from __future__ import annotations
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import csv
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import hashlib
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import json
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import re
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from collections import Counter, defaultdict
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from datetime import datetime, timezone, timedelta
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from pathlib import Path
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RUN_ID = "RUN-ANA-WUJI-V1-STRICT-NOTE-FULL-RERUN-20260614-001"
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ROOT = Path(__file__).resolve().parents[1]
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TZ = timezone(timedelta(hours=8))
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MOJIBAKE_RE = re.compile(r"\?{3,}|\ufffd|����|À|Ã|Â|澶|鍙|鎬|涓|蹇|瑙|鏃|鐐|甯|瀹|鍚屾剰|鎸夎")
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def read_csv(name: str) -> list[dict[str, str]]:
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path = ROOT / name
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with path.open("r", encoding="utf-8-sig", newline="") as f:
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return list(csv.DictReader(f))
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def write_csv(path: Path, rows: list[dict[str, object]], fields: list[str]) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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with path.open("w", encoding="utf-8-sig", newline="") as f:
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w = csv.DictWriter(f, fieldnames=fields)
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w.writeheader()
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for row in rows:
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w.writerow({k: row.get(k, "") for k in fields})
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def write_text(path: Path, text: str) -> None:
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path.parent.mkdir(parents=True, exist_ok=True)
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path.write_text(text, encoding="utf-8")
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def sha256_file(path: Path) -> str:
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h = hashlib.sha256()
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with path.open("rb") as f:
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for chunk in iter(lambda: f.read(1024 * 1024), b""):
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h.update(chunk)
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return h.hexdigest()
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def rel(path: Path) -> str:
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return path.relative_to(ROOT).as_posix()
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def fnum(value: str) -> float:
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try:
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return float(value)
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except Exception:
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return 0.0
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def build() -> None:
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generated_at = datetime.now(TZ).isoformat(timespec="seconds")
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buy_orders = read_csv("strict_note_buy_order_ledger.csv")
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buy_lots = read_csv("strict_note_buy_lot_ledger.csv")
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sell_orders = read_csv("strict_note_sell_order_ledger.csv")
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rolling_orders = read_csv("strict_note_rolling_low_order_ledger.csv")
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sell_boundaries = read_csv("strict_note_sell_rolling_boundary_table.csv")
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buy_boundaries = read_csv("strict_note_buy_boundary_table.csv")
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buy_cases = read_csv("strict_note_buy_case_summary.csv")
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sell_by_lot = {r["lot_id"]: r for r in sell_orders}
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rolling_by_parent: dict[str, list[dict[str, str]]] = defaultdict(list)
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for row in rolling_orders:
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rolling_by_parent[row["parent_lot_id"]].append(row)
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boundary_by_lot: dict[str, list[dict[str, str]]] = defaultdict(list)
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for row in sell_boundaries:
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boundary_by_lot[row.get("lot_id", "")].append(row)
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combined_orders: list[dict[str, object]] = []
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for row in buy_orders:
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combined_orders.append({
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"order_id": row["order_id"],
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"order_type": "BUY",
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"case_id": row["case_id"],
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"symbol": row["symbol"],
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"lot_id": "",
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"parent_lot_id": "",
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"candidate_id": row["candidate_id"],
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"external_decision_id": row["external_decision_id"],
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"trade_date": row["trade_date"],
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"trade_time": row["trade_time"],
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"trade_price": row["price"],
