20260327 今日开发完成：三个策略新增+结构化适配+消息风控+任务跟踪

guanyu-risk/common/README.md

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+# 通用风控模块 - 个股利好利空风险监控
+
+## 模块说明
+
+`news_risk_monitor.py` + `structural_market_risk.py` 是通用模块，适配A股结构化行情，可被各个策略共享使用。
+
+### 核心功能
+
+基于公开数据，提前预判潜在消息面风险和板块风险：
+
+| 监控维度 | 预警规则 | 预判逻辑 |
+|----------|----------|----------|
+| **量价异常** | 近5日放量下跌 >7%，无公开消息 | 可能利空提前泄露，资金先跑 |
+| | 成交量放天量 >2倍，但股价不涨 | 可能利好兑现，主力出货 |
+| | 向下跳空缺口未回补 | 技术面偏空，趋势向下 |
+| **融资变化** | 一周融资余额减少 >20% | 杠杆资金出逃，不看好后市 |
+| | 融资买入占成交额 >20% | 杠杆比例过高，波动风险大 |
+| **龙虎大宗** | 机构大额卖出上榜 | 机构出逃，看空 |
+| | 大宗折价 >8% | 大股东折价出货，利空 |
+| **舆情监控** | 讨论量突然暴涨 >5倍 | 热度太高，往往见顶 |
+| | 舆情情感分 < -0.5 | 市场一致看空，情绪偏空 |
+| **国际联动** | A+H股H股隔夜跌幅 >3% | A股大概率跟随下跌 |
+| | 大宗商品股对应期货跌幅 >4% | 个股价格承压 |
+| | 美股隔夜跌幅 >2% | A股开盘承压，系统性风险 |
+| | 重大国际利空消息 | 直接预警，建议降仓 |
+| **结构化行情风控** | 单板块仓位 >15% | 提示减仓分散 |
+| | 板块累计涨幅 >50% | 风险评分放大，警惕利好出尽 |
+| | 单一风格仓位 >40% | 提示超配风险 |
+| | 冷门板块连续大跌 >20% | 如果基本面没问题，提示低吸机会 |
+| | 热点板块消息 | 风险评分放大2倍，灵敏度提高
+
+### 量化规则
+
+**风险等级划分：**
+
+| 总分 | 等级 | 操作建议 |
+|------|------|----------|
+| ≥40 | 🔴 EXIT | 建议清仓离场 |
+| 25~40 | 🟠 REDUCE | 建议减仓 |
+| 10~25 | 🟡 WATCH | 继续观察，不新开仓 |
+| <10 | 🟢 SAFE | 安全，按计划操作 |
+
+**整合进五维风险评估：**
+
+原来四个维度 + 消息风险维度：
+1. 流动性风险 (15%)
+2. 估值风险 (20%)
+3. 技术面风险 (20%)
+4. 基本面风险 (20%)
+5. **消息面风险 (25%)** → 权重更高，因为黑天鹅危害大
+
+### 使用方法
+
+```python
+# 导入通用模块
+import sys
+sys.path.append("../../../guanyu-risk/common/")
+from news_risk_monitor import NewsRiskMonitor, StockNewsData, FiveDimensionRiskAssessment
+
+# 初始化监控器
+monitor = NewsRiskMonitor()
+
+# 填充数据
+data = StockNewsData(
+    code="600000",
+    name="浦发银行",
+    recent_vol_change=1.2,       # 成交量较20日均变化
+    recent_pct_change=-8.5,      # 近5日涨跌幅%
+    gap_down=True,              # 是否有向下缺口
+    finance_balance_change=-0.25, # 融资余额周变化比例
+    has_large_order=False,       # 龙虎榜是否大额卖出
+    has_bulk_discount=False,      # 是否有大宗折价
+    discussion_count_change=3.0,  # 讨论量较上周变化倍数
+    sentiment_score=-0.6,         # 舆情情感分-1~1
+    # 国际联动数据
+    is_ah=False,
+    ah_hk_overnight_change=0,
+    is_commodity_related=False,
+    commodity_future_overnight_change=0,
+    us_index_overnight_change=-1.2,
+    has_major_international_news=False
+)
+
+# 分析
+result = monitor.analyze_stock(data)
+print(monitor.get_risk_report(result))
+print(f"风险等级: {result.risk_level}")
+print(f"建议: {result.suggestion}")
+
+# 整合到五维风险评估
+five_dim = FiveDimensionRiskAssessment(monitor)
+eval_result = five_dim.calculate_total_risk(
+    liquidity_risk=0.2,
+    valuation_risk=0.5,
+    technical_risk=0.3,
+    fundamental_risk=0.4,
+    news_data=data
+)
+print(f"综合风险分: {eval_result['total_risk_score']:.2f}")
+print(f"建议: {eval_result['suggestion']}")
+```
+
+### 数据获取说明
+
+公开数据都可以从这些渠道获取：
+- 量价、融资：东方财富、TuShare、AkShare 直接接口
+- 龙虎榜：交易所官网、东方财富
+- 大宗交易：交易所、TuShare
+- 舆情：雪球、股吧公开数据，可以用爬虫获取讨论量
+
+## 设计思路
+
+核心思想：**A股很多消息会提前泄露，从量价、资金、情绪上能提前发现痕迹**，不等公告出来再反应，提前减仓规避黑天鹅。
+
+层层设防：就算技术面、基本面都没问题，消息面不对劲，直接降仓，把风险拦在前面。
+
+## 作者
+
+关羽（云长）  
+风险都督  
+2026-03-27

guanyu-risk/common/news_risk_monitor.py

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+"""
+个股+板块利好利空风险监控模块
+功能：基于公开数据提前预判消息面风险，适配结构化行情
+
+监控维度：
+1. 量价异常：没有消息但突然大幅波动，可能是提前泄密
+2. 融资余额变化：融资快速增减可能预示资金面变化
+3. 龙虎榜/大宗交易：机构大额进出
+4. 舆情频率：股吧雪球讨论热度异常变化
+5. 国际联动风险：外盘、商品、国际消息对A股影响预判
+6. 结构化行情风控：板块集中度风险、热点透支风险
+
+Author: 关羽（云长）
+Date: 2026-03-27
+"""
+
+from dataclasses import dataclass
+from typing import List, Dict, Optional, Tuple
+from enum import Enum
+
+
+class NewsSentiment(Enum):
+    STRONG_BULL = 2
+    BULL = 1
+    NEUTRAL = 0
+    BEAR = -1
+    STRONG_BEAR = -2
+
+
+class RiskLevel(Enum):
+    SAFE = 0
+    WATCH = 1
+    REDUCE = 2
+    EXIT = 3
+
+
+@dataclass
+class StockNewsData:
+    """个股监控数据"""
+    code: str
+    name: str
+    
+    # 1. 量价数据
+    recent_vol_change: float  # 近5日成交量相比20日均值变化比例，比如0.5就是放量50%
+    recent_pct_change: float  # 近5日涨跌幅（%）
+    gap_up: bool = False  # 是否高开缺口未回补
+    gap_down: bool = False  # 是否低开缺口未回补
+    
+    # 2. 融资数据
+    finance_balance_change: float = 0.0  # 融资余额变化比例（周）
+    finance_pct_of_volume: float = 0.0  # 融资买入额占成交额比例
+    
+    # 3. 龙虎榜/大宗
