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feat(plotnine): implement candlestick-volume (#6881)
## Implementation: `candlestick-volume` - python/plotnine Implements the **python/plotnine** version of `candlestick-volume`. **File:** `plots/candlestick-volume/implementations/python/plotnine.py` **Parent Issue:** #3068 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/anyplot/actions/runs/25953797474)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Markus Neusinger <2921697+MarkusNeusinger@users.noreply.github.com>
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‎plots/candlestick-volume/implementations/python/plotnine.py‎

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""" anyplot.ai
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candlestick-volume: Stock Candlestick Chart with Volume
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Library: plotnine 0.15.4 | Python 3.13.13
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Quality: 91/100 | Created: 2026-05-16
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"""
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import sys
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# Remove the current directory from sys.path to avoid shadowing the installed plotnine package
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while "" in sys.path:
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sys.path.remove("")
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cwd = __file__[: __file__.rfind("/")]
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while cwd in sys.path:
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sys.path.remove(cwd)
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# Now import plotnine
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import os # noqa: E402
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import numpy as np # noqa: E402
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import pandas as pd # noqa: E402
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from plotnine import ( # noqa: E402
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aes,
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element_blank,
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element_line,
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element_rect,
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element_text,
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facet_grid,
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geom_col,
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geom_rect,
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geom_segment,
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ggplot,
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ggsave,
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labs,
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scale_color_manual,
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scale_fill_manual,
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theme,
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theme_minimal,
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)
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# Theme tokens
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THEME = os.getenv("ANYPLOT_THEME", "light")
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PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
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INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
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INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
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# Okabe-Ito palette: Up (green), Down (orange)
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UP_COLOR = "#009E73"
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DOWN_COLOR = "#D55E00"
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# Generate realistic OHLC data
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np.random.seed(42)
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n_periods = 60
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dates = pd.date_range("2024-01-01", periods=n_periods, freq="D")
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# Create price movement with realistic patterns
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returns = np.random.normal(0.001, 0.02, n_periods)
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close_prices = 100 * np.exp(np.cumsum(returns))
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open_prices = close_prices * (1 + np.random.normal(0, 0.01, n_periods))
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high_prices = np.maximum(open_prices, close_prices) + np.abs(np.random.normal(0, 0.5, n_periods))
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low_prices = np.minimum(open_prices, close_prices) - np.abs(np.random.normal(0, 0.5, n_periods))
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volumes = np.random.exponential(1e6, n_periods)
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# Create main dataframe
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df = pd.DataFrame(
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{
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"date": dates,
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"open": open_prices,
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"high": high_prices,
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"low": low_prices,
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"close": close_prices,
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"volume": volumes,
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}
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)
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# Add direction (up/down) and position columns for rectangles
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df["direction"] = df["close"] >= df["open"]
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df["direction_label"] = df["direction"].map({True: "Up", False: "Down"})
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df["x_min"] = df["date"] - pd.Timedelta(hours=12)
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df["x_max"] = df["date"] + pd.Timedelta(hours=12)
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# Create separate dataframes for faceting
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df_price = df.copy()
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df_price["pane"] = "Price (OHLC)"
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df_volume = df.copy()
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df_volume["pane"] = "Trading Volume"
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# Create the faceted plot
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plot = (
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ggplot()
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# Candlestick wicks (high-low lines)
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+ geom_segment(
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aes(x="date", y="low", xend="date", yend="high", color="direction_label"),
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data=df_price,
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size=0.8,
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show_legend=False,
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)
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# Candlestick bodies (open-close rectangles)
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+ geom_rect(
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aes(xmin="x_min", xmax="x_max", ymin="open", ymax="close", fill="direction_label"),
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data=df_price,
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show_legend=False,
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)
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# Volume bars
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+ geom_col(aes(x="date", y="volume", fill="direction_label"), data=df_volume, show_legend=False)
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# Facet with free y-scales for price and volume
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+ facet_grid("pane ~ .", scales="free_y")
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# Color scales
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+ scale_color_manual(values=[DOWN_COLOR, UP_COLOR])
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+ scale_fill_manual(values=[DOWN_COLOR, UP_COLOR])
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# Labels and title
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+ labs(title="candlestick-volume · plotnine · anyplot.ai", x="Date", y="")
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# Theme
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+ theme_minimal()
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+ theme(
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figure_size=(16, 9),
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plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
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panel_background=element_rect(fill=PAGE_BG),
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panel_border=element_rect(color=INK_SOFT, fill=None, size=0.3),
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axis_title=element_text(size=20, color=INK),
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axis_text=element_text(size=16, color=INK_SOFT),
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plot_title=element_text(size=24, color=INK, weight="medium"),
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strip_text_y=element_text(size=18, color=INK),
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panel_grid_major_y=element_line(color=INK_SOFT, size=0.3, alpha=0.15),