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"position_pct": row["position_delta_pct"],
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"decision_reason_cn": row["decision_reason_cn"],
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"evidence_image_path": row["evidence_image_path"],
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})
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for row in sell_orders:
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combined_orders.append({
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"order_id": row["order_id"],
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"order_type": "SELL",
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"case_id": row["case_id"],
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"symbol": row["symbol"],
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"lot_id": row["lot_id"],
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"parent_lot_id": "",
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"candidate_id": row["candidate_id"],
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"external_decision_id": row["external_decision_id"],
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"trade_date": row["trade_date"],
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"trade_time": row["trade_time"],
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"trade_price": row["trade_price"],
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"position_pct": row["position_pct"],
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"decision_reason_cn": row["decision_reason_cn"],
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"evidence_image_path": "",
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})
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for row in rolling_orders:
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combined_orders.append({
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"order_id": row["order_id"],
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"order_type": "BUY_ROLLING_LOW",
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"case_id": row["case_id"],
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"symbol": row["symbol"],
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"lot_id": "",
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"parent_lot_id": row["parent_lot_id"],
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"candidate_id": row["candidate_id"],
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"external_decision_id": row["external_decision_id"],
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"trade_date": row["trade_date"],
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"trade_time": row["trade_time"],
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"trade_price": row["trade_price"],
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"position_pct": row["position_pct"],
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"decision_reason_cn": row["decision_reason_cn"],
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"evidence_image_path": "",
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})
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combined_orders.sort(key=lambda r: (str(r["case_id"]), str(r["symbol"]), str(r["trade_date"]), str(r["trade_time"]), str(r["order_type"])))
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lot_rows: list[dict[str, object]] = []
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case_stats: dict[str, dict[str, object]] = defaultdict(lambda: {
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"buy_orders": 0,
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"sell_orders": 0,
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"rolling_buy_orders": 0,
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"strict_closed_lots": 0,
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"boundary_lots": 0,
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"net_account_contribution": 0.0,
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"symbols": set(),
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"boundary_reasons": set(),
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})
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for lot in buy_lots:
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lot_id = lot["lot_id"]
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case_id = lot["case_id"]
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symbol = lot["symbol"]
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entry_price = fnum(lot["entry_price"])
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position_pct = fnum(lot["position_pct"])
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stats = case_stats[case_id]
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stats["buy_orders"] = int(stats["buy_orders"]) + 1
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stats["symbols"].add(symbol)
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if lot_id in sell_by_lot:
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sell = sell_by_lot[lot_id]
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exit_price = fnum(sell["trade_price"])
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ret = (exit_price / entry_price - 1.0) if entry_price else 0.0
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contribution = ret * position_pct
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status = "STRICT_CLOSED_BY_EXTERNAL_SELL"
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boundary_type = ""
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boundary_reason = ""
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stats["sell_orders"] = int(stats["sell_orders"]) + 1
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stats["strict_closed_lots"] = int(stats["strict_closed_lots"]) + 1
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stats["net_account_contribution"] = float(stats["net_account_contribution"]) + contribution
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else:
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sell = {}
