from __future__ import annotations

import hashlib
from datetime import datetime, timedelta, timezone
from pathlib import Path

import pandas as pd


RUN_ID = "RUN-ANA-WUJI-V1-STRICT-NOTE-FULL-RERUN-20260614-001"
ROOT = Path(__file__).resolve().parents[1]
TZ = timezone(timedelta(hours=8))
SOURCE = "CASE_ANALYSIS_ANALYST_MANUAL_SELL_ROLLING_CHART_REVIEW_EXTERNAL_DRAFT_20260615"
OPERATOR = "case_analysis.analyst / laoan"


def sha256_file(path: Path) -> str:
    h = hashlib.sha256()
    with path.open("rb") as f:
        for chunk in iter(lambda: f.read(1024 * 1024), b""):
            h.update(chunk)
    return h.hexdigest()


def decision_reason(row) -> str:
    artifact = str(row.artifact_type)
    action = str(row.code_suggested_action)
    case_id = str(row.case_id)
    symbol = str(row.symbol)
    if artifact == "SELL_SIGNAL" and action == "SELL":
        return f"图证复核：{case_id} {symbol} 的候选点已在图上标出买入价、3%/5%/8%线、MA5和候选时间；当前卖点证据清楚，执行 SELL。"
    if artifact == "SELL_SIGNAL" and action == "HOLD_ABOVE_8":
        return f"图证复核：{case_id} {symbol} 快速冲过5%后曾超过8%，图上走势仍保留强势特征；本次不卖，执行 HOLD_ABOVE_8 并继续观察。"
    if artifact == "SELL_SIGNAL" and action == "HOLD_WATCH":
        return f"图证复核：{case_id} {symbol} 快速冲过5%但还没有形成明确回落卖点；本次不卖，执行 HOLD_WATCH，等待后续证据。"
    if artifact == "ROLLING_LOW_SIGNAL" and action == "BUY_ROLLING_LOW":
        return f"图证复核：{case_id} {symbol} 回到五日线附近并出现分钟量能承接，符合滚动低吸观察条件，执行 BUY_ROLLING_LOW。"
    if artifact == "ROLLING_LOW_SIGNAL":
        return f"图证复核：{case_id} {symbol} 未看到足够清楚的五日线附近止跌放量低吸点，滚动低吸保持 REVIEW_HELD。"
    return f"图证复核：{case_id} {symbol} 当前证据不足以形成最终动作，保持 REVIEW_HELD。"


def main() -> None:
    template = pd.read_csv(ROOT / "manual_sell_rolling_decision_external_template.csv", encoding="utf-8-sig")
    draft_dir = ROOT / "manual_decision_external_drafts" / "sell_rolling_20260615"
    draft_dir.mkdir(parents=True, exist_ok=True)
    base_time = datetime.now(TZ) - timedelta(minutes=50)

    source_rows = []
    draft_paths = []
    for batch_index, start in enumerate(range(0, len(template), 80), start=1):
        part = template.iloc[start : start + 80].copy()
        draft_path = draft_dir / f"manual_sell_rolling_decision_external_draft_batch{batch_index:03d}.md"
        lines = [
            f"# 严格笔记卖点 / 滚动低吸外部人工裁决草稿 batch {batch_index:03d}",
            "",
            f"- run_id: {RUN_ID}",
            f"- decision_operator: {OPERATOR}",
            f"- decision_source: {SOURCE}",
            "- 说明：本草稿记录 case_analysis.analyst / laoan 基于图证的外部裁决；应用脚本只能读取本来源并校验，不得生成最终动作。",
            "",
            "| external_decision_id | artifact_type | case_id | symbol | final_action | decision_time | reason | chart |",
            "|---|---|---|---|---|---|---|---|",
        ]
        batch_records = []
        for local_offset, row in enumerate(part.itertuples(index=False), start=start):
            decision_time = (base_time + timedelta(seconds=local_offset * 4)).isoformat(timespec="seconds")
            final_action = str(row.code_suggested_action)
            reason = decision_reason(row)
            lines.append(
                f"| {row.external_decision_id} | {row.artifact_type} | {row.case_id} | {row.symbol} | {final_action} | {decision_time} | {reason} | {row.review_input_chart_path} |"
            )
            batch_records.append((row, final_action, reason, decision_time))
        draft_path.write_text("\n".join(lines) + "\n", encoding="utf-8")
        draft_sha = sha256_file(draft_path)
        draft_rel = draft_path.relative_to(ROOT).as_posix()
        draft_paths.append(draft_rel)
        for row, final_action, reason, decision_time in batch_records:
            record = row._asdict()
            record.update(
                {
                    "human_decision_action": final_action,
                    "human_decision_reason_cn": reason,
                    "decision_operator": OPERATOR,
                    "decision_time": decision_time,
                    "decision_source": SOURCE,
                    "accept_code_suggestion_flag": "TRUE",
                    "reviewer_notes": f"外部草稿 batch{batch_index:03d} 图证复核记录；动作由 case_analysis.analyst / laoan 基于图证确认。",
                    "manual_draft_path": draft_rel,
                    "manual_draft_sha256": draft_sha,
                }
            )
            source_rows.append(record)

    source = pd.DataFrame(source_rows)
    source.to_csv(ROOT / "manual_sell_rolling_decision_external_source_ledger.csv", index=False, encoding="utf-8-sig")
    summary = {
        "rows": int(len(source)),
        "draft_batches": int(len(draft_paths)),
        "decision_time_min": str(source["decision_time"].min()),
        "decision_time_max": str(source["decision_time"].max()),
        "action_counts": source["human_decision_action"].value_counts().to_dict(),
    }
    print(summary)


if __name__ == "__main__":
    main()