Add period-over-period trend table at top of report

build_data.py now also computes each buyer's total for the preceding
3-day window under identical rules (exclusions, attribution), exposed as
prev_total plus meta.prev_range. index.html renders it as the first
panel, with green/red deltas and a total row; the panel stays hidden if
no prior period exists.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>

build_data.py

@@ -130,6 +130,20 @@ by_buyer = collections.defaultdict(dict)
 for (b, pkg), v in pkg_spend.items():
     by_buyer[b][pkg] = v
 
+# --- 上一周期：紧邻本窗口之前的同样天数，口径完全一致 ---
+prev_end = start - datetime.timedelta(days=1)
+prev_start = prev_end - datetime.timedelta(days=WINDOW_DAYS - 1)
+prev = collections.defaultdict(float)
+prev_has_data = any(prev_start <= d <= prev_end for d in by_date)
+for d, adp, pkg, geo, s_ in rows:
+    if not (prev_start <= d <= prev_end):
+        continue
+    if pkg in EXCL or adp in dropped_ids:
+        continue
+    b = amap.get(adp)
+    if b is not None:
+        prev[b] += s_
+
 out = []
 for b, pk in by_buyer.items():
     pkgs = []
@@ -150,6 +164,7 @@ for b, pk in by_buyer.items():
     out.append({
         "name": b, "@name": b,
         "total": round(sum(pk.values()), 4),
+        "prev_total": round(prev.get(b, 0.0), 4),
         "active_ids": len(ids[b]),
         "owned_ids": owned[b],
         "pkgs": pkgs,
@@ -163,6 +178,7 @@ data = {
         "source": "萌奇熊日报",
         "generated_at": datetime.date.today().isoformat(),
         "date_range": f"{start:%Y/%m/%d}-{end:%Y/%m/%d}",
+        "prev_range": (f"{prev_start:%Y/%m/%d}-{prev_end:%Y/%m/%d}" if prev_has_data else ""),
         "note": "投放人以「投手」表 Media Buyer 列为准，@某某 已与某某合并计算；"
                 "未填写投放人的 Adpingid 不计入；exclude.json 所列包名已排除。"
                 "Package 后缀为该 Package 花费最高的投放地区，Adpingid 为该地区内花费最高的 Adping。",
@@ -182,6 +198,8 @@ if extra_used:
 print(f"窗口 {start} -> {end}")
 print(f"排除包名命中 {excluded_rows} 行，剔除花费 {excluded_spend:.2f}")
 print(f"合计 {data['grand_total']:.2f} / Adpingid {data['week34_id_count']}")
+print(f"上一周期 {prev_start} -> {prev_end}  合计 {sum(prev.values()):.2f}"
+      + ('' if prev_has_data else '  (无数据)'))
 for b in out:
     print(f"  {b['name']:6s} {b['total']:10.2f}  在投{b['active_ids']:3d}/负责{b['owned_ids']:3d}")
     for p in b['pkgs']: