"""LLM 工具执行器

根据工具名调用现有数据层，返回 JSON 字符串供 LLM 使用。
"""

import json
import logging
import math

from app.db.error_logger import log_error

logger = logging.getLogger(__name__)

_chat_user_context: dict | None = None


def set_chat_user_context(user: dict | None) -> None:
    global _chat_user_context
    _chat_user_context = user


async def execute_tool(name: str, arguments: dict) -> str:
    """执行工具调用，返回 JSON 字符串"""
    try:
        if name == "get_strategy_board":
            return await _get_strategy_board()
        elif name == "get_market_temperature":
            return await _get_market_temperature()
        elif name == "get_hot_sectors":
            return await _get_hot_sectors(arguments.get("limit", 10))
        elif name == "get_latest_recommendations":
            return await _get_latest_recommendations()
        elif name == "get_user_watchlist_snapshot":
            return await _get_user_watchlist_snapshot()
        elif name == "get_stock_kline":
            return await _get_stock_kline(
                arguments["ts_code"], arguments.get("days", 60)
            )
        elif name == "get_stock_capital_flow":
            return await _get_stock_capital_flow(
                arguments["ts_code"], arguments.get("days", 10)
            )
        elif name == "search_stock":
            return await _search_stock(arguments["keyword"])
        elif name == "get_stock_technical_signal":
            return await _get_stock_technical_signal(arguments["ts_code"])
        elif name == "diagnose_stock":
            return await _diagnose_stock(arguments["ts_code"], arguments.get("mode", "entry"))
        elif name == "get_sector_performance":
            return await _get_sector_performance(arguments["sector_name"])
        elif name == "get_realtime_indices":
            return await _get_realtime_indices()
        else:
            return json.dumps({"error": f"未知工具: {name}"}, ensure_ascii=False)
    except Exception as e:
        logger.error(f"工具执行失败 {name}: {e}")
        await log_error(
            "llm_tool",
            f"工具执行失败 {name}: {e}",
            detail=f"arguments={json.dumps(arguments, ensure_ascii=False, default=str)}",
        )
        return json.dumps({"error": str(e)}, ensure_ascii=False)


def _clean_for_json(obj):
    """清理 float NaN/Inf 为 None"""
    if isinstance(obj, dict):
        return {k: _clean_for_json(v) for k, v in obj.items()}
    if isinstance(obj, list):
        return [_clean_for_json(v) for v in obj]
    if isinstance(obj, float) and (math.isnan(obj) or math.isinf(obj)):
        return None
    return obj


async def _get_strategy_board() -> str:
    """返回当日市场概况 + 策略状态，替代已删除的 strategy_board 模块。"""
    from app.engine.recommender import get_latest_recommendations

    result = await get_latest_recommendations()
    mt = result.get("market_temp")
    recs = result.get("recommendations", [])
    strategy = result.get("strategy_profile") or {}

    actionable = [r for r in recs if r.action_plan == "可操作"]
    watch = [r for r in recs if r.action_plan == "重点关注"]

    payload = {
        "market_temperature": mt.temperature if mt else 0,
        "up_count": mt.up_count if mt else 0,
        "down_count": mt.down_count if mt else 0,
        "limit_up_count": mt.limit_up_count if mt else 0,
        "strategy_name": strategy.get("name", "未知"),
        "market_stance": strategy.get("market_stance", ""),
        "decision_note": strategy.get("decision_note", ""),
        "actionable_count": len(actionable),
        "watch_count": len(watch),
        "total_recommendations": len(recs),
    }
    return json.dumps(_clean_for_json(payload), ensure_ascii=False, default=str)


async def _get_market_temperature() -> str:
    from app.engine.recommender import get_latest_recommendations
    result = await get_latest_recommendations()
    mt = result.get("market_temp")
    if not mt:
        return json.dumps({"error": "暂无市场温度数据"}, ensure_ascii=False)
    return json.dumps(mt.model_dump(), ensure_ascii=False, default=str)


