"""
统一算法模块 — 全部核心算法的唯一定义处（Single Source of Truth）

包含:
1. compute_recommend   — 统一推荐逻辑（买入/卖出/加仓/观望等，严格遵循suanfa.md）
2. get_kline_data      — 获取K线数据并返回 DataFrame（优先本地DB → 阿里云 → 腾讯 → 麦蕊 → AKShare）
3. fetch_kline_rows    — 获取K线数据并返回 tuple 行列表（用于写入DB同步）
4. get_latest_price    — 获取股票最新价格
5. code_to_market      — 股票代码→市场判断（SH/SZ/BJ）
6. 各 API session 管理
7. compute_bull_stage  — 牛股阶段识别（底部→起爆→确立→加速→补涨）
8. find_bull_stocks    — 从扫描结果中找出潜在牛股

调用方:
- routes/analysis.py   → compute_recommend, get_kline_data
- services/scheduler.py → compute_recommend, get_latest_price
- full_signal_scan.py  → get_kline_data, API sessions
- sync_kline.py        → fetch_kline_rows, API sessions
- routes/market.py     → get_kline_data
"""

import threading
from datetime import datetime, timedelta

import requests
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

from config import Config


# ═══════════════════════════════════════════════
# 1. 股票代码 → 市场 工具函数
# ═══════════════════════════════════════════════

def code_to_market(code):
    """6位股票代码 → 市场代码（SH/SZ/BJ）"""
    if code.startswith(('0', '3')):
        return 'SZ'
    elif code.startswith(('8', '9')):
        return 'BJ'
    else:
        return 'SH'


def is_bj_stock(code):
    """是否是北交所股票"""
    return code.startswith(('8', '9'))


def code_to_ali_symbol(code):
    """6位股票代码 → 阿里云API格式（SH600519 / SZ000001 / BJ920720）"""
    return f'{code_to_market(code)}{code}'


def code_to_tencent_symbol(code):
    """6位股票代码 → 腾讯API格式（sh600519 / sz000001 / bj920720）"""
    return f'{code_to_market(code).lower()}{code}'


# ═══════════════════════════════════════════════
# 2. API Session 管理（线程安全，连接池复用）
# ═══════════════════════════════════════════════

_ali_session = None
_ali_lock = threading.Lock()

_tencent_session = None
_tencent_lock = threading.Lock()


def get_ali_session():
    """获取阿里云API专用 Session（线程安全，单例）"""
    global _ali_session
    if _ali_session is None:
        with _ali_lock:
            if _ali_session is None:
                s = requests.Session()
                retry = Retry(total=2, backoff_factor=0.3,
                              status_forcelist=[500, 502, 503, 504])
                adapter = HTTPAdapter(max_retries=retry,
                                      pool_connections=20, pool_maxsize=20)
                s.mount('https://', adapter)
                s.headers.update({
                    'Authorization': f'APPCODE {Config.ALICLOUD_APPCODE}',
                    'Content-Type': 'application/x-www-form-urlencoded',
                })
                _ali_session = s
    return _ali_session


def get_tencent_session():
    """获取腾讯K线API专用 Session（线程安全，单例）"""
    global _tencent_session
    if _tencent_session is None:
        with _tencent_lock:
            if _tencent_session is None:
                s = requests.Session()
                retry = Retry(total=2, backoff_factor=0.3,
                              status_forcelist=[500, 502, 503, 504])
                adapter = HTTPAdapter(max_retries=retry,
                                      pool_connections=20, pool_maxsize=20)
                s.mount('https://', adapter)
                s.headers.update({
                    'User-Agent': ('Mozilla/5.0 (Windows NT 10.0; Win64; x64) '
                                   'AppleWebKit/537.36'),
                })
                _tencent_session = s
    return _tencent_session


# API URL 常量
ALICLOUD_KLINE_URL = Config.ALICLOUD_KLINE_URL
TENCENT_KLINE_URL = 'https://proxy.finance.qq.com/ifzqgtimg/appstock/app/newfqkline/get'


# ═══════════════════════════════════════════════
# 3. K线数据获取 — DataFrame 格式（供信号检测/分析用）
# ═══════════════════════════════════════════════

def get_kline_data(stock_code, days=120, use_local_db=True):
    """
    获取K线数据，返回 pandas DataFrame (columns: date, open, high, low, close, volume)
    
    数据源优先级: 本地DB → 阿里云API → 腾讯API → 麦蕊API → AKShare
    
    参数:
        stock_code: 6位股票代码
        days: 获取天数
        use_local_db: 是否优先使用本地DB（全景扫描时为True, 实时分析时可为False）
    
    返回:
        DataFrame 或 None
    """
    import pandas as pd

    # 1. 优先从本地数据库读取
    if use_local_db:
        df = _get_kline_from_local_db(stock_code, days)
        if df is not None:
            return df

    # 2. 阿里云K线API（沪深最稳定，北交所可能不支持）
    if not is_bj_stock(stock_code):
        df = _fetch_ali_kline_df(stock_code, days)
        if df is not None and len(df) >= 30:
            return df

    # 3. 腾讯K线API（全市场，含北交所）
    df = _fetch_tencent_kline_df(stock_code, days)
    if df is not None and len(df) >= 30:
        return df

    # 4. 麦蕊API
    df = _fetch_mairui_kline_df(stock_code, days)
    if df is not None and len(df) >= 30:
        return df

    # 5. AKShare
    df = _fetch_akshare_kline_df(stock_code, days)
    if df is not None and len(df) >= 30:
        return df

    return None


def _get_kline_from_local_db(stock_code, days=120):
    """从本地数据库读取K线（最快，毫秒级）"""
    import pandas as pd
    try:
        from db import get_db, put_db
        conn = get_db()
        if not conn:
            return None
        conn.autocommit = True
        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
        try:
            with conn.cursor() as cur:
                cur.execute("""
                    SELECT trade_date, open, high, low, close, volume
                    FROM stock_kline_daily
                    WHERE code = %s AND trade_date >= %s
                    ORDER BY trade_date
                """, (stock_code, start_date))
                rows = cur.fetchall()
        finally:
            put_db(conn)

        if rows and len(rows) >= 30:
            df = pd.DataFrame(rows, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
            df['date'] = df['date'].astype(str)
            for col in ('open', 'high', 'low', 'close', 'volume'):
                df[col] = df[col].astype(float)
            return df
    except Exception:
        pass
    return None


