from __future__ import annotations
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import hashlib
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import json
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from datetime import datetime
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from pathlib import Path
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import pandas as pd
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RUN_ID = "RUN-ANA-WUJI-BASELINE-PILOT-20260607-001"
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CASE_MATTER_ID = "ANA-WUJI-BASELINE-2023-2026"
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DESIGN_ID = "DESIGN-WUJI-BASELINE-FLOW-20260607"
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DESIGN_AUDIT_ID = "AUDIT-ANA-WUJI-BASELINE-FLOW-20260607-001"
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ROOT = Path(__file__).resolve().parents[1]
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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 read_json(path: Path) -> dict:
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return json.loads(path.read_text(encoding="utf-8"))
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def manifest_files() -> list[dict]:
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rows: list[dict] = []
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for path in sorted(ROOT.rglob("*")):
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if not path.is_file():
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continue
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rel = path.relative_to(ROOT).as_posix()
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if rel == "manifest.json":
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continue
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rows.append(
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{
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"path": rel,
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"exists": True,
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"size": path.stat().st_size,
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"sha256": sha256_file(path),
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}
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)
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return rows
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def main() -> None:
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generated_at = datetime.now().astimezone().isoformat(timespec="seconds")
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candidate_summary = read_json(ROOT / "candidate_generation_summary.json")
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candidate_counts = candidate_summary["candidate_counts"]
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candidate_image_summary = read_json(ROOT / "candidate_image_generation_summary.json")
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entry_review_summary = read_json(ROOT / "entry_review_generation_summary.json")
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entry_ai_summary = read_json(ROOT / "entry_ai_review_summary.json")
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exit_review_summary = read_json(ROOT / "exit_review_generation_summary.json")
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exit_ai_summary = read_json(ROOT / "exit_ai_review_summary.json")
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self_check = read_json(ROOT / "self_check.json")
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source_db = read_json(ROOT / "source_db_direct_check.json")
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source_boundaries = source_db["execution_boundaries"]
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daily_range = source_boundaries["daily_price_usable_range"]
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minute_range = source_boundaries["minute_price_usable_range_for_intraday_replay"]
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breadth_range = source_boundaries["market_breadth_daily_usable_range"]
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image_manifest = pd.read_csv(ROOT / "image_manifest.csv", encoding="utf-8-sig")
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decisions = pd.read_csv(ROOT / "decision_log.csv", encoding="utf-8-sig")
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orders = pd.read_csv(ROOT / "order_ledger.csv", encoding="utf-8-sig")
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lots = pd.read_csv(ROOT / "position_lot_ledger.csv", encoding="utf-8-sig")
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case_summary = pd.read_csv(ROOT / "case_summary.csv", encoding="utf-8-sig")
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completed_outputs = [
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"run_config.md",
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"run_config.json",
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"baseline_rule_mapping.csv",
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"baseline_excluded_rule_table.csv",
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"code_validation_report.md",
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"code_validation_checks.json",
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"baseline_mapping_check.csv",
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"sample_recalc_check.csv",
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"chart_smoke_manifest.csv",
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"source_db_direct_check.json",
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"candidate_ledger.csv",
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"candidate_date_summary.csv",
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"case_index.csv",
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"selected_candidate_ledger.csv",
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"image_manifest.csv",
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"case_image_board.md",
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"case_story_board.md",
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"entry_review_generation_summary.json",
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"entry_ai_review_summary.json",
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"sell_signal_candidates.csv",
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"sell_decision_log.csv",
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"exit_resolution_log.csv",
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"order_ledger.csv",
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"position_lot_ledger.csv",
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"daily_account_ledger.csv",
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"case_summary.csv",
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"chart_evidence_audit.csv",
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"self_check.json",
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"self_check.md",
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]
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held_items = [
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{
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"item": "unresolved_lots",
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"count": int(exit_ai_summary["unresolved_lot_count"]),
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"reason": "Exit review has resolved all previous held lots under existing rules. Remaining non-SELL lots are explicit WINDOW_END_VALUATION_ONLY or EXIT_DATA_GAP_HELD rows, not forced SELL.",
