# -*- 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())