cfdaily
630 lines
25 KiB
Python
630 lines
25 KiB
Python
"""
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个股+板块利好利空风险监控模块
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功能：基于公开数据提前预判消息面风险，适配结构化行情
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监控维度：
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1. 量价异常：没有消息但突然大幅波动，可能是提前泄密
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2. 融资余额变化：融资快速增减可能预示资金面变化
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3. 龙虎榜/大宗交易：机构大额进出
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4. 舆情频率：股吧雪球讨论热度异常变化
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5. 国际联动风险：外盘、商品、国际消息对A股影响预判
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6. 结构化行情风控：板块集中度风险、热点透支风险
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Author: 关羽（云长）
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Date: 2026-03-27
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"""
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from dataclasses import dataclass
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from typing import List, Dict, Optional, Tuple
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from enum import Enum
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class NewsSentiment(Enum):
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STRONG_BULL = 2
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BULL = 1
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NEUTRAL = 0
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BEAR = -1
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STRONG_BEAR = -2
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class RiskLevel(Enum):
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SAFE = 0
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WATCH = 1
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REDUCE = 2
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EXIT = 3
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@dataclass
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class StockNewsData:
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"""个股监控数据"""
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code: str
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name: str
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# 1. 量价数据
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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%
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recent_pct_change: float # 近5日涨跌幅（%）
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gap_up: bool = False # 是否高开缺口未回补
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gap_down: bool = False # 是否低开缺口未回补
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# 2. 融资数据
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finance_balance_change: float = 0.0 # 融资余额变化比例（周）
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finance_pct_of_volume: float = 0.0 # 融资买入额占成交额比例
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# 3. 龙虎榜/大宗
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has_large_order: bool = False # 近日是否有龙虎榜大额卖出
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has_bulk_discount: bool = False # 是否有大宗折价交易
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bulk_discount_pct: float = 0.0 # 大宗折价幅度
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# 4. 舆情数据
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discussion_count_change: float = 0.0 # 讨论量相比上周变化倍数
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sentiment_score: float = 0.0 # 舆情情感分 -1~1，负=利空
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# 5. 国际联动数据
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is_ah: bool = False # 是否A+H股
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ah_hk_overnight_change: float = 0.0 # 港股H股隔夜涨跌幅%
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is_commodity_related: bool = False # 是否大宗商品相关（有色/化工/农业）
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commodity_future_overnight_change: float = 0.0 # 对应期货隔夜涨跌幅%
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us_index_overnight_change: float = 0.0 # 美股隔夜涨跌%
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has_major_international_news: bool = False # 是否有重大国际消息（加息/地缘政治等）
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international_news_sentiment: int = 0 # -1利空 0中性 1利好
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# 6. 结构化行情数据
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sector_name: str = "" # 板块名称
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sector_style: str = "" # 风格名称（AI/新能源/消费等）
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sector_total_gain: float = 0.0 # 板块近期累计涨幅%
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sector_position_ratio: float = 0.0 # 当前板块仓位占总仓位比例
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@dataclass
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class NewsRiskResult:
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"""消息风险评估结果"""
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code: str
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name: str
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risk_level: RiskLevel
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sentiment: NewsSentiment
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risk_score: float # 总分 0~100，越高风险越大
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bear_points: List[str] # 利空信号点
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bull_points: List[str] # 利好信号点
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suggestion: str # 操作建议
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class PriceVolumeMonitor:
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"""量价异常监控"""
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def __init__(self,
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vol_alert_threshold: float = 2.0, # 放量超过2倍预警
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vol_crush_threshold: float = -0.5, # 缩量超过50%预警
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drop_alert_threshold: float = -7.0): # 5日跌幅超过7%预警
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self.vol_alert_threshold = vol_alert_threshold
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self.vol_crush_threshold = vol_crush_threshold
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self.drop_alert_threshold = drop_alert_threshold
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def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
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"""
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返回：(风险分增量, 利空点列表, 利好点列表)
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"""
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risk_score = 0
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bear_points = []
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bull_points = []
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# 天量天价无消息，警惕利好兑现出货
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if data.recent_vol_change >= self.vol_alert_threshold and data.recent_pct_change >= 5:
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risk_score += 15
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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")
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# 莫名其妙大跌，可能有利空提前泄露
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if data.recent_pct_change <= self.drop_alert_threshold and data.recent_vol_change >= 0.5:
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risk_score += 20
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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")
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# 向下跳空缺口
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if data.gap_down:
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risk_score += 10
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bear_points.append("存在向下跳空缺口未回补，技术形态偏空")
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# 向上跳空缺口
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if data.gap_up:
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bull_points.append("存在向上跳空缺口未回补，技术形态偏多")
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# 突然严重缩量，警惕流动性枯竭
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if data.recent_vol_change <= self.vol_crush_threshold:
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risk_score += 10
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bear_points.append(f"成交量缩量{(data.recent_vol_change*100):.0f}%，警惕流动性风险")
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return risk_score, bear_points, bull_points
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class FinanceMonitor:
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"""融资余额监控"""
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def __init__(self,
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increase_threshold: float = 0.3, # 融资余额增加超30%
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decrease_threshold: float = -0.2): # 融资余额减少超20%
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self.increase_threshold = increase_threshold
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self.decrease_threshold = decrease_threshold
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def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
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risk_score = 0
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bear_points = []
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bull_points = []
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# 融资快速增加，看多情绪升温
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if data.finance_balance_change >= self.increase_threshold:
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bull_points.append(f"融资余额一周增加{data.finance_balance_change:.1%}，杠杆资金看多")
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# 融资快速减少，资金出逃，利空
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if data.finance_balance_change <= self.decrease_threshold:
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risk_score += 15
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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")
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# 融资买入占比过高，波动会放大
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if data.finance_pct_of_volume >= 0.2:
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risk_score += 5
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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")
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return risk_score, bear_points, bull_points
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class InstitutionalMonitor:
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"""龙虎榜/大宗交易监控"""
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def __init__(self,
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bulk_discount_threshold: float = -0.08): # 折价超过8%预警
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self.bulk_discount_threshold = bulk_discount_threshold
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def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
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risk_score = 0
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bear_points = []
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bull_points = []
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# 龙虎榜大额机构卖出
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if data.has_large_order:
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risk_score += 20
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bear_points.append("龙虎榜出现机构大额卖出，机构出逃")
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# 大宗折价交易
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if data.has_bulk_discount and data.bulk_discount_pct <= self.bulk_discount_threshold:
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risk_score += 15
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bear_points.append(f"大宗交易折价{data.bulk_discount_pct:.1%}，大股东折价出货")
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elif data.has_bulk_discount:
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bull_points.append("大宗交易平价/溢价成交，有机构接盘")
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return risk_score, bear_points, bull_points
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class SentimentMonitor:
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"""舆情热度监控"""
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def __init__(self,
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hot_threshold: float = 5.0, # 讨论量增加5倍，太热预警
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cold_threshold: float = -0.8): # 讨论量减少80%，太凉预警
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self.hot_threshold = hot_threshold
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self.cold_threshold = cold_threshold
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def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
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risk_score = 0
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bear_points = []
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bull_points = []
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# 讨论量突然暴涨，关注度太高，往往是见顶信号
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if data.discussion_count_change >= self.hot_threshold:
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risk_score += 10
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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")
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# 舆情已经明显偏空
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if data.sentiment_score <= -0.5:
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risk_score += 10
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bear_points.append(f"市场舆情偏空，情感分{data.sentiment_score:.2f}")
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# 舆情明显偏多
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elif data.sentiment_score >= 0.5:
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bull_points.append(f"市场舆情偏多，情感分{data.sentiment_score:.2f}")
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return risk_score, bear_points, bull_points
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class InternationalLinkageMonitor:
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"""国际联动风险监控"""
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def __init__(self,
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ah_change_threshold: float = -3.0, # H股隔夜跌幅超过3%预警
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commodity_change_threshold: float = -4.0, # 商品期货跌幅超过4%预警
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us_index_change_threshold: float = -2.0): # 美股跌幅超过2%预警
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self.ah_change_threshold = ah_change_threshold
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self.commodity_change_threshold = commodity_change_threshold
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self.us_index_change_threshold = us_index_change_threshold
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def analyze(self, data: StockNewsData) -> Tuple[int, List[str], List[str]]:
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risk_score = 0
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bear_points = []
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bull_points = []
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# 1. A+H股，H股隔夜大跌预警
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if data.is_ah:
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if data.ah_hk_overnight_change <= self.ah_change_threshold:
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risk_score += 15
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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")
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elif data.ah_hk_overnight_change >= 3.0:
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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")
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# 2. 大宗商品相关个股，对应期货隔夜大跌预警
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if data.is_commodity_related:
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if data.commodity_future_overnight_change <= self.commodity_change_threshold:
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risk_score += 15
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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")
