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