# ---- 线程本地连接（供多线程扫描时使用，避免频繁建连） ----
_thread_local = threading.local()


def get_kline_from_local_db_threaded(stock_code, days=120):
    """多线程扫描专用：使用线程本地连接从本地DB读取K线"""
    import pandas as pd
    try:
        conn = getattr(_thread_local, 'kline_conn', None)
        if conn is None or conn.closed:
            import psycopg2
            conn = psycopg2.connect(
                host=Config.DB_HOST, port=Config.DB_PORT,
                dbname=Config.DB_NAME, user=Config.DB_USER, password=Config.DB_PASSWORD,
            )
            conn.autocommit = True
            _thread_local.kline_conn = conn

        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y-%m-%d')
        with conn.cursor() as cur:
            cur.execute("""
                SELECT trade_date, open, high, low, close, volume
                FROM stock_kline_daily
                WHERE code = %s AND trade_date >= %s
                ORDER BY trade_date
            """, (stock_code, start_date))
            rows = cur.fetchall()

        if rows and len(rows) >= 30:
            df = pd.DataFrame(rows, columns=['date', 'open', 'high', 'low', 'close', 'volume'])
            df['date'] = df['date'].astype(str)
            for col in ('open', 'high', 'low', 'close', 'volume'):
                df[col] = df[col].astype(float)
            return df
    except Exception:
        pass
    return None


def _fetch_ali_kline_df(stock_code, days=120):
    """阿里云K线API → DataFrame"""
    import pandas as pd
    try:
        session = get_ali_session()
        symbol = code_to_ali_symbol(stock_code)
        resp = session.post(ALICLOUD_KLINE_URL, data={
            'symbol': symbol,
            'type': '240',
            'limit': str(min(days, 300)),
            'ma': '5',
        }, timeout=10)
        if resp.status_code == 200:
            data = resp.json()
            if data.get('success') and data.get('data', {}).get('list'):
                records = []
                for item in data['data']['list']:
                    day_str = item.get('day', '')
                    if not day_str or len(day_str) < 10:
                        continue
                    records.append({
                        'date': day_str[:10],
                        'open': float(item.get('open', 0)),
                        'high': float(item.get('high', 0)),
                        'low': float(item.get('low', 0)),
                        'close': float(item.get('close', 0)),
                        'volume': float(item.get('volume', 0)),
                    })
                if records:
                    return pd.DataFrame(records)
    except Exception:
        pass
    return None


def _fetch_tencent_kline_df(stock_code, days=120):
    """腾讯K线API → DataFrame（全市场含北交所）"""
    import pandas as pd
    try:
        session = get_tencent_session()
        symbol = code_to_tencent_symbol(stock_code)
        start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y-%m-%d')
        resp = session.get(TENCENT_KLINE_URL, params={
            'param': f'{symbol},day,{start_date},,{min(days, 300)},qfq',
        }, timeout=15)
        if resp.status_code == 200:
            data = resp.json()
            stock_data = data.get('data', {}).get(symbol, {})
            klines = stock_data.get('qfqday') or stock_data.get('day') or []
            if klines:
                records = []
                for item in klines:
                    if len(item) < 6:
                        continue
                    # 腾讯格式: [date, open, close, high, low, volume, ...]
                    records.append({
                        'date': item[0][:10],
                        'open': float(item[1]),
                        'high': float(item[3]),   # high = position 3
                        'low': float(item[4]),    # low = position 4
                        'close': float(item[2]),  # close = position 2
                        'volume': float(item[5]),
                    })
                if records:
                    return pd.DataFrame(records)
    except Exception:
        pass
    return None


def _fetch_mairui_kline_df(stock_code, days=120):
    """麦蕊API → DataFrame"""
    import pandas as pd
    try:
        from services.mairui_api import get_kline
        result = get_kline(stock_code, period='d', days=days, adjust='f')
        if result['success'] and result['data']:
            df = pd.DataFrame(result['data'])
            df.rename(columns={
                'date': 'date', 'open': 'open', 'high': 'high',
                'low': 'low', 'close': 'close', 'volume': 'volume',
            }, inplace=True)
            if len(df) >= 30:
                return df
    except Exception:
        pass
    return None


def _fetch_akshare_kline_df(stock_code, days=120):
    """AKShare → DataFrame (支持自动降级到腾讯数据源)"""
    import pandas as pd
    try:
        from utils.data_fetcher import fetch_stock_hist
        end_date = datetime.now().strftime('%Y%m%d')
        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d')
        df = fetch_stock_hist(
            stock_code=stock_code, period='daily',
            start_date=start_date, end_date=end_date, adjust='qfq',
        )
        if df is not None and not df.empty:
            df = df.rename(columns={
                '日期': 'date', '开盘': 'open', '最高': 'high',
                '最低': 'low', '收盘': 'close', '成交量': 'volume',
            })
            df = df[['date', 'open', 'high', 'low', 'close', 'volume']]
            return df
    except Exception:
        pass
    return None


# ═══════════════════════════════════════════════
# 4. K线数据获取 — tuple行格式（供 sync_kline.py 写入DB用）
# ═══════════════════════════════════════════════

def fetch_kline_rows(code, days):
    """
    获取K线数据，返回 list of tuple: (code, date, open, high, low, close, volume, amount)
    
    数据源优先级: 阿里云API → 腾讯API → 麦蕊API → AKShare
    北交所(8XX/9XX)直接走腾讯API
    
    供 sync_kline.py 同步到本地数据库使用。
    """
    bj = is_bj_stock(code)

    # 北交所：直接用腾讯API（阿里云/Mairui不支持BJ）
    if bj:
        rows = _fetch_tencent_kline_rows(code, days)
        if rows:
            return rows
        return None