+    has_large_order: bool = False  # 近日是否有龙虎榜大额卖出
+    has_bulk_discount: bool = False  # 是否有大宗折价交易
+    bulk_discount_pct: float = 0.0  # 大宗折价幅度
+    
+    # 4. 舆情数据
+    discussion_count_change: float = 0.0  # 讨论量相比上周变化倍数
+    sentiment_score: float = 0.0  # 舆情情感分 -1~1，负=利空
+    
+    # 5. 国际联动数据
+    is_ah: bool = False  # 是否A+H股
+    ah_hk_overnight_change: float = 0.0  # 港股H股隔夜涨跌幅%
+    is_commodity_related: bool = False  # 是否大宗商品相关（有色/化工/农业）
+    commodity_future_overnight_change: float = 0.0  # 对应期货隔夜涨跌幅%
+    us_index_overnight_change: float = 0.0  # 美股隔夜涨跌%
+    has_major_international_news: bool = False  # 是否有重大国际消息（加息/地缘政治等）
+    international_news_sentiment: int = 0  # -1利空 0中性 1利好
+    
+    # 6. 结构化行情数据
+    sector_name: str = ""  # 板块名称
+    sector_style: str = ""  # 风格名称（AI/新能源/消费等）
+    sector_total_gain: float = 0.0  # 板块近期累计涨幅%
+    sector_position_ratio: float = 0.0  # 当前板块仓位占总仓位比例
+
+
+@dataclass
+class NewsRiskResult:
+    """消息风险评估结果"""
+    code: str
+    name: str
+    risk_level: RiskLevel
+    sentiment: NewsSentiment
+    risk_score: float  # 总分 0~100，越高风险越大
+    bear_points: List[str]  # 利空信号点
+    bull_points: List[str]  # 利好信号点
+    suggestion: str  # 操作建议
+
+
+class PriceVolumeMonitor:
+    """量价异常监控"""
+    
+    def __init__(self, 
+                 vol_alert_threshold: float = 2.0,  # 放量超过2倍预警
+                 vol_crush_threshold: float = -0.5,  # 缩量超过50%预警
+                 drop_alert_threshold: float = -7.0):  # 5日跌幅超过7%预警
+        self.vol_alert_threshold = vol_alert_threshold
+        self.vol_crush_threshold = vol_crush_threshold
+        self.drop_alert_threshold = drop_alert_threshold
+    
+    def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
+        """
+        返回：(风险分增量, 利空点列表, 利好点列表)
+        """
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 天量天价无消息，警惕利好兑现出货
+        if data.recent_vol_change >= self.vol_alert_threshold and data.recent_pct_change >= 5:
+            risk_score += 15
+            bear_points.append(f"近5日放量{data.recent_vol_change:.1f}倍，涨幅{data.recent_pct_change:.1f}%，可能利好提前泄露，警惕出货")
+        
+        # 莫名其妙大跌，可能有利空提前泄露
+        if data.recent_pct_change <= self.drop_alert_threshold and data.recent_vol_change >= 0.5:
+            risk_score += 20
+            bear_points.append(f"近5日放量下跌{data.recent_pct_change:.1f}%，无公开消息，警惕利空提前泄露")
+        
+        # 向下跳空缺口
+        if data.gap_down:
+            risk_score += 10
+            bear_points.append("存在向下跳空缺口未回补，技术形态偏空")
+        
+        # 向上跳空缺口
+        if data.gap_up:
+            bull_points.append("存在向上跳空缺口未回补，技术形态偏多")
+        
+        # 突然严重缩量，警惕流动性枯竭
+        if data.recent_vol_change <= self.vol_crush_threshold:
+            risk_score += 10
+            bear_points.append(f"成交量缩量{(data.recent_vol_change*100):.0f}%，警惕流动性风险")
+        
+        return risk_score, bear_points, bull_points
+
+
+class FinanceMonitor:
+    """融资余额监控"""
+    
+    def __init__(self,
+                 increase_threshold: float = 0.3,  # 融资余额增加超30%
+                 decrease_threshold: float = -0.2):  # 融资余额减少超20%
+        self.increase_threshold = increase_threshold
+        self.decrease_threshold = decrease_threshold
+    
+    def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 融资快速增加，看多情绪升温
+        if data.finance_balance_change >= self.increase_threshold:
+            bull_points.append(f"融资余额一周增加{data.finance_balance_change:.1%}，杠杆资金看多")
+        # 融资快速减少，资金出逃，利空
+        if data.finance_balance_change <= self.decrease_threshold:
+            risk_score += 15
+            bear_points.append(f"融资余额一周减少{data.finance_balance_change:.1%}，杠杆资金快速出逃，警惕利空")
+        
+        # 融资买入占比过高，波动会放大
+        if data.finance_pct_of_volume >= 0.2:
+            risk_score += 5
+            bear_points.append(f"融资买入占成交额{data.finance_pct_of_volume:.1%}，杠杆比例高，波动风险大")
+        
+        return risk_score, bear_points, bull_points
+
+
+class InstitutionalMonitor:
+    """龙虎榜/大宗交易监控"""
+    
+    def __init__(self,
+                 bulk_discount_threshold: float = -0.08):  # 折价超过8%预警
+        self.bulk_discount_threshold = bulk_discount_threshold
