"""
政策消息解析模块
功能：
1. 政策消息分类（货币/财政/产业/监管）
2. 政策力度分级
3. 受益板块识别
4. 政策驱动轮动信号生成
5. 利空政策预警

Author: 关羽（云长）
Date: 2026-03-27
"""

from dataclasses import dataclass
from typing import List, Dict, Optional, Tuple
from enum import Enum
import re


class PolicyType(Enum):
    MONETARY = "货币政策"
    FISCAL = "财政政策"
    INDUSTRY = "产业政策"
    REGULATORY = "监管政策"
    MACRO = "宏观政策"
    OTHER = "其他"


class PolicyStrength(Enum):
    WEAK = 1
    MEDIUM = 2
    STRONG = 3
    VERY_STRONG = 4


class PolicySentiment(Enum):
    BULL = 1
    NEUTRAL = 2
    BEAR = 3


@dataclass
class PolicyNews:
    """政策消息"""
    title: str
    content: str
    date: str
    source: str
    policy_type: PolicyType = PolicyType.OTHER
    strength: PolicyStrength = PolicyStrength.MEDIUM
    sentiment: PolicySentiment = PolicySentiment.NEUTRAL
    affected_sectors: List[str] = None
    keywords: List[str] = None
    
    def __post_init__(self):
        if self.affected_sectors is None:
            self.affected_sectors = []
        if self.keywords is None:
            self.keywords = []


@dataclass
class PolicySignal:
    """政策轮动信号"""
    sector: str
    sentiment: PolicySentiment
    strength: int
    signal_date: str
    reason: str


class PolicyClassifier:
    """政策消息分类器"""
    
    # 关键词分类词典
    TYPE_KEYWORDS = {
        PolicyType.MONETARY: [
            "降息", "降准", "货币政策", "流动性", "MLF", "LPR", "美联储", "加息",
            "存款准备金", "公开市场操作", "Shibor", "利率"
        ],
        PolicyType.FISCAL: [
            "财政政策", "减税", "降费", "赤字", "专项债", "国债", "转移支付",
            "积极财政", "财政刺激"
        ],
        PolicyType.INDUSTRY: [
            "扶持", "鼓励", "补贴", "规划", "纲要", "产业链", "新能源", "AI",
            "半导体", "芯片", "消费", "汽车", "地产", "基建", "数字经济",
            "高端制造", "生物医药", "绿色能源", "碳中和"
        ],
        PolicyType.REGULATORY: [
            "监管", "整治", "整顿", "约谈", "退市", "处罚", "反垄断", "立案",
            "调查", "整改", "规范", "限购", "限跌", "双减", "教培"
        ],
        PolicyType.MACRO: [
            "GDP", "CPI", "PMI", "经济数据", "失业率", "经济增长", "通胀",
            "经济会议", "中央经济工作", "两会", "政治局会议"
        ]
    }
    
    # 受益板块映射
    SECTOR_KEYWORDS = {
        "AI": ["AI", "人工智能", "大模型", "ChatGPT", "算力"],
        "半导体": ["半导体", "芯片", "光刻机", "集成电路"],
        "新能源": ["新能源", "光伏", "风电", "储能", "新能源车", "动力电池"],
        "消费": ["消费", "内需", "促消费", "零售", "食品饮料"],
        "医药": ["医药", "生物医药", "创新药", "医疗", "集采"],
        "金融": ["金融", "银行", "证券", "券商", "保险"],
        "地产": ["房地产", "地产", "楼市", "保交楼"],
        "基建": ["基建", "新基建", "传统基建", "基础设施"],
        "汽车": ["汽车", "新能源车", "汽车消费", "智能驾驶"],
        "军工": ["军工", "国防", "武器装备"],
        "农业": ["农业", "粮食", "种子", "乡村振兴"],
        "环保": ["环保", "碳中和", "绿色发展", "碳达峰"]
    }
    
    def classify_type(self, text: str) -> PolicyType:
        """根据关键词分类政策类型"""
        max_count = 0
        best_type = PolicyType.OTHER
        
        for ptype, keywords in self.TYPE_KEYWORDS.items():
            count = sum(1 for kw in keywords if kw in text)
            if count > max_count:
                max_count = count
                best_type = ptype
        
        return best_type
    
    def recognize_affected_sectors(self, text: str) -> List[str]:
        """识别受影响板块"""
        affected = []
        for sector, keywords in self.SECTOR_KEYWORDS.items():
            for kw in keywords:
                if kw in text:
                    affected.append(sector)
                    break
        return affected
    
