风险管理智能体是 TradingAgents 框架的安全守护者，负责识别、评估和控制投资风险。通过多层次的风险评估机制，确保交易决策在可接受的风险范围内，保护投资组合免受重大损失。
class RiskManagementSystem:
"""风险管理系统 - 统筹所有风险管理活动"""
def __init__(self, config):
self.config = config
self.risk_assessors = self._initialize_risk_assessors()
self.risk_monitor = RiskMonitor()
self.risk_controller = RiskController()
def _initialize_risk_assessors(self):
"""初始化风险评估智能体"""
return {
"aggressive": AggressiveRiskAssessor(self.config),
"conservative": ConservativeRiskAssessor(self.config),
"neutral": NeutralRiskAssessor(self.config)
}
def assess_trading_risk(self, trading_decision: Dict, market_data: Dict) -> Dict:
"""评估交易风险"""
# 1. 多角度风险评估
risk_assessments = {}
for assessor_type, assessor in self.risk_assessors.items():
risk_assessments[assessor_type] = assessor.assess_risk(
trading_decision, market_data
)
# 2. 综合风险评估
consolidated_risk = self._consolidate_risk_assessments(risk_assessments)
# 3. 风险等级分类
risk_classification = self._classify_risk_level(consolidated_risk)
# 4. 风险控制建议
control_recommendations = self._generate_control_recommendations(
consolidated_risk, risk_classification
)
return {
"individual_assessments": risk_assessments,
"consolidated_risk": consolidated_risk,
"risk_classification": risk_classification,
"control_recommendations": control_recommendations,
"approval_status": self._determine_approval_status(risk_classification)
}
class AggressiveRiskAssessor:
"""激进风险评估 - 支持高风险高收益策略"""
def __init__(self, config):
self.config = config
self.risk_appetite = "high"
self.return_focus = "high"
专业特点:
- 高风险容忍度
- 关注收益最大化
- 支持杠杆使用
- 鼓励机会把握
评估重点:
- 收益潜力分析
- 机会成本评估
- 市场时机把握
- 竞争优势利用
def assess_risk(self, trading_decision: Dict, market_data: Dict) -> Dict:
"""激进角度的风险评估"""
# 基础风险指标
base_risk_metrics = self._calculate_base_risk_metrics(trading_decision, market_data)
# 激进调整因子
aggressive_adjustments = {
"volatility_tolerance": 1.5, # 高波动容忍度
"drawdown_tolerance": 1.3, # 高回撤容忍度
"leverage_acceptance": 1.2, # 接受适度杠杆
"opportunity_weight": 1.4 # 高机会权重
}
# 调整风险评估
adjusted_risk = self._apply_aggressive_adjustments(
base_risk_metrics, aggressive_adjustments
)
# 收益风险比分析
return_risk_analysis = self._analyze_return_risk_ratio(
trading_decision, adjusted_risk
)
# 机会成本评估
opportunity_cost = self._assess_opportunity_cost(trading_decision, market_data)
return {
"risk_score": adjusted_risk["overall_risk"],
"return_potential": return_risk_analysis["return_potential"],
"risk_adjusted_return": return_risk_analysis["risk_adjusted_return"],
"opportunity_cost": opportunity_cost,
"recommendation": self._generate_aggressive_recommendation(
adjusted_risk, return_risk_analysis, opportunity_cost
),
"key_considerations": [
"高收益潜力值得承担相应风险",
"市场机会稍纵即逝，应果断行动",
"适度风险是获得超额收益的必要条件",
"风险可通过主动管理得到控制"
]
}
def _analyze_return_risk_ratio(self, decision: Dict, risk_metrics: Dict) -> Dict:
"""分析收益风险比"""
expected_return = decision.get("take_profit", 0.1)
risk_level = risk_metrics.get("overall_risk", 0.5)
# 激进视角下的收益风险比计算
# 更看重收益潜力，对风险相对宽容
risk_adjusted_return = expected_return / max(risk_level * 0.8, 0.1)
return_potential_score = min(expected_return * 10, 1.0) # 收益潜力评分
return {
"return_potential": return_potential_score,