+    
+    def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 龙虎榜大额机构卖出
+        if data.has_large_order:
+            risk_score += 20
+            bear_points.append("龙虎榜出现机构大额卖出，机构出逃")
+        
+        # 大宗折价交易
+        if data.has_bulk_discount and data.bulk_discount_pct <= self.bulk_discount_threshold:
+            risk_score += 15
+            bear_points.append(f"大宗交易折价{data.bulk_discount_pct:.1%}，大股东折价出货")
+        elif data.has_bulk_discount:
+            bull_points.append("大宗交易平价/溢价成交，有机构接盘")
+        
+        return risk_score, bear_points, bull_points
+
+
+class SentimentMonitor:
+    """舆情热度监控"""
+    
+    def __init__(self,
+                 hot_threshold: float = 5.0,  # 讨论量增加5倍，太热预警
+                 cold_threshold: float = -0.8):  # 讨论量减少80%，太凉预警
+        self.hot_threshold = hot_threshold
+        self.cold_threshold = cold_threshold
+    
+    def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 讨论量突然暴涨，关注度太高，往往是见顶信号
+        if data.discussion_count_change >= self.hot_threshold:
+            risk_score += 10
+            bear_points.append(f"股吧/雪球讨论量增加{data.discussion_count_change:.1f}倍，热度异常，可能见顶")
+        
+        # 舆情已经明显偏空
+        if data.sentiment_score <= -0.5:
+            risk_score += 10
+            bear_points.append(f"市场舆情偏空，情感分{data.sentiment_score:.2f}")
+        # 舆情明显偏多
+        elif data.sentiment_score >= 0.5:
+            bull_points.append(f"市场舆情偏多，情感分{data.sentiment_score:.2f}")
+        
+        return risk_score, bear_points, bull_points
+
+
+class InternationalLinkageMonitor:
+    """国际联动风险监控"""
+    
+    def __init__(self,
+                 ah_change_threshold: float = -3.0,  # H股隔夜跌幅超过3%预警
+                 commodity_change_threshold: float = -4.0,  # 商品期货跌幅超过4%预警
+                 us_index_change_threshold: float = -2.0):  # 美股跌幅超过2%预警
+        self.ah_change_threshold = ah_change_threshold
+        self.commodity_change_threshold = commodity_change_threshold
+        self.us_index_change_threshold = us_index_change_threshold
+    
+    def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 1. A+H股，H股隔夜大跌预警
+        if data.is_ah:
+            if data.ah_hk_overnight_change <= self.ah_change_threshold:
+                risk_score += 15
+                bear_points.append(f"A+H股，H股隔夜大跌{data.ah_hk_overnight_change:.1f}%，A股大概率跟随低开，风险预警")
+            elif data.ah_hk_overnight_change >= 3.0:
+                bull_points.append(f"A+H股，H股隔夜大涨{data.ah_hk_overnight_change:.1f}%，对A股有正面带动")
+        
+        # 2. 大宗商品相关个股，对应期货隔夜大跌预警
+        if data.is_commodity_related:
+            if data.commodity_future_overnight_change <= self.commodity_change_threshold:
+                risk_score += 15
+                bear_points.append(f"大宗商品股，对应期货隔夜大跌{data.commodity_future_overnight_change:.1f}%，个股承压")
+            elif data.commodity_future_overnight_change >= 4.0:
+                bull_points.append(f"大宗商品股，对应期货隔夜大涨{data.commodity_future_overnight_change:.1f}%，对个股有利")
+        
+        # 3. 美股隔夜大跌，系统性风险预警
+        if data.us_index_overnight_change <= self.us_index_change_threshold:
+            risk_score += 10
+            bear_points.append(f"美股隔夜大跌{data.us_index_overnight_change:.1f}%，A股开盘可能承压，系统性风险")
+        elif data.us_index_overnight_change >= 2.0:
+            bull_points.append(f"美股隔夜大涨{data.us_index_overnight_change:.1f}%，对A股开盘有利")
+        
+        # 4. 重大国际消息
+        if data.has_major_international_news:
+            if data.international_news_sentiment == -1:
+                risk_score += 20
+                bear_points.append("重大国际利空消息（加息/地缘政治等），系统性风险上升，建议降仓")
+            elif data.international_news_sentiment == 1:
+                bull_points.append("重大国际利好消息，市场情绪偏向乐观")
+        
+        return risk_score, bear_points, bull_points
+
+
+class StructuralMarketRisk:
+    """
+    结构化行情风控
+    A股现在经常是结构化行情，少数板块上涨，其他板块不动
+    需要控制板块集中度，防范热点透支
+    """
+    
+    def __init__(self,
+                 single_sector_threshold: float = 0.15,  # 单板块仓位超15%预警
+                 sector_rally_threshold: float = 50.0,  # 板块累计涨幅超50%预警