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},
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{
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"item": "return_statistics",
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"count": 1,
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"reason": "Execution audit is pending and strict_baseline_return_ready_flag=false.",
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},
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]
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summary = {
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"run_id": RUN_ID,
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"case_matter_id": CASE_MATTER_ID,
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"design_id": DESIGN_ID,
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"design_audit_id": DESIGN_AUDIT_ID,
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"created_at": "2026-06-07T23:37:00+08:00",
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"updated_at": generated_at,
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"role_instance_id": "case_analysis.analyst",
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"stage": "STRUCTURE_PILOT_EXIT_REVIEW_RESOLVED_SELF_CHECK_DONE",
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"status": "exit_review_held_lots_resolved_self_check_pass_return_stat_held_execution_review_submission_ready",
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"completed_outputs": completed_outputs,
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"held_items": held_items,
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"conclusion_boundary": "Structure pilot only. No complete baseline return, success rate, win rate, drawdown, or strategy effectiveness conclusion. Closed-lot contribution is an internal ledger check and must not be quoted as final return.",
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"source_db_validation": {
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"status": "PASS_WITH_MINUTE_BOUNDARY",
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"evidence": "source_db_direct_check.json",
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"daily_price_range": f"{daily_range['min_date']} to {daily_range['max_date']}",
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"minute_price_range": f"{minute_range['min_date']} to {minute_range['max_date']}",
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"market_breadth_range": f"{breadth_range['min_date']} to {breadth_range['max_date']}",
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"market_breadth_scope": "ALL_A_SHARE/ALL",
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"market_breadth_run_id": breadth_range["min_run_id"],
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},
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"candidate_pool": {
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"candidate_rows": candidate_counts["candidate_rows"],
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"candidate_entry_dates": candidate_counts["candidate_entry_dates"],
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"selected_pilot_cases": len(candidate_summary["selected_cases"]),
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"market_gate_open_dates": candidate_counts["market_gate_open_entry_dates"],
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"market_gate_closed_dates": candidate_counts["market_gate_closed_entry_dates"],
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},
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"image_package": {
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"image_count": len(image_manifest),
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"role_counts": image_manifest.chart_role.value_counts().to_dict(),
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"candidate_daily_images": candidate_image_summary["image_count"],
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"entry_review_images": entry_review_summary["entry_review_images"],
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"buy_decision_images": entry_ai_summary["buy_decision_image_count"],
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"exit_signal_images": exit_review_summary["exit_signal_images"],
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"sell_decision_images": int((image_manifest.chart_role == "exit_1m_sell_decision_view").sum()),
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},
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"trade_ledger": {
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"order_counts": orders.action.value_counts().to_dict(),
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"lot_status_counts": lots.lot_status.value_counts().to_dict(),
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"case_count": len(case_summary),
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"closed_lot_account_return_sum_for_recalc_only": exit_ai_summary["closed_lot_account_return_sum"],
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},
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"decision_counts": decisions.groupby(["decision_stage", "action_status"]).size().astype(int).to_dict(),
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"self_check": {
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"status": self_check["overall_status"],
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"check_count": self_check["check_count"],
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"fail_count": self_check["fail_count"],
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"evidence": "self_check.json",
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},
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"repair_context": {
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"previous_execution_audit_id": "AUDIT-ANA-WUJI-BASELINE-PILOT-20260608-EXEC-001",
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"previous_review_message_id": "msg_20260608012706751_a5dbc58b",
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"fixed_issue": "ANA-ISSUE-WUJI-ACCOUNT-LEDGER-DIRECTION-20260608-001",
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"repair_summary": "Fixed daily_account_ledger cash/open-position direction, added account semantic self-checks, refreshed root case_image_board.md and run_config.md status.",
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},
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"exit_review_resolution_context": {
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"previous_issue": "ANA-ISSUE-WUJI-EXIT-REVIEW-20260608-001",
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"resolution_summary": "Re-scanned the approved 10-trading-day observation window with 1-minute evidence. Four previously held lots were closed by minute-confirmed SELL points; one lot is WINDOW_END_VALUATION_ONLY; one lot remains EXIT_DATA_GAP_HELD because minute data coverage ends before the signal date.",
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"evidence": "exit_resolution_log.csv",
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},
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"strict_baseline_return_ready_flag": False,
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"execution_review_status": "SUBMISSION_READY",
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}
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# Convert tuple keys from groupby for JSON stability.
