#!/usr/bin/env python3 """Derive capacity_timeseries.csv and company_gw_chart.html from forecast_vintage.csv.""" from __future__ import annotations import csv from pathlib import Path BASE = Path(__file__).resolve().parent VINTAGE = BASE / "forecast_vintage.csv" TIMESERIES = BASE / "capacity_timeseries.csv" CHART = BASE / "company_gw_chart.html" QUARTERS = [ "FY2024-FQ4", "FY2025-FQ1", "FY2025-FQ2", "FY2025-FQ3", "FY2025-FQ4", "FY2026-FQ1", "FY2026-FQ2", "FY2026-FQ3", ] # First quarter with FY2026 full-year capex guidance vintage (as-of FY2025-FQ4). GUIDANCE_ORIGIN_QUARTER = "FY2025-FQ4" FIELDS = [ "company", "ticker", "fiscal_quarter", "actual_gw", "latest_forecast_gw", "original_forecast_gw", "under_construction_gw", "capex_actual_usd_b", "capex_guidance_latest_usd_b", "capex_guidance_original_usd_b", "capex_actual_metric", "capex_actual_is_derived", "caveat", ] def period_key(p: str) -> tuple[int, int]: fy = int(p[2:6]) fq = int(p.split("-FQ")[1]) return (fy, fq) def load_vintage() -> list[dict[str, str]]: with VINTAGE.open(encoding="utf-8") as f: return list(csv.DictReader(f)) def guidance_vintages(rows: list[dict[str, str]]) -> list[tuple[str, float]]: out: list[tuple[str, float]] = [] for row in rows: if row["metric_name"] == "capex_guidance" and row["target_period"] == "FY2026": out.append((row["as_of_period"], float(row["raw_value"]))) return sorted(out, key=lambda x: period_key(x[0])) def fmt_usd_b(value: float) -> str: if value == int(value): return str(int(value)) return f"{value:.3f}".rstrip("0").rstrip(".") def guidance_as_of(quarter: str, vintages: list[tuple[str, float]]) -> str: """Return latest FY2026 guidance vintage available as-of quarter (blank if none yet).""" qk = period_key(quarter) latest = "" for period, value in vintages: if period_key(period) <= qk: latest = fmt_usd_b(value) return latest def original_guidance_as_of(quarter: str, vintages: list[tuple[str, float]]) -> str: """Original FY2026 guide only visible from first guidance vintage quarter onward.""" if period_key(quarter) < period_key(GUIDANCE_ORIGIN_QUARTER): return "" if not vintages: return "" return fmt_usd_b(vintages[0][1]) def capex_actual_for_quarter(quarter: str, rows: list[dict[str, str]]) -> tuple[str, str, str]: for row in rows: if ( row["as_of_period"] == quarter and row["metric_name"] == "capex_actual" and row["target_period"] == quarter ): return row["raw_value"], "capex_actual", row["is_derived"] if quarter == "FY2024-FQ4": for row in rows: if ( row["as_of_period"] == quarter and row["metric_name"] == "capex_actual" and row["target_period"] == "FY2024" ): return row["raw_value"], "capex_actual_fy", row["is_derived"] if quarter == "FY2025-FQ4": for row in rows: if ( row["as_of_period"] == quarter and row["metric_name"] == "capex_actual" and row["target_period"] == "FY2025-FQ4" ): return row["raw_value"], "capex_actual", row["is_derived"] return "", "", "" def build_timeseries(rows: list[dict[str, str]]) -> list[dict[str, str]]: guidance = guidance_vintages(rows) caveat = ( "Microsoft does not disclose data-center MW/GW; GW columns blank. " "capex_*_usd_b are USD billions proxies from SEC/IR primary sources. " "FY2024-FQ4 capex_actual is full-year FY2024 (not quarterly-comparable). " "FY2026 guidance vintages apply only from FY2025-FQ4 