From 75a6d3b95e86de4d03bb02a493d820f25b5780dd Mon Sep 17 00:00:00 2001
From: Orchestrator <orchestrator@opencode.ai>
Date: Thu, 4 Jun 2026 17:22:16 -0500
Subject: [PATCH] feat(chart): S&P 500 P/E and dividend yield historical

---
 src/charts/pe_dividend.py | 84 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 84 insertions(+)
 create mode 100644 src/charts/pe_dividend.py

diff --git a/src/charts/pe_dividend.py b/src/charts/pe_dividend.py
new file mode 100644
index 0000000..ebc33dd
--- /dev/null
+++ b/src/charts/pe_dividend.py
@@ -0,0 +1,84 @@
+"""S&P 500 P/E and Dividend Yield Charts"""
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+from src.data.market_bubbles import sp500_pe, sp500_dividend_yield
+from src.utils.styling import get_theme, EXPORT_DPI, BUBBLE_ZONE, WARNING_ZONE, NORMAL_ZONE, GRAY_DARK
+from src.utils.export import save_chart_tight
+
+
+def plot_pe_dividend() -> str:
+    """Generate 2-panel S&P 500 P/E Ratio and Dividend Yield chart."""
+    plt.rcParams.update(get_theme())
+    fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(14, 10), sharex=True)
+
+    # ── Panel 1: P/E ───────────────────────────────────────────────────────
+    years_pe = [d["year"] for d in sp500_pe]
+    values_pe = [d["value"] for d in sp500_pe]
+    ax1.plot(years_pe, values_pe, color=GRAY_DARK, linewidth=1.5)
+    ax1.axhspan(0, 15, alpha=0.15, color=NORMAL_ZONE)
+    ax1.axhspan(15, 25, alpha=0.15, color=WARNING_ZONE)
+    ax1.axhspan(25, 80, alpha=0.15, color=BUBBLE_ZONE)
+    ax1.axhline(y=18.2, color="#333", linestyle="--", linewidth=1, alpha=0.7)
+    ax1.text(1960, 18.7, "Mean: 18.2", fontsize=10)
+    ax1.set_ylabel("P/E Ratio", fontsize=12)
+    ax1.set_ylim(0, 80)
+
+    pe_events = [
+        (1999, 32.92, "1999 Peak"),
+        (2009, 70.91, "2009 Anomaly"),
+        (2021, 35.96, "2021"),
+        (2026, 29.60, "2026"),
+    ]
+    for year, val, label in pe_events:
+        ax1.annotate(label, xy=(year, val), xytext=(year + 3, val + 3),
+                      arrowprops=dict(arrowstyle="->", color="gray", lw=0.8),
+                      fontsize=8)
+
+    # ── Panel 2: Dividend Yield ────────────────────────────────────────────
+    years_dy = [d["year"] for d in sp500_dividend_yield]
+    values_dy = [d["value"] for d in sp500_dividend_yield]
+    ax2.plot(years_dy, values_dy, color="#e74c3c", linewidth=1.5)
+    ax2.axhline(y=3.2, color="#333", linestyle="--", linewidth=1, alpha=0.7)
+    ax2.text(1960, 3.5, "Mean: 3.2%", fontsize=10)
+    ax2.set_ylabel("Dividend Yield (%)", fontsize=12)
+    ax2.set_xlabel("Year", fontsize=12)
+    ax2.set_ylim(0, 8)
+
+    dy_events = [
+        (1950, 7.44, "1950: 7.44%"),
+        (2000, 1.22, "2000: 1.22%"),
+        (2026, 1.04, "2026: 1.04%"),
+    ]
+    for year, val, label in dy_events:
+        ax2.annotate(label, xy=(year, val), xytext=(year + 3, val + 0.5),
+                      arrowprops=dict(arrowstyle="->", color="gray", lw=0.8),
+                      fontsize=8)
+
+    fig.suptitle(
+        "S&P 500 Valuation Metrics — P/E Ratio and Dividend Yield",
+        fontsize=16, fontweight="bold",
+    )
+
+    for ax in (ax1, ax2):
+        ax.grid(True, alpha=0.3)
+
+    # Save — avoid tight_layout() and bbox_inches="tight" to bypass
+    # Python 3.14 + matplotlib deepcopy RecursionError
+    import os
+    output_dir = "output/charts"
+    os.makedirs(output_dir, exist_ok=True)
+    path = os.path.join(output_dir, "03_pe_dividend.png")
+    fig.subplots_adjust(top=0.90, bottom=0.06, left=0.08, right=0.95, hspace=0.28)
+    plt.savefig(path, dpi=EXPORT_DPI, facecolor=fig.get_facecolor(), edgecolor="none")
+    plt.close(fig)
+    return path
+
+
+def main():
+    path = plot_pe_dividend()
+    print(f"Chart saved: {path}")
+
+
+if __name__ == "__main__":
+    main()