async def _get_hot_sectors(limit: int) -> str:
    from app.engine.recommender import get_latest_sectors
    sectors = await get_latest_sectors()
    data = [s.model_dump() for s in sectors[:limit]]
    return json.dumps(data, ensure_ascii=False, default=str)


async def _get_latest_recommendations() -> str:
    from app.engine.recommender import get_latest_recommendations
    result = await get_latest_recommendations()
    recs = result.get("recommendations", [])
    data = []
    for rec in recs:
        item = rec.model_dump(exclude={"created_at"})
        item["llm_analysis"] = ""
        data.append(item)
    return json.dumps(data, ensure_ascii=False, default=str)


async def _get_user_watchlist_snapshot() -> str:
    from sqlalchemy import text
    from app.db.database import get_db

    user_id = (_chat_user_context or {}).get("id")
    if not user_id:
        return json.dumps({"error": "当前会话缺少用户上下文"}, ensure_ascii=False)

    async with get_db() as db:
        rows = (await db.execute(
            text(
                "SELECT w.id, w.ts_code, w.name, w.note, w.watch_group, w.cost_price, w.updated_at, "
                "a.conclusion, a.advice, a.trigger_condition, a.risk_note, a.summary, "
                "a.analysis_mode, a.created_at AS analysis_created_at "
                "FROM user_watchlists w "
                "LEFT JOIN watchlist_analyses a ON a.id = ("
                "  SELECT id FROM watchlist_analyses "
                "  WHERE watchlist_id = w.id ORDER BY created_at DESC, id DESC LIMIT 1"
                ") "
                "WHERE w.user_id = :uid AND COALESCE(w.is_active, 1) = 1 "
                "ORDER BY CASE w.watch_group "
                "  WHEN 'focus' THEN 1 "
                "  WHEN 'candidate' THEN 2 "
                "  WHEN 'holding' THEN 3 "
                "  ELSE 4 END, w.updated_at DESC, w.id DESC"
            ),
            {"uid": user_id},
        )).fetchall()

    items = [dict(row._mapping) for row in rows]
    grouped = {"focus": 0, "candidate": 0, "holding": 0, "observe": 0}
    for item in items:
        key = item.get("watch_group") or "observe"
        if key in grouped:
            grouped[key] += 1

    actionable = [
        item for item in items
        if item.get("conclusion") in {"可操作", "重点关注"}
    ][:8]

    payload = {
        "count": len(items),
        "group_counts": grouped,
        "high_priority": actionable,
        "items": items[:20],
    }
    return json.dumps(_clean_for_json(payload), ensure_ascii=False, default=str)


async def _get_stock_kline(ts_code: str, days: int) -> str:
    from app.data.tushare_client import tushare_client
    from app.analysis.technical import add_all_indicators
    df = tushare_client.get_stock_daily(ts_code, days=days)
    if df.empty:
        return json.dumps({"error": f"未找到 {ts_code} 的K线数据"}, ensure_ascii=False)
    df = df.sort_values("trade_date").reset_index(drop=True)
    df = add_all_indicators(df)
    # 只返回最近 20 条以节省 token
    records = df.tail(20).to_dict(orient="records")
    records = _clean_for_json(records)
    return json.dumps(records, ensure_ascii=False, default=str)


async def _get_stock_capital_flow(ts_code: str, days: int) -> str:
    from app.data.tushare_client import tushare_client
    df = tushare_client.get_stock_moneyflow(ts_code, days=days)
    if df.empty:
        return json.dumps({"error": f"未找到 {ts_code} 的资金流向数据"}, ensure_ascii=False)
    df = df.sort_values("trade_date")
    records = []
    for _, row in df.iterrows():
        main_net = (
            (row.get("buy_elg_amount", 0) or 0) - (row.get("sell_elg_amount", 0) or 0) +
            (row.get("buy_lg_amount", 0) or 0) - (row.get("sell_lg_amount", 0) or 0)
        )
        records.append({
            "trade_date": row["trade_date"],
            "main_net_inflow": round(main_net, 2),
            "net_mf_amount": round(float(row.get("net_mf_amount", 0) or 0), 2),
        })
    return json.dumps(records, ensure_ascii=False, default=str)