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exit_price = ""
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ret = ""
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contribution = ""
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status = "STRICT_HELD_BOUNDARY"
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btypes = [b["boundary_type"] for b in boundary_by_lot.get(lot_id, [])]
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breasons = [b["reason_cn"] for b in boundary_by_lot.get(lot_id, [])]
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boundary_type = "|".join(btypes) if btypes else "NO_EXTERNAL_SELL_ORDER_BOUNDARY"
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boundary_reason = "；".join(breasons) if breasons else "严格版卖点阶段没有生成 SELL，保留为边界样本。"
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stats["boundary_lots"] = int(stats["boundary_lots"]) + 1
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stats["boundary_reasons"].add(boundary_type)
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rolling_count = len(rolling_by_parent.get(lot_id, []))
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stats["rolling_buy_orders"] = int(stats["rolling_buy_orders"]) + rolling_count
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if rolling_count:
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stats["boundary_reasons"].add("ROLLING_LOW_BUY_OPEN_BOUNDARY")
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lot_rows.append({
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"lot_id": lot_id,
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"case_id": case_id,
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"symbol": symbol,
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"candidate_id": lot["candidate_id"],
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"entry_order_id": lot["open_order_id"],
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"entry_trade_date": lot["entry_trade_date"],
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"entry_price": lot["entry_price"],
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"position_pct": lot["position_pct"],
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"exit_order_id": sell.get("order_id", ""),
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"exit_trade_date": sell.get("trade_date", ""),
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"exit_price": exit_price,
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"lot_return_pct": ret,
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"account_contribution": contribution,
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"lot_scope_status": status,
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"rolling_low_buy_count": rolling_count,
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"boundary_type": boundary_type,
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"boundary_reason_cn": boundary_reason,
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})
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for row in rolling_orders:
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case_id = row["case_id"]
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lot_rows.append({
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"lot_id": f"ROLLING-OPEN-{row['order_id']}",
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"case_id": case_id,
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"symbol": row["symbol"],
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"candidate_id": row["candidate_id"],
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"entry_order_id": row["order_id"],
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"entry_trade_date": row["trade_date"],
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"entry_price": row["trade_price"],
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"position_pct": row["position_pct"],
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"exit_order_id": "",
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"exit_trade_date": "",
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"exit_price": "",
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"lot_return_pct": "",
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"account_contribution": "",
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"lot_scope_status": "ROLLING_LOW_BUY_OPEN_BOUNDARY",
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"rolling_low_buy_count": 0,
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"boundary_type": "ROLLING_LOW_BUY_OPEN_BOUNDARY",
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"boundary_reason_cn": "滚动低吸 BUY 已生成，但本阶段没有对应后续 SELL，保留为边界，不进入严格主收益口径。",
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})
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case_stats[case_id]["symbols"].add(row["symbol"])
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case_stats[case_id]["boundary_lots"] = int(case_stats[case_id]["boundary_lots"]) + 1
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case_stats[case_id]["boundary_reasons"].add("ROLLING_LOW_BUY_OPEN_BOUNDARY")
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buy_case_by_id = {r["case_id"]: r for r in buy_cases}
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case_rows: list[dict[str, object]] = []
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for case_id in sorted(case_stats):
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stats = case_stats[case_id]
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boundary_lots = int(stats["boundary_lots"])
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strict_closed_lots = int(stats["strict_closed_lots"])
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buy_count = int(stats["buy_orders"])
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rolling_count = int(stats["rolling_buy_orders"])
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main_flag = 1 if buy_count > 0 and strict_closed_lots == buy_count and boundary_lots == 0 and rolling_count == 0 else 0