    # 1. 首选：阿里云K线API
    rows = _fetch_ali_kline_rows(code, days)
    if rows:
        return rows

    # 2. 回退：腾讯API
    rows = _fetch_tencent_kline_rows(code, days)
    if rows:
        return rows

    # 3. 回退：Mairui API
    rows = _fetch_mairui_kline_rows(code, days)
    if rows:
        return rows

    # 4. 最后回退：AKShare
    rows = _fetch_akshare_kline_rows(code, days)
    if rows:
        return rows

    return None


def _fetch_ali_kline_rows(code, days):
    """阿里云K线API → tuple rows"""
    try:
        session = get_ali_session()
        symbol = code_to_ali_symbol(code)
        resp = session.post(ALICLOUD_KLINE_URL, data={
            'symbol': symbol,
            'type': '240',
            'limit': str(min(days, 300)),
            'ma': '5',
        }, timeout=10)
        if resp.status_code == 200:
            data = resp.json()
            if data.get('success') and data.get('data', {}).get('list'):
                rows = []
                for item in data['data']['list']:
                    day_str = item.get('day', '')
                    if not day_str or len(day_str) < 10:
                        continue
                    rows.append((
                        code,
                        day_str[:10],
                        float(item.get('open', 0)),
                        float(item.get('high', 0)),
                        float(item.get('low', 0)),
                        float(item.get('close', 0)),
                        int(item.get('volume', 0)),
                        float(item.get('amount', 0)),
                    ))
                if rows:
                    return rows
    except Exception:
        pass
    return None


def _fetch_tencent_kline_rows(code, days):
    """腾讯K线API → tuple rows（全市场含北交所）"""
    try:
        symbol = code_to_tencent_symbol(code)
        session = get_tencent_session()
        start_date = (datetime.now() - timedelta(days=days + 30)).strftime('%Y-%m-%d')
        resp = session.get(TENCENT_KLINE_URL, params={
            'param': f'{symbol},day,{start_date},,{min(days, 300)},qfq',
        }, timeout=15)
        if resp.status_code == 200:
            data = resp.json()
            stock_data = data.get('data', {}).get(symbol, {})
            klines = stock_data.get('qfqday') or stock_data.get('day') or []
            if klines:
                rows = []
                for item in klines:
                    if len(item) < 6:
                        continue
                    date_str = item[0]
                    if not date_str or len(date_str) < 10:
                        continue
                    rows.append((
                        code,
                        date_str[:10],
                        float(item[1]),          # open
                        float(item[3]),          # high (position 3)
                        float(item[4]),          # low (position 4)
                        float(item[2]),          # close (position 2)
                        int(float(item[5])),     # volume
                        float(item[8]) * 10000 if len(item) > 8 and item[8] else 0,
                    ))
                if rows:
                    return rows
    except Exception:
        pass
    return None


def _fetch_mairui_kline_rows(code, days):
    """麦蕊API → tuple rows"""
    try:
        from services.mairui_api import get_kline
        result = get_kline(code, period='d', days=days, adjust='f')
        if result['success'] and result['data']:
            rows = []
            for item in result['data']:
                date_str = item.get('date', '')
                if not date_str:
                    continue
                rows.append((
                    code,
                    date_str,
                    item.get('open', 0),
                    item.get('high', 0),
                    item.get('low', 0),
                    item.get('close', 0),
                    int(item.get('volume', 0)),
                    item.get('amount', 0),
                ))
            if rows:
                return rows
    except Exception:
        pass
    return None


def _fetch_akshare_kline_rows(code, days):
    """AKShare → tuple rows (支持自动降级到腾讯数据源)"""
    try:
        from utils.data_fetcher import fetch_stock_hist
        end_date = datetime.now().strftime('%Y%m%d')
        start_date = (datetime.now() - timedelta(days=days)).strftime('%Y%m%d')
        df = fetch_stock_hist(
            stock_code=code, period='daily',
            start_date=start_date, end_date=end_date, adjust='qfq',
        )
        if df is not None and not df.empty:
            rows = []
            for _, r in df.iterrows():
                rows.append((
                    code,
                    str(r['日期']),
                    float(r['开盘']),
                    float(r['最高']),
                    float(r['最低']),
                    float(r['收盘']),
                    int(r['成交量']),
                    float(r.get('成交额', 0)),
                ))
            if rows:
                return rows
    except Exception:
        pass
    return None


# ═══════════════════════════════════════════════
# 5. 统一推荐算法
# ═══════════════════════════════════════════════

def compute_recommend(signal_status, indicators, triggered_count, is_holding):
    """
    统一推荐逻辑 — 全局唯一定义（严格遵循 suanfa.md 体系最强战法）。
    
    体系最强战法流程（suanfa.md）：
    1. 日线底背离 → 纳入关注范围
    2. 龙抬头出现 → 执行买入操作（实操核心买点）
    3. 真龙/主升浪 → 持有仓位+加仓（不是新买入！）
    4. 不见主升浪 → 不出场
    
    参数:
        signal_status: list[dict] 信号状态列表（来自 signal_detector._check_all_signal_status）
        indicators: dict 最新技术指标（含 macd.dif, macd.dea 等）
        triggered_count: int 触发信号数量
        is_holding: bool 当前是否持仓该股票
    
    返回:
        tuple: (signal_type, display_text, reason, recommend_rate)
        - signal_type: 'buy' | 'sell' | 'watch'
        - display_text: '买入' | '卖出' | '加仓' | '持有' | '关注' | '观察' | '观望'
        - reason: str 推荐理由
        - recommend_rate: int 推荐评分 0-100
    
    使用场景:
        - 全景扫描结果推荐列
        - 策略建议分档
        - 提醒 tab 买卖推荐
        - 模拟交易自动买卖决策
    """
    if not signal_status:
        return ('watch', '观望', '暂无信号数据', 0)

    ss = signal_status
    sig_map = {}
    for s in ss:
        sig_map[s.get('type', '')] = s

    has_main_wave = sig_map.get('main_rising_wave', {}).get('triggered', False)
    has_divergence = sig_map.get('daily_bottom_divergence', {}).get('triggered', False)
    has_dragon = sig_map.get('dragon_head', {}).get('triggered', False)
    has_real_dragon = sig_map.get('true_dragon', {}).get('triggered', False)

    macd = (indicators or {}).get('macd', {})
    dif = macd.get('dif', 0)
    dea = macd.get('dea', 0)

    triggered_signals = [s.get('name', s.get('type', '')) for s in ss if s.get('triggered')]

