"""
外部因素分析模块（P2/P3/P4/P7）

包含：
- P2: 北向资金（外资动向）
- P3: 美股隔夜板块变化
- P4: 大宗商品价格
- P7: 汇率变化

数据源：AKShare（开源免费）
所有数据采集均带超时和异常处理，失败时返回中性评分不影响主流程。
"""
import logging
from datetime import datetime, timedelta

logger = logging.getLogger(__name__)

# 缓存（当日有效）
_cache = {}
_cache_date = {}


def _get_cache(key):
    """获取当日缓存"""
    today = datetime.now().strftime('%Y-%m-%d')
    if _cache_date.get(key) == today:
        return _cache.get(key)
    return None


def _set_cache(key, value):
    """设置当日缓存"""
    today = datetime.now().strftime('%Y-%m-%d')
    _cache[key] = value
    _cache_date[key] = today


# ═══════════════════════════════════════════════
# P2: 北向资金
# ═══════════════════════════════════════════════

def get_northbound_capital():
    """
    获取北向资金净流入数据

    返回:
        dict: {
            'net_inflow': float,       # 今日净流入（亿）
            'score': int,              # 评分增减（-10 ~ +10）
            'summary': str,            # 白话总结
            'reasons': list,           # 评分原因
        }
    """
    cached = _get_cache('northbound')
    if cached:
        return cached

    try:
        import akshare as ak

        # 获取北向资金净流入数据
        df = ak.stock_hsgt_north_net_flow_in_em(symbol="北向")
        if df is None or df.empty:
            return _neutral_result('北向资金数据为空')

        # 取最近5个交易日
        recent = df.tail(5)
        today_inflow = float(recent.iloc[-1].get('当日净流入', 0) or 0)

        # 连续流入/流出天数
        consecutive_inflow = 0
        consecutive_outflow = 0
        for _, row in recent[::-1].iterrows():
            val = float(row.get('当日净流入', 0) or 0)
            if val > 0:
                if consecutive_outflow > 0:
                    break
                consecutive_inflow += 1
            elif val < 0:
                if consecutive_inflow > 0:
                    break
                consecutive_outflow += 1

        # 评分
        score = 0
        reasons = []
        summary_parts = []

        if today_inflow > 50:
            score += 5
            reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+5)')
            summary_parts.append(f'外资今日大幅买入{today_inflow:.1f}亿元')
        elif today_inflow > 20:
            score += 3
            reasons.append(f'北向今日净流入{today_inflow:.1f}亿(+3)')
            summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元')
        elif today_inflow < -50:
            score -= 5
            reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-5)')
            summary_parts.append(f'外资今日大幅卖出{abs(today_inflow):.1f}亿元')
        elif today_inflow < -20:
            score -= 3
            reasons.append(f'北向今日净流出{abs(today_inflow):.1f}亿(-3)')
            summary_parts.append(f'外资今日净流出{abs(today_inflow):.1f}亿元')
        else:
            summary_parts.append(f'外资今日净流入{today_inflow:.1f}亿元，方向不明')

        if consecutive_inflow >= 3:
            score += 3
            reasons.append(f'北向连续{consecutive_inflow}日净流入(+3)')
            summary_parts.append(f'已连续{consecutive_inflow}天买入')

        if consecutive_outflow >= 3:
            score -= 3
            reasons.append(f'北向连续{consecutive_outflow}日净流出(-3)')
            summary_parts.append(f'已连续{consecutive_outflow}天卖出')

        score = max(-10, min(10, score))

        result = {
            'net_inflow': round(today_inflow, 2),
            'consecutive_inflow': consecutive_inflow,
            'consecutive_outflow': consecutive_outflow,
            'score': score,
            'summary': '，'.join(summary_parts),
            'reasons': reasons,
        }
        _set_cache('northbound', result)
        return result

    except Exception as e:
        logger.warning(f"获取北向资金数据失败: {e}")
        return _neutral_result('北向资金数据获取失败')


