strong bounce
+returns[45] = -0.05  # Another reversal
 close_prices = 150 * np.cumprod(1 + returns)
 
 # Generate OHLC from close prices
 open_prices = np.roll(close_prices, 1)
 open_prices[0] = 150
-high_prices = np.maximum(open_prices, close_prices) * (1 + np.abs(np.random.normal(0, 0.01, n_days)))
-low_prices = np.minimum(open_prices, close_prices) * (1 - np.abs(np.random.normal(0, 0.01, n_days)))
+high_prices = np.maximum(open_prices, close_prices) * (1 + np.abs(np.random.normal(0, 0.012, n_days)))
+low_prices = np.minimum(open_prices, close_prices) * (1 - np.abs(np.random.normal(0, 0.012, n_days)))
 
-# Generate volume with some correlation to price movement
+# Generate volume with correlation to price movement
 base_volume = 5_000_000
 volatility = np.abs(close_prices - open_prices) / open_prices
 volume = base_volume * (1 + volatility * 10 + np.random.uniform(-0.3, 0.3, n_days))
 volume = volume.astype(int)
 
-# Determine up/down days for coloring (shorter labels to avoid truncation)
-direction = ["Up Day" if c >= o else "Down Day" for c, o in zip(close_prices, open_prices)]
+# Determine up/down days for coloring
+direction = ["Up" if c >= o else "Down" for c, o in zip(close_prices, open_prices)]
 
-# Create date labels for x-axis (show every 10th trading day)
+# Create date labels for x-axis (show every 10 trading days)
 date_labels = [d.strftime("%b %d") for d in dates]
 date_breaks = list(range(0, n_days, 10))
 date_tick_labels = [date_labels[i] for i in date_breaks]
@@ -64,69 +65,67 @@ def format_volume(val):
     }
 )
 
-# Colorblind-safe colors (blue for up, orange for down)
-color_up = "#0077BB"
-color_down = "#EE7733"
+# Colorblind-safe colors (first series Okabe-Ito, second is orange)
+color_up = "#009E73"
+color_down = "#D55E00"
+
+# Create volume breaks and labels (inline formatting)
+vol_min, vol_max = df["volume"].min(), df["volume"].max()
+vol_breaks = [int(vol_min), int((vol_min + vol_max) / 2), int(vol_max)]
+vol_labels = [f"{v / 1_000_000:.1f}M" if v >= 1_000_000 else f"{v / 1_000:.0f}K" for v in vol_breaks]
+
+# Theme-adaptive styling
+anyplot_theme = theme(
+    plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
+    panel_background=element_rect(fill=PAGE_BG),
+    panel_grid_major=element_line(color=INK_SOFT, size=0.25),
+    panel_grid_minor=element_blank(),
+    axis_title=element_text(color=INK, size=20),
+    axis_text=element_text(color=INK_SOFT, size=16),
+    axis_line=element_line(color=INK_SOFT, size=0.3),
+    plot_title=element_text(color=INK, size=24),
+    legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
+    legend_text=element_text(color=INK_SOFT, size=16),
+    legend_title=element_text(color=INK, size=18),
+)
 
 # Create candlestick chart (main pane)
 candle_plot = (
     ggplot(df)
-    # Wicks (high-low lines)
-    + geom_segment(aes(x="date_idx", xend="date_idx", y="low", yend="high", color="direction"), size=1.0)
+    # Wicks (high-low lines) - thicker for visibility
+    + geom_segment(aes(x="date_idx", xend="date_idx", y="low", yend="high", color="direction"), size=1.5)
     # Bodies (open-close rectangles)
-    + geom_segment(aes(x="date_idx", xend="date_idx", y="open", yend="close", color="direction"), size=5.0)
-    + scale_color_manual(values={"Up Day": color_up, "Down Day": color_down}, name="Direction")
+    + geom_segment(aes(x="date_idx", xend="date_idx", y="open", yend="close", color="direction"), size=6.0)
+    + scale_color_manual(values={"Up": color_up, "Down": color_down}, name="Direction")
     + scale_x_continuous(breaks=date_breaks, labels=date_tick_labels)
-    + labs(title="candlestick-volume · letsplot · pyplots.ai", y="Price ($)", x="")
-    # Enable crosshair cursor for precise reading
-    + coord_cartesian()
-    + theme_minimal()
+    + labs(title="Stock Trading · candlestick-volume · letsplot · anyplot.ai", y="Price ($)", x="")
+    + anyplot_theme
     + theme(
-        plot_title=element_text(size=24),
-        axis_title_y=element_text(size=20),
-        axis_text_y=element_text(size=16),
         axis_text_x=element_blank(),
-        legend_position=[0.5, 0.98],
+        legend_position=[0.5, 0.95],
         legend_justification=[0.5, 1.0],
         legend_direction="horizontal",
-        legend_title=element_text(size=18),
-        legend_text=element_text(size=16),
-        panel_grid_major=element_line(color="#E5E7EB", size=0.5),
-        panel_grid_minor=element_blank(),
-        plot_margin=[40, 20, 5, 10],
+        plot_margin=[40, 20, 2, 10],
     )
     + ggsize(1600, 630)
 )
 
-# Create volume breaks and labels for human-readable format
-vol_min, vol_max = df["volume"].min(), df["volume"].max()
-vol_breaks = [int(vol_min), int((vol_min + vol_max) / 2), int(vol_max)]
-vol_labels = [format_volume(v) for v in vol_breaks]
-
 # Volume chart (lower pane)
 volume_plot = (
     ggplot(df)
     + geom_bar(aes(x="date_idx", y="volume", fill="direction"), stat="identity", width=0.8)
-    + scale_fill_manual(values={"Up Day": color_up, "Down Day": color_down}, name="Direction")
+    + scale_fill_manual(values={"Up": color_up, "Down": color_down}, name="Direction")
     + scale_x_continuous(breaks=date_breaks, labels=date_tick_labels)
     + scale_y_continuous(breaks=vol_breaks, labels=vol_labels)
     + labs(x="Date (2024)", y="Volume (shares)")
-    # Enable crosshair cursor for precise reading (interactive HTML)
-    + coord_cartesian()
-    + theme_minimal()
-    + theme(
-        axis_title=element_text(size=20),
-        axis_text=element_text(size=16),
-        legend_position="none",
-        panel_grid_major=element_line(color="#E5E7EB", size=0.5),
-        panel_grid_minor=element_blank(),
-    )
+    + anyplot_theme
+    + theme(legend_position="none", plot_margin=[2, 20, 10, 10])
     + ggsize(1600, 270)
 )
 
-# Use gggrid for dual-pane layout (replaces deprecated GGBunch)
+# Use gggrid for dual-pane layout with tighter spacing
 combined = gggrid([candle_plot, volume_plot], ncol=1, heights=[0.7, 0.3])
 
-# Save outputs (path='' ensures files are saved in current directory)
-ggsave(combined, "plot.png", scale=3, path=".")
-ggsave(combined, "plot.html", path=".")
+# Save outputs
+ggsave(combined, f"plot-{THEME}.png", scale=3, path=".")
+ggsave(combined, f"plot-{THEME}.html", path=".")