    # ════════════════════════════════════════════
    # 持仓逻辑（suanfa.md 步骤3-4）
    # ════════════════════════════════════════════
    if is_holding:
        # 卖出条件: MACD死叉 + 无主升浪 → 趋势走弱，不见主升浪则出场
        if dif < dea and not has_main_wave:
            return ('sell', '卖出',
                    f"MACD死叉(DIF={dif:.3f}<DEA={dea:.3f})+主升浪消失", 75)
        # 主升浪 → 加仓（suanfa.md: 主升浪=持有仓位+加仓）
        if has_main_wave:
            return ('buy', '加仓', '主升浪信号 → 加速拉升阶段，建议加仓', 90)
        # 真龙 → 持有（suanfa.md: 真龙=趋势确认，持有）
        if has_real_dragon:
            return ('buy', '持有', '真龙信号 → 趋势确立，继续持有', 70)
        return ('watch', '观望', '持仓中，等待主升浪信号', 50)

    # ════════════════════════════════════════════
    # 非持仓逻辑（suanfa.md 步骤1-2）
    # ════════════════════════════════════════════

    # 最佳买入: 底背离+龙抬头（suanfa.md 步骤1→2 的理想组合）
    # MACD 死叉时降级，避免与持仓卖出信号矛盾
    if has_divergence and has_dragon:
        macd_golden = (dif is None or dea is None or dif >= dea)
        if macd_golden:
            return ('buy', '买入', '日线底背离+龙抬头 → 最佳买入信号', 95)
        return ('watch', '关注',
                f'底背离+龙抬头但MACD死叉(DIF={dif:.3f}<DEA={dea:.3f}) → 等待MACD金叉确认', 65)

    # 核心买入: 龙抬头（suanfa.md: "龙抬头出现 → 执行买入操作"）
    # 但需要 MACD 配合：如果 MACD 死叉则信号冲突，降级为关注
    if has_dragon:
        macd_ok = (dif is None or dea is None or dif >= dea)  # MACD 金叉或无数据
        if has_main_wave and macd_ok:
            return ('buy', '买入', '龙抬头+主升浪 → 强势买入信号', 90)
        if macd_ok:
            return ('buy', '买入', '龙抬头出现 → 短线起爆点，执行买入', 80)
        # MACD死叉 + 龙抬头 → 信号冲突，降级为关注
        return ('watch', '关注',
                f'龙抬头出现但MACD死叉(DIF={dif:.3f}<DEA={dea:.3f}) → 信号冲突，谨慎观望', 55)

    # 主升浪（非持仓）→ 不是新买入！suanfa.md: 主升浪=加速段，属于持有/加仓信号
    if has_main_wave:
        return ('watch', '关注', '主升浪(加速段) → 已过最佳买点，关注回调机会', 75)

    # 真龙 → 关注（趋势刚启动，可跟踪）
    if has_real_dragon:
        return ('watch', '关注', '真龙出现 → 趋势启动，等待龙抬头确认', 65)

    # MACD死叉 → 回避（非持仓不能卖出，应为回避/观望）
    if dif is not None and dea is not None and dif < dea:
        return ('watch', '回避', f"MACD死叉(DIF={dif:.3f}<DEA={dea:.3f})，趋势偏弱", 25)

    # 底背离 → 关注（suanfa.md 步骤1: 纳入关注范围）
    if has_divergence:
        return ('watch', '关注', '日线底背离出现 → 纳入关注，等待龙抬头', 60)

    # 其他信号 → 观察
    if triggered_count and triggered_count > 0:
        sigs = '、'.join(triggered_signals[:3])
        return ('watch', '观察', f"触发{triggered_count}个信号: {sigs}", 40)

    return ('watch', '观望', '无核心信号触发', 0)


# ═══════════════════════════════════════════════
# 6. 获取最新价格
# ═══════════════════════════════════════════════

def get_latest_price(stock_code):
    """
    获取股票最新价格（从本地 stock_realtime_price 表）
    
    返回:
        float: 最新价格, 失败返回 0
    """
    from db import get_db, put_db
    conn = get_db()
    if not conn:
        return 0
    try:
        cur = conn.cursor()
        cur.execute("""
            SELECT price FROM stock_realtime_price
            WHERE code = %s AND price > 0
        """, (stock_code,))
        row = cur.fetchone()
        if row:
            return float(row[0])
    except Exception:
        pass
    finally:
        put_db(conn)
    return 0


# ═══════════════════════════════════════════════
# 7. 牛股阶段识别（suanfa.md 标准牛股启动信号先后顺序）
# ═══════════════════════════════════════════════

# 标准牛股启动流程（底部→拉升）:
# 阶段1 → 日线底背离/短底背离（跌到底部，停止下跌）
# 阶段2 → 龙抬头（资金进场，短线起爆）
# 阶段3 → 真龙（趋势正式确立）
# 阶段4 → ★主升浪（进入加速段，利润兑现最快）
# 阶段5 → 反弹（中途回调后的补涨信号）
# 补充  → 老鼠仓可在底部任意位置提前出现