"risk_adjusted_return": risk_adjusted_return,
"return_risk_ratio": expected_return / max(risk_level, 0.1),
"attractiveness": "高" if risk_adjusted_return > 1.5 else "中" if risk_adjusted_return > 1.0 else "低"
}
def _assess_opportunity_cost(self, decision: Dict, market_data: Dict) -> Dict:
"""评估机会成本"""
# 不行动的机会成本
inaction_cost = self._calculate_inaction_cost(decision, market_data)
# 延迟行动的成本
delay_cost = self._calculate_delay_cost(decision, market_data)
# 保守策略的机会成本
conservative_cost = self._calculate_conservative_cost(decision)
total_opportunity_cost = inaction_cost + delay_cost + conservative_cost
return {
"inaction_cost": inaction_cost,
"delay_cost": delay_cost,
"conservative_cost": conservative_cost,
"total_cost": total_opportunity_cost,
"urgency_level": "高" if total_opportunity_cost > 0.1 else "中" if total_opportunity_cost > 0.05 else "低"
}
class ConservativeRiskAssessor:
"""保守风险评估 - 强调风险控制和资本保护"""
def __init__(self, config):
self.config = config
self.risk_appetite = "low"
self.capital_preservation_focus = True
专业特点:
- 低风险容忍度
- 强调资本保护
- 谨慎的仓位管理
- 严格的风险控制
评估重点:
- 下行风险保护
- 最大回撤控制
- 流动性风险管理
- 尾部风险防范
def assess_risk(self, trading_decision: Dict, market_data: Dict) -> Dict:
"""保守角度的风险评估"""
# 基础风险指标
base_risk_metrics = self._calculate_base_risk_metrics(trading_decision, market_data)
# 保守调整因子
conservative_adjustments = {
"volatility_penalty": 1.5, # 高波动惩罚
"drawdown_penalty": 1.8, # 高回撤惩罚
"liquidity_requirement": 1.3, # 高流动性要求
"safety_margin": 1.4 # 高安全边际
}
# 调整风险评估
adjusted_risk = self._apply_conservative_adjustments(
base_risk_metrics, conservative_adjustments
)
# 下行风险分析
downside_risk = self._analyze_downside_risk(trading_decision, market_data)
# 尾部风险评估
tail_risk = self._assess_tail_risk(trading_decision, market_data)
# 流动性风险评估
liquidity_risk = self._assess_liquidity_risk(trading_decision, market_data)
return {
"risk_score": adjusted_risk["overall_risk"],
"downside_risk": downside_risk,
"tail_risk": tail_risk,
"liquidity_risk": liquidity_risk,
"capital_at_risk": self._calculate_capital_at_risk(adjusted_risk),
"recommendation": self._generate_conservative_recommendation(
adjusted_risk, downside_risk, tail_risk
),
"risk_mitigation_measures": [
"严格设置止损位，控制最大损失",
"分散投资，降低单一资产风险",
"保持充足现金储备",
"定期重新评估风险状况",
"避免过度集中和杠杆"
]
}
def _analyze_downside_risk(self, decision: Dict, market_data: Dict) -> Dict:
"""分析下行风险"""
stop_loss = decision.get("stop_loss", 0.05)
position_size = decision.get("quantity", 0.05)
# 最大可能损失
max_loss = stop_loss * position_size
# 下行波动率
downside_volatility = market_data.get("downside_volatility", 0.2)
# VaR 计算 (95% 置信度)
var_95 = position_size * downside_volatility * 1.65 # 95% VaR
# CVaR 计算 (条件风险价值)
cvar_95 = var_95 * 1.3 # 估算 CVaR
return {
"max_loss": max_loss,
"var_95": var_95,
"cvar_95": cvar_95,
"downside_volatility": downside_volatility,
"risk_level": "高" if max_loss > 0.02 else "中" if max_loss > 0.01 else "低"
}
def _assess_tail_risk(self, decision: Dict, market_data: Dict) -> Dict:
"""评估尾部风险"""
# 极端市场情况下的风险
market_stress_scenarios = {
"market_crash": {"probability": 0.05, "impact": -0.3},
"liquidity_crisis": {"probability": 0.03, "impact": -0.2},