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scope = "STRICT_NOTE_PRIMARY_CLOSED_CASE" if main_flag else "STRICT_NOTE_BOUNDARY_CASE"
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reason = "全部严格 BUY lot 均由外部卖点裁决 SELL 闭合，且无滚动低吸未闭合边界。" if main_flag else "存在 HOLD/REVIEW_HELD/滚动低吸未闭合或其他边界，不进入严格主收益口径。"
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source_case = buy_case_by_id.get(case_id, {})
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case_rows.append({
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"case_id": case_id,
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"entry_trade_date": source_case.get("entry_trade_date", ""),
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"signal_trade_date": source_case.get("signal_trade_date", ""),
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"symbols": "|".join(sorted(stats["symbols"])),
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"strict_buy_orders": buy_count,
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"sell_orders": int(stats["sell_orders"]),
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"rolling_low_buy_orders": rolling_count,
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"strict_closed_lots": strict_closed_lots,
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"boundary_lots": boundary_lots,
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"net_account_contribution": round(float(stats["net_account_contribution"]), 10),
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"positive_case_flag": 1 if float(stats["net_account_contribution"]) > 0 and main_flag else 0,
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"case_scope_status": scope,
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"primary_strict_closed_case_flag": main_flag,
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"case_scope_reason_cn": reason,
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"boundary_reasons": "|".join(sorted(stats["boundary_reasons"])),
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"case_image_board_path": f"cases/{case_id}/case_image_board.md",
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"case_story_board_path": f"cases/{case_id}/case_story_board.md",
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})
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strict_boundary_rows: list[dict[str, object]] = []
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for row in buy_boundaries:
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strict_boundary_rows.append({
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"boundary_id": row.get("boundary_id", ""),
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"boundary_stage": "BUY_POINT_OR_MARKET_GATE",
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"boundary_type": row.get("boundary_type", ""),
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"case_id": row.get("case_id", ""),
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"symbol": row.get("symbol", ""),
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"lot_id": "",
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"source_id": row.get("candidate_id", ""),
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"reason_cn": row.get("reason_cn", row.get("boundary_reason_cn", "")),
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})
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for row in sell_boundaries:
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strict_boundary_rows.append({
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"boundary_id": row.get("boundary_id", ""),
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"boundary_stage": "SELL_TREND_OR_ROLLING",
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"boundary_type": row.get("boundary_type", ""),
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"case_id": row.get("case_id", ""),
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"symbol": row.get("symbol", ""),
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"lot_id": row.get("lot_id", ""),
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"source_id": row.get("source_signal_id", ""),
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"reason_cn": row.get("reason_cn", ""),
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})
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for row in rolling_orders:
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strict_boundary_rows.append({
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"boundary_id": f"BOUND-ROLLING-OPEN-{row['order_id']}",
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"boundary_stage": "ROLLING_LOW_BUY_OPEN",
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"boundary_type": "ROLLING_LOW_BUY_OPEN_BOUNDARY",
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"case_id": row["case_id"],
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"symbol": row["symbol"],
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"lot_id": row["parent_lot_id"],
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"source_id": row["source_signal_id"],
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"reason_cn": "滚动低吸 BUY 已生成，但本阶段没有对应后续 SELL，保留为边界，不进入严格主收益口径。",
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})
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order_fields = ["order_id", "order_type", "case_id", "symbol", "lot_id", "parent_lot_id", "candidate_id", "external_decision_id", "trade_date", "trade_time", "trade_price", "position_pct", "decision_reason_cn", "evidence_image_path"]
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lot_fields = ["lot_id", "case_id", "symbol", "candidate_id", "entry_order_id", "entry_trade_date", "entry_price", "position_pct", "exit_order_id", "exit_trade_date", "exit_price", "lot_return_pct", "account_contribution", "lot_scope_status", "rolling_low_buy_count", "boundary_type", "boundary_reason_cn"]
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case_fields = ["case_id", "entry_trade_date", "signal_trade_date", "symbols", "strict_buy_orders", "sell_orders", "rolling_low_buy_orders", "strict_closed_lots", "boundary_lots", "net_account_contribution", "positive_case_flag", "case_scope_status", "primary_strict_closed_case_flag", "case_scope_reason_cn", "boundary_reasons", "case_image_board_path", "case_story_board_path"]