BULL_STAGES = {
    1: {'name': '底部探测', 'icon': '', 'color': '#2196F3',
        'desc': '日线底背离/短底背离 → 跌到底部，停止下跌'},
    2: {'name': '资金进场', 'icon': '', 'color': '#4CAF50',
        'desc': '龙抬头 → 资金进场，短线起爆点（最佳买入时机）'},
    3: {'name': '趋势确立', 'icon': '', 'color': '#FF9800',
        'desc': '真龙 → 中期趋势正式确立'},
    4: {'name': '加速拉升', 'icon': '', 'color': '#F44336',
        'desc': '★主升浪 → 进入加速段，利润兑现最快'},
    5: {'name': '回调补涨', 'icon': '', 'color': '#9C27B0',
        'desc': '反弹 → 中途回调后的补涨信号'},
}


def compute_bull_stage(signal_status):
    """
    识别股票在标准牛股启动流程中的阶段。
    
    参数:
        signal_status: list[dict] 信号状态列表
    
    返回:
        dict: {
            'stage': int (0-5, 0=未进入流程),
            'stage_name': str,
            'stage_icon': str,
            'stage_color': str,
            'stage_desc': str,
            'signals_active': list[str],   # 当前活跃的信号名称
            'progress': int (0-100),        # 牛股流程进度百分比
            'next_signal': str,             # 下一个期待的信号
            'investment_advice': str,       # 投资建议
            'has_rat_trading': bool,        # 是否有老鼠仓（提前埋伏信号）
        }
    """
    if not signal_status:
        return {
            'stage': 0, 'stage_name': '观望', 'stage_icon': '',
            'stage_color': '#9E9E9E', 'stage_desc': '无信号触发',
            'signals_active': [], 'progress': 0,
            'next_signal': '等待底背离/短底背离', 'investment_advice': '暂无操作机会',
            'has_rat_trading': False,
        }

    sig_map = {}
    for s in signal_status:
        sig_map[s.get('type', '')] = s

    has_divergence = sig_map.get('daily_bottom_divergence', {}).get('triggered', False)
    has_short_div = sig_map.get('short_bottom_divergence', {}).get('triggered', False)
    has_dragon = sig_map.get('dragon_head', {}).get('triggered', False)
    has_true_dragon = sig_map.get('true_dragon', {}).get('triggered', False)
    has_main_wave = sig_map.get('main_rising_wave', {}).get('triggered', False)
    has_rebound = sig_map.get('rebound', {}).get('triggered', False)
    has_rat = sig_map.get('rat_trading', {}).get('triggered', False)

    signals_active = []
    if has_divergence:
        signals_active.append('日线底背离')
    if has_short_div:
        signals_active.append('短底背离')
    if has_dragon:
        signals_active.append('龙抬头')
    if has_true_dragon:
        signals_active.append('真龙')
    if has_main_wave:
        signals_active.append('主升浪')
    if has_rebound:
        signals_active.append('反弹')
    if has_rat:
        signals_active.append('老鼠仓')

    # 确定阶段（按最高阶段判定）
    stage = 0
    if has_main_wave:
        stage = 4
    elif has_true_dragon:
        stage = 3
    elif has_dragon:
        stage = 2
    elif has_divergence or has_short_div:
        stage = 1
    elif has_rebound:
        stage = 5
    elif has_rat:
        stage = 1  # 老鼠仓归入底部阶段

    if stage == 0:
        return {
            'stage': 0, 'stage_name': '观望', 'stage_icon': '',
            'stage_color': '#9E9E9E', 'stage_desc': '无核心信号触发',
            'signals_active': signals_active, 'progress': 0,
            'next_signal': '等待底背离/短底背离',
            'investment_advice': '暂无操作机会',
            'has_rat_trading': has_rat,
        }

    info = BULL_STAGES[stage]

    # 计算流程进度（越靠后越高）
    # 加分项：多信号叠加说明流程更完整
    base_progress = {1: 20, 2: 45, 3: 65, 4: 85, 5: 50}
    progress = base_progress.get(stage, 0)
    if stage <= 2 and has_divergence:
        progress += 10  # 有底背离做基础更好
    if stage >= 2 and has_dragon:
        progress += 5
    if stage >= 3 and has_true_dragon:
        progress += 5
    if has_rat:
        progress += 5  # 老鼠仓加分
    progress = min(progress, 100)

    # 下一步信号期待
    next_signals = {
        1: '等待龙抬头（资金进场信号）',
        2: '等待真龙（趋势确认信号）',
        3: '等待主升浪（加速拉升信号）',
        4: '持有！不见主升浪消失不出场',
        5: '等待龙抬头/真龙确认趋势',
    }

    # 投资建议
    advices = {
        1: '纳入关注池，等待龙抬头出现后买入',
        2: '最佳买入时机！龙抬头=实操核心买点',
        3: '趋势已确立，可以追入，建议等回调买入',
        4: '已在加速段，持仓者加仓/持有，新入者谨慎追高',
        5: '回调中可关注，但需确认不是假反弹',
    }

    return {
        'stage': stage,
        'stage_name': info['name'],
        'stage_icon': info['icon'],
        'stage_color': info['color'],
        'stage_desc': info['desc'],
        'signals_active': signals_active,
        'progress': progress,
        'next_signal': next_signals.get(stage, ''),
        'investment_advice': advices.get(stage, ''),
        'has_rat_trading': has_rat,
    }


def find_bull_stocks(scan_rows, holding_codes=None, scores_map=None):
    """
    从扫描结果中找出潜在牛股，按阶段分组排序。
    
    参数:
        scan_rows: list[dict] 扫描结果列表 (含 code, name, signal_status, indicators, triggered_count)
        holding_codes: set 持仓代码集合
        scores_map: dict 综合评分映射 {code: {technical_score, external_score, final_score, verdict}}
                    当提供时，每只股票附加三项得分，并按综合得分排序
    
    返回:
        dict: {
            'stages': {1: [...], 2: [...], ...},  # 按阶段分组的股票列表
            'summary': {1: count, 2: count, ...},  # 各阶段数量统计
            'total': int,  # 有信号的总数
        }
    """
    if holding_codes is None:
        holding_codes = set()

    stages = {1: [], 2: [], 3: [], 4: [], 5: []}
    summary = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0}

    for row in scan_rows:
        signal_status = row.get('signal_status') or []
        if not signal_status:
            summary[0] += 1
            continue

        # 判断牛股阶段
        bull = compute_bull_stage(signal_status)
        stage = bull['stage']
        summary[stage] += 1

        if stage == 0:
            continue

        # 计算推荐
        is_holding = row.get('code', '') in holding_codes
        st, disp, reason, rate = compute_recommend(
            signal_status, row.get('indicators'),
            row.get('triggered_count'), is_holding,
        )