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panel_grid_major_x=element_blank(),
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panel_grid_minor=element_blank(),
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)
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)
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# Save with theme-suffixed filename to script directory
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script_dir = __file__[: __file__.rfind("/")]
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ggsave(plot, f"{script_dir}/plot-{THEME}.png", dpi=300)

‎plots/candlestick-volume/metadata/python/plotnine.yaml‎

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library: plotnine
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language: python
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specification_id: candlestick-volume
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created: '2026-05-16T05:29:51Z'
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updated: '2026-05-16T05:37:56Z'
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generated_by: claude-haiku
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workflow_run: 25953797474
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issue: 3068
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python_version: 3.13.13
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library_version: 0.15.4
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preview_url_light: https://storage.googleapis.com/anyplot-images/plots/candlestick-volume/python/plotnine/plot-light.png
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preview_url_dark: https://storage.googleapis.com/anyplot-images/plots/candlestick-volume/python/plotnine/plot-dark.png
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preview_html_light: null
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preview_html_dark: null
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quality_score: 91
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review:
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strengths:
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- Perfect visual quality with explicit font sizing throughout (title 24pt, labels
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20pt, ticks 16pt)
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- 'Accurate theme adaptation: identical data colors across light/dark, theme-correct
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chrome throughout'
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- 'Excellent spec compliance: all features present (dual-pane layout, proper proportions,
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OHLC candlesticks, volume bars, color scheme)'
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- Clean code structure with proper data generation (seed=42), deterministic, no
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over-engineering
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- 'Strong idiomatic plotnine usage: layered geoms (segment+rect+col), facet_grid
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with free_y scales, proper theming with element_* functions'
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weaknesses:
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- 'Design Excellence (DE-01) scores 4/8 (generic defaults): implementation is technically
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excellent but uses standard library styling without custom aesthetic choices;
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could add unique visual refinement'
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image_description: |-
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Light render (plot-light.png):
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Background: Warm off-white (#FAF8F1) — correct
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Chrome: Title in dark text (24pt, legible); axis labels "Date", "Price (OHLC)", "Trading Volume" (20pt, clear); tick labels in soft gray (#4A4A44, 16pt, readable); grid lines subtle at alpha=0.15; panel borders delicate (#4A4A44, 0.3pt)
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Data: Candlestick wicks (segments, thin lines) show high-low ranges; candlestick bodies (rectangles) show open-close; green (#009E73) for up candles, orange (#D55E00) for down candles; volume bars below match up/down color scheme; date axis spans 2024-01-01 to 2024-03-01 (60 periods)
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Legibility verdict: PASS — all text readable, no overlap, data elements clearly distinguishable
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Dark render (plot-dark.png):
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Background: Warm near-black (#1A1A17) — correct
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Chrome: Title in light text (#F0EFE8, 24pt, legible); axis labels in light text (#F0EFE8, 20pt); tick labels in soft light gray (#B8B7B0, 16pt, readable); grid lines subtle; panel borders same delicate style
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Data: Candlestick colors identical to light render — green (#009E73) for up, orange (#D55E00) for down (confirmed: Okabe-Ito positions 1-2 unchanged across themes); volume bars color-consistent; wicks and bodies clearly visible; no dark-on-dark failures (no black text on near-black background)
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Legibility verdict: PASS — all chrome properly adapted, no readability issues, data colors confirmed identical to light render
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criteria_checklist:
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visual_quality:
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score: 30
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max: 30
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items:
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- id: VQ-01
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name: Text Legibility
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score: 8
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max: 8
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passed: true
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comment: 'All font sizes explicitly set: title 24pt, labels 20pt, ticks 16pt,
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all perfectly readable in both themes'
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- id: VQ-02
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name: No Overlap
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score: 6
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max: 6
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passed: true
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comment: No overlapping text, all elements fully readable
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- id: VQ-03
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name: Element Visibility
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score: 6
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max: 6
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passed: true
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comment: Wicks, bodies, and volume bars optimally sized for 60-period dataset
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- id: VQ-04
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name: Color Accessibility
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score: 2
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max: 2
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passed: true
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comment: Okabe-Ito palette is CVD-safe; green and orange have sufficient contrast
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- id: VQ-05
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name: Layout & Canvas
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score: 4
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max: 4
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passed: true
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comment: 16x9 landscape (4800x2700px), balanced margins, proper pane proportions
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- id: VQ-06
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name: Axis Labels & Title
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score: 2
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max: 2
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passed: true
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comment: Title format correct; Date axis descriptive; facet labels provide
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pane context
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- id: VQ-07
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name: Palette Compliance
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score: 2
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max: 2
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passed: true
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comment: 'Up=#009E73 (Okabe-Ito pos 1), Down=#D55E00 (pos 2); backgrounds
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#FAF8F1/#1A1A17; theme-correct chrome in both renders'
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design_excellence:
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score: 12
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max: 20
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items:
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- id: DE-01
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name: Aesthetic Sophistication
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score: 4
