fix(chart): add quarterly granularity to hyperscaler capex chart

src/charts/spending_debt.py

@@ -3,7 +3,11 @@ import matplotlib
 matplotlib.use("Agg")
 import matplotlib.pyplot as plt
 import numpy as np
-from src.data.ai_infrastructure import hyperscaler_capex_annual, hyperscaler_capex_meta
+from src.data.ai_infrastructure import (
+    hyperscaler_capex_annual,
+    hyperscaler_capex_quarterly,
+    hyperscaler_capex_meta,
+)
 from src.utils.styling import get_theme, EXPORT_DPI, get_company_colors, GRAY_DARK
 from src.utils.export import save_chart_tight
 
@@ -14,47 +18,84 @@ def plot_hyperscaler_capex() -> str:
 
     company_colors = get_company_colors()
     companies = ["Microsoft", "Alphabet", "Meta", "Amazon"]
-    years = list(range(2020, 2027))
 
-    # Organize data by company
-    data = {c: [] for c in companies}
+    # Build combined timeline: annual 2020-2023 + quarterly 2024-Q1 through 2026-Q1
+    # Periods: 2020, 2021, 2022, 2023, 2024-Q1, 2024-Q2, 2024-Q3, 2024-Q4,
+    #           2025-Q1, 2025-Q2, 2026-Q1
+    annual_years = [2020, 2021, 2022, 2023]
+    quarterly_periods = [
+        (2024, "Q1"), (2024, "Q2"), (2024, "Q3"), (2024, "Q4"),
+        (2025, "Q1"), (2025, "Q2"),
+        (2026, "Q1"),
+    ]
+
+    x_labels = [str(y) for y in annual_years] + [
+        f"{y}-Q{q}" for y, q in quarterly_periods
+    ]
+    n_periods = len(x_labels)
+    x_positions = list(range(n_periods))
+
+    # Organize annual data by company (2020-2023)
+    annual_data = {c: {} for c in companies}
     for entry in hyperscaler_capex_annual:
-        data[entry["company"]].append(entry["capex_billions"])
+        if entry["year"] in annual_years:
+            annual_data[entry["company"]][entry["year"]] = entry["capex_billions"]
 
-    # Fill missing years with 0
+    # Organize quarterly data by company
+    quarterly_data = {c: {} for c in companies}
+    for entry in hyperscaler_capex_quarterly:
+        key = (entry["year"], entry["quarter"])
+        quarterly_data[entry["company"]][key] = entry["capex_billions"]
+
+    # Build per-company value arrays
+    data = {}
     for c in companies:
-        while len(data[c]) < len(years):
-            data[c].append(0)
+        vals = []
+        # Annual portion
+        for y in annual_years:
+            vals.append(annual_data[c].get(y, 0))
+        # Quarterly portion
+        for y, q in quarterly_periods:
+            vals.append(quarterly_data[c].get((y, q), 0))
+        data[c] = np.array(vals)
 
     # Stacked area
-    y_offset = np.zeros(len(years))
-    totals = []
+    y_offset = np.zeros(n_periods)
     for c in companies:
-        values = np.array(data[c])
-        ax.fill_between(years, y_offset, y_offset + values,
+        values = data[c]
+        ax.fill_between(x_positions, y_offset, y_offset + values,
                         alpha=0.8, color=company_colors.get(c, "#666"), label=c)
-        ax.plot(years, y_offset + values, color=company_colors.get(c, "#666"), linewidth=1)
+        ax.plot(x_positions, y_offset + values, color=company_colors.get(c, "#666"), linewidth=1)
         y_offset += values
-        totals.append(sum(values))
 
     # Total annotations
-    for i, (year, total) in enumerate(zip(years, y_offset)):
-        label = f"${total:.0f}B"
-        if year >= 2026:
-            label += "*"
-        ax.text(year, total + 5, label, ha="center", fontsize=9, fontweight="bold")
+    for i, (pos, label, total) in enumerate(zip(x_positions, x_labels, y_offset)):
+        total_label = f"${total:.0f}B"
+        # Mark 2026 as projected
+        if label.startswith("2026"):
+            total_label += "*"
+        # Only annotate every period (avoid crowding)
+        ax.text(pos, total + 4, total_label, ha="center", fontsize=9, fontweight="bold")
 
-    # Dashed vertical line at 2026 to mark guided/projected boundary
-    ax.axvline(x=2025.5, color=GRAY_DARK, linestyle="--", alpha=0.4, linewidth=1)
+    # Dashed vertical line between last quarterly data (2026-Q1) and remaining 2026
+    ax.axvline(x=10.5, color=GRAY_DARK, linestyle="--", alpha=0.4, linewidth=1)
 
-    ax.set_title("Hyperscaler AI Infrastructure Capex — 2020 to 2026", fontsize=16, fontweight="bold")
-    ax.set_xlabel("Year", fontsize=12)
+    ax.set_xticks(x_positions)
+    ax.set_xticklabels(x_labels, rotation=45, ha="right", fontsize=9)
+    ax.set_title("Hyperscaler AI Infrastructure Capex — 2020 to 2026-Q1",
+                 fontsize=16, fontweight="bold")
+    ax.set_xlabel("Period", fontsize=12)
     ax.set_ylabel("Capex (Billions USD)", fontsize=12)
     ax.legend(loc="upper left", fontsize=10)
     ax.grid(True, alpha=0.3)
     ax.text(0.02, 0.97, "*2026 = guided/projected", transform=ax.transAxes,
             fontsize=9, style="italic", color="gray", va="top")
 
+    # Granularity note
+    ax.text(0.50, 0.03, "2020-2023: annual | 2024-2026-Q1: quarterly",
+            transform=ax.transAxes, fontsize=9, style="italic",
+            color="gray", ha="center", va="bottom")
+
     # AI-related capex share note
     ax.text(0.98, 0.03, "80-90% of 2025/2026 capex is AI-related",
             transform=ax.transAxes, fontsize=9, style="italic",