        # 附加综合评分（如果提供了 scores_map）
        code = row.get('code', '')
        technical_score = rate
        external_score = 0
        final_score = rate
        verdict = ''
        if scores_map and code in scores_map:
            sc = scores_map[code]
            technical_score = sc.get('technical_score', rate)
            external_score = sc.get('external_score', 0)
            final_score = sc.get('final_score', rate)
            verdict = sc.get('verdict', '')

        item = {
            'code': code,
            'name': row.get('name', ''),
            'stage': stage,
            'stage_name': bull['stage_name'],
            'stage_icon': bull['stage_icon'],
            'stage_color': bull['stage_color'],
            'signals_active': bull['signals_active'],
            'progress': bull['progress'],
            'next_signal': bull['next_signal'],
            'investment_advice': bull['investment_advice'],
            'has_rat_trading': bull['has_rat_trading'],
            'recommend_type': st,
            'recommend_text': disp,
            'recommend_reason': reason,
            'recommend_rate': final_score,
            'is_holding': is_holding,
            'triggered_count': row.get('triggered_count', 0),
            'technical_score': technical_score,
            'external_score': external_score,
            'final_score': final_score,
            'verdict': verdict,
        }

        stages[stage].append(item)

    # 每个阶段内排序：有综合评分时按综合得分→技术得分→进度，否则按推荐评分→进度
    if scores_map:
        for stage_num in stages:
            stages[stage_num].sort(
                key=lambda x: (-x.get('final_score', 0), -x.get('technical_score', 0), -x['progress'])
            )
    else:
        for stage_num in stages:
            stages[stage_num].sort(key=lambda x: (-x['recommend_rate'], -x['progress']))

    total = sum(len(v) for v in stages.values())

    return {
        'stages': stages,
        'summary': summary,
        'total': total,
        'stage_info': BULL_STAGES,
    }


# ═══════════════════════════════════════════════
# 9. 单股深度分析（价格位置、压力支撑、量价、空间估算）
# ═══════════════════════════════════════════════

def _generate_plain_summary(price, change_pct, ma_trend, position, supports,
                            resistances, vol_ratio, vol_trend, patterns,
                            space, score, verdict, reasons):
    """根据技术分析结果生成通俗易懂的中文解说"""
    parts = []

    # 1. 当前走势概况
    if change_pct > 3:
        trend_desc = f'今天涨了{change_pct:.1f}%，涨势比较猛'
    elif change_pct > 0:
        trend_desc = f'今天小涨{change_pct:.1f}%'
    elif change_pct > -3:
        trend_desc = f'今天小跌{abs(change_pct):.1f}%'
    else:
        trend_desc = f'今天跌了{abs(change_pct):.1f}%，跌幅较大'

    if ma_trend == 'bullish':
        trend_desc += '，均线呈多头排列，说明中短期整体向上'
    elif ma_trend == 'bearish':
        trend_desc += '，均线呈空头排列，中短期趋势偏弱'
    else:
        trend_desc += '，均线交叉纠缠，短期方向还不太明确'
    parts.append(trend_desc + '。')

    # 2. 价格位置（用大白话）
    pos_20 = position.get('20d', {})
    pct_20 = pos_20.get('pct', 50)
    if pct_20 > 80:
        parts.append(f'当前股价处于近20天的高位区间（{pct_20:.0f}%位置），已经涨了不少，追高要小心。')
    elif pct_20 > 50:
        parts.append(f'股价在近20天的中高位置（{pct_20:.0f}%），还有一定上涨空间。')
    elif pct_20 > 20:
        parts.append(f'股价在近20天的中低位置（{pct_20:.0f}%），相对安全。')
    else:
        parts.append(f'股价处于近20天的低位区间（{pct_20:.0f}%），可能存在反弹机会。')

    # 3. 上方压力和下方支撑
    if resistances:
        nearest_r = resistances[0]
        r_gap = round((nearest_r['level'] - price) / price * 100, 1) if price > 0 else 0
        if r_gap > 0:
            parts.append(f'往上最近的压力位在{nearest_r["level"]:.2f}元（{nearest_r["name"]}），距离约{r_gap:.1f}%。')
    if supports:
        nearest_s = supports[0]
        s_gap = round((price - nearest_s['level']) / price * 100, 1) if price > 0 else 0
        if s_gap > 0:
            parts.append(f'往下最近的支撑位在{nearest_s["level"]:.2f}元（{nearest_s["name"]}），有{s_gap:.1f}%的安全垫。')

    # 4. 成交量情况
    if vol_ratio >= 2:
        parts.append(f'成交量明显放大（量比{vol_ratio:.1f}倍），市场关注度很高，要留意是主力进场还是出货。')
    elif vol_ratio >= 1.3:
        parts.append(f'成交量温和放大（量比{vol_ratio:.1f}倍），有资金在活跃参与。')
    elif vol_ratio < 0.6:
        parts.append(f'成交量萎缩（量比{vol_ratio:.1f}倍），市场比较冷清，短期可能震荡。')
    else:
        parts.append(f'成交量正常（量比{vol_ratio:.1f}倍）。')

    # 5. 形态识别
    if patterns:
        pattern_names = [p['name'] for p in patterns]
        bullish_p = [p['name'] for p in patterns if p.get('bullish') is True]
        bearish_p = [p['name'] for p in patterns if p.get('bullish') is False]
        if bullish_p:
            parts.append(f'发现看涨信号：{"、".join(bullish_p)}，这是积极的技术形态。')
        if bearish_p:
            parts.append(f'注意看跌信号：{"、".join(bearish_p)}，需要警惕。')

    # 6. 综合建议（大白话）
    action_tip = ''
    if score >= 75:
        action_tip = '综合来看比较乐观，可以考虑逢低关注或适量参与，但注意控制仓位。'
    elif score >= 60:
        action_tip = '整体偏积极，可以少量关注，等回调到支撑位附近再考虑。'
    elif score >= 45:
        action_tip = '目前多空力量比较均衡，建议观望为主，等方向更明确再做决定。'
    elif score >= 30:
        action_tip = '目前偏弱势，不建议急于买入。如果持有，可以在反弹时适当减仓。'
    else:
        action_tip = '当前走势比较弱，建议回避。已经持有的可以考虑止损或等待反弹减仓。'

