cfdaily
291 lines
10 KiB
Python
291 lines
10 KiB
Python
"""
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风控模块 - 量化策略风控系统
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功能：
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1. 单票15%止损规则
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2. 整体回撤分级风控（10%/20%/25% 分级降仓）
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3. 黑天鹅过滤（ST、跌停、财务造假排除）
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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
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import pandas as pd
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@dataclass
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class StockInfo:
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"""单票基本信息"""
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code: str
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name: str
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cost_price: float
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current_price: float
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is_st: bool = False
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is_limit_down: bool = False
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is_fraud: bool = False
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volume: float = 0.0 # 日成交额（亿）
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@dataclass
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class PortfolioInfo:
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"""组合信息"""
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total_capital: float
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current_capital: float
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positions: Dict[str, float] # code -> position_size
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class SingleStockRiskControl:
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"""单票风控：15%止损规则"""
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def __init__(self, stop_loss_pct: float = 0.15):
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"""
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初始化
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:param stop_loss_pct: 止损比例，默认15%
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"""
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self.stop_loss_pct = stop_loss_pct
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def check_stop_loss(self, stock: StockInfo) -> bool:
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"""
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检查是否触发止损
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:return: True = 需要止损，False = 持有
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"""
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if stock.cost_price <= 0:
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return False
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drawdown = (stock.current_price - stock.cost_price) / stock.cost_price
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# 亏损超过止损比例，触发止损
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return drawdown <= -self.stop_loss_pct
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def get_drawdown(self, stock: StockInfo) -> float:
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"""计算单票当前回撤"""
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if stock.cost_price <= 0:
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return 0.0
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return (stock.current_price - stock.cost_price) / stock.cost_price
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class PortfolioDrawdownRiskControl:
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"""
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整体回撤分级风控
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10%回撤 → 降仓50%
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20%回撤 → 降仓75%
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25%回撤 → 清仓休息
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"""
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def __init__(self,
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drawdown_levels: List[float] = None,
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reduce_ratios: List[float] = None):
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"""
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初始化分级风控
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:param drawdown_levels: 回撤阈值
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:param reduce_ratios: 对应降仓比例（剩余仓位比例）
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"""
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# 默认分级：10%/20%/25%
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self.drawdown_levels = drawdown_levels or [0.10, 0.20, 0.25]
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# 对应剩余仓位：50% / 25% / 0%
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self.reduce_ratios = reduce_ratios or [0.50, 0.25, 0.00]
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def calculate_total_drawdown(self, portfolio: PortfolioInfo) -> float:
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"""计算组合总回撤"""
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if portfolio.total_capital <= 0:
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return 0.0
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return (portfolio.total_capital - portfolio.current_capital) / portfolio.total_capital
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def get_target_position_ratio(self, portfolio: PortfolioInfo) -> float:
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"""
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获取目标仓位比例
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:return: 目标仓位占当前总资金的比例
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"""
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drawdown = self.calculate_total_drawdown(portfolio)
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# 从大到小检查，触发最高级别
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for level, ratio in reversed(list(zip(self.drawdown_levels, self.reduce_ratios))):
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if drawdown >= level:
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return ratio
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# 没有触发任何分级，保持满仓
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return 1.0
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def need_rebalance(self, portfolio: PortfolioInfo) -> tuple[bool, float]:
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"""
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检查是否需要降仓
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:return: (是否需要调整, 目标仓位比例)
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"""
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target_ratio = self.get_target_position_ratio(portfolio)
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current_ratio = sum(portfolio.positions.values()) / portfolio.current_capital
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# 当前仓位高于目标，需要降仓
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if current_ratio > target_ratio + 0.05: # 5%容差
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return True, target_ratio
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return False, target_ratio
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class BlackSwanFilter:
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"""黑天鹅过滤：排除ST、跌停、财务造假、低流动性票"""
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def __init__(self, min_daily_volume: float = 0.5):
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"""
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初始化过滤器
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:param min_daily_volume: 最小日成交额（亿），低于此排除
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"""
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self.min_daily_volume = min_daily_volume
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def filter_stock(self, stock: StockInfo) -> tuple[bool, str]:
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"""
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过滤个股
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:return: (是否通过过滤, 不通过原因)
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True = 可以买入，False = 排除
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"""
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# 1. 排除ST股
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if stock.is_st:
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return False, "ST股票"
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# 2. 排除跌停
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if stock.is_limit_down:
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return False, "跌停股票"
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# 3. 排除财务造假
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if stock.is_fraud:
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return False, "财务造假问题股"
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# 4. 排除低流动性
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if stock.volume < self.min_daily_volume and stock.volume > 0:
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return False, f"成交额不足{self.min_daily_volume}亿，流动性不足"
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# 全部通过
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return True, ""