+                 hot_news_sensitivity: float = 2.0):  # 热点板块消息风险放大倍数
+        self.single_sector_threshold = single_sector_threshold
+        self.sector_rally_threshold = sector_rally_threshold
+        self.hot_news_sensitivity = hot_news_sensitivity
+    
+    def analyze_sector_risk(self, data: StockNewsData, total_portfolio_sectors: Dict[str, float]) -> Tuple[int, List[str], List[str]]:
+        """
+        分析板块风险
+        total_portfolio_sectors: {板块名称: 板块仓位比例}，用来计算整体板块集中度
+        """
+        risk_score = 0
+        bear_points = []
+        bull_points = []
+        
+        # 1. 单板块仓位超过阈值，提醒分散
+        if data.sector_position_ratio > self.single_sector_threshold:
+            extra_risk = int((data.sector_position_ratio - self.single_sector_threshold) * 100)
+            risk_score += extra_risk
+            bear_points.append(f"单板块仓位{data.sector_position_ratio:.1%}，超过{self.single_sector_threshold:.1%}预警，建议分散减仓")
+        
+        # 2. 板块连续大涨，提醒风险
+        if data.sector_total_gain >= self.sector_rally_threshold:
+            risk_score += 15
+            bear_points.append(f"板块{data.sector_name}累计涨幅{data.sector_total_gain:.1f}%，超过{self.sector_rally_threshold:.0f}%，警惕利好出尽")
+        
+        # 3. 冷门板块连续大跌，基本面没问题提示低吸机会
+        # 这里只做提示，实际需要结合基本面判断
+        if data.sector_total_gain <= -20 and data.sector_total_gain >= -40:
+            bull_points.append(f"板块{data.sector_name}累计跌幅{data.sector_total_gain:.1f}%，如果基本面没问题，可考虑适度低吸")
+        
+        return risk_score, bear_points, bull_points
+    
+    def adjust_news_risk_for_hot_sector(self, base_risk: int, data: StockNewsData) -> int:
+        """热点板块消息风险灵敏度提高，放大风险分"""
+        if data.sector_total_gain >= 30:
+            # 热点板块，消息更容易透支，放大风险评分
+            return int(base_risk * self.hot_news_sensitivity)
+        return base_risk
+
+
+class StyleConcentrationRisk:
+    """风格集中度风险控制，不要全仓押注一种风格"""
+    
+    def __init__(self, single_style_threshold: float = 0.4):  # 单一风格超40%预警
+        self.single_style_threshold = single_style_threshold
+    
+    def analyze_style_risk(self, total_style_pos: Dict[str, float]) -> Tuple[int, List[str]]:
+        """检查风格集中度"""
+        risk_score = 0
+        bear_points = []
+        
+        for style, ratio in total_style_pos.items():
+            if ratio >= self.single_style_threshold:
+                extra_risk = int((ratio - self.single_style_threshold) * 50)
+                risk_score += extra_risk
+                bear_points.append(f"单一风格{style}仓位{ratio:.1%}，超过{self.single_style_threshold:.0%}，建议分散配置")
+        
+        return risk_score, bear_points
+
+
+class NewsRiskMonitor:
+    """总消息风险监控器，整合所有监控维度，适配结构化行情"""
+    
+    def __init__(self):
+        self.pv_monitor = PriceVolumeMonitor()
+        self.fin_monitor = FinanceMonitor()
+        self.inst_monitor = InstitutionalMonitor()
+        self.sent_monitor = SentimentMonitor()
+        self.intl_monitor = InternationalLinkageMonitor()
+        self.structural_rc = StructuralMarketRisk()
+    
+    def analyze_stock(self, data: StockNewsData, total_portfolio_sectors: Dict[str, float] = None) -> NewsRiskResult:
+        """综合分析个股消息风险，支持结构化板块风控"""
+        total_portfolio_sectors = total_portfolio_sectors or {}
+        
+        total_risk = 0
+        all_bear = []
+        all_bull = []
+        
+        # 原有各维度打分
+        r1, b1, bl1 = self.pv_monitor.analyze(data)
+        r2, b2, bl2 = self.fin_monitor.analyze(data)
+        r3, b3, bl3 = self.inst_monitor.analyze(data)
+        r4, b4, bl4 = self.sent_monitor.analyze(data)
+        r5, b5, bl5 = self.intl_monitor.analyze(data)
+        
+        total_risk = r1 + r2 + r3 + r4 + r5
+        all_bear = b1 + b2 + b3 + b4 + b5
+        all_bull = bl1 + bl2 + bl3 + bl4 + bl5
+        
+        # 新增结构化行情板块风控
+        if data.sector_name:
+            r6, b6, bl6 = self.structural_rc.analyze_sector_risk(data, total_portfolio_sectors)
+            # 热点板块消息风险放大
+            base_news_risk = total_risk
+            total_risk = self.structural_rc.adjust_news_risk_for_hot_sector(base_news_risk, data) + r6
+            all_bear.extend(b6)