    # 7. 空间估算
    rr = space.get('risk_reward', 0)
    if rr and rr > 0:
        if rr >= 2:
            parts.append(f'从空间来看，潜在收益是风险的{rr:.1f}倍，性价比不错。')
        elif rr >= 1:
            parts.append(f'收益风险比{rr:.1f}:1，性价比一般。')
        else:
            parts.append(f'收益风险比仅{rr:.1f}:1，下行风险大于上涨空间，不太划算。')

    summary_text = ''.join(parts)

    return {
        'text': summary_text,
        'action_tip': action_tip,
        'confidence': '高' if score >= 70 or score <= 30 else '中',
    }


def compute_deep_analysis(df, signal_result=None, realtime_info=None):
    """
    对单只股票进行深度分析，返回结构化的分析报告。
    
    参数:
        df: DataFrame (含技术指标的K线数据)
        signal_result: dict (detect_all_signals 返回的结果，可选)
        realtime_info: dict (stock_realtime_price 行数据，可选)
    
    返回:
        dict: 完整的深度分析报告
    """
    import numpy as np
    if df is None or len(df) < 30:
        return {'error': 'K线数据不足（需要至少30天）'}

    last = df.iloc[-1]
    cl = float(last['close'])
    n = len(df)

    # ---- 1. 均线系统 ----
    ma_data = {}
    for period in [5, 10, 20, 60]:
        col = f'ma{period}'
        if col in df.columns and n >= period:
            ma_data[f'ma{period}'] = round(float(df[col].iloc[-1]), 2)

    ma_list = sorted(ma_data.items(), key=lambda x: x[1], reverse=True)
    ma_trend = 'bullish' if all(
        ma_data.get(f'ma{a}', 0) >= ma_data.get(f'ma{b}', 0)
        for a, b in [(5, 10), (10, 20)]
    ) else 'bearish' if all(
        ma_data.get(f'ma{a}', 0) <= ma_data.get(f'ma{b}', 0)
        for a, b in [(5, 10), (10, 20)]
    ) else 'mixed'

    ma_trend_label = {'bullish': '多头排列', 'bearish': '空头排列', 'mixed': '交叉整理'}

    # ---- 2. 价格位置分析 ----
    position = {}
    for days in [20, 60, 120]:
        subset = df.tail(days) if n >= days else df
        h = float(subset['high'].max())
        l = float(subset['low'].min())
        rng = h - l
        pct = round((cl - l) / rng * 100, 0) if rng > 0 else 50
        position[f'd{days}'] = {
            'high': round(h, 2), 'low': round(l, 2),
            'range_pct': pct,
            'up_space': round((h / cl - 1) * 100, 1),
            'down_risk': round((1 - l / cl) * 100, 1),
        }

    # ---- 3. 支撑与压力位 ----
    supports = []
    resistances = []

    for name, val in ma_data.items():
        if val < cl:
            supports.append({'level': val, 'type': 'ma', 'name': name.upper()})
        elif val > cl:
            resistances.append({'level': val, 'type': 'ma', 'name': name.upper()})

    for days_key in ['d20', 'd60', 'd120']:
        p = position.get(days_key, {})
        label = days_key.replace('d', '') + '日'
        if p.get('low', 0) < cl:
            supports.append({'level': p['low'], 'type': 'low', 'name': f'{label}低点'})
        if p.get('high', 0) > cl:
            resistances.append({'level': p['high'], 'type': 'high', 'name': f'{label}高点'})

    supports.sort(key=lambda x: x['level'], reverse=True)
    resistances.sort(key=lambda x: x['level'])

    # ---- 4. 成交量分析 ----
    vol = float(last['volume'])
    vol_5 = float(df['volume'].tail(5).mean()) if n >= 5 else vol
    vol_20 = float(df['volume'].tail(20).mean()) if n >= 20 else vol
    vol_ratio = round(vol / vol_20, 1) if vol_20 > 0 else 1.0

    vol_trend = '缩量' if vol_ratio < 0.7 else '平量' if vol_ratio < 1.3 else '温和放量' if vol_ratio < 2.0 else '大幅放量'

    # ---- 5. 形态识别（增强版） ----
    patterns = []
    closes_10 = [float(x) for x in df['close'].tail(10)]
    if n >= 10:
        std_10 = np.std(closes_10)
        mean_10 = np.mean(closes_10)
        cv_10 = std_10 / mean_10 if mean_10 > 0 else 0

        if cv_10 < 0.015 and cl > max(closes_10[:-1]):
            patterns.append({'name': '平台突破', 'bullish': True,
                'desc': f'近10日波动率仅{cv_10*100:.1f}%，今日突破平台'})
        elif cv_10 < 0.015:
            patterns.append({'name': '窄幅整理', 'bullish': None,
                'desc': f'近10日波动率{cv_10*100:.1f}%，蓄势待变'})

        if n >= 20:
            h20 = float(df.tail(20)['high'].max())
            if cl >= h20 * 0.99:
                patterns.append({'name': '创20日新高', 'bullish': True,
                    'desc': f'触及20日高点{h20:.2f}'})

        # 双底形态：近30日内两个低点价格接近（差异<3%），且当前价格高于两低点之间的高点
        if n >= 30:
            lows_30 = [float(x) for x in df['low'].tail(30)]
            # 找最低点和次低点
            min_idx = int(np.argmin(lows_30))
            min_val = lows_30[min_idx]
            # 在最低点之前找次低点
            if min_idx > 5:
                before_lows = lows_30[:min_idx]
                second_min_idx = int(np.argmin(before_lows))
                second_min_val = before_lows[second_min_idx]
                if abs(min_val - second_min_val) / min_val < 0.03:
                    # 两低点之间的高点
                    between_high = max(lows_30[second_min_idx:min_idx])
                    if cl > between_high:
                        patterns.append({'name': '双底突破', 'bullish': True,
                            'desc': f'双底形态（低点{min_val:.2f}和{second_min_val:.2f}），已突破颈线{between_high:.2f}'})

        # 量价齐升：近5日成交量递增且价格递增
        if n >= 5:
            vols_5 = [float(x) for x in df['volume'].tail(5)]
            closes_5 = [float(x) for x in df['close'].tail(5)]
            if all(vols_5[i] <= vols_5[i+1] for i in range(len(vols_5)-1)) and \
               all(closes_5[i] <= closes_5[i+1] for i in range(len(closes_5)-1)):
                patterns.append({'name': '量价齐升', 'bullish': True,
                    'desc': '近5日成交量与价格同步递增，强势特征'})

