feat(plotnine): implement indicator-sma

plots/indicator-sma/implementations/plotnine.py

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+""" pyplots.ai
+indicator-sma: Simple Moving Average (SMA) Indicator Chart
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2026-01-11
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    element_line,
+    element_text,
+    geom_line,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_x_datetime,
+    theme,
+    theme_minimal,
+)
+
+
+# Data - Generate realistic stock price data with trend and volatility
+np.random.seed(42)
+n_days = 300
+dates = pd.date_range("2024-01-01", periods=n_days, freq="B")  # Business days
+
+# Create a price series with trends and mean reversion
+base_price = 150
+returns = np.random.normal(0.0003, 0.015, n_days)  # Daily returns
+# Add some trending behavior
+trend = np.sin(np.linspace(0, 3 * np.pi, n_days)) * 0.001
+returns = returns + trend
+close = base_price * np.cumprod(1 + returns)
+
+# Calculate SMAs
+df = pd.DataFrame({"date": dates, "close": close})
+df["sma_20"] = df["close"].rolling(window=20).mean()
+df["sma_50"] = df["close"].rolling(window=50).mean()
+df["sma_200"] = df["close"].rolling(window=200).mean()
+
+# Reshape data for plotnine (long format for multiple lines with legend)
+df_long = pd.melt(
+    df, id_vars=["date"], value_vars=["close", "sma_20", "sma_50", "sma_200"], var_name="series", value_name="price"
+)
+
+# Rename series for legend
+series_labels = {"close": "Price", "sma_20": "SMA 20", "sma_50": "SMA 50", "sma_200": "SMA 200"}
+df_long["series"] = df_long["series"].map(series_labels)
+
+# Set order for legend
+series_order = ["Price", "SMA 20", "SMA 50", "SMA 200"]
+df_long["series"] = pd.Categorical(df_long["series"], categories=series_order, ordered=True)
+
+# Define colors matching the other implementations
+colors = {"Price": "#306998", "SMA 20": "#FFD43B", "SMA 50": "#E74C3C", "SMA 200": "#2ECC71"}
+
+# Plot
+plot = (
+    ggplot(df_long, aes(x="date", y="price", color="series"))
+    + geom_line(size=1.5, alpha=0.9)
+    + scale_color_manual(values=colors)
+    + scale_x_datetime(date_breaks="2 months", date_labels="%b %Y")
+    + labs(x="Date", y="Price ($)", title="indicator-sma · plotnine · pyplots.ai", color="")
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        axis_text_x=element_text(rotation=30, ha="right"),
+        legend_text=element_text(size=16),
+        legend_position=(0.02, 0.98),
+        legend_direction="vertical",
+        panel_grid_major_y=element_line(color="#cccccc", size=0.5, alpha=0.3),
+        panel_grid_major_x=element_line(alpha=0),
+        panel_grid_minor=element_line(alpha=0),
+    )
+)
+
+plot.save("plot.png", dpi=300)

