#!/usr/bin/env python3
"""collect_evaluation_data.py — 六维评估原始数据采集

纯数据收集脚本（no_agent），不做任何评估/判断/RR计算。
输出：data/evaluation_input.json — 供 21:00 LLM cron 使用。

采集内容：
D1 宏观环境 — 五大指数（上证/深证/恒生/恒科/A50）
D2 行业表现 — 持仓+自选按行业分组
D3 技术面（当前） — 今开/今高/今低/昨收/现价/成交量
D4 基本面 — PE/PB/总市值/52周高/52周低
D5 消息面 — (此脚本不采集，LLM cron web_search)
D6 资金面 — 成交额/换手率/量比

日期：2026-06-18 v1 — 初始版本
"""

import json
import os
import sys
from datetime import datetime
from pathlib import Path

# ── 消息通道统一路由(broadcast/xmpp by delivery) ──
try:
    from messenger import install_stdio_hook as _msh
    _msh()
except Exception:
    pass

DATA_DIR = Path(__file__).parent.parent / "data"
PROFILES_PATH = DATA_DIR / "stock_profiles.json"
OUTPUT_PATH = DATA_DIR / "evaluation_input.json"


def load_json(path, default=None):
    try:
        with open(path, encoding="utf-8") as f:
            return json.load(f)
    except (FileNotFoundError, json.JSONDecodeError):
        return {} if default is None else default


def save_json(path, data):
    Path(path).parent.mkdir(parents=True, exist_ok=True)
    with open(path, "w", encoding="utf-8") as f:
        json.dump(data, f, ensure_ascii=False, indent=2)


def fetch_tencent_data(symbols):
    """批量拉行情。读 DB live_prices + stock_daily（2026-08-26 分层铁律：消费层不直连腾讯API）"""
    if not symbols:
        return {}
    import sqlite3
    result = {}
    try:
        conn = sqlite3.connect('/home/hmo/MoFin/data/mofin.db', timeout=5)
        syms = list(symbols)
        ph = ",".join("?" * len(syms))
        # 实时价：一次查
        price_rows = conn.execute(
            f"SELECT code, price, change_pct FROM live_prices WHERE code IN ({ph})", syms
        ).fetchall()
        prices = {r[0]: (r[1], r[2]) for r in price_rows}
        # 名称
        name_rows = conn.execute(
            f"SELECT code, name FROM stocks WHERE code IN ({ph})", syms
        ).fetchall()
        names = {r[0]: r[1] or "" for r in name_rows}
        # 最近日K（昨收/今开/高低/量）
        sd = {}
        for c in syms:
            row = conn.execute(
                "SELECT close, open, high, low, volume FROM stock_daily "
                "WHERE code=? ORDER BY date DESC LIMIT 1", (c,)
            ).fetchone()
            if row:
                sd[c] = row
        conn.close()

        for code in syms:
            if code not in prices:
                continue
            price = prices[code][0]
            change_pct = prices[code][1] or 0
            if not price or price <= 0:
                continue
            row = sd.get(code)
            row_c = row[0] if row else 0
            row_o = row[1] if row else 0
            row_h = row[2] if row else 0
            row_l = row[3] if row else 0
            row_v = row[4] if row else 0
            result[code] = {
                "name": names.get(code, ""),
                "price": price,
                "prev_close": row_c,
                "open": row_o,
                "change_pct": change_pct,
                "high": row_h,
                "low": row_l,
                "volume": row_v,
            }
    except Exception as e:
        print(f"行情拉取失败: {e}", file=sys.stderr)
        return {}
    return result


def fetch_indices():
    """拉指数：读 DB stock_daily 最近收盘（2026-08-26 分层铁律：消费层不直连腾讯API）"""
    import sqlite3
    index_codes = {
        "sh000001": "上证指数",
        "sz399001": "深证成指",
        "sz399006": "创业板指",
        "hkHSI": "恒生指数",
        "hkHSTECH": "恒生科技",
    }
    result = {}
    try:
        conn = sqlite3.connect('/home/hmo/MoFin/data/mofin.db', timeout=5)
        for c, n in index_codes.items():
            rows = conn.execute(
                "SELECT date, close, open, high, low FROM stock_daily "
                "WHERE code=? ORDER BY date DESC LIMIT 2", (c,)
            ).fetchall()
            if not rows:
                continue  # 读不到则跳过（中性）
            latest = rows[0]
            prev = rows[1] if len(rows) > 1 else None
            prev_close = prev[1] if prev else latest[1]
            price = latest[1] or 0
            change_pct = (price - prev_close) / prev_close * 100 if prev_close else 0
            result[n] = {
                "price": safe_float(price),
                "prev_close": safe_float(prev_close if prev else None),
                "change_pct": safe_float(change_pct),
                "high": safe_float(latest[3] or price),
                "low": safe_float(latest[4] or price),
                "timestamp": latest[0],
            }
        conn.close()
    except Exception as e:
        print(f"指数拉取失败: {e}", file=sys.stderr)
        return {}
    return result


