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feat(seaborn): implement renko-basic (#3312)
## Implementation: `renko-basic` - seaborn Implements the **seaborn** version of `renko-basic`. **File:** `plots/renko-basic/implementations/seaborn.py` **Parent Issue:** #3294 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20823037316)* --------- 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/renko-basic/implementations/seaborn.py‎

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""" pyplots.ai
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renko-basic: Basic Renko Chart
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Library: seaborn 0.13.2 | Python 3.13.11
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Quality: 68/100 | Created: 2026-01-08
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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import seaborn as sns
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from matplotlib.patches import Rectangle
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# Set seaborn style
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sns.set_style("whitegrid")
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sns.set_context("talk", font_scale=1.2)
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# Generate synthetic stock price data
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np.random.seed(42)
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n_days = 250
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dates = pd.date_range("2025-01-01", periods=n_days, freq="D")
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# Simulate price movement with random walk
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returns = np.random.normal(0.0005, 0.015, n_days)
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prices = 100 * np.cumprod(1 + returns)
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df = pd.DataFrame({"date": dates, "close": prices})
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# Renko brick calculation
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brick_size = 2.0 # $2 per brick
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bricks = []
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# Start from first price
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current_price = df["close"].iloc[0]
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brick_high = np.floor(current_price / brick_size) * brick_size
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brick_low = brick_high
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for price in df["close"]:
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# Check for upward bricks
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while price >= brick_high + brick_size:
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brick_high += brick_size
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brick_low = brick_high - brick_size
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bricks.append({"direction": "up", "open": brick_low, "close": brick_high})
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# Check for downward bricks
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while price <= brick_low - brick_size:
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brick_low -= brick_size
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brick_high = brick_low + brick_size
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bricks.append({"direction": "down", "open": brick_high, "close": brick_low})
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renko_df = pd.DataFrame(bricks)
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# Colorblind-accessible colors (blue/orange scheme instead of red/green)
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bullish_color = "#1976D2" # Blue for up bricks (colorblind-safe)
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bearish_color = "#E65100" # Orange for down bricks (colorblind-safe)
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# Create figure
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fig, ax = plt.subplots(figsize=(16, 9))
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# Gap ratio for small separation between bricks
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gap_ratio = 0.08
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brick_width = 1.0 - gap_ratio
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# Draw bricks in proper stair-step pattern
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# Each brick is positioned at (x, bottom) with height = brick_size
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# The key for Renko: next brick starts at the SAME y-level where previous ended
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bullish_patches = []
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bearish_patches = []
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for idx, row in renko_df.iterrows():
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bottom = min(row["open"], row["close"])
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x_pos = idx + gap_ratio / 2 # Center the brick with gap
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rect = Rectangle((x_pos, bottom), brick_width, brick_size, linewidth=1.5, edgecolor="white")
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if row["direction"] == "up":
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rect.set_facecolor(bullish_color)
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bullish_patches.append(rect)
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else:
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rect.set_facecolor(bearish_color)
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bearish_patches.append(rect)
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ax.add_patch(rect)
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# Add legend handles with updated labels
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legend_handles = [
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Rectangle((0, 0), 1, 1, facecolor=bullish_color, edgecolor="white", label="Bullish (Up)"),
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Rectangle((0, 0), 1, 1, facecolor=bearish_color, edgecolor="white", label="Bearish (Down)"),
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]
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# Set axis limits with padding
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price_min = renko_df[["open", "close"]].min().min() - brick_size
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price_max = renko_df[["open", "close"]].max().max() + brick_size
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ax.set_ylim(price_min, price_max)
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ax.set_xlim(-0.5, len(renko_df) + 0.5)
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# Labels and styling - more descriptive axis labels
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ax.set_xlabel("Brick Number", fontsize=20)
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ax.set_ylabel("Stock Price (USD)", fontsize=20)
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ax.set_title("renko-basic · seaborn · pyplots.ai", fontsize=24, fontweight="bold")
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ax.tick_params(axis="both", labelsize=16)
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# Add legend positioned outside plot area to avoid data overlap
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ax.legend(handles=legend_handles, fontsize=16, loc="upper right", framealpha=0.9)
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# Add subtle grid on y-axis only
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ax.grid(True, alpha=0.3, linestyle="--", axis="y")
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ax.set_axisbelow(True)
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# Simplify x-axis ticks for cleaner look
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tick_step = max(1, len(renko_df) // 10)
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ax.set_xticks(range(0, len(renko_df), tick_step))
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# Add annotation about brick size
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ax.annotate(
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f"Brick Size: ${brick_size:.0f}",
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xy=(0.98, 0.02),
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xycoords="axes fraction",
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fontsize=14,
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ha="right",
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va="bottom",
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bbox={"boxstyle": "round,pad=0.5", "facecolor": "white", "edgecolor": "gray", "alpha": 0.8},
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)
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/renko-basic/metadata/seaborn.yaml‎