plots/indicator-sma/metadata/plotnine.yaml

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+library: plotnine
+specification_id: indicator-sma
+created: '2026-01-11T00:53:45Z'
+updated: '2026-01-11T00:56:27Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20886981190
+issue: 3651
+python_version: 3.13.11
+library_version: 0.15.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/indicator-sma/plotnine/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/indicator-sma/plotnine/plot_thumb.png
+preview_html: null
+quality_score: 91
+review:
+  strengths:
+  - Excellent use of plotnine grammar of graphics with proper long-format data transformation
+  - Clean color scheme that distinguishes all four series effectively
+  - Proper title format following the spec-id · library · pyplots.ai convention
+  - Good handling of date axis with appropriate rotation to prevent overlap
+  - Realistic synthetic stock price data with visible trend behavior
+  - All three SMA periods (20, 50, 200) are correctly calculated and displayed
+  weaknesses:
+  - Legend could benefit from a background/box for better readability against data
+  - The SMA 200 line only appears in the final third of the chart due to the 200-day
+    window requirement
+  image_description: 'The plot displays a Simple Moving Average (SMA) indicator chart
+    with a dark blue price line showing daily stock prices over approximately one
+    year (Jan 2024 - Feb 2025). Three SMA overlay lines are visible: a yellow SMA
+    20 (short-term), a red SMA 50 (medium-term), and a green SMA 200 (long-term).
+    The price fluctuates between roughly $130-$170, showing a downward trend from
+    Jan-Aug 2024 followed by an upward trend from Oct 2024 onwards. The legend is
+    positioned in the upper left corner with entries for Price, SMA 20, SMA 50, and
+    SMA 200. The title follows the correct format "indicator-sma · plotnine · pyplots.ai"
+    displayed in bold at the top. X-axis shows dates in "Mon YYYY" format with labels
+    rotated 30 degrees to prevent overlap. Y-axis shows "Price ($)" with values ranging
+    from 130 to 170. Grid lines are subtle, appearing only on the y-axis with low
+    opacity. The minimal theme provides a clean background. All four lines are clearly
+    distinguishable with consistent 1.5 line width and 0.9 alpha transparency.'
+  criteria_checklist:
+    visual_quality:
+      score: 36
+      max: 40
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 10
+        max: 10
+        passed: true
+        comment: Title is bold (24pt), axis labels (20pt) and tick labels (16pt) are
+          clearly readable at full resolution
+      - id: VQ-02
+        name: No Overlap
+        score: 8
+        max: 8
+        passed: true
+        comment: No overlapping text elements, date labels rotated 30 degrees to prevent
+          overlap
+      - id: VQ-03
+        name: Element Visibility
+        score: 7
+        max: 8
+        passed: true
+        comment: Line width of 1.5 and alpha of 0.9 provide good visibility, all four
+          series clearly distinguishable
+      - id: VQ-04
+        name: Color Accessibility
+        score: 5
+        max: 5
+        passed: true
+        comment: Blue, yellow, red, green color scheme is colorblind-friendly and
+          provides good contrast
+      - id: VQ-05
+        name: Layout Balance
+        score: 4
+        max: 5
+        passed: true
+        comment: Good canvas utilization, legend well positioned in upper-left, minimal
+          wasted space
+      - id: VQ-06
+        name: Axis Labels
+        score: 2
+        max: 2
+        passed: true
+        comment: Y-axis has units 'Price ($)', X-axis labeled 'Date'
+      - id: VQ-07
+        name: Grid & Legend
+        score: 0
+        max: 2
+        passed: false
+        comment: Grid is appropriately subtle but legend lacks background frame for
+          readability against data
+    spec_compliance:
+      score: 25
+      max: 25
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 8
+        max: 8
+        passed: true
+        comment: Correct line chart with price and three SMA overlays as specified
+      - id: SC-02
+        name: Data Mapping
+        score: 5
+        max: 5
+        passed: true
+        comment: Date on X-axis, price values on Y-axis, all correctly mapped
+      - id: SC-03
+        name: Required Features
+        score: 5
+        max: 5
+        passed: true
+        comment: 'All required features present: price line, 20/50/200-day SMAs, legend
+          with periods'
+      - id: SC-04
+        name: Data Range
+        score: 3
+        max: 3
+        passed: true
+        comment: All data visible, axes accommodate full range from ~130 to ~170
+      - id: SC-05
+        name: Legend Accuracy
+        score: 2
+        max: 2
+        passed: true
+        comment: Legend correctly shows 'Price', 'SMA 20', 'SMA 50', 'SMA 200'
+      - id: SC-06
+        name: Title Format
+        score: 2
+        max: 2
+        passed: true
+        comment: Correct format 'indicator-sma · plotnine · pyplots.ai'
+    data_quality:
+      score: 18
+      max: 20
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 7
+        max: 8
+        passed: true
+        comment: Shows uptrend and downtrend, SMAs crossing, different lag behavior
+          of each SMA period
+      - id: DQ-02
+        name: Realistic Context
+        score: 7
+        max: 7
+        passed: true
+        comment: Realistic stock price scenario with plausible daily volatility and
+          price range (~$130-$170)
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 5
+        passed: true
+        comment: 300 business days is appropriate for showing 200-day SMA; price range
+          realistic
+    code_quality:
+      score: 9
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: 'Clean linear structure: imports → data generation → SMA calculation
+          → reshape → plot → save'
+      - id: CQ-02
+        name: Reproducibility
+        score: 3
+        max: 3
+        passed: true
+        comment: np.random.seed(42) set at the beginning for reproducible data
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: 'All imports are used: numpy, pandas, and plotnine components'
+      - id: CQ-04
+        name: No Deprecated API
+        score: 1
+        max: 1
+        passed: true
+        comment: Uses current plotnine API
+      - id: CQ-05
+        name: Output Correct
+        score: 0
+        max: 1
+        passed: false
+        comment: Saves as plot.png correctly but minor formatting
+    library_features:
+      score: 3
+      max: 5
+      items:
+      - id: LF-01
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: true
+        comment: Uses plotnine's ggplot grammar, theme_minimal, scale_color_manual,
+          element_text, and proper long-format data transformation with pd.melt which
+          is idiomatic for plotnine
+  verdict: APPROVED
+impl_tags:
+  dependencies: []
+  techniques:
+  - layer-composition
+  patterns:
+  - data-generation
+  - wide-to-long
+  dataprep:
+  - rolling-window
+  - time-series
+  styling:
+  - alpha-blending
+  - grid-styling