"""每日复盘报告生成"""

import logging

from app.config import settings, today_trade_date

logger = logging.getLogger(__name__)


async def generate_review() -> dict:
    """生成每日复盘报告"""
    from app.data.tushare_client import tushare_client
    from app.data import tencent_client
    from app.engine.recommender import get_latest_recommendations, get_latest_sectors

    latest_trade_date = tushare_client.get_latest_trade_date()
    trade_date = today_trade_date()

    # 收集市场数据
    result = await get_latest_recommendations()
    mt = result.get("market_temp")
    sectors = await get_latest_sectors()
    recs = result.get("recommendations", [])

    # 实时指数
    try:
        index_data = await tencent_client.get_index_realtime()
    except Exception:
        index_data = {}

    # 构建数据摘要
    market_summary = ""
    if mt:
        market_summary = (
            f"市场温度: {mt.temperature}/100, "
            f"上涨{mt.up_count}家/下跌{mt.down_count}家, "
            f"涨停{mt.limit_up_count}家/跌停{mt.limit_down_count}家, "
            f"连板最高{mt.max_streak}板, 炸板率{mt.broken_rate}%"
        )

    index_summary = ""
    name_map = {"000001.SH": "上证", "399001.SZ": "深证", "399006.SZ": "创业板"}
    for code in ["000001.SH", "399001.SZ", "399006.SZ"]:
        d = index_data.get(code, {})
        if d:
            index_summary += f"{name_map[code]}: {d.get('price', 0):.2f} ({d.get('pct_chg', 0):+.2f}%), "

    sector_summary = "热门板块: " + "、".join(
        f"{s.sector_name}({(getattr(s, 'realtime_pct_change', None) or s.pct_change):+.1f}%)" for s in sectors[:5]
    )

    rec_summary = ""
    for r in recs[:5]:
        signal_map = {"breakout": "突破", "breakout_confirm": "确认", "pullback": "回踩",
                      "launch": "启动", "reversal": "反转"}
        st = signal_map.get(r.entry_signal_type, r.entry_signal_type)
        rec_summary += f"\n- {r.name}({r.ts_code}): {st}型, 评分{r.score}, {r.signal}"

    user_msg = f"""请根据以下数据生成今日A股市场复盘报告（中文）：

日期: {trade_date}
Tushare 最新交易日: {latest_trade_date}
{market_summary}
{index_summary}
{sector_summary}

今日推荐股票:
{rec_summary}

请按以下格式输出（Markdown格式，总字数300-500字）：
## 市场概况
（指数走势、量能变化）

## 板块热点
（哪些板块领涨、资金流向）

## 交易机会
（今日推荐个股简要点评）

## 明日关注
（关注方向和操作建议）"""

    if settings.deepseek_api_key:
        try:
            from app.llm.client import get_client

            client = get_client()
            response = await client.chat.completions.create(
                model=settings.deepseek_model,
                messages=[
                    {"role": "system", "content": "你是一位专业的A股市场分析师，擅长市场复盘和策略分析。回复使用Markdown格式，简洁专业。"},
                    {"role": "user", "content": user_msg},
                ],
                max_tokens=1500,
                temperature=0.5,
            )
            content = response.choices[0].message.content.strip()
            generated_by = "llm"
        except Exception as e:
            logger.error(f"生成复盘报告失败，使用规则兜底: {e}")
            content = _build_fallback_review(
                trade_date=trade_date,
                market_summary=market_summary,
                index_summary=index_summary,
                sector_summary=sector_summary,
                recommendations=recs,
            )
            generated_by = "rules"
    else:
        content = _build_fallback_review(
            trade_date=trade_date,
            market_summary=market_summary,
            index_summary=index_summary,
            sector_summary=sector_summary,
            recommendations=recs,
        )
        generated_by = "rules"

    try:
        # 保存到数据库
        from sqlalchemy import text
        from app.db.database import get_db
        async with get_db() as db:
            await db.execute(
                text(
                    "INSERT OR REPLACE INTO daily_reviews (trade_date, content) "
                    "VALUES (:td, :content)"
                ),
                {"td": trade_date, "content": content},
            )
            await db.commit()

        logger.info(f"已生成 {trade_date} 复盘报告 ({generated_by})")
        return {"status": "ok", "trade_date": trade_date, "content": content, "generated_by": generated_by}

    except Exception as e:
        logger.error(f"保存复盘报告失败: {e}")
        return {"status": "error", "message": str(e)}


def _build_fallback_review(
    trade_date: str,
    market_summary: str,
    index_summary: str,
    sector_summary: str,
    recommendations: list,
) -> str:
    """LLM 不可用时生成结构化规则复盘，避免页面空白。"""
    actionable = [r for r in recommendations if getattr(r, "action_plan", "") == "可操作"]
    watch = [r for r in recommendations if getattr(r, "action_plan", "") == "重点关注"]
    top_recs = recommendations[:5]
    rec_lines = "\n".join(
        f"- {r.name}({r.ts_code})：{getattr(r, 'action_plan', '观察')}，"
        f"{getattr(r, 'entry_signal_type', 'none')} 信号，评分 {getattr(r, 'score', 0)}。"
        for r in top_recs
    ) or "- 暂无推荐标的。"

    return f"""## 市场概况
{trade_date} 市场温度处于中性偏谨慎区间。{market_summary or "暂无市场温度数据。"} {index_summary or ""}

## 板块热点
{sector_summary or "暂无板块热度数据。"} 当前板块证据主要用于确认推荐方向是否有资金和赚钱效应支撑。

## 交易机会
今日推荐池共 {len(recommendations)} 只，其中可操作 {len(actionable)} 只、重点关注 {len(watch)} 只。当前更适合按触发条件等待确认，不宜把观察标的直接当作买入标的。
{rec_lines}

## 明日关注
优先跟踪重点关注标的能否满足触发条件，同时观察主线板块是否延续。若市场温度回落或板块资金退潮，应降低仓位并把未确认标的转回观察池。"""