# ═══════════════════════════════════════════════
# P3: 美股隔夜板块变化
# ═══════════════════════════════════════════════

# 美股板块 → A股板块映射
US_A_SECTOR_MAP = {
    '科技': ['半导体', '软件', '消费电子', '芯片', 'IT服务'],
    '新能源车': ['锂电池', '汽车零部件', '新能源车', '充电桩'],
    '金融': ['银行', '保险', '券商'],
    '能源': ['石油开采', '化工', '页岩气'],
    '医药': ['创新药', '医疗器械', '生物制品', 'CXO'],
    '消费': ['白酒', '食品饮料', '零售', '免税'],
    '房地产': ['房地产', '建材', '家居'],
    '工业': ['机械', '军工', '工业4.0'],
    '材料': ['有色金属', '钢铁', '化工新材料'],
    '公用事业': ['电力', '环保', '水务'],
}


def get_us_market_overview():
    """
    获取美股隔夜收盘数据，计算外盘情绪

    返回:
        dict: {
            'indices': dict,          # 三大指数涨跌
            'sectors': dict,          # 主要板块涨跌
            'score': int,             # 评分增减（-10 ~ +10）
            'summary': str,           # 白话总结
            'reasons': list,          # 评分原因
            'affected_a_sectors': dict, # 对A股板块的影响
        }
    """
    cached = _get_cache('us_market')
    if cached:
        return cached

    try:
        import akshare as ak

        # 获取全球主要指数
        df = ak.index_global()
        if df is None or df.empty:
            return _neutral_result('美股指数数据为空')

        # 筛选美股主要指数
        us_indices = {}
        for _, row in df.iterrows():
            name = str(row.get('名称', ''))
            if '纳斯达克' in name:
                us_indices['nasdaq'] = {
                    'name': name,
                    'change_pct': float(row.get('涨跌幅', 0) or 0),
                }
            elif '道琼斯' in name:
                us_indices['dow'] = {
                    'name': name,
                    'change_pct': float(row.get('涨跌幅', 0) or 0),
                }
            elif '标普500' in name:
                us_indices['sp500'] = {
                    'name': name,
                    'change_pct': float(row.get('涨跌幅', 0) or 0),
                }

        if not us_indices:
            return _neutral_result('未找到美股指数')

        # 计算综合涨跌
        avg_change = sum(v['change_pct'] for v in us_indices.values()) / len(us_indices)

        # 评分
        score = 0
        reasons = []
        summary_parts = []

        if avg_change > 2:
            score += 5
            reasons.append(f'美股三大指数平均涨幅{avg_change:.1f}%(+5)')
            summary_parts.append(f'美股大涨，平均涨幅{avg_change:.1f}%')
        elif avg_change > 0.5:
            score += 2
            reasons.append(f'美股偏强，平均涨幅{avg_change:.1f}%(+2)')
            summary_parts.append(f'美股小幅上涨，平均{avg_change:.1f}%')
        elif avg_change < -2:
            score -= 5
            reasons.append(f'美股三大指数平均跌幅{abs(avg_change):.1f}%(-5)')
            summary_parts.append(f'美股大跌，平均跌幅{abs(avg_change):.1f}%')
        elif avg_change < -0.5:
            score -= 2
            reasons.append(f'美股偏弱，平均跌幅{abs(avg_change):.1f}%(-2)')
            summary_parts.append(f'美股小幅下跌，平均{avg_change:.1f}%')
        else:
            summary_parts.append(f'美股基本平盘，平均变化{avg_change:.1f}%')

        # 对A股板块的影响
        affected_sectors = {}
        if avg_change > 1:
            for us_sector, a_sectors in US_A_SECTOR_MAP.items():
                affected_sectors[us_sector] = {
                    'a_sectors': a_sectors,
                    'direction': '利好',
                    'note': f'美股{us_sector}板块偏强，A股{"、".join(a_sectors[:3])}可能高开',
                }
        elif avg_change < -1:
            for us_sector, a_sectors in US_A_SECTOR_MAP.items():
                affected_sectors[us_sector] = {
                    'a_sectors': a_sectors,
                    'direction': '利空',
                    'note': f'美股{us_sector}板块偏弱，A股{"、".join(a_sectors[:3])}可能低开',
                }

        score = max(-10, min(10, score))

        result = {
            'indices': us_indices,
            'avg_change': round(avg_change, 2),
            'score': score,
            'summary': '，'.join(summary_parts),
            'reasons': reasons,
            'affected_a_sectors': affected_sectors,
        }
        _set_cache('us_market', result)
        return result

    except Exception as e:
        logger.warning(f"获取美股数据失败: {e}")
        return _neutral_result('美股数据获取失败')