+            all_bull.extend(bl6)
+        
+        # 计算风险等级
+        
+        # 计算风险等级
+        if total_risk >= 40:
+            risk_level = RiskLevel.EXIT
+        elif total_risk >= 25:
+            risk_level = RiskLevel.REDUCE
+        elif total_risk >= 10:
+            risk_level = RiskLevel.WATCH
+        else:
+            risk_level = RiskLevel.SAFE
+        
+        # 计算整体情绪
+        bull_count = len(all_bull)
+        bear_count = len(all_bear)
+        if bull_count - bear_count >= 2:
+            sentiment = NewsSentiment.STRONG_BULL
+        elif bull_count - bear_count >= 1:
+            sentiment = NewsSentiment.BULL
+        elif bear_count - bull_count >= 2:
+            sentiment = NewsSentiment.STRONG_BEAR
+        elif bear_count - bull_count >= 1:
+            sentiment = NewsSentiment.BEAR
+        else:
+            sentiment = NewsSentiment.NEUTRAL
+        
+        # 生成操作建议
+        suggestion = self._get_suggestion(risk_level, sentiment)
+        
+        return NewsRiskResult(
+            code=data.code,
+            name=data.name,
+            risk_level=risk_level,
+            sentiment=sentiment,
+            risk_score=total_risk,
+            bear_points=all_bear,
+            bull_points=all_bull,
+            suggestion=suggestion
+        )
+    
+    def analyze_stocks(self, datas: List[StockNewsData]) -> List[NewsRiskResult]:
+        """批量分析"""
+        return [self.analyze_stock(d) for d in datas]
+    
+    def _get_suggestion(self, risk_level: RiskLevel, sentiment: NewsSentiment) -> str:
+        """根据风险和情绪给出操作建议"""
+        if risk_level == RiskLevel.EXIT:
+            return "⚠️ 风险较高，建议减仓或清仓离场，规避黑天鹅"
+        elif risk_level == RiskLevel.REDUCE:
+            return "⚠️ 存在明显利空信号，建议降低仓位，控制风险"
+        elif risk_level == RiskLevel.WATCH:
+            if sentiment in [NewsSentiment.BULL, NewsSentiment.STRONG_BULL]:
+                return "👀 有少量异常信号，但整体偏多，可继续观察，谨慎加仓"
+            else:
+                return "👀 存在轻度风险信号，继续观察，不新开仓"
+        else:
+            if sentiment in [NewsSentiment.BULL, NewsSentiment.STRONG_BULL]:
+                return "✅ 无明显风险，整体偏多，可按计划操作"
+            else:
+                return "✅ 无明显风险，按计划持有"
+    
+    def get_risk_report(self, result: NewsRiskResult) -> str:
+        """生成风险报告"""
+        levels = {
+            RiskLevel.SAFE: "🟢 安全",
+            RiskLevel.WATCH: "🟡 关注",
+            RiskLevel.REDUCE: "🟠 减仓",
+            RiskLevel.EXIT: "🔴 离场",
+        }
+        sentiments = {
+            NewsSentiment.STRONG_BULL: "🔼 强烈看多",
+            NewsSentiment.BULL: "▶️ 看多",
+            NewsSentiment.NEUTRAL: "➖ 中性",
+            NewsSentiment.BEAR: "◀️ 看空",
+            NewsSentiment.STRONG_BEAR: "🔽 强烈看空",
+        }
+        
+        lines = []
+        lines.append("=" * 60)
+        lines.append(f"个股消息风险评估: {result.name}({result.code})")
+        lines.append("=" * 60)
+        lines.append(f"风险等级: {levels[result.risk_level]}   风险分: {result.risk_score}/100")
+        lines.append(f"市场情绪: {sentiments[result.sentiment]}")
+        lines.append("")
+        
+        if result.bull_points:
+            lines.append("✅ 利好信号:")
+            for point in result.bull_points:
+                lines.append(f"  • {point}")
+            lines.append("")
+        
+        if result.bear_points:
+            lines.append("⚠️ 利空信号:")
+            for point in result.bear_points:
+                lines.append(f"  • {point}")
+            lines.append("")
+        
+        lines.append(f"💡 操作建议: {result.suggestion}")
+        lines.append("=" * 60)
+        return "\n".join(lines)
+    
+    def get_risk_score_for_risk_system(self, result: NewsRiskResult) -> float:
+        """
+        获取归一化风险分，给五维风险评估体系使用
+        返回 0~1，越高风险越大
+        """
+        return min(result.risk_score / 50, 1.0)
+
+
+# 整合到原有风控体系的五维风险评估
+class FiveDimensionRiskAssessment:
+    """
+    五维度风险评估
+    整合：流动性风险 + 估值风险 + 技术面风险 + 基本面风险 + 消息面风险
+    """
+    
+    def __init__(self, news_monitor: NewsRiskMonitor = None):
+        self.news_monitor = news_monitor or NewsRiskMonitor()
+    