        # 均线粘合后发散：MA5/10/20 三线粘合后开始发散
        if n >= 20:
            ma5_val = ma_data.get('ma5', 0)
            ma10_val = ma_data.get('ma10', 0)
            ma20_val = ma_data.get('ma20', 0)
            if ma5_val and ma10_val and ma20_val:
                ma_spread = max(ma5_val, ma10_val, ma20_val) - min(ma5_val, ma10_val, ma20_val)
                ma_pct = ma_spread / cl * 100
                if ma_pct < 1.0 and ma5_val > ma10_val > ma20_val:
                    patterns.append({'name': '均线粘合发散', 'bullish': True,
                        'desc': f'MA5/10/20粘合（离散{ma_pct:.1f}%）后多头排列'})

    # 涨跌幅计算：如果最后一条是今天（可能未收盘），用前一日收盘价计算
    from datetime import date
    last_date_str = str(df['date'].values[-1])[:10]
    today_str = date.today().isoformat()
    if last_date_str == today_str and n >= 3:
        # 今天未收盘，用倒数第二根K线的收盘价对比倒数第三根
        change_today = round((cl / float(df.iloc[-2]['close']) - 1) * 100, 2)
    else:
        change_today = round((cl / float(df.iloc[-2]['close']) - 1) * 100, 2) if n >= 2 else 0
    if change_today >= 5:
        patterns.append({'name': '大阳线', 'bullish': True,
            'desc': f'涨幅{change_today:.1f}%'})
    elif change_today <= -5:
        patterns.append({'name': '大阴线', 'bullish': False,
            'desc': f'跌幅{change_today:.1f}%'})

    # ---- 6. 空间估算 ----
    first_resist = resistances[0] if resistances else None
    first_support = supports[0] if supports else None

    space = {
        'nearest_resist': first_resist,
        'nearest_support': first_support,
        'risk_reward': None,
    }
    if first_resist and first_support:
        upside = first_resist['level'] - cl
        downside = cl - first_support['level']
        space['risk_reward'] = round(upside / downside, 1) if downside > 0 else 99

    # ---- 7. 综合评估 ----
    score = 50
    reasons = []

    if ma_trend == 'bullish':
        score += 10
        reasons.append('均线多头排列(+10)')
    elif ma_trend == 'bearish':
        score -= 10
        reasons.append('均线空头排列(-10)')

    if vol_ratio >= 1.3:
        score += 5
        reasons.append(f'放量{vol_ratio}倍(+5)')
    elif vol_ratio < 0.6:
        score -= 3
        reasons.append(f'缩量{vol_ratio}倍(-3)')

    any_breakout = any(p['name'] == '平台突破' for p in patterns)
    if any_breakout:
        score += 10
        reasons.append('平台突破(+10)')

    any_new_high = any(p['name'] == '创20日新高' for p in patterns)
    if any_new_high:
        score += 5
        reasons.append('创20日新高(+5)')

    any_double_bottom = any(p['name'] == '双底突破' for p in patterns)
    if any_double_bottom:
        score += 10
        reasons.append('双底突破(+10)')

    any_vol_price_rise = any(p['name'] == '量价齐升' for p in patterns)
    if any_vol_price_rise:
        score += 8
        reasons.append('量价齐升(+8)')

    any_ma_converge = any(p['name'] == '均线粘合发散' for p in patterns)
    if any_ma_converge:
        score += 7
        reasons.append('均线粘合发散(+7)')

    pos_120 = position.get('d120', {}).get('range_pct', 50)
    if pos_120 < 30:
        score += 5
        reasons.append(f'120日位置偏低{pos_120}%(+5)')
    elif pos_120 > 80:
        score -= 5
        reasons.append(f'120日位置偏高{pos_120}%(-5)')

    # 20日位置也纳入评分
    pos_20 = position.get('d20', {}).get('range_pct', 50)
    if pos_20 < 25:
        score += 3
        reasons.append(f'20日位置偏低{pos_20}%(+3)')
    elif pos_20 > 85:
        score -= 3
        reasons.append(f'20日位置偏高{pos_20}%(-3)')

    if signal_result:
        sig_count = signal_result.get('signal_summary', {}).get('total_signals', 0)
        if sig_count >= 3:
            score += 15
            reasons.append(f'{sig_count}信号共振(+15)')
        elif sig_count >= 2:
            score += 10
            reasons.append(f'{sig_count}信号叠加(+10)')
        elif sig_count >= 1:
            score += 5
            reasons.append(f'{sig_count}个信号(+5)')

    if space.get('risk_reward') and space['risk_reward'] >= 2:
        score += 5
        reasons.append(f'风险收益比{space["risk_reward"]}:1(+5)')
    elif space.get('risk_reward') and space['risk_reward'] < 0.8:
        score -= 5
        reasons.append(f'风险收益比{space["risk_reward"]}:1(-5)')

    score = max(0, min(100, score))

    verdict = '强烈看多' if score >= 80 else '看多' if score >= 65 else '中性偏多' if score >= 50 else '中性偏空' if score >= 35 else '看空'

    ai_summary = _generate_plain_summary(
        cl, change_today, ma_trend, position, supports, resistances,
        vol_ratio, vol_trend, patterns, space, score, verdict, reasons
    )

    return {
        'price': cl,
        'change_pct': change_today,
        'ma': ma_data,
        'ma_trend': ma_trend,
        'ma_trend_label': ma_trend_label[ma_trend],
        'position': position,
        'supports': supports[:5],
        'resistances': resistances[:5],
        'volume': {
            'today': vol,
            'avg_5': round(vol_5),
            'avg_20': round(vol_20),
            'ratio': vol_ratio,
            'trend': vol_trend,
        },
        'patterns': patterns,
        'space': space,
        'deep_score': score,
        'verdict': verdict,
        'score_reasons': reasons,
        'ai_summary': ai_summary,
        'kline_days': n,
    }