# ═══════════════════════════════════════════════
# P4: 大宗商品价格
# ═══════════════════════════════════════════════

# 大宗商品 → A股板块影响映射
COMMODITY_A_SECTOR_MAP = {
    '原油': {
        'beneficiary': ['石油开采', '油服', '化工'],
        'victim': ['航空', '物流', '化工下游'],
        'direction': '油价涨→开采受益，航空受损',
    },
    '黄金': {
        'beneficiary': ['黄金股', '珠宝', '有色'],
        'victim': [],
        'direction': '金价涨→黄金企业受益',
    },
    '铜': {
        'beneficiary': ['铜矿', '有色', '电缆'],
        'victim': [],
        'direction': '铜价涨→铜企受益',
    },
    '螺纹钢': {
        'beneficiary': ['钢铁', '钢矿'],
        'victim': ['基建', '地产'],
        'direction': '钢价涨→钢企受益，基建成本增',
    },
    '碳酸锂': {
        'beneficiary': ['锂矿', '锂电池'],
        'victim': ['新能源车'],
        'direction': '锂价涨→锂矿受益，新能源车成本增',
    },
}


def get_commodity_overview():
    """
    获取主要大宗商品价格变化

    返回:
        dict: {
            'commodities': dict,      # 各商品涨跌
            'score': int,             # 评分增减（-5 ~ +5）
            'summary': str,           # 白话总结
            'reasons': list,          # 评分原因
            'affected_sectors': dict, # 对A股板块影响
        }
    """
    cached = _get_cache('commodity')
    if cached:
        return cached

    try:
        import akshare as ak

        # 获取国内商品期货行情
        df = ak.futures_main_sina()
        if df is None or df.empty:
            return _neutral_result('大宗商品数据为空')

        # 关注的商品
        target_commodities = ['原油', '黄金', '铜', '螺纹钢', '碳酸锂']
        commodities = {}

        for _, row in df.iterrows():
            symbol = str(row.get('symbol', ''))
            for target in target_commodities:
                if target in symbol:
                    change = float(row.get('change', 0) or 0)
                    pct = float(row.get('change_pct', 0) or 0)
                    commodities[target] = {
                        'symbol': symbol,
                        'change_pct': round(pct, 2),
                    }
                    break

        if not commodities:
            return _neutral_result('未找到关注的大宗商品')

        # 评分和影响
        score = 0
        reasons = []
        summary_parts = []
        affected = {}

        for name, data in commodities.items():
            pct = data['change_pct']
            if abs(pct) < 0.5:
                continue

            mapping = COMMODITY_A_SECTOR_MAP.get(name)
            if not mapping:
                continue

            if pct > 2:
                score += 1
                reasons.append(f'{name}涨{pct:.1f}%，利好{"、".join(mapping["beneficiary"][:2])}(+1)')
                summary_parts.append(f'{name}大涨{pct:.1f}%')
                affected[name] = {
                    'direction': '利好',
                    'beneficiary': mapping['beneficiary'],
                    'victim': mapping['victim'],
                    'note': mapping['direction'],
                }
            elif pct < -2:
                score -= 1
                reasons.append(f'{name}跌{abs(pct):.1f}%，利空{"、".join(mapping["beneficiary"][:2])}(-1)')
                summary_parts.append(f'{name}大跌{pct:.1f}%')
                affected[name] = {
                    'direction': '利空',
                    'beneficiary': mapping['victim'],
                    'victim': mapping['beneficiary'],
                    'note': mapping['direction'],
                }

        score = max(-5, min(5, score))

        result = {
            'commodities': commodities,
            'score': score,
            'summary': '，'.join(summary_parts) if summary_parts else '大宗商品整体平稳',
            'reasons': reasons,
            'affected_sectors': affected,
        }
        _set_cache('commodity', result)
        return result

    except Exception as e:
        logger.warning(f"获取大宗商品数据失败: {e}")
        return _neutral_result('大宗商品数据获取失败')