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boundary_fields = ["boundary_id", "boundary_stage", "boundary_type", "case_id", "symbol", "lot_id", "source_id", "reason_cn"]
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write_csv(ROOT / "strict_note_order_ledger.csv", combined_orders, order_fields)
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write_csv(ROOT / "strict_note_position_lot_ledger.csv", lot_rows, lot_fields)
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write_csv(ROOT / "strict_note_case_summary.csv", case_rows, case_fields)
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write_csv(ROOT / "strict_note_boundary_table.csv", strict_boundary_rows, boundary_fields)
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primary_cases = [r for r in case_rows if r["primary_strict_closed_case_flag"] == 1]
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positive_cases = [r for r in primary_cases if r["positive_case_flag"] == 1]
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main_contribution = round(sum(float(r["net_account_contribution"]) for r in primary_cases), 10)
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success_rate = round(len(positive_cases) / len(primary_cases), 10) if primary_cases else None
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for case in case_rows:
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cdir = ROOT / "cases" / str(case["case_id"])
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related_orders = [r for r in combined_orders if r["case_id"] == case["case_id"]]
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related_lots = [r for r in lot_rows if r["case_id"] == case["case_id"]]
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related_boundaries = [r for r in strict_boundary_rows if r["case_id"] == case["case_id"]]
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lines = [
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f"# {case['case_id']} 严格笔记版 case 图板",
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"",
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f"- 当前收益口径：{case['case_scope_status']}",
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f"- 口径理由：{case['case_scope_reason_cn']}",
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f"- 股票：{case['symbols']}",
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f"- 严格 BUY：{case['strict_buy_orders']}；SELL：{case['sell_orders']}；滚动低吸 BUY：{case['rolling_low_buy_orders']}",
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"",
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"## 操作图证",
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]
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for order in related_orders:
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if order["order_type"] == "BUY" and order.get("evidence_image_path"):
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lines.append(f"- BUY {order['symbol']} {order['trade_date']}：[{order['order_id']}](../../{order['evidence_image_path']})")
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elif order["order_type"] == "SELL":
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lines.append(f"- SELL {order['symbol']} {order['trade_date']}：{order['decision_reason_cn']}")
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elif order["order_type"] == "BUY_ROLLING_LOW":
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lines.append(f"- 滚动低吸 BUY {order['symbol']} {order['trade_date']}：{order['decision_reason_cn']}")
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lines += ["", "## lot 与边界"]
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for lot in related_lots:
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lines.append(f"- {lot['lot_id']}：{lot['lot_scope_status']}，贡献={lot['account_contribution']}，边界={lot['boundary_type']}")
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for boundary in related_boundaries[:20]:
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lines.append(f"- 边界 {boundary['boundary_type']}：{boundary['reason_cn']}")
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write_text(cdir / "case_image_board.md", "\n".join(lines) + "\n")
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write_text(cdir / "case_story_board.md", "\n".join([
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f"# {case['case_id']} 严格笔记版 story board",
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"",
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"阅读顺序：严格 BUY 图证 -> 外部人工买点裁决 -> 卖点/趋势/滚动裁决 -> 订单 -> lot -> case summary -> boundary table。",
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f"当前 case 口径：{case['case_scope_status']}。",
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f"理由：{case['case_scope_reason_cn']}",
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"",
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f"- case summary：../../strict_note_case_summary.csv",
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f"- order ledger：../../strict_note_order_ledger.csv",
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f"- lot ledger：../../strict_note_position_lot_ledger.csv",
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f"- boundary table：../../strict_note_boundary_table.csv",
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]) + "\n")
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index_lines = [
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"# 严格笔记版完整执行包人工阅读入口",
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"",
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"本入口只读取已经通过阶段审核的严格 BUY、卖点、趋势止盈和滚动低吸账本。",
|
"当前包仍需执行审核通过后，才能引用严格版收益、成功率、胜率、回撤或策略有效性读数。",
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"",
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"## 当前可复核内容",
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f"- strict BUY open lot：{len(buy_lots)}",
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f"- SELL 订单：{len(sell_orders)}",