def safe_float(v):
    try:
        return float(v) if v else None
    except (ValueError, TypeError):
        return None


def parse_stock_data(code, fields, is_hk=False):
    """从腾讯 API 字段解析为结构化数据"""
    # fields可能是dict(已解析)或list(原始split)
    if isinstance(fields, dict):
        data = {
            "code": code,
            "name": fields.get("name", code),
            "price": safe_float(fields.get("price", 0)),
            "prev_close": safe_float(fields.get("prev_close", 0)),
            "open": safe_float(fields.get("open", 0)) if not is_hk else None,
            "change_pct": safe_float(fields.get("change_pct", 0)),
            "high": safe_float(fields.get("high", 0)),
            "low": safe_float(fields.get("low", 0)),
            "volume": safe_float(fields.get("volume", 0)),
        }
    else:
        data = {
            "code": code,
            "name": fields[1] if len(fields) > 1 else code,
            "price": safe_float(fields[3]),
            "prev_close": safe_float(fields[4]),
            "open": safe_float(fields[5]) if not is_hk else None,
            "change_pct": safe_float(fields[32]),
            "high": safe_float(fields[33]),
            "low": safe_float(fields[34]),
            "volume": safe_float(fields[6]),
        }
    # A股特有字段 (index 35+)
    if not is_hk:
        if isinstance(fields, dict):
            data["turnover_rate"] = safe_float(fields.get("turnover_rate", 0))
            data["amplitude"] = safe_float(fields.get("amplitude", 0))
            data["pe"] = safe_float(fields.get("pe", 0))
            data["market_cap"] = safe_float(fields.get("market_cap", 0))
        else:
            data["turnover_rate"] = safe_float(fields[38]) if len(fields) > 38 else 0
            data["amplitude"] = safe_float(fields[39]) if len(fields) > 39 else 0
            data["pe"] = safe_float(fields[39]) if len(fields) > 39 else 0
            data["market_cap"] = safe_float(fields[45]) if len(fields) > 45 else 0
    # 港股特有
    if is_hk:
        data["market"] = "HK"
        if isinstance(fields, dict):
            data["high_52w"] = safe_float(fields.get("high_52w", 0))
            data["low_52w"] = safe_float(fields.get("low_52w", 0))
            data["amplitude"] = safe_float(fields.get("amplitude", 0))
        else:
            data["high_52w"] = safe_float(fields[48]) if len(fields) > 48 else 0
            data["low_52w"] = safe_float(fields[49]) if len(fields) > 49 else 0
            data["amplitude"] = safe_float(fields[43]) if len(fields) > 43 else 0
    # 港股特有字段
    if is_hk:
        if isinstance(fields, dict):
            data["pe"] = safe_float(fields.get("pe", 0))
            data["total_market_cap"] = safe_float(fields.get("total_market_cap", 0))
            data["high_52w"] = safe_float(fields.get("high_52w", 0))
            data["low_52w"] = safe_float(fields.get("low_52w", 0))
        else:
            if len(fields) > 70:
                data["pe"] = safe_float(fields[71])
                data["total_market_cap"] = safe_float(fields[69])
                data["high_52w"] = safe_float(fields[48])
                data["low_52w"] = safe_float(fields[49])
    return data


def get_sector_mapping(profiles, decisions):
    """
    从 stock_profiles.json 和 decisions.json 建立
    {code: {name, sector, business, market, type}} 映射
    """
    mapping = {}
    # 先读 stock_profiles
    profile_list = profiles.get("profiles", []) if isinstance(profiles, dict) else profiles
    if isinstance(profile_list, list):
        for p in profile_list:
            code = p.get("code", "")
            if code:
                mapping[code] = {
                    "name": p.get("name", ""),
                    "sector": p.get("sector", ""),
                    "business": p.get("business", ""),
                    "market": p.get("market", ""),
                    "type": p.get("type", ""),
                }
    # 再补全 decisions.json 中的信息
    for d in decisions.get("decisions", []):
        code = d.get("code", "")
        if code and code not in mapping:
            trig = d.get("trigger", {})
            mapping[code] = {
                "name": d.get("name", code),
                "sector": trig.get("sector_name", d.get("sector_name", "")),
                "business": "",
                "market": "港股" if len(code) == 5 else "A股",
                "type": d.get("type", "持仓策略"),
            }
    return mapping


def get_portfolio_info(portfolio):
    """建立 {code: {cost, shares, position_pct}} 映射"""
    result = {}
    for h in portfolio.get("holdings", []):
        code = h.get("code", "")
        result[code] = {
            "cost": h.get("cost", 0),
            "shares": h.get("shares", 0),
            "position_pct": h.get("position_pct", 0),
        }
    return result


def get_decisions_info(decisions):
    """提取 decisions.json 中的策略参数"""
    return decisions.get("decisions", [])


def run():
    # 加载数据
    from mo_data import read_decisions, read_portfolio
    decisions = read_decisions()
    portfolio = read_portfolio()
    profiles = load_json(PROFILES_PATH, {"profiles": []})