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library: seaborn
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specification_id: renko-basic
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created: '2026-01-08T16:02:10Z'
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updated: '2026-01-08T16:32:04Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20823037316
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issue: 3294
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python_version: 3.13.11
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library_version: 0.13.2
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preview_url: https://storage.googleapis.com/pyplots-images/plots/renko-basic/seaborn/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/renko-basic/seaborn/plot_thumb.png
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preview_html: null
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quality_score: 68
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review:
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strengths:
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- Correct Renko chart algorithm with proper stair-step brick positioning
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- Good data generation showing realistic price movements with multiple trend reversals
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- Clean code structure following KISS principles
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- Appropriate brick sizing and gaps for visual clarity
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- Helpful annotation showing brick size
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weaknesses:
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- Title format uses hyphens instead of required middle dots (·)
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- 'Color mismatch: code defines colorblind-safe blue/orange but rendered image shows
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traditional red/green'
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- Seaborn is only used for styling - no seaborn plotting functions are utilized
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image_description: 'The plot displays a Renko chart showing stock price movements
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over approximately 40 bricks. Green bricks represent bullish (upward) price movements
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while red bricks represent bearish (downward) movements. The chart shows a clear
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pattern: an initial rise from ~$100 to ~$105, followed by a significant decline
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to ~$87, then a recovery back up to ~$112. Bricks are uniform in size ($2 each
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as noted in the annotation), clearly separated with small gaps, and properly stair-stepped.
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The Y-axis shows "Stock Price (USD)" ranging from ~$85 to ~$112, and the X-axis
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shows "Brick Number" from 0 to ~40. A legend in the upper left identifies the
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brick colors. An annotation in the lower right displays "Brick Size: $2". The
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title reads "renko-basic - seaborn - pyplots.ai" using hyphens instead of middle
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dots.'
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criteria_checklist:
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visual_quality:
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score: 32
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max: 40
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items:
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- id: VQ-01
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name: Text Legibility
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score: 10
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max: 10
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passed: true
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comment: Title at 24pt, labels at 20pt, ticks at 16pt, all perfectly readable
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- id: VQ-02
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name: No Overlap
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score: 8
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max: 8
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passed: true
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comment: No overlapping text elements
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- id: VQ-03
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name: Element Visibility
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score: 8
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max: 8
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passed: true
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comment: Bricks are well-sized and clearly visible with appropriate gaps
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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: 5
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passed: false
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comment: Uses red-green color scheme instead of colorblind-safe colors
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- id: VQ-05
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name: Layout Balance
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score: 5
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max: 5
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passed: true
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comment: Good proportions, plot fills canvas appropriately
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- id: VQ-06
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name: Axis Labels
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score: 2
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max: 2
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passed: true
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comment: 'Descriptive labels with units: Stock Price (USD), Brick Number'
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- id: VQ-07
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name: Grid & Legend
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score: 0
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max: 2
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passed: false
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comment: Legend uses wrong colors (shows green/red but code defines blue/orange)
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spec_compliance:
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score: 21
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max: 25
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items:
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- id: SC-01
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name: Plot Type
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score: 8
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max: 8
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passed: true
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comment: Correct Renko chart implementation
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- id: SC-02
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name: Data Mapping
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score: 5
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max: 5
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passed: true
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comment: Brick index on X, price on Y correctly assigned
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- id: SC-03
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name: Required Features
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score: 5
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max: 5
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passed: true
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comment: 'All features present: bullish/bearish bricks, uniform size, gaps,
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grid, legend'
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- id: SC-04
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name: Data Range
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score: 3
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max: 3
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passed: true
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comment: Axes show all data with appropriate padding
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- id: SC-05
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name: Legend Accuracy
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score: 2
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max: 2
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passed: true
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comment: Legend labels correctly identify bullish (Up) and bearish (Down)
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- id: SC-06
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name: Title Format
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score: 0
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max: 2
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passed: false
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comment: Uses hyphens instead of required middle dots (·)
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data_quality:
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score: 18
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max: 20
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items:
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- id: DQ-01
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name: Feature Coverage
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score: 8
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max: 8
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passed: true
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comment: Shows both bullish and bearish movements, multiple trend reversals,
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clear patterns
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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: 7
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passed: true
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comment: Plausible stock price scenario with realistic volatility pattern
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- id: DQ-03
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name: Appropriate Scale
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score: 5
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max: 5
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passed: true
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comment: Sensible stock price values around $100 with $2 brick size
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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: Follows imports → data → plot → save structure, no functions/classes
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- id: CQ-02
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name: Reproducibility
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score: 3
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max: 3
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passed: true
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comment: Uses np.random.seed(42)
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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 are used
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- id: CQ-04
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name: No Deprecated API
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score: 1
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max: 1
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passed: true
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comment: Uses current seaborn/matplotlib API
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- id: CQ-05
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name: Output Correct
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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.png
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library_features:
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score: 0
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max: 5
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items:
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- id: LF-01
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name: Distinctive Features
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score: 0
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max: 5
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passed: false
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comment: Seaborn is only used for styling (set_style, set_context). All plotting
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is done with matplotlib patches. No seaborn plotting functions are used.
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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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- patches
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- custom-legend
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- annotations
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- manual-ticks
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patterns:
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- data-generation
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- iteration-over-groups
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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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- edge-highlighting

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