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