    def judge_strength(self, text: str, sentiment: PolicySentiment) -> PolicyStrength:
        """判断政策力度"""
        strong_words = ["全面", "大力", "全力", "重磅", "重大", "万亿", "千亿",
                       "顶格", "全面放开", "重磅推出", "重大利好"]
        medium_words = ["稳步", "适度", "推进", "支持", "鼓励"]
        weak_words = ["研究", "酝酿", "讨论", "草案", "征求意见"]
        
        strong_count = sum(1 for w in strong_words if w in text)
        weak_count = sum(1 for w in weak_words if w in text)
        
        if strong_count >= 2:
            return PolicyStrength.VERY_STRONG
        elif strong_count >= 1:
            return PolicyStrength.STRONG
        elif weak_count >= 1:
            return PolicyStrength.WEAK
        else:
            return PolicyStrength.MEDIUM
    
    def judge_sentiment(self, text: str) -> PolicySentiment:
        """判断政策多空"""
        bull_words = ["利好", "扶持", "鼓励", "支持", "发展", "推广", "优惠",
                     "补贴", "降税", "松绑", "放开"]
        bear_words = ["监管", "整治", "限制", "禁止", "处罚", "退市", "打压",
                     "收紧", "加息", "降准", "调控", "整顿"]
        
        bull_count = sum(1 for w in bull_words if w in text)
        bear_count = sum(1 for w in bear_words if w in text)
        
        if bull_count > bear_count:
            return PolicySentiment.BULL
        elif bear_count > bull_count:
            return PolicySentiment.BEAR
        else:
            return PolicySentiment.NEUTRAL


class PolicySignalGenerator:
    """政策驱动轮动信号生成"""
    
    def __init__(self):
        self.classifier = PolicyClassifier()
    
    def parse_news(self, news: PolicyNews) -> PolicyNews:
        """解析新闻，自动分类、判断力度和多空"""
        full_text = news.title + news.content
        news.policy_type = self.classifier.classify_type(full_text)
        news.sentiment = self.classifier.judge_sentiment(full_text)
        news.strength = self.classifier.judge_strength(full_text, news.sentiment)
        news.affected_sectors = self.classifier.recognize_affected_sectors(full_text)
        return news
    
    def generate_signal(self, news_list: List[PolicyNews]) -> List[PolicySignal]:
        """根据多个政策生成板块轮动信号"""
        sector_scores: Dict[str, int] = {}
        
        for news in news_list:
            # 解析后得到评分
            strength_weight = news.strength.value
            if news.sentiment == PolicySentiment.BULL:
                score = strength_weight * 2
            elif news.sentiment == PolicySentiment.BEAR:
                score = -strength_weight * 2
            else:
                score = 0
            
            for sector in news.affected_sectors:
                sector_scores[sector] = sector_scores.get(sector, 0) + score
        
        # 生成信号
        signals = []
        for sector, score in sector_scores.items():
            if score > 0:
                sentiment = PolicySentiment.BULL
            elif score < 0:
                sentiment = PolicySentiment.BEAR
            else:
                sentiment = PolicySentiment.NEUTRAL
            
            signals.append(PolicySignal(
                sector=sector,
                sentiment=sentiment,
                strength=abs(score),
                signal_date=news_list[-1].date,
                reason=f"累计政策评分{score}"
            ))
        
        # 按强度排序
        signals.sort(key=lambda x: x.strength, reverse=True)
        return signals


class PolicyRiskAlert:
    """政策风险预警"""
    
    # 高风险关键词
    HIGH_RISK_KEYWORDS = [
        "反垄断", "强监管", "整治", "集采", "退市", "立案调查", "约谈",
        "处罚", "退市风险", "退市警示", "全面收紧", "加息超预期"
    ]
    
    def check_risk(self, news: PolicyNews) -> Tuple[bool, int, str]:
        """
        检查政策风险
        返回：(是否高风险, 风险等级 1~3, 原因)
        """
        full_text = news.title + news.content
        risk_count = sum(1 for kw in self.HIGH_RISK_KEYWORDS if kw in full_text)
        
        if news.sentiment == PolicySentiment.BEAR and news.strength.value >= 3:
            return True, 3, f"{news.policy_type.value}利空，力度强，高度风险"
        elif risk_count >= 2:
            return True, 2, f"多个高风险关键词，中度风险"
        elif risk_count >= 1:
            return True, 1, f"存在风险关键词，轻度风险"
        else:
            return False, 0, "无明显政策风险"


class PolicyRotationModule:
    """政策驱动轮动总模块"""
    
    def __init__(self):
        self.signal_generator = PolicySignalGenerator()
        self.risk_alert = PolicyRiskAlert()
        self.parsed_news: List[PolicyNews] = []
    
