feat(shadow-desk): P0对账spike(runner加initial_positions通道+final_portfolio产出): BacktestEngine续跑注入初始持仓(引擎原生支持,不消耗现金); 期末现金/持仓/总值从engine.context.portfolio直取; run_backtest_json透传initial_positions+final_portfolio; scripts/shadow_desk/spike_p0全量vs 3段续跑对账(持仓逐只/现金总值/净值曲线<1e-6,自动月初分段或--split压除权边界); 6测试 [vps]

scripts/shadow_desk/spike_p0_checkpoint_replay.py

@@ -0,0 +1,182 @@
+# -*- coding: utf-8 -*-
+"""影子柜台 P0 对账 spike（docs/design/paper-shadow-desk-design.md §6 P0）。
+
+验证目标：checkpoint 续跑（分段跑，段间用 initial_positions + 段末现金恢复）
+与全量重放（一次跑完整个区间）账目一致。
+
+判定标准：
+  - 期末持仓逐只证券数量完全一致；均价差 < 1e-6（相对）
+  - 期末现金 / 总值差 < 1e-6（相对）
+  - 两边净值曲线在共同交易日上的偏差 < 1e-6（相对）
+跨除权：区间拉长到年，段边界自然落在除权日前后的概率极高；可通过
+--split 显式把边界压在已知除权日上（前复权因子是否随段变化由对账暴露）。
+
+用法（VPS，真实数据）:
+    cd C:\\sanguo_vnpy_v2
+    C:\\Python310\\python.exe -X utf8 scripts\\shadow_desk\\spike_p0_checkpoint_replay.py ^
+        --strategy all_weather --start 2024-01-01 --end 2024-12-31 --cash 1000000
+
+输出末行 SPIKE_P0_PASS / SPIKE_P0_FAIL。
+"""
+from __future__ import annotations
+
+import argparse
+import json
+import sys
+
+TOL = 1e-6  # 相对容差
+
+
+def _run_segment(params: dict) -> dict:
+    """跑一段回测,返回含 final_portfolio 的结果 dict。"""
+    from sanguo_portfolio.runner_backtest import run_backtest_json
+    return run_backtest_json(params)
+
+
+def _positions_map(final: dict | None) -> dict[str, dict]:
+    fp = (final or {}).get("final_portfolio") or {}
+    return {p["security"]: p for p in fp.get("positions", [])}
+
+
+def _rel_diff(a: float, b: float) -> float:
+    denom = max(abs(a), abs(b), 1e-12)
+    return abs(a - b) / denom
+
+
+def compare(full: dict, segmented: dict) -> list[str]:
+    """返回问题清单(空 = 通过)。"""
+    problems: list[str] = []
+    fpf = (full.get("final_portfolio") or {})
+    fps = (segmented.get("final_portfolio") or {})
+    if not fpf or not fps:
+        return [f"缺 final_portfolio: full={bool(fpf)} segmented={bool(fps)}"]
+
+    # 1. 期末持仓
+    pf, ps = _positions_map(full), _positions_map(segmented)
+    for code in sorted(set(pf) | set(ps)):
+        a, b = pf.get(code), ps.get(code)
+        if a is None or b is None:
+            problems.append(f"持仓 {code}: 单边缺失 full={a is not None} seg={b is not None}")
+            continue
+        if int(a["amount"]) != int(b["amount"]):
+            problems.append(f"持仓 {code}: 数量 full={a['amount']} seg={b['amount']}")
+        if _rel_diff(a["avg_cost"], b["avg_cost"]) > TOL:
+            problems.append(
+                f"持仓 {code}: 均价 full={a['avg_cost']:.6f} seg={b['avg_cost']:.6f}")
+    # 2. 现金 / 总值
+    for key in ("cash", "total_value"):
+        d = _rel_diff(fpf[key], fps[key])
+        if d > TOL:
+            problems.append(f"{key}: full={fpf[key]:.4f} seg={fps[key]:.4f} rel_diff={d:.2e}")
+
+    # 3. 净值曲线共同交易日
+    ef = {p["date"]: float(p["equity"]) for p in full.get("equity_curve") or []}
+    es = {p["date"]: float(p["equity"]) for p in segmented.get("equity_curve") or []}
+    common = sorted(set(ef) & set(es))
+    if not common:
+        problems.append("净值曲线无共同交易日")
+    worst, worst_d = "", 0.0
+    for d in common:
+        rd = _rel_diff(ef[d], es[d])
+        if rd > worst_d:
+            worst, worst_d = d, rd
+    if worst_d > TOL:
+        problems.append(f"净值曲线最大偏差 {worst_d:.2e} @ {worst}")
+    return problems
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="影子柜台 P0 对账 spike")
+    ap.add_argument("--strategy", default="all_weather")