+    def calculate_total_risk(self,
+                            liquidity_risk: float,  # 0~1
+                            valuation_risk: float,  # 0~1
+                            technical_risk: float,  # 0~1
+                            fundamental_risk: float,  # 0~1
+                            news_data: StockNewsData) -> dict:
+        """
+        计算五维综合风险
+        每个维度输入都是0~1，越高风险越大
+        """
+        # 先算消息面风险
+        news_result = self.news_monitor.analyze_stock(news_data)
+        news_risk = self.news_monitor.get_risk_score_for_risk_system(news_result)
+        
+        # 加权平均，消息面风险权重高一些，因为黑天鹅突发危害大
+        total_risk = (
+            liquidity_risk * 0.15 +
+            valuation_risk * 0.20 +
+            technical_risk * 0.20 +
+            fundamental_risk * 0.20 +
+            news_risk * 0.25
+        )
+        
+        return {
+            "total_risk_score": total_risk,  # 0~1
+            "dimension_scores": {
+                "liquidity": liquidity_risk,
+                "valuation": valuation_risk,
+                "technical": technical_risk,
+                "fundamental": fundamental_risk,
+                "news": news_risk
+            },
+            "news_result": news_result,
+            "overall_risk_level": self._get_level(total_risk),
+            "suggestion": self._get_suggestion(total_risk)
+        }
+    
+    def _get_level(self, score: float) -> str:
+        if score >= 0.7:
+            return "高风险"
+        elif score >= 0.4:
+            return "中风险"
+        else:
+            return "低风险"
+    
+    def _get_suggestion(self, score: float) -> str:
+        if score >= 0.7:
+            return "不参与，规避"
+        elif score >= 0.4:
+            return "轻仓参与，严格止损"
+        else:
+            return "正常参与，按计划执行"
+
+
+if __name__ == "__main__":
+    # 测试案例
+    
+    print("=== 测试1: 疑似提前泄露利空的个股 + 国际利空 ===")
+    data1 = StockNewsData(
+        code="600XXX",
+        name="XX股份",
+        recent_vol_change=1.2,  # 放量120%
+        recent_pct_change=-8.5,  # 5天下跌8.5%
+        gap_down=True,
+        finance_balance_change=-0.25,  # 融资减少25%
+        discussion_count_change=3.0,
+        sentiment_score=-0.6,
+        has_major_international_news=True,
+        international_news_sentiment=-1
+    )
+    
+    monitor = NewsRiskMonitor()
+    result1 = monitor.analyze_stock(data1)
+    print(monitor.get_risk_report(result1))
+    
+    print("\n=== 测试2: 明显利好信号个股 ===")
+    data2 = StockNewsData(
+        code="002XXX",
+        name="XX科技",
+        recent_vol_change=0.8,
+        recent_pct_change=4.2,
+        gap_up=True,
+        finance_balance_change=0.4,  # 融资增加40%
+        has_large_order=False,
+        discussion_count_change=2.0,
+        sentiment_score=0.5
+    )
+    result2 = monitor.analyze_stock(data2)
+    print(monitor.get_risk_report(result2))
+    
+    print("\n=== 测试2: 大宗商品股 + 商品隔夜大跌 ===")
+    data2 = StockNewsData(
+        code="601899",
+        name="紫金矿业",
+        recent_vol_change=0.5,
+        recent_pct_change=-2.0,
+        is_commodity_related=True,
+        commodity_future_overnight_change=-5.2,
+        us_index_overnight_change=-2.5
+    )
+    result2 = monitor.analyze_stock(data2)
+    print(monitor.get_risk_report(result2))
+    
+    print("\n=== 测试3: A+H股 + H股隔夜大跌 ===")
+    data3 = StockNewsData(
+        code="600016",
+        name="民生银行",
+        recent_vol_change=0.1,
+        recent_pct_change=-1.2,
+        is_ah=True,
+        ah_hk_overnight_change=-4.5
+    )
+    result3 = monitor.analyze_stock(data3)
+    print(monitor.get_risk_report(result3))
+    
+    print("\n=== 测试4: 五维风险评估 ===")
+    five_dim = FiveDimensionRiskAssessment()
+    eval_result = five_dim.calculate_total_risk(
+        liquidity_risk=0.2,
+        valuation_risk=0.5,
+        technical_risk=0.3,
+        fundamental_risk=0.4,
+        news_data=data1
+    )
+    print(f"五维综合风险评分: {eval_result['total_risk_score']:.2f}")
+    print(f"风险等级: {eval_result['overall_risk_level']}")
+    print(f"建议: {eval_result['suggestion']}")
+    print(f"各维度分数: {eval_result['dimension_scores']}")