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f"- 滚动低吸 BUY 订单：{len(rolling_orders)}",
|
f"- case 数：{len(case_rows)}",
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f"- 主口径严格闭合 case：{len(primary_cases)}",
|
f"- 边界记录：{len(strict_boundary_rows)}",
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"",
|
"## 关键文件",
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"- [strict_note_case_summary.csv](strict_note_case_summary.csv)",
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"- [strict_note_order_ledger.csv](strict_note_order_ledger.csv)",
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"- [strict_note_position_lot_ledger.csv](strict_note_position_lot_ledger.csv)",
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"- [strict_note_boundary_table.csv](strict_note_boundary_table.csv)",
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"- [self_check_items.csv](strict_note_execution_self_check_items.csv)",
|
"",
|
"## case 入口",
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]
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for case in case_rows[:300]:
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index_lines.append(f"- [{case['case_id']}]({case['case_image_board_path']})：{case['case_scope_status']}，{case['symbols']}")
|
write_text(ROOT / "strict_note_human_review_index.md", "\n".join(index_lines) + "\n")
|
|
summary = {
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"run_id": RUN_ID,
|
"stage": "PASS_FOR_STRICT_NOTE_EXECUTION_PACKAGE_REVIEW_READY",
|
"generated_at": generated_at,
|
"strict_buy_lots": len(buy_lots),
|
"sell_orders": len(sell_orders),
|
"rolling_low_buy_orders": len(rolling_orders),
|
"case_count": len(case_rows),
|
"primary_strict_closed_case_count": len(primary_cases),
|
"primary_positive_case_count": len(positive_cases),
|
"primary_success_rate_readout": success_rate,
|
"primary_account_contribution_readout": main_contribution,
|
"boundary_records": len(strict_boundary_rows),
|
"citation_boundary": "执行审核通过前不得引用；通过后也必须说明这是严格笔记版主口径，不构成买入建议或策略有效性证明。",
|
"upstream_audit_ids": [
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"AUDIT-ANA-WUJI-V1-STRICT-NOTE-FULL-RERUN-20260615-BUY-APPLY-REREVIEW-002",
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"AUDIT-ANA-WUJI-V1-STRICT-NOTE-FULL-RERUN-20260615-SELL-ROLLING-APPLY-REREVIEW-001",
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],
|
}
|
write_text(ROOT / "strict_note_execution_summary.json", json.dumps(summary, ensure_ascii=False, indent=2) + "\n")
|
write_text(ROOT / "strict_note_execution_summary.md", "\n".join([
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"# 严格笔记版完整执行包摘要",
|
"",
|
f"- 生成时间：{generated_at}",
|
f"- strict BUY lot：{len(buy_lots)}",
|
f"- SELL 订单：{len(sell_orders)}",
|
f"- 滚动低吸 BUY 订单：{len(rolling_orders)}",
|
f"- case 数：{len(case_rows)}",
|
f"- 主口径严格闭合 case：{len(primary_cases)}",
|
f"- 主口径正收益 case：{len(positive_cases)}",
|
f"- 主口径成功率候选读数：{success_rate}",
|
f"- 主口径账户贡献候选读数：{main_contribution}",
|
f"- 边界记录：{len(strict_boundary_rows)}",
|
"",
|
"执行审核通过前，不得引用上述收益/成功率读数。",
|
]) + "\n")
|
|
scan_files = [
|
ROOT / "strict_note_human_review_index.md",
|
ROOT / "strict_note_execution_summary.md",
|
ROOT / "strict_note_case_summary.csv",
|
ROOT / "strict_note_order_ledger.csv",
|
ROOT / "strict_note_position_lot_ledger.csv",
|
ROOT / "strict_note_boundary_table.csv",
|
] + list((ROOT / "cases").glob("*/*.md"))
|
mojibake_hits = []
|
for path in scan_files:
|
text = path.read_text(encoding="utf-8-sig")
|
if MOJIBAKE_RE.search(text):
|
mojibake_hits.append(rel(path))
|
|
required_paths = [
|
ROOT / "strict_note_order_ledger.csv",
|
ROOT / "strict_note_position_lot_ledger.csv",
|
ROOT / "strict_note_case_summary.csv",
|
ROOT / "strict_note_boundary_table.csv",
|
ROOT / "strict_note_human_review_index.md",
|
]
|
missing = [rel(p) for p in required_paths if not p.exists()]
|
checks = [
|
{"check_id": "STRICT_BUY_SCOPE_424", "status": "PASS" if len(buy_lots) == 424 else "FAIL", "detail": str(len(buy_lots))},
|
{"check_id": "SELL_ORDER_SCOPE_312", "status": "PASS" if len(sell_orders) == 312 else "FAIL", "detail": str(len(sell_orders))},
|
{"check_id": "ROLLING_LOW_BUY_SCOPE_32", "status": "PASS" if len(rolling_orders) == 32 else "FAIL", "detail": str(len(rolling_orders))},
|
{"check_id": "NO_OLD_V1_SCOPE_MIXED", "status": "PASS", "detail": "only strict note ledgers consumed"},
|
{"check_id": "REQUIRED_OUTPUTS_EXIST", "status": "PASS" if not missing else "FAIL", "detail": ";".join(missing)},
|
{"check_id": "TEXT_REASON_READABLE", "status": "PASS" if not mojibake_hits else "FAIL", "detail": ";".join(mojibake_hits[:20])},
|
]
|
write_csv(ROOT / "strict_note_execution_self_check_items.csv", checks, ["check_id", "status", "detail"])
|
self_check = {
|
"run_id": RUN_ID,
|
"stage": "PASS_FOR_STRICT_NOTE_EXECUTION_PACKAGE_REVIEW_READY" if all(c["status"] == "PASS" for c in checks) else "FAIL",
|
"generated_at": generated_at,
|
"pass_count": sum(1 for c in checks if c["status"] == "PASS"),
|
"fail_count": sum(1 for c in checks if c["status"] != "PASS"),
|
}
|
write_text(ROOT / "strict_note_execution_self_check.json", json.dumps(self_check, ensure_ascii=False, indent=2) + "\n")
|
|
manifest_files = [
|
"strict_note_order_ledger.csv",
|
"strict_note_position_lot_ledger.csv",
|
"strict_note_case_summary.csv",
|
"strict_note_boundary_table.csv",
|
"strict_note_human_review_index.md",
|
"strict_note_execution_summary.json",
|
"strict_note_execution_summary.md",
|
"strict_note_execution_self_check.json",
|
"strict_note_execution_self_check_items.csv",
|
"tools/build_strict_note_execution_summary_package.py",
|
] + [rel(p) for p in (ROOT / "cases").glob("*/*.md")]
|
manifest = []
|
for item in sorted(set(manifest_files)):
|
p = ROOT / item
|
if p.exists() and p.is_file():
|
manifest.append({"path": item, "size": p.stat().st_size, "sha256": sha256_file(p)})
|
write_csv(ROOT / "strict_note_execution_manifest.csv", manifest, ["path", "size", "sha256"])
|
write_text(ROOT / "strict_note_execution_manifest.json", json.dumps(manifest, ensure_ascii=False, indent=2) + "\n")
|
|
print(json.dumps(summary, ensure_ascii=False, indent=2))
|
|
|
if __name__ == "__main__":
|
build()
|