# ═══════════════════════════════════════════════
# P7: 汇率变化
# ═══════════════════════════════════════════════

# 汇率 → A股板块影响
FX_SECTOR_MAP = {
    '升值': {
        'beneficiary': ['航空', '造纸', '房地产'],
        'victim': ['纺织', '家电出口', '电子代工'],
    },
    '贬值': {
        'beneficiary': ['纺织', '家电', '电子代工'],
        'victim': ['航空', '造纸'],
    },
}


def get_fx_overview():
    """
    获取人民币汇率变化

    返回:
        dict: {
            'usd_cny': float,         # 美元兑人民币汇率
            'change_pct': float,      # 涨跌幅
            'direction': str,         # 升值/贬值/稳定
            'score': int,             # 评分增减（-3 ~ +3）
            'summary': str,           # 白话总结
            'reasons': list,          # 评分原因
            'affected_sectors': dict, # 对A股板块影响
        }
    """
    cached = _get_cache('fx')
    if cached:
        return cached

    try:
        import akshare as ak

        # 获取人民币汇率
        df = ak.currency_boc_sina(symbol="美元")
        if df is None or df.empty:
            return _neutral_result('汇率数据为空')

        # 取最近2条计算变化
        recent = df.tail(2)
        if len(recent) < 2:
            return _neutral_result('汇率数据不足')

        today_rate = float(recent.iloc[-1].get('中行折算价', 0) or 0)
        prev_rate = float(recent.iloc[-2].get('中行折算价', 0) or 0)

        if prev_rate == 0:
            return _neutral_result('汇率数据异常')

        change_pct = round((today_rate / prev_rate - 1) * 100, 3)

        # 判断方向（美元兑人民币：涨=人民币贬值，跌=人民币升值）
        if change_pct > 0.1:
            direction = '贬值'
            score = -2
            summary = f'人民币贬值{abs(change_pct):.3f}%'
            reasons = [f'人民币贬值{abs(change_pct):.3f}%(-2)']
            affected = FX_SECTOR_MAP['贬值']
        elif change_pct < -0.1:
            direction = '升值'
            score = 2
            summary = f'人民币升值{abs(change_pct):.3f}%'
            reasons = [f'人民币升值{abs(change_pct):.3f}%(+2)']
            affected = FX_SECTOR_MAP['升值']
        else:
            direction = '稳定'
            score = 0
            summary = '人民币汇率基本稳定'
            reasons = []
            affected = {}

        score = max(-3, min(3, score))

        result = {
            'usd_cny': round(today_rate, 4),
            'change_pct': change_pct,
            'direction': direction,
            'score': score,
            'summary': summary,
            'reasons': reasons,
            'affected_sectors': affected,
        }
        _set_cache('fx', result)
        return result

    except Exception as e:
        logger.warning(f"获取汇率数据失败: {e}")
        return _neutral_result('汇率数据获取失败')


# ═══════════════════════════════════════════════
# 综合外部因素
# ═══════════════════════════════════════════════

def get_all_external_factors():
    """
    获取所有外部因素数据，返回综合结果

    返回:
        dict: 包含北向资金、美股、大宗商品、汇率的综合数据
    """
    northbound = get_northbound_capital()
    us_market = get_us_market_overview()
    commodity = get_commodity_overview()
    fx = get_fx_overview()

    total_score = (
        northbound.get('score', 0) +
        us_market.get('score', 0) +
        commodity.get('score', 0) +
        fx.get('score', 0)
    )

    all_reasons = []
    all_reasons.extend(northbound.get('reasons', []))
    all_reasons.extend(us_market.get('reasons', []))
    all_reasons.extend(commodity.get('reasons', []))
    all_reasons.extend(fx.get('reasons', []))

    summaries = []
    for name, data in [('北向资金', northbound), ('美股', us_market), ('大宗商品', commodity), ('汇率', fx)]:
        s = data.get('summary', '')
        if s and '失败' not in s and '为空' not in s:
            summaries.append(f'{name}：{s}')

    return {
        'northbound_capital': northbound,
        'us_market': us_market,
        'commodity': commodity,
        'fx': fx,
        'total_score': total_score,
        'all_reasons': all_reasons,
        'summary': ' | '.join(summaries),
    }


def _neutral_result(reason):
    """返回中性结果"""
    return {
        'score': 0,
        'summary': reason,
        'reasons': [],
    }