guanyu-risk/common/structural_market_risk.py

@@ -0,0 +1,241 @@
+"""
+结构化行情择时风控模块
+专门处理A股结构化行情下的板块集中度风险和择时信号
+
+功能：
+1. 择时：单板块连续大涨提醒风险，连续大跌提示低吸
+2. 风控：控制板块和风格集中度，不押注单一方向
+3. 消息风险：热点板块灵敏度提高，提前预警利好出尽
+
+Author: 关羽（云长）
+Date: 2026-03-27
+"""
+
+from dataclasses import dataclass
+from typing import List, Dict, Tuple
+
+@dataclass
+class SectorInfo:
+    """板块信息"""
+    name: str
+    style: str  # 风格：AI/新能源/消费/周期等
+    recent_gain: float  # 近期累计涨幅%
+    position_ratio: float  # 组合中该板块仓位比例
+    stock_count: int  # 持有股票数量
+    is_hot: bool = False  # 是否是当前热点板块
+
+
+class StructuredMarketTiming:
+    """结构化行情择时"""
+    
+    def __init__(self,
+                 high_position_threshold: float = 0.15,  # 单板块仓位超15%提示减仓
+                 sector_rally_warning: float = 30.0,  # 累计涨30%提示风险
+                 sector_rally_stop: float = 50.0,  # 累计涨50%强制提高风控权重
+                 sector_dump_opportunity: float = -20.0):  # 跌20%提示机会
+        self.high_position_threshold = high_position_threshold
+        self.sector_rally_warning = sector_rally_warning
+        self.sector_rally_stop = sector_rally_stop
+        self.sector_dump_opportunity = sector_dump_opportunity
+    
+    def timing_sector(self, sector: SectorInfo) -> Tuple[str, str, int]:
+        """
+        板块择时
+        返回：(信号类型, 建议, 风险增量评分)
+        信号类型: bull/bear/neutral
+        """
+        risk_increment = 0
+        suggestion = ""
+        signal = "neutral"
+        
+        # 单板块仓位过高提醒
+        if sector.position_ratio > self.high_position_threshold:
+            risk_increment += int((sector.position_ratio - self.high_position_threshold) * 100)
+            suggestion += f"\n⚠️ 单板块仓位{sector.position_ratio:.1%}，超过{self.high_position_threshold:.1%}阈值，建议适度减仓分散"
+            signal = "bear"
+        
+        # 板块连续大涨提醒风险
+        if sector.recent_gain >= self.sector_rally_stop:
+            risk_increment += 20
+            suggestion += f"\n🔴 板块累计涨幅{sector.recent_gain:.1f}%，超过{self.sector_rally_stop:.0f}%，警惕过热，建议整体止盈"
+            signal = "bear"
+        elif sector.recent_gain >= self.sector_rally_warning:
+            risk_increment += 10
+            suggestion += f"\n🟡 板块累计涨幅{sector.recent_gain:.1f}%，已有较大涨幅，提高风控警惕"
+            signal = "bear"
+        
+        # 板块连续大跌提示机会
+        if sector.recent_gain <= self.sector_dump_opportunity and sector.recent_gain >= -40:
+            suggestion += f"\n✅ 板块累计跌幅{-sector.recent_gain:.1f}%，如果基本面没问题，可考虑适度低吸布局"
+            signal = "bull"
+        
+        if not suggestion:
+            suggestion = "✅ 板块仓位和涨幅正常，无特殊风险"
+            signal = "neutral"
+        
+        return signal, suggestion.strip(), risk_increment
+
+
+class SectorConcentrationRisk:
+    """板块和风格集中度风控"""
+    
+    def __init__(self,
+                 max_single_sector: float = 0.25,  # 单板块最大仓位25%
+                 max_single_style: float = 0.40,  # 单风格最大仓位40%
+                 max_hot_sectors_total: float = 0.50):  # 所有热点板块合计最大50%
+        self.max_single_sector = max_single_sector
+        self.max_single_style = max_single_style
+        self.max_hot_sectors_total = max_hot_sectors_total
+    
+    def check_concentration(self, sectors: List[SectorInfo]) -> Tuple[int, List[str]]:
+        """检查集中度风险，返回总风险增量和警告列表"""
+        risk_increment = 0
+        warnings = []
+        
+        # 1. 单板块检查
+        for sector in sectors:
+            if sector.position_ratio > self.max_single_sector:
+                extra = (sector.position_ratio - self.max_single_sector) * 100
+                risk_increment += int(extra)
+                warnings.append(f"⚠️ 板块【{sector.name}】仓位{sector.position_ratio:.1%}，超过最大限制{self.max_single_sector:.1%}")
+        
+        # 2. 按风格汇总检查
+        style_summary: Dict[str, float] = {}
+        for sector in sectors:
+            if sector.style not in style_summary:
+                style_summary[sector.style] = 0.0
+            style_summary[sector.style] += sector.position_ratio
+        
+        for style, ratio in style_summary.items():
+            if ratio > self.max_single_style:
+                extra = (ratio - self.max_single_style) * 50
