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feat(plotly): implement candlestick-volume (#3103)
## Implementation: `candlestick-volume` - plotly Implements the **plotly** version of `candlestick-volume`. **File:** `plots/candlestick-volume/implementations/plotly.py` **Parent Issue:** #3068 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620310502)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/candlestick-volume/implementations/plotly.py‎

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""" pyplots.ai
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candlestick-volume: Stock Candlestick Chart with Volume
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Library: plotly 6.5.0 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-31
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"""
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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# Data - Generate 60 trading days of realistic OHLC + volume data
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np.random.seed(42)
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n_days = 60
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dates = pd.date_range("2024-01-02", periods=n_days, freq="B") # Business days
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# Start price and generate random walk
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start_price = 150.0
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returns = np.random.normal(0.0005, 0.02, n_days)
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prices = start_price * np.cumprod(1 + returns)
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# Generate OHLC from close prices
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close = prices
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open_prices = np.roll(close, 1)
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open_prices[0] = start_price
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# Generate high/low with realistic intraday range
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daily_range = np.abs(np.random.normal(0.015, 0.008, n_days)) * close
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high = np.maximum(open_prices, close) + daily_range * np.random.uniform(0.3, 0.7, n_days)
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low = np.minimum(open_prices, close) - daily_range * np.random.uniform(0.3, 0.7, n_days)
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# Volume with correlation to price movement (higher volume on bigger moves)
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base_volume = 5_000_000
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price_change = np.abs(close - open_prices) / open_prices
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volume = base_volume * (1 + 3 * price_change) * np.random.uniform(0.7, 1.3, n_days)
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volume = volume.astype(int)
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# Determine up/down days for coloring
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is_up = close >= open_prices
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colors = ["#306998" if up else "#FFD43B" for up in is_up]
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# Create subplot with shared x-axis
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# Price pane: 75% height, Volume pane: 25% height
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fig = make_subplots(rows=2, cols=1, shared_xaxes=True, vertical_spacing=0.03, row_heights=[0.75, 0.25])
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# Add candlestick chart
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fig.add_trace(
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go.Candlestick(
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x=dates,
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open=open_prices,
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high=high,
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low=low,
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close=close,
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increasing={"line": {"color": "#306998", "width": 2}, "fillcolor": "#306998"},
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decreasing={"line": {"color": "#FFD43B", "width": 2}, "fillcolor": "#FFD43B"},
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name="Price",
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showlegend=False,
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),
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row=1,
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col=1,
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)
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# Add volume bars
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fig.add_trace(
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go.Bar(x=dates, y=volume, marker={"color": colors, "line": {"width": 0}}, name="Volume", showlegend=False),
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row=2,
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col=1,
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)
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# Update layout for professional appearance
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fig.update_layout(
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title={"text": "candlestick-volume · plotly · pyplots.ai", "font": {"size": 32}, "x": 0.5, "xanchor": "center"},
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template="plotly_white",
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hovermode="x unified",
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# Add crosshair cursor
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xaxis={
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"showspikes": True,
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"spikemode": "across",
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"spikesnap": "cursor",
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"spikecolor": "#888888",
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"spikethickness": 1,
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"spikedash": "solid",
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},
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xaxis2={
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"showspikes": True,
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"spikemode": "across",
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"spikesnap": "cursor",
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"spikecolor": "#888888",
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"spikethickness": 1,
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"spikedash": "solid",
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},
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yaxis={
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"showspikes": True,
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"spikemode": "across",
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"spikesnap": "cursor",
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"spikecolor": "#888888",
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"spikethickness": 1,
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"spikedash": "solid",
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},
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yaxis2={
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"showspikes": True,
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"spikemode": "across",
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"spikesnap": "cursor",
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"spikecolor": "#888888",
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"spikethickness": 1,
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"spikedash": "solid",
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},
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# Disable range slider
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xaxis_rangeslider_visible=False,
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# Margins for proper spacing
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margin={"l": 80, "r": 40, "t": 100, "b": 60},
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)
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# Update axes styling
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fig.update_xaxes(
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title={"text": "Date", "font": {"size": 22}},
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tickfont={"size": 18},
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gridcolor="rgba(128,128,128,0.2)",
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gridwidth=1,
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row=2,
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col=1,
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)
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fig.update_yaxes(
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title={"text": "Price ($)", "font": {"size": 22}},
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tickfont={"size": 18},
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tickformat="$.0f",
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gridcolor="rgba(128,128,128,0.2)",
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gridwidth=1,
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row=1,
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col=1,
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)
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fig.update_yaxes(
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title={"text": "Volume", "font": {"size": 22}},
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tickfont={"size": 18},
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tickformat=".2s",
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gridcolor="rgba(128,128,128,0.2)",
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gridwidth=1,
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row=2,
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col=1,
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)
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# Hide x-axis title on price pane
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fig.update_xaxes(title=None, row=1, col=1)
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# Save as PNG (4800 x 2700 pixels)
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fig.write_image("plot.png", width=1600, height=900, scale=3)
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# Save as HTML for interactivity
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fig.write_html("plot.html", include_plotlyjs="cdn")

‎plots/candlestick-volume/metadata/plotly.yaml‎

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library: plotly
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specification_id: candlestick-volume
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created: '2025-12-31T13:54:24Z'
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updated: '2025-12-31T14:05:27Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20620310502
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issue: 3068
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python_version: 3.13.11
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library_version: 6.5.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/candlestick-volume/plotly/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/candlestick-volume/plotly/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/candlestick-volume/plotly/plot.html
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quality_score: 92
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review:
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strengths:
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- Excellent dual-pane layout with proper 75/25 vertical split as specified
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- 'Professional use of Plotly features: unified hover, crosshair spikes spanning
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both panes, shared x-axis'
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- Color scheme is consistent between candlesticks and volume bars for visual coherence
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- Realistic OHLC data generation with volume correlated to price movement magnitude
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- Both PNG and HTML outputs for static and interactive viewing
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- Clean, well-organized code following KISS principles
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weaknesses:
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- Volume axis label lacks units (should be "Volume (shares)" or similar)
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- No legend or annotation explaining that blue=up day, yellow=down day

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