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elif data.commodity_future_overnight_change >= 4.0:
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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")
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# 3. 美股隔夜大跌，系统性风险预警
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if data.us_index_overnight_change <= self.us_index_change_threshold:
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risk_score += 10
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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")
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elif data.us_index_overnight_change >= 2.0:
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bull_points.append(f"美股隔夜大涨{data.us_index_overnight_change:.1f}%，对A股开盘有利")
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# 4. 重大国际消息
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if data.has_major_international_news:
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if data.international_news_sentiment == -1:
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risk_score += 20
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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")
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elif data.international_news_sentiment == 1:
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bull_points.append("重大国际利好消息，市场情绪偏向乐观")
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return risk_score, bear_points, bull_points
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class StructuralMarketRisk:
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"""
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结构化行情风控
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A股现在经常是结构化行情，少数板块上涨，其他板块不动
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需要控制板块集中度，防范热点透支
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"""
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def __init__(self,
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single_sector_threshold: float = 0.15, # 单板块仓位超15%预警
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sector_rally_threshold: float = 50.0, # 板块累计涨幅超50%预警
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hot_news_sensitivity: float = 2.0): # 热点板块消息风险放大倍数
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self.single_sector_threshold = single_sector_threshold
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self.sector_rally_threshold = sector_rally_threshold
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self.hot_news_sensitivity = hot_news_sensitivity
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def analyze_sector_risk(self, data: StockNewsData, total_portfolio_sectors: Dict[str, float]) -> Tuple[int, List[str], List[str]]:
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"""
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分析板块风险
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total_portfolio_sectors: {板块名称: 板块仓位比例}，用来计算整体板块集中度
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"""
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risk_score = 0
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bear_points = []
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bull_points = []
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# 1. 单板块仓位超过阈值，提醒分散
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if data.sector_position_ratio > self.single_sector_threshold:
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extra_risk = int((data.sector_position_ratio - self.single_sector_threshold) * 100)
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risk_score += extra_risk
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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")
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# 2. 板块连续大涨，提醒风险
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if data.sector_total_gain >= self.sector_rally_threshold:
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risk_score += 15
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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")
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# 3. 冷门板块连续大跌，基本面没问题提示低吸机会
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# 这里只做提示，实际需要结合基本面判断
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if data.sector_total_gain <= -20 and data.sector_total_gain >= -40:
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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")
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return risk_score, bear_points, bull_points
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def adjust_news_risk_for_hot_sector(self, base_risk: int, data: StockNewsData) -> int:
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"""热点板块消息风险灵敏度提高，放大风险分"""
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if data.sector_total_gain >= 30:
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# 热点板块，消息更容易透支，放大风险评分
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return int(base_risk * self.hot_news_sensitivity)
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return base_risk
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class StyleConcentrationRisk:
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"""风格集中度风险控制，不要全仓押注一种风格"""
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def __init__(self, single_style_threshold: float = 0.4): # 单一风格超40%预警
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self.single_style_threshold = single_style_threshold
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def analyze_style_risk(self, total_style_pos: Dict[str, float]) -> Tuple[int, List[str]]:
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"""检查风格集中度"""
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risk_score = 0
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bear_points = []
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for style, ratio in total_style_pos.items():
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if ratio >= self.single_style_threshold:
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extra_risk = int((ratio - self.single_style_threshold) * 50)
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risk_score += extra_risk
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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")
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return risk_score, bear_points
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class NewsRiskMonitor:
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"""总消息风险监控器，整合所有监控维度，适配结构化行情"""
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def __init__(self):
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self.pv_monitor = PriceVolumeMonitor()
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self.fin_monitor = FinanceMonitor()
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self.inst_monitor = InstitutionalMonitor()
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self.sent_monitor = SentimentMonitor()
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self.intl_monitor = InternationalLinkageMonitor()
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self.structural_rc = StructuralMarketRisk()
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def analyze_stock(self, data: StockNewsData, total_portfolio_sectors: Dict[str, float] = None) -> NewsRiskResult:
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"""综合分析个股消息风险，支持结构化板块风控"""
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total_portfolio_sectors = total_portfolio_sectors or {}
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total_risk = 0
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all_bear = []
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all_bull = []
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# 原有各维度打分
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r1, b1, bl1 = self.pv_monitor.analyze(data)
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r2, b2, bl2 = self.fin_monitor.analyze(data)
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r3, b3, bl3 = self.inst_monitor.analyze(data)
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r4, b4, bl4 = self.sent_monitor.analyze(data)
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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']}")
|