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summary["decision_counts"] = {f"{k[0]}::{k[1]}": v for k, v in summary["decision_counts"].items()}
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(ROOT / "summary.json").write_text(
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json.dumps(summary, ensure_ascii=False, indent=2) + "\n",
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encoding="utf-8",
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)
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summary_md = [
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f"# {RUN_ID} summary",
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"",
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"当前阶段：`STRUCTURE_PILOT_EXIT_REVIEW_RESOLVED_SELF_CHECK_DONE`",
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"当前状态：6 笔待审 lot 已按现有规则复核，结构试点自检通过；执行审核待提交；`RETURN_STAT_READY=false`。",
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"",
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"## 已完成",
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"",
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"1. 完成 run_config、蓝本映射、代码验证、T+1 / 账本烟测、中文 K 线烟测和源库直连校验。",
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f"2. 生成真实候选池：{candidate_counts['candidate_rows']} 行，覆盖 {candidate_counts['candidate_entry_dates']} 个入场日。",
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f"3. 选择 {len(candidate_summary['selected_cases'])} 个代表性小样本案例，并生成 100 日选股日 K 图。",
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f"4. 生成买入分时复核图 {entry_review_summary['entry_review_images']} 张，AI 买入裁决图 {entry_ai_summary['buy_decision_image_count']} 张。",
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f"5. 生成卖点日 K 信号图 {exit_review_summary['exit_signal_images']} 张，AI SELL 分时裁决图 {summary['image_package']['sell_decision_images']} 张。",
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f"6. 建立订单、lot、事件账户账本和案例汇总：BUY {summary['trade_ledger']['order_counts'].get('BUY', 0)} 笔，SELL {summary['trade_ledger']['order_counts'].get('SELL', 0)} 笔。",
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f"7. 完成机器自检：{self_check['check_count']} 项，失败 {self_check['fail_count']} 项，图片 manifest 共 {len(image_manifest)} 张图且 hash 可反查。",
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"8. 根据执行审核反馈 `AUDIT-ANA-WUJI-BASELINE-PILOT-20260608-EXEC-001` 修复账户流水方向，并补充现金 / 持仓方向自检。",
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"9. 针对 `ANA-ISSUE-WUJI-EXIT-REVIEW-20260608-001` 重新按 1 分钟 evidence 扫描观察窗口：4 笔原待审 lot 转为分钟线确认 SELL，1 笔为窗口末估值保留，1 笔为分钟数据缺口保留。",
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"",
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"## 未完成 / 保留项",
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"",
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"1. 本次退出复核执行审核尚未通过，审核通过前不得把本包读成正式结论。",
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f"2. 未闭合 / 非真实 SELL lot 共 {exit_ai_summary['unresolved_lot_count']} 笔：1 笔窗口末估值保留，1 笔分钟数据缺口保留。",
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"3. 当前闭合 lot 的账户贡献合计只用于账本复算，不是完整 baseline 收益率、成功率、胜率或回撤。",
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"",
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"## 结论边界",
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"",
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"本包只证明第一轮小样本结构试点可以串起候选、图片、AI 手工裁决、订单 / lot / 账户账本、自检和人工审核入口。",
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"它不产生完整无忌 baseline 收益、成功率、胜率、回撤或策略有效性结论。",
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"",
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"后续必须提交 `case_analysis.reviewer` 执行审核；若审核要求继续修正口径、图或账本，按审计意见回写后再继续下一批。",
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"",
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]
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(ROOT / "summary.md").write_text("\n".join(summary_md), encoding="utf-8")
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files = manifest_files()
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manifest = {
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"schema_version": "1.0",
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"run_id": RUN_ID,
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"manifest_stage": "STRUCTURE_PILOT_EXIT_REVIEW_RESOLVED_SELF_CHECK_DONE",
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"generated_at": generated_at,
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"hash_status": "size_and_sha256_recorded_for_current_artifacts_manifest_self_excluded",
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"overall_status": self_check["overall_status"],
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"file_count": len(files),
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"files": files,
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"directories": sorted([p.relative_to(ROOT).as_posix() for p in ROOT.rglob("*") if p.is_dir()]),
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"notes": "Exit-review-resolved structure pilot package manifest. manifest.json itself is excluded from hashing for stability. Execution review submission is pending; return statistics are not ready.",
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}
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(ROOT / "manifest.json").write_text(
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json.dumps(manifest, ensure_ascii=False, indent=2) + "\n",
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encoding="utf-8",
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)
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if __name__ == "__main__":
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main()
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