async def _search_stock(keyword: str) -> str:
    from app.data.tushare_client import tushare_client
    basic = tushare_client.get_stock_basic()
    if basic.empty:
        return json.dumps([], ensure_ascii=False)
    matches = basic[
        basic["name"].str.contains(keyword, na=False) |
        basic["ts_code"].str.contains(keyword, na=False) |
        basic["symbol"].str.contains(keyword, na=False)
    ].head(10)
    data = matches[["ts_code", "name", "industry"]].to_dict(orient="records")
    return json.dumps(data, ensure_ascii=False, default=str)


async def _get_stock_technical_signal(ts_code: str) -> str:
    """获取个股技术面信号详情"""
    from app.analysis.signals import generate_signals
    signal = generate_signals(ts_code)
    data = _clean_for_json(signal.model_dump())
    return json.dumps(data, ensure_ascii=False, default=str)


async def _diagnose_stock(ts_code: str, mode: str = "entry") -> str:
    """生成系统化个股会诊，供作战问答智能体调用。"""
    from sqlalchemy import text
    from app.db.database import get_db
    from app.db import tables
    from app.llm.client import chat_completion

    mode_map = {
        "entry": "建仓前诊断",
        "holding": "持仓复核",
        "review": "回撤复盘",
        "tracking": "继续跟踪",
    }
    mode = mode if mode in mode_map else "entry"

    strategy_board = await _get_strategy_board()
    latest_recommendations = await _get_latest_recommendations()
    hot_sectors = await _get_hot_sectors(8)
    kline = await _get_stock_kline(ts_code, 80)
    capital_flow = await _get_stock_capital_flow(ts_code, 15)
    technical_signal = await _get_stock_technical_signal(ts_code)

    latest_rec = None
    stock_name = ts_code
    try:
        recs = json.loads(latest_recommendations)
        latest_rec = next((item for item in recs if item.get("ts_code") == ts_code), None)
    except Exception:
        latest_rec = None
    if latest_rec and latest_rec.get("name"):
        stock_name = latest_rec["name"]

    recent_diagnoses = []
    try:
        async with get_db() as db:
            rows = (await db.execute(
                text(
                    "SELECT name, diagnosis_mode, diagnosis, created_at "
                    "FROM stock_diagnoses WHERE ts_code = :ts_code "
                    "ORDER BY created_at DESC, id DESC LIMIT 3"
                ),
                {"ts_code": ts_code},
            )).fetchall()
            recent_diagnoses = [dict(row._mapping) for row in rows]
            if recent_diagnoses and recent_diagnoses[0].get("name"):
                stock_name = recent_diagnoses[0]["name"]
    except Exception:
        recent_diagnoses = []

    prompt = f"""请在 A 股作战台语境下，对 {ts_code} 做一次系统化个股会诊。

诊断模式: {mode_map[mode]}

今日作战结论:
{strategy_board}

最新推荐池中该股记录:
{json.dumps(latest_rec, ensure_ascii=False, default=str) if latest_rec else "不在最新推荐池"}

