feat数据统计曲线改为各机累计已实现盈亏叠加，不再用余额采样。

Co-authored-by: Cursor <cursoragent@cursor.com>

lib/stats_agg_lib.py

@@ -1,12 +1,12 @@
-"""中控统计汇总:KPI 对比 + 余额曲线."""
+"""中控统计汇总:KPI 对比 + 已实现盈亏曲线."""
 from __future__ import annotations
 
+import time
 from typing import Any
 
-from lib.bridge_client_lib import pull_all_enabled, pull_all_stats
+from lib.bridge_client_lib import pull_all_enabled, pull_all_pnl_curves, pull_all_stats
 from lib.cash_sample_lib import (
     first_cash_by_instance,
-    query_cash_series,
     record_cash_samples_from_overview,
 )
 
@@ -185,7 +185,84 @@ def build_stats_summary(
 
 
 def build_stats_curve(range_key: str = "7d") -> dict[str, Any]:
-    # 曲线只读本地采样库;余额采样由总览常驻 pull 负责
-    data = query_cash_series(range_key=range_key)
-    data["ok"] = True
-    return data
+    """多机累计已实现盈亏曲线(各机台阶叠加;划转/提现不影响)."""
+    rk = normalize_window(range_key)
+    now_ms = int(time.time() * 1000)
+    rows = pull_all_pnl_curves(rk)
+    series: list[dict[str, Any]] = []
+    by_id: dict[Any, list[dict[str, float]]] = {}
+    start_ms = None
+    end_ms = now_ms
+    for row in rows:
+        iid = row.get("id")
+        pts_raw = row.get("points") if isinstance(row.get("points"), list) else []
+        pts: list[dict[str, float]] = []
+        for p in pts_raw:
+            if not isinstance(p, dict):
+                continue
+            try:
+                t = int(p.get("t"))
+                v = float(p.get("v"))
+            except (TypeError, ValueError):
+                continue
+            pts.append({"t": t, "v": v})
+        pts.sort(key=lambda x: x["t"])
+        if row.get("start_ms") is not None:
+            try:
+                sm = int(row.get("start_ms"))
+                start_ms = sm if start_ms is None else min(start_ms, sm)
+            except (TypeError, ValueError):
+                pass
+        if row.get("end_ms") is not None:
+            try:
+                end_ms = max(end_ms, int(row.get("end_ms")))
+            except (TypeError, ValueError):
+                pass
+        by_id[iid] = pts
+        series.append(
+            {
+                "id": iid,
+                "name": row.get("name") or str(iid),
+                "online": bool(row.get("online")),
+                "error": row.get("error") or "",
+                "points": pts,
+                "trade_count": int(row.get("trade_count") or 0),
+                "latest": row.get("latest"),
+            }
+        )
+
+    # 合计:按 1 分钟桶,各机该桶最后值求和并前向填充
+    last_in_bucket: dict[int, dict[Any, float]] = {}
+    for iid, pts in by_id.items():
+        for p in pts:
+            bt = (int(p["t"]) // 60000) * 60000
+            last_in_bucket.setdefault(bt, {})[iid] = float(p["v"])
+    running: dict[Any, float] = {}
+    total_pts: list[dict[str, float]] = []
+    for bt in sorted(last_in_bucket.keys()):
+        running.update(last_in_bucket[bt])
+        if running:
+            total_pts.append({"t": bt, "v": round(sum(running.values()), 4)})
+
+    if start_ms is None:
+        if rk in ("today", "1d"):
+            import datetime as dt
+
+            start = dt.datetime.now().replace(hour=0, minute=0, second=0, microsecond=0)
+            start_ms = int(start.timestamp() * 1000)
+        elif rk in ("30d", "30"):
+            start_ms = now_ms - 30 * 86400 * 1000
+        elif rk in ("all", "0"):
+            start_ms = 0
+        else:
+            start_ms = now_ms - 7 * 86400 * 1000
+
+    return {
+        "ok": True,
+        "metric": "realized_pnl",
+        "series": series,
+        "total": total_pts,
+        "range": rk,
+        "start_ms": start_ms,
+        "end_ms": end_ms,
+    }