    # 获取行业映射
    sector_mapping = get_sector_mapping(profiles, decisions)

    # 获取持仓信息
    portfolio_info = get_portfolio_info(portfolio)

    # 收集所有代码
    all_codes = set()
    for d in decisions.get("decisions", []):
        code = d.get("code", "")
        if code:
            all_codes.add(code)
    for h in portfolio.get("holdings", []):
        code = h.get("code", "")
        if code:
            all_codes.add(code)

    # 区分 A/H 股
    a_codes = [c for c in all_codes if len(c) != 5]
    hk_codes = [c for c in all_codes if len(c) == 5]

    # 拉行情
    a_prices = fetch_tencent_data(a_codes) if a_codes else {}
    hk_prices = fetch_tencent_data(hk_codes) if hk_codes else {}

    # 拉指数
    index_data = fetch_indices()

    # 解析个股数据
    stock_data = {}
    for code in a_codes:
        if code in a_prices:
            stock_data[code] = parse_stock_data(code, a_prices[code], is_hk=False)
    for code in hk_codes:
        if code in hk_prices:
            stock_data[code] = parse_stock_data(code, hk_prices[code], is_hk=True)

    # 组装输出
    stocks = []
    all_codes_sorted = sorted(all_codes)

    for code in all_codes_sorted:
        raw = stock_data.get(code, {})
        sector_info = sector_mapping.get(code, {})
        port = portfolio_info.get(code, {})
        strategy = None
        for d in decisions.get("decisions", []):
            if d.get("code") == code:
                trig = d.get("trigger", {})
                strategy = {
                    "action": trig.get("action", d.get("action", "")),
                    "entry_zone": trig.get("entry_zone", ""),
                    "stop_loss": trig.get("stop_loss", d.get("stop_loss", "")),
                    "take_profit": trig.get("take_profit", d.get("take_profit", "")),
                    "type": d.get("type", "持仓策略"),
                    "tech_snapshot": trig.get("tech_snapshot", d.get("tech_snapshot", "")),
                }
                break

        stock_entry = {
            "code": code,
            "name": raw.get("name", sector_info.get("name", code)),
            "market": "港股" if len(code) == 5 else "A股",
            "type": sector_info.get("type", "持仓策略"),
            "sector": sector_info.get("sector", ""),
            "business": sector_info.get("business", ""),
            # 当天行情
            "price": raw.get("price"),
            "prev_close": raw.get("prev_close"),
            "open": raw.get("open"),
            "high": raw.get("high"),
            "low": raw.get("low"),
            "change_pct": raw.get("change_pct"),
            "volume": raw.get("volume"),
            # 基本面
            "pe": raw.get("pe"),
            "total_market_cap": raw.get("total_market_cap"),
            "high_52w": raw.get("high_52w"),
            "low_52w": raw.get("low_52w"),
            "turnover_rate": raw.get("turnover_rate"),
            "amplitude": raw.get("amplitude"),
            # 持仓
            "cost": port.get("cost", 0),
            "shares": port.get("shares", 0),
            "position_pct": port.get("position_pct", 0),
            # 现策略
            "strategy": strategy,
        }
        # 浮亏%
        cost = port.get("cost", 0)
        price = raw.get("price", 0)
        if cost > 0 and price > 0:
            stock_entry["pnl_pct"] = round((price - cost) / cost * 100, 2)
        else:
            stock_entry["pnl_pct"] = None

        stocks.append(stock_entry)

    # 按行业分组统计
    sector_groups = {}
    for s in stocks:
        sector = s.get("sector", "未分类")
        if sector not in sector_groups:
            sector_groups[sector] = []
        sector_groups[sector].append({
            "code": s["code"],
            "name": s["name"],
            "change_pct": s["change_pct"],
            "pnl_pct": s["pnl_pct"],
            "type": s["type"],
        })

    # 汇总
    total = len(stocks)
    up_count = sum(1 for s in stocks if s["change_pct"] is not None and s["change_pct"] > 0)
    down_count = sum(1 for s in stocks if s["change_pct"] is not None and s["change_pct"] < 0)
    deep_loss = sum(1 for s in stocks if s["pnl_pct"] is not None and s["pnl_pct"] < -20)

    output = {
        "collected_at": datetime.now().isoformat(),
        "total_stocks": total,
        "summary": {
            "up_count": up_count,
            "down_count": down_count,
            "deep_loss_count": deep_loss,
            "holdings_count": len(portfolio_info),
            "watchlist_count": total - len(portfolio_info),
        },
        "index_data": index_data,
        "sector_groups": sector_groups,
        "stocks": stocks,
    }

    save_json(OUTPUT_PATH, output)
    print(f"数据收集完成: {total}只股票, {len(index_data)}个指数, {len(sector_groups)}个行业分组")
    print(f"  上涨{up_count} 下跌{down_count} 深套{deep_loss}")
    print(f"  输出: {OUTPUT_PATH}")


if __name__ == "__main__":
    run()