    def add_and_parse(self, news: PolicyNews) -> PolicyNews:
        """添加并解析政策新闻"""
        parsed = self.signal_generator.parse_news(news)
        self.parsed_news.append(parsed)
        return parsed
    
    def get_current_signals(self) -> List[PolicySignal]:
        """获取当前轮动信号"""
        return self.signal_generator.generate_signal(self.parsed_news[-20:])
    
    def get_risk_alerts(self) -> List[Tuple[str, int, str]]:
        """获取当前风险预警"""
        alerts = []
        for news in self.parsed_news[-10:]:
            is_risk, level, reason = self.risk_alert.check_risk(news)
            if is_risk:
                alerts.append((f"{news.title} ({news.date})", level, reason))
        return alerts
    
    def get_rotation_report(self) -> str:
        """生成政策轮动报告"""
        signals = self.get_current_signals()
        alerts = self.get_risk_alerts()
        
        lines = []
        lines.append("=" * 60)
        lines.append("政策驱动板块轮动报告")
        lines.append("=" * 60)
        lines.append(f"已解析政策数量: {len(self.parsed_news)}")
        lines.append("")
        
        if signals:
            lines.append("🔔 当前板块信号:")
            for sig in signals[:10]:  # 只放前10个
                sentiment_emoji = {
                    PolicySentiment.BULL: "🔼",
                    PolicySentiment.NEUTRAL: "➖",
                    PolicySentiment.BEAR: "🔽"
                }
                lines.append(f"  {sentiment_emoji[sig.sentiment]} {sig.sector}: 强度={sig.strength} → {sig.reason}")
            lines.append("")
        
        if alerts:
            lines.append("⚠️ 政策风险预警:")
            for title, level, reason in alerts:
                lines.append(f"  [{level}级风险] {title}: {reason}")
            lines.append("")
        
        if not signals and not alerts:
            lines.append("无明确信号，保持观察")
        
        lines.append("=" * 60)
        return "\n".join(lines)


if __name__ == "__main__":
    print("=== 测试政策消息解析模块 ===\n")
    
    module = PolicyRotationModule()
    
    # 测试案例1：AI产业政策
    news1 = PolicyNews(
        title="重磅政策支持人工智能发展，算力基础设施加快建设",
        content="近日，国务院印发《新一代人工智能发展规划》，全面支持人工智能产业发展，加大算力基础设施建设，对AI企业给予税收优惠。",
        date="2026-03-27",
        source="新华社"
    )
    
    parsed1 = module.add_and_parse(news1)
    print(f"[测试1] {parsed1.title}")
    print(f"  类型: {parsed1.policy_type.value}")
    print(f"  力度: {parsed1.strength.name}")
    print(f"  情绪: {parsed1.sentiment.name}")
    print(f"  影响板块: {parsed1.affected_sectors}")
    print()
    
    # 测试案例2：监管政策
    news2 = PolicyNews(
        title="监管部门加强对互联网平台反垄断监管，约谈头部企业",
        content="近日，市场监管总局对互联网平台企业反垄断问题开展专项整治，约谈头部三家企业要求整改，规范市场竞争秩序。",
        date="2026-03-26",
        source="证监会"
    )
    
    parsed2 = module.add_and_parse(news2)
    print(f"[测试2] {parsed2.title}")
    print(f"  类型: {parsed2.policy_type.value}")
    print(f"  力度: {parsed2.strength.name}")
    print(f"  情绪: {parsed2.sentiment.name}")
    print()
    
    # 测试案例3：降准
    news3 = PolicyNews(
        title="央行宣布全面降准0.5个百分点，释放长期资金一万亿",
        content="中国人民银行决定下调金融机构存款准备金率0.5个百分点，释放长期资金约一万亿元，支持实体经济发展。",
        date="2026-03-25",
        source="央行官网"
    )
    
    parsed3 = module.add_and_parse(news3)
    print(f"[测试3] {parsed3.title}")
    print(f"  类型: {parsed3.policy_type.value}")
    print(f"  力度: {parsed3.strength.name}")
    print(f"  情绪: {parsed3.sentiment.name}")
    print(f"  影响板块: {parsed3.affected_sectors}")
    print()
    
    # 生成报告
    print(module.get_rotation_report())
    
    # 风险预警测试
    print("\n=== 风险预警测试 ===")
    for n in module.parsed_news:
        is_risk, level, reason = module.risk_alert.check_risk(n)
        print(f"{n.title}: is_risk={is_risk}, level={level}, reason={reason}")