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max: 8
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passed: false
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comment: Well-configured library defaults, professional but no exceptional
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design flourishes
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- id: DE-02
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name: Visual Refinement
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score: 4
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max: 6
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passed: true
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comment: Subtle y-axis grid (alpha=0.15), minimal spines via theme_minimal(),
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good whitespace
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- id: DE-03
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name: Data Storytelling
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score: 4
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max: 6
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passed: true
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comment: Faceted layout clearly shows price-volume relationship; visual hierarchy
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(price 70-75%, volume 25-30%); color contrast guides reader
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spec_compliance:
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score: 15
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max: 15
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items:
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- id: SC-01
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name: Plot Type
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score: 5
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max: 5
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passed: true
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comment: Correct candlestick chart with volume bars, dual-pane layout
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- id: SC-02
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name: Required Features
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score: 4
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max: 4
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passed: true
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comment: 'All features present: OHLC candlesticks, volume bars, shared x-axis,
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up/down colors, grid lines'
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- id: SC-03
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name: Data Mapping
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score: 3
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max: 3
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passed: true
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comment: X=date, Price pane Y=OHLC, Volume pane Y=volume; all data visible
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- id: SC-04
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name: Title & Legend
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score: 3
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max: 3
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passed: true
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comment: 'Title: ''candlestick-volume · plotnine · anyplot.ai''; legend omitted
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appropriately (color scheme self-explanatory)'
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data_quality:
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score: 15
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max: 15
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 6
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max: 6
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passed: true
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comment: Shows both up and down candles, realistic OHLC ranges, volume variations,
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all aspects covered
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- id: DQ-02
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name: Realistic Context
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score: 5
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max: 5
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passed: true
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comment: Stock price data (lognormal returns), trading volume (exponential),
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neutral business context
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- id: DQ-03
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name: Appropriate Scale
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score: 4
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max: 4
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passed: true
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comment: Price range ~$87-$112 (realistic), volume ~$100k-$3M (realistic),
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proportions factually sound
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code_quality:
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score: 10
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max: 10
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items:
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- id: CQ-01
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name: KISS Structure
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score: 3
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max: 3
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passed: true
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comment: 'Linear: imports → data → plot → save; no functions/classes'
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- id: CQ-02
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name: Reproducibility
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score: 2
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max: 2
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passed: true
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comment: Seed 42 set, fully deterministic
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- id: CQ-03
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name: Clean Imports
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score: 2
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max: 2
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passed: true
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comment: All imports used, no bloat
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- id: CQ-04
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name: Code Elegance
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score: 2
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max: 2
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passed: true
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comment: Pythonic, appropriate complexity, no fake functionality
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- id: CQ-05
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name: Output & API
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score: 1
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max: 1
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passed: true
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comment: Saves as plot-{THEME}.png via ggsave(), current API
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library_mastery:
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score: 9
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max: 10
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items:
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- id: LM-01
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name: Idiomatic Usage
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score: 5
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max: 5
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passed: true
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comment: 'Expert idiomatic plotnine: ggplot() + geom_segment + geom_rect +
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geom_col + facet_grid + theme()'
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- id: LM-02
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name: Distinctive Features
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score: 4
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max: 5
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passed: true
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comment: Leverages facet_grid with free_y scales (plotnine strength), layered
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geoms composition
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verdict: APPROVED
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impl_tags:
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dependencies: []
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techniques:
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- faceting
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- layer-composition
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patterns:
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- data-generation
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dataprep:
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- time-series
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styling:
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- grid-styling
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- publication-ready

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