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feat(matplotlib): implement point-and-figure-basic (#3876)
## Implementation: `point-and-figure-basic` - matplotlib Implements the **matplotlib** version of `point-and-figure-basic`. **File:** `plots/point-and-figure-basic/implementations/matplotlib.py` **Parent Issue:** #3755 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/21049143205)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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‎plots/point-and-figure-basic/implementations/matplotlib.py‎

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""" pyplots.ai
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point-and-figure-basic: Point and Figure Chart
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Library: matplotlib 3.10.8 | Python 3.13.11
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Quality: 91/100 | Created: 2026-01-15
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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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from matplotlib.lines import Line2D
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# Generate synthetic price data simulating stock price movements
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np.random.seed(42)
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n_days = 300
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# Starting price and random walk with trend
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base_price = 100
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returns = np.random.normal(0.001, 0.02, n_days) # Daily returns
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close = base_price * np.cumprod(1 + returns)
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# Generate high/low around close
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volatility = np.abs(np.random.normal(0, 0.01, n_days))
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high = close * (1 + volatility)
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low = close * (1 - volatility)
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# P&F chart parameters
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box_size = 2.0 # Price increment per box
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reversal = 3 # Boxes needed to reverse
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# Build P&F columns from price data
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columns = [] # Each column: {'type': 'X' or 'O', 'start': price, 'end': price}
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current_col = None
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current_price = None
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for i in range(len(close)):
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price = close[i]
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if current_col is None:
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# Initialize first column based on price direction
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current_col = {
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"type": "X",
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"start": np.floor(price / box_size) * box_size,
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"end": np.floor(price / box_size) * box_size,
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}
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current_price = current_col["end"]
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continue
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if current_col["type"] == "X":
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# In an X (up) column
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new_high = np.floor(price / box_size) * box_size
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if new_high > current_col["end"]:
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# Extend column up
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current_col["end"] = new_high
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current_price = new_high
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elif price <= current_price - reversal * box_size:
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# Reversal to O column
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columns.append(current_col.copy())
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new_start = current_col["end"] - box_size # Start one box below
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new_end = np.ceil(price / box_size) * box_size
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current_col = {"type": "O", "start": new_start, "end": new_end}
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current_price = new_end
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else:
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# In an O (down) column
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new_low = np.ceil(price / box_size) * box_size
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if new_low < current_col["end"]:
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# Extend column down
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current_col["end"] = new_low
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current_price = new_low
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elif price >= current_price + reversal * box_size:
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# Reversal to X column
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columns.append(current_col.copy())
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new_start = current_col["end"] + box_size # Start one box above
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new_end = np.floor(price / box_size) * box_size
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current_col = {"type": "X", "start": new_start, "end": new_end}
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current_price = new_end
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# Append last column
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if current_col is not None:
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columns.append(current_col)
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# Create figure
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fig, ax = plt.subplots(figsize=(16, 9))
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# Define colors
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x_color = "#306998" # Python Blue for bullish
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o_color = "#D62728" # Red for bearish
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# Plot each column
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for col_idx, col in enumerate(columns):
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if col["type"] == "X":
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# X column goes from start to end (upward)
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start = min(col["start"], col["end"])
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end = max(col["start"], col["end"])
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boxes = np.arange(start, end + box_size / 2, box_size)
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for box_price in boxes:
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ax.text(col_idx, box_price, "X", fontsize=14, fontweight="bold", ha="center", va="center", color=x_color)
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else:
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# O column goes from start to end (downward)
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start = max(col["start"], col["end"])
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end = min(col["start"], col["end"])
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boxes = np.arange(end, start + box_size / 2, box_size)
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for box_price in boxes:
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ax.text(col_idx, box_price, "O", fontsize=14, fontweight="bold", ha="center", va="center", color=o_color)
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# Calculate y-axis limits from the data
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all_prices = []
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for col in columns:
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all_prices.extend([col["start"], col["end"]])
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y_min = min(all_prices) - 2 * box_size
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y_max = max(all_prices) + 2 * box_size
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# Configure axes
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ax.set_xlim(-0.5, len(columns) - 0.5)
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ax.set_ylim(y_min, y_max)
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# Grid at box size intervals
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ax.set_yticks(
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np.arange(np.floor(y_min / box_size) * box_size, np.ceil(y_max / box_size) * box_size + box_size, box_size)
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)
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ax.grid(True, alpha=0.3, linestyle="-", which="both")
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# Labels and title
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ax.set_xlabel("Column (Reversal Number)", fontsize=20)
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ax.set_ylabel("Price ($)", fontsize=20)
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ax.set_title("point-and-figure-basic · matplotlib · pyplots.ai", fontsize=24)
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ax.tick_params(axis="both", labelsize=16)
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# Add legend
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legend_elements = [
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Line2D(
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[0],
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[0],
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marker="$X$",
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color="w",
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markerfacecolor=x_color,
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markersize=20,
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label="Rising (X)",
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markeredgecolor=x_color,
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),
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Line2D(
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[0],
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[0],
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marker="$O$",
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color="w",
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markerfacecolor=o_color,
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markersize=20,
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label="Falling (O)",
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markeredgecolor=o_color,
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),
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]
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ax.legend(handles=legend_elements, fontsize=16, loc="upper left")
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plt.tight_layout()
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plt.savefig("plot.png", dpi=300, bbox_inches="tight")