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def filter_universe(self, stocks: List[StockInfo]) -> List[StockInfo]:
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"""批量过滤选股池"""
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passed = []
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for stock in stocks:
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ok, _ = self.filter_stock(stock)
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if ok:
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passed.append(stock)
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return passed
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class RiskController:
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"""总风控控制器，整合所有风控规则"""
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def __init__(self):
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self.single_stock_rc = SingleStockRiskControl()
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self.portfolio_rc = PortfolioDrawdownRiskControl()
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self.black_swan_filter = BlackSwanFilter()
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def pre_trade_check(self, stock: StockInfo, portfolio: PortfolioInfo) -> tuple[bool, str]:
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"""
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交易前检查：开仓前调用
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:return: (是否允许开仓, 原因)
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"""
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# 第一步：黑天鹅过滤
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ok, reason = self.black_swan_filter.filter_stock(stock)
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if not ok:
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return False, f"黑天鹅过滤: {reason}"
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# 第二步：检查整体回撤是否允许新开仓
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target_ratio = self.portfolio_rc.get_target_position_ratio(portfolio)
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current_ratio = sum(portfolio.positions.values()) / portfolio.current_capital
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if current_ratio >= target_ratio:
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return False, f"整体回撤已触发降仓，当前仓位{current_ratio:.1%}，目标仓位{target_ratio:.1%}，不允许新开仓"
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return True, ""
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def post_trade_check(self, stocks: List[StockInfo], portfolio: PortfolioInfo) -> dict:
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"""
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收盘后检查：止损检查 + 降仓检查
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:return: 风控结果，包含需要止损的票和需要降仓的信息
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"""
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# 1. 检查单票止损
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stop_loss_list = []
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for stock in stocks:
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if self.single_stock_rc.check_stop_loss(stock):
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stop_loss_list.append({
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"code": stock.code,
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"name": stock.name,
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"current_drawdown": self.single_stock_rc.get_drawdown(stock)
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})
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# 2. 检查组合降仓
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need_rebalance, target_ratio = self.portfolio_rc.need_rebalance(portfolio)
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current_drawdown = self.portfolio_rc.calculate_total_drawdown(portfolio)
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return {
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"stop_loss_required": len(stop_loss_list) > 0,
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"stop_loss_stocks": stop_loss_list,
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"rebalance_required": need_rebalance,
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"current_drawdown": current_drawdown,
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"target_position_ratio": target_ratio,
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"current_position_ratio": sum(portfolio.positions.values()) / portfolio.current_capital if portfolio.current_capital > 0 else 0
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}
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def get_risk_report(self, stocks: List[StockInfo], portfolio: PortfolioInfo) -> str:
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"""生成风控报告"""
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result = self.post_trade_check(stocks, portfolio)
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report = []
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report.append("=" * 50)
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report.append("风控日报")
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report.append("=" * 50)
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report.append(f"组合总回撤: {result['current_drawdown']:.2%}")
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report.append(f"当前仓位比例: {result['current_position_ratio']:.2%}")
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report.append(f"目标仓位比例: {result['target_position_ratio']:.2%}")
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report.append("")
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if result['stop_loss_required']:
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report.append("触发止损个股:")
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for s in result['stop_loss_stocks']:
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report.append(f" {s['code']} {s['name']} 回撤: {s['current_drawdown']:.2%}")
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else:
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report.append("无个股触发止损")
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report.append("")
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if result['rebalance_required']:
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report.append(f"⚠️ 需要降仓调整，当前仓位高于目标")
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else:
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report.append("✅ 仓位符合风控要求")
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report.append("=" * 50)
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return "\n".join(report)
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if __name__ == "__main__":
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# 简单测试
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from pprint import pprint
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# 测试单票止损
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stock1 = StockInfo(code="000001", name="平安银行", cost_price=10.0, current_price=8.4)
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rc_single = SingleStockRiskControl()
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print(f"测试单票止损，当前价8.4，成本10，亏损16%: {rc_single.check_stop_loss(stock1)} → 应该是True")
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stock2 = StockInfo(code="000002", name="万科A", cost_price=20.0, current_price=18.0)
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print(f"测试单票止损，当前价18，成本20，亏损10%: {rc_single.check_stop_loss(stock2)} → 应该是False")
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# 测试组合回撤
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portfolio = PortfolioInfo(
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total_capital=1000000,
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current_capital=850000,
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positions={"000001": 200000, "000002": 150000}
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)
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rc_port = PortfolioDrawdownRiskControl()
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dd = rc_port.calculate_total_drawdown(portfolio)
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print(f"\n组合回撤: {dd:.2%} → 15%")
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print(f"目标仓位比例: {rc_port.get_target_position_ratio(portfolio)} → 50%（触发10%档）")
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# 测试黑天鹅过滤
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filter_bs = BlackSwanFilter()
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st_stock = StockInfo(code="000003", name="ST基蛋", cost_price=10, current_price=10, is_st=True)
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ok, reason = filter_bs.filter_stock(st_stock)
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print(f"\nST过滤: ok={ok}, reason={reason}")
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normal_stock = StockInfo(code="600000", name="浦发银行", cost_price=10, current_price=10, volume=1.2)
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ok, reason = filter_bs.filter_stock(normal_stock)
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print(f"正常票过滤: ok={ok}, reason={reason}")
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# 整合风控测试
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rc = RiskController()
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print("\n风控报告:")
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print(rc.get_risk_report([stock1, stock2], portfolio))
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