热门板块:
{hot_sectors}

资金流:
{capital_flow}

K线与价格行为:
{kline}

技术信号:
{technical_signal}

最近诊断:
{json.dumps(recent_diagnoses, ensure_ascii=False, default=str)}

输出要求:
- 先给明确结论，只能是「可操作 / 重点关注 / 观察 / 回避」
- 明确当前动作、触发条件、失效条件、仓位边界、下一步观察点
- 分析顺序必须是：资金与主线 > 量价与价格行为 > 位置与边界 > 技术指标备注
- A 股优先看资金顺势、主线板块、量价承接、价格行为和位置；技术指标只做节奏与风控确认
- RSI、MACD、KDJ 的超买超卖不能单独决定买卖，也不能放在核心依据第一位
- 输出必须包含以下小节：当前结论、资金与主线、量价与价格行为、位置与边界、执行动作、风险清单、技术指标备注
- 不写传统研报，不堆原始数据，不承诺收益
- 用 Markdown 输出，保持简洁"""

    resp = await chat_completion([
        {
            "role": "system",
            "content": (
                "你是 A 股投研作战台的个股会诊智能体。"
                "你必须优先分析资金流向、主线板块、量价关系、价格行为和位置边界，"
                "技术指标只能作为最后的节奏与风控备注。"
                "输出可执行但带风险边界的会诊结论。"
            ),
        },
        {"role": "user", "content": prompt},
    ])
    if not resp or not resp.content:
        return json.dumps({"error": "个股会诊生成失败，LLM 未返回内容"}, ensure_ascii=False)

    diagnosis = resp.content.strip()
    try:
        async with get_db() as db:
            await db.execute(
                tables.stock_diagnoses_table.insert().values(
                    ts_code=ts_code,
                    name=stock_name,
                    diagnosis_mode=mode,
                    diagnosis=diagnosis,
                )
            )
            await db.commit()
    except Exception as e:
        logger.warning(f"保存聊天会诊结果失败 {ts_code}: {e}")

    return json.dumps({
        "ts_code": ts_code,
        "name": stock_name,
        "mode": mode,
        "diagnosis": diagnosis,
        "saved": True,
    }, ensure_ascii=False, default=str)


async def _get_sector_performance(sector_name: str) -> str:
    """获取板块表现数据"""
    from app.engine.recommender import get_latest_sectors
    sectors = await get_latest_sectors()
    # 模糊匹配板块名称
    matched = [s for s in sectors if sector_name in s.sector_name or s.sector_name in sector_name]
    if not matched:
        # 返回所有热门板块概览
        data = [{"sector_name": s.sector_name, "pct_change": s.pct_change,
                 "capital_inflow": s.capital_inflow, "limit_up_count": s.limit_up_count,
                 "heat_score": s.heat_score, "stage": s.stage}
                for s in sectors[:10]]
        return json.dumps({"matched": False, "available_sectors": data}, ensure_ascii=False, default=str)
    data = _clean_for_json([s.model_dump() for s in matched])
    return json.dumps({"matched": True, "sectors": data}, ensure_ascii=False, default=str)


async def _get_realtime_indices() -> str:
    """获取指数实时行情数据（盘中用腾讯实时数据）"""
    from app.data.tencent_client import get_index_realtime
    from app.config import is_market_session

    is_trading = is_market_session()

    try:
        index_data = await get_index_realtime()
        if not index_data:
            return json.dumps({"error": "获取指数数据失败"}, ensure_ascii=False)

        # 格式化数据
        results = []
        for ts_code, data in index_data.items():
            results.append({
                "ts_code": ts_code,
                "name": data.get("name", ts_code),
                "price": round(data.get("price", 0), 2),
                "pct_chg": round(data.get("pct_chg", 0), 2),
                "volume": data.get("volume", 0),
                "amount": data.get("amount", 0),
                "high": round(data.get("high", 0), 2),
                "low": round(data.get("low", 0), 2),
                "open": round(data.get("open", 0), 2),
                "pre_close": round(data.get("pre_close", 0), 2),
            })

        return json.dumps({
            "is_realtime": is_trading,
            "mode": "盘中实时" if is_trading else "盘后收盘",
            "indices": results
        }, ensure_ascii=False, default=str)
    except Exception as e:
        logger.error(f"获取实时指数失败: {e}")
        await log_error("llm_tool", f"获取实时指数失败: {e}")
        return json.dumps({"error": f"获取指数数据失败: {e}"}, ensure_ascii=False)