‎plots/point-and-figure-basic/metadata/matplotlib.yaml‎

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library: matplotlib
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specification_id: point-and-figure-basic
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created: '2026-01-15T22:54:30Z'
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updated: '2026-01-15T22:57:16Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 21049143205
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issue: 3755
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python_version: 3.13.11
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library_version: 3.10.8
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preview_url: https://storage.googleapis.com/pyplots-images/plots/point-and-figure-basic/matplotlib/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/point-and-figure-basic/matplotlib/plot_thumb.png
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preview_html: null
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quality_score: 91
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review:
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strengths:
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- Excellent P&F algorithm implementation correctly handling box sizes and 3-box
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reversals
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- Clean, readable code structure following KISS principles
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- Proper use of X-axis representing columns/reversals rather than time (key P&F
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feature)
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- Good color choices with blue for bullish and red for bearish
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- Grid lines aligned with box size intervals as recommended in spec
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- Appropriate text sizing for high-resolution output
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weaknesses:
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- Missing support/resistance trend lines mentioned in spec notes (45-degree lines
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connecting ascending lows/descending highs)
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- Legend marker style does not perfectly match the text characters displayed on
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chart
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image_description: The plot displays a Point and Figure (P&F) chart showing price
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movements over 13 columns (0-12). The chart uses blue "X" characters for rising
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price columns (bullish) and red "O" characters for falling price columns (bearish).
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The Y-axis shows "Price ($)" ranging from approximately $80 to $132 with grid
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lines at $2 intervals (matching the box size). The X-axis shows "Column (Reversal
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Number)" from 0 to 12. The title correctly follows the format "point-and-figure-basic
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· matplotlib · pyplots.ai". A legend in the upper left shows "Rising (X)" in blue
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and "Falling (O)" in red. The chart demonstrates alternating X and O columns showing
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price reversals, with an overall upward trend visible as prices rise from ~$100
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to ~$127 by the end.
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criteria_checklist:
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visual_quality:
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score: 37
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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, axis labels at 20pt, tick labels at 16pt, all perfectly
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readable at full resolution
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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 or elements; X and O symbols are well-spaced
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within their columns
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- id: VQ-03
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name: Element Visibility
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score: 7
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max: 8
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passed: true
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comment: X and O symbols are clearly visible at fontsize=14 with bold weight;
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could be slightly larger for optimal visibility
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- id: VQ-04
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name: Color Accessibility
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score: 5
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max: 5
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passed: true
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comment: Blue and red provide good contrast and are distinguishable for colorblind
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users
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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: Plot fills canvas appropriately with balanced margins
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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: Price ($) includes units, Column (Reversal Number) is descriptive
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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 marker style doesn't perfectly match the text characters displayed
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on chart
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spec_compliance:
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score: 23
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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 Point and Figure chart with X for rising and O for falling
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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: Y-axis correctly shows price, X-axis shows column/reversal number
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- id: SC-03
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name: Required Features
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score: 4
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max: 5
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passed: true
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comment: Has X/O symbols, box size intervals, grid lines. Missing trend lines
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from spec notes
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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: All data visible within axes limits 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 correctly labels Rising (X) and Falling (O)
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- id: SC-06
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name: Title Format
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score: 1
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max: 2
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passed: true
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comment: Uses correct format with middle dot separator
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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: 7
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max: 8
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passed: true
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comment: Shows both bullish and bearish columns, multiple reversals, overall
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trend
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- id: DQ-02
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name: Realistic Context
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score: 7
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max: 7
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passed: true
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comment: Stock price simulation starting at $100 with realistic daily returns
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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: 5
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passed: true
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comment: Price range $80-132 is reasonable; box size of $2 is sensible
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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: 'Clean linear flow: imports, data generation, P&F algorithm, plot,
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save'
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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 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: 3
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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: 3
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max: 5
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passed: true
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comment: Uses ax.text() for character placement and Line2D for custom legend
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markers
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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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- annotations
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- custom-legend
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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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styling:
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- grid-styling

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