+                risk_increment += int(extra)
+                warnings.append(f"⚠️ 风格【{style}】总仓位{ratio:.1%}，超过最大限制{self.max_single_style:.1%}，建议分散")
+        
+        # 3. 热点板块合计检查
+        hot_total = sum(s.position_ratio for s in sectors if s.is_hot)
+        if hot_total > self.max_hot_sectors_total:
+            extra = (hot_total - self.max_hot_sectors_total) * 80
+            risk_increment += int(extra)
+            warnings.append(f"⚠️ 所有热点板块合计仓位{hot_total:.1%}，超过{self.max_hot_sectors_total:.1%}，总体过热风险")
+        
+        return risk_increment, warnings
+
+
+class HotSectorNewsRiskAdjust:
+    """热点板块消息风险调整，热点更容易利好出尽，灵敏度提高"""
+    
+    def __init__(self,
+                 hot_risk_multiplier: float = 2.0,  # 热点板块风险分放大倍数
+                 medium_risk_multiplier: float = 1.5):  # 中度上涨板块放大倍数
+        self.hot_risk_multiplier = hot_risk_multiplier
+        self.medium_risk_multiplier = medium_risk_multiplier
+    
+    def adjust_risk_score(self, base_score: int, sector_recent_gain: float) -> int:
+        """根据板块涨幅调整风险分"""
+        if sector_recent_gain >= 50:
+            return int(base_score * self.hot_risk_multiplier)
+        elif sector_recent_gain >= 30:
+            return int(base_score * self.medium_risk_multiplier)
+        else:
+            return base_score
+
+
+def get_structural_risk_report(sectors: List[SectorInfo], 
+                               timing: StructuredMarketTiming,
+                               concentration: SectorConcentrationRisk) -> str:
+    """生成结构化行情风险报告"""
+    timing = StructuredMarketTiming()
+    concentration = SectorConcentrationRisk()
+    
+    total_risk = 0
+    all_warnings = []
+    all_suggestions = []
+    
+    # 逐个板块择时
+    for sector in sectors:
+        signal, suggestion, risk_inc = timing.timing_sector(sector)
+        total_risk += risk_inc
+        if signal != "neutral":
+            all_suggestions.append(f"【{sector.name}】{suggestion}")
+    
+    # 集中度检查
+    risk_inc, warnings = concentration.check_concentration(sectors)
+    total_risk += risk_inc
+    all_warnings.extend(warnings)
+    
+    # 生成报告
+    lines = []
+    lines.append("=" * 60)
+    lines.append("结构化行情板块风控择时报告")
+    lines.append("=" * 60)
+    lines.append(f"监控板块数量: {len(sectors)}")
+    lines.append(f"总风险增量评分: {total_risk}")
+    lines.append("")
+    
+    if all_warnings:
+        lines.append("⚠️ 集中度风险警告:")
+        for w in all_warnings:
+            lines.append(f"  • {w}")
+        lines.append("")
+    
+    if all_suggestions:
+        lines.append("💡 择时建议:")
+        for s in all_suggestions:
+            lines.append(f"  • {s}")
+        lines.append("")
+    
+    if total_risk >= 30:
+        lines.append("⚠️ 整体结论：板块风险较高，建议减仓分散")
+    elif total_risk >= 10:
+        lines.append("🟡 整体结论：存在一定板块风险，建议密切观察")
+    else:
+        lines.append("✅ 整体结论：板块结构正常，无明显风险")
+    
+    lines.append("=" * 60)
+    return "\n".join(lines)
+
+
+if __name__ == "__main__":
+    # 测试
+    print("=== 测试结构化行情风控 ===\n")
+    
+    sectors = [
+        SectorInfo(
+            name="AI算力",
+            style="AI",
+            recent_gain=65.0,
+            position_ratio=0.22,
+            stock_count=4,
+            is_hot=True
+        ),
+        SectorInfo(
+            name="新能源",
+            style="新能源",
+            recent_gain=-25.0,
+            position_ratio=0.10,
+            stock_count=2,
+            is_hot=False
+        ),
+        SectorInfo(
+            name="消费",
+            style="消费",
+            recent_gain=8.0,
+            position_ratio=0.12,
+            stock_count=3,
+            is_hot=False
+        )
+    ]
+    
+    timing = StructuredMarketTiming()
+    concentration = SectorConcentrationRisk()
+    
+    print(get_structural_risk_report(sectors, timing, concentration))
+    
+    # 测试热点风险放大
+    adjust = HotSectorNewsRiskAdjust()
+    base_score = 10
+    print(f"\n基础风险分10，不同涨幅放大后:")
+    print(f"  涨幅10% → {adjust.adjust_risk_score(10, 10)}")
+    print(f"  涨幅35% → {adjust.adjust_risk_score(10, 35)}")
+    print(f"  涨幅60% → {adjust.adjust_risk_score(10, 60)}")