+    ap.add_argument("--start", default="2024-01-01")
+    ap.add_argument("--end", default="2024-12-31")
+    ap.add_argument("--cash", type=float, default=1_000_000.0)
+    ap.add_argument("--benchmark", default="000300.XSHG")
+    ap.add_argument("--max-pool", type=int, default=30)
+    ap.add_argument(
+        "--split", nargs="*", default=[],
+        help="段边界日期(升序, 2 个 = 3 段)。缺省自动取 1/3, 2/3 处的月初。")
+    ap.add_argument("--provider", default="unified")
+    ap.add_argument("--provider-config", default="{}")
+    ap.add_argument("--slippage", type=float, default=0.0)
+    args = ap.parse_args()
+
+    base = {
+        "strategy": args.strategy,
+        "start_date": args.start,
+        "end_date": args.end,
+        "initial_cash": args.cash,
+        "benchmark": args.benchmark,
+        "max_pool": args.max_pool,
+        "commission_rate": 0.0003,
+        "stamp_duty_rate": 0.001,
+        "min_commission": 5.0,
+        "slippage": args.slippage,
+        "provider": args.provider,
+        "provider_config": args.provider_config,
+    }
+
+    splits = args.split or _auto_splits(args.start, args.end)
+    print(f"[spike] 全量重放 {args.start} ~ {args.end} ...", flush=True)
+    full = _run_segment(base)
+
+    # 分段续跑: 每段用上一段期末现金 + 期末持仓恢复
+    bounds = [args.start] + list(splits) + [args.end]
+    carry_cash, carry_positions = args.cash, None
+    seg_results = []
+    for i in range(len(bounds) - 1):
+        seg = dict(base)
+        seg["start_date"], seg["end_date"] = bounds[i], bounds[i + 1]
+        seg["initial_cash"] = carry_cash
+        if carry_positions:
+            seg["initial_positions"] = carry_positions
+        print(f"[spike] 段{i + 1}/{len(bounds) - 1}: {seg['start_date']} ~ {seg['end_date']} "
+              f"cash={carry_cash:.2f} positions={len(carry_positions or [])}", flush=True)
+        r = _run_segment(seg)
+        seg_results.append(r)
+        fp = r.get("final_portfolio") or {}
+        carry_cash = fp.get("cash", 0.0)
+        carry_positions = [
+            {"security": p["security"], "amount": p["amount"], "avg_cost": p["avg_cost"]}
+            for p in fp.get("positions", [])
+        ]
+
+    last_seg = seg_results[-1]
+    # 分段侧期末 end_date 是最后一段区间,与全量一致
+    problems = compare(full, last_seg)
+
+    fpf = (full.get("final_portfolio") or {})
+    print("\n[spike] ===== 对账结果 =====", flush=True)
+    print(f"  全量期末: 现金={fpf.get('cash', 0):.2f} 总值={fpf.get('total_value', 0):.2f} "
+          f"持仓数={len(fpf.get('positions', []))}", flush=True)
+    if problems:
+        for p in problems:
+            print(f"  ❌ {p}", flush=True)
+        print("SPIKE_P0_FAIL", flush=True)
+        return 1
+    print("  ✅ 期末持仓逐只一致; 现金/总值/净值曲线相对偏差 < 1e-6", flush=True)
+    print("SPIKE_P0_PASS", flush=True)
+    return 0
+
+
+def _auto_splits(start: str, end: str) -> list[str]:
+    """缺省段边界: 区间 1/3 与 2/3 处最近月初(月度调仓策略段边界取月初更干净)。"""
+    from datetime import date
+
+    def to_date(s: str) -> date:
+        y, m, d = map(int, s.split("-"))
+        return date(y, m, d)
+
+    s, e = to_date(start), to_date(end)
+    total = (e - s).days
+    marks = []
+    for frac in (1 / 3, 2 / 3):
+        target = s + __import__("datetime").timedelta(days=int(total * frac))
+        # 回退到月初
+        first = target.replace(day=1)
+        marks.append(first.isoformat())
+    return marks
+
+
+if __name__ == "__main__":
+    sys.exit(main())