onward." ) out: list[dict[str, str]] = [] for quarter in QUARTERS: capex, metric, derived = capex_actual_for_quarter(quarter, rows) out.append( { "company": "Microsoft", "ticker": "MSFT", "fiscal_quarter": quarter, "actual_gw": "", "latest_forecast_gw": "", "original_forecast_gw": "", "under_construction_gw": "", "capex_actual_usd_b": capex, "capex_guidance_latest_usd_b": guidance_as_of(quarter, guidance), "capex_guidance_original_usd_b": original_guidance_as_of(quarter, guidance), "capex_actual_metric": metric, "capex_actual_is_derived": derived, "caveat": caveat, } ) return out def svg_chart(rows: list[dict[str, str]]) -> str: labels = [r["fiscal_quarter"] for r in rows] raw_actual = [float(r["capex_actual_usd_b"]) if r["capex_actual_usd_b"] else None for r in rows] latest = [float(r["capex_guidance_latest_usd_b"]) if r["capex_guidance_latest_usd_b"] else None for r in rows] original = [ float(r["capex_guidance_original_usd_b"]) if r["capex_guidance_original_usd_b"] else None for r in rows ] # Exclude FY2024 annual anchor from quarterly-comparable blue polyline. quarterly_actual = [None if r["capex_actual_metric"] == "capex_actual_fy" else v for r, v in zip(rows, raw_actual)] annual_anchor_idx = next( (i for i, r in enumerate(rows) if r["capex_actual_metric"] == "capex_actual_fy"), None, ) w, h, ml, mr, mt, mb = 920, 480, 70, 30, 70, 90 pw, ph = w - ml - mr, h - mt - mb all_vals = [v for v in quarterly_actual + latest + original if v is not None] if annual_anchor_idx is not None and raw_actual[annual_anchor_idx] is not None: all_vals.append(raw_actual[annual_anchor_idx]) ymax = max(all_vals) * 1.1 if all_vals else 100 ymin = 0 def x(i: int) -> float: return ml + (i / max(len(labels) - 1, 1)) * pw def y(v: float) -> float: return mt + ph - ((v - ymin) / (ymax - ymin)) * ph def polyline(vals: list[float | None], color: str) -> str: pts = [] for i, v in enumerate(vals): if v is not None: pts.append(f"{x(i):.1f},{y(v):.1f}") if len(pts) < 2: return "" return f'' annual_marker = "" if annual_anchor_idx is not None and raw_actual[annual_anchor_idx] is not None: ax, ay = x(annual_anchor_idx), y(raw_actual[annual_anchor_idx]) annual_marker = ( f'' f'' f"FY2024 annual" ) grid = [] step = 10 if ymax <= 100 else 20 for tick in range(0, int(ymax) + 1, step): yy = y(tick) grid.append(f'') grid.append( f'{tick}' ) xlabels = "".join( f'{lab}' for i, lab in enumerate(labels) ) return f""" MSFT — Capex Actual vs FY2026 Guidance Vintages (GW Proxy)

Microsoft (MSFT) — Infrastructure Spend Proxy: Actual vs Guidance

{''.join(grid)} Fiscal quarter (as-of) USD billions (capex proxy) {polyline(quarterly_actual, '#2563eb')} {polyline(latest, '#16a34a')} {polyline(original, '#dc2626')} {annual_marker} {xlabels}
Capex actual (quarterly proxy) FY2024 full-year actual (annual anchor) Latest FY2026 capex guidance vintage (as-of) Original FY2026 capex guidance (~$77.5B, from FY2025-FQ4)
""" def main() -> None: vintage = load_vintage() ts = build_timeseries(vintage) with TIMESERIES.open("w", newline="", encoding="utf-8") as f: writer = csv.DictWriter(f, fieldnames=FIELDS) writer.writeheader() writer.writerows(ts) CHART.write_text(svg_chart(ts), encoding="utf-8") print(f"Wrote {len(ts)} rows to {TIMESERIES}") print(f"Wrote chart to {CHART}") if __name__ == "__main__": main()