"sector_collapse": {"probability": 0.08, "impact": -0.25},
"black_swan": {"probability": 0.01, "impact": -0.5}
}
position_size = decision.get("quantity", 0.05)
tail_risk_exposure = 0
scenario_impacts = {}
for scenario, params in market_stress_scenarios.items():
scenario_loss = position_size * abs(params["impact"])
expected_loss = scenario_loss * params["probability"]
tail_risk_exposure += expected_loss
scenario_impacts[scenario] = {
"potential_loss": scenario_loss,
"expected_loss": expected_loss,
"probability": params["probability"]
}
return {
"total_tail_risk": tail_risk_exposure,
"scenario_impacts": scenario_impacts,
"tail_risk_level": "高" if tail_risk_exposure > 0.01 else "中" if tail_risk_exposure > 0.005 else "低",
"mitigation_priority": "高" if tail_risk_exposure > 0.01 else "中"
}
class NeutralRiskAssessor:
"""中性风险评估 - 平衡风险和收益"""
def __init__(self, config):
self.config = config
self.risk_appetite = "medium"
self.balanced_approach = True
专业特点:
- 平衡的风险观
- 理性的收益预期
- 适度的风险承担
- 全面的风险考量
评估重点:
- 风险收益平衡
- 多维度风险分析
- 情景分析
- 概率加权评估
def assess_risk(self, trading_decision: Dict, market_data: Dict) -> Dict:
"""中性角度的风险评估"""
# 全面风险分析
comprehensive_risk = self._conduct_comprehensive_risk_analysis(
trading_decision, market_data
)
# 情景分析
scenario_analysis = self._conduct_scenario_analysis(trading_decision, market_data)
# 风险收益平衡评估
risk_return_balance = self._assess_risk_return_balance(
trading_decision, comprehensive_risk
)
# 概率加权风险评估
probability_weighted_risk = self._calculate_probability_weighted_risk(
scenario_analysis
)
return {
"comprehensive_risk": comprehensive_risk,
"scenario_analysis": scenario_analysis,
"risk_return_balance": risk_return_balance,
"probability_weighted_risk": probability_weighted_risk,
"overall_assessment": self._generate_balanced_assessment(
comprehensive_risk, scenario_analysis, risk_return_balance
),
"balanced_recommendation": self._generate_balanced_recommendation(
comprehensive_risk, risk_return_balance
)
}
def _conduct_scenario_analysis(self, decision: Dict, market_data: Dict) -> Dict:
"""进行情景分析"""
scenarios = {
"bull_case": {"probability": 0.3, "return": 0.15, "risk": 0.1},
"base_case": {"probability": 0.4, "return": 0.08, "risk": 0.15},
"bear_case": {"probability": 0.3, "return": -0.05, "risk": 0.25}
}
position_size = decision.get("quantity", 0.05)
scenario_outcomes = {}
expected_return = 0
expected_risk = 0
for scenario_name, params in scenarios.items():
scenario_return = params["return"] * position_size
scenario_risk = params["risk"] * position_size
probability = params["probability"]
expected_return += scenario_return * probability
expected_risk += scenario_risk * probability
scenario_outcomes[scenario_name] = {
"return": scenario_return,
"risk": scenario_risk,
"probability": probability,
"risk_adjusted_return": scenario_return / max(scenario_risk, 0.01)
}
return {
"scenarios": scenario_outcomes,
"expected_return": expected_return,
"expected_risk": expected_risk,
"risk_return_ratio": expected_return / max(expected_risk, 0.01),
"scenario_dispersion": self._calculate_scenario_dispersion(scenario_outcomes)
}
def _assess_risk_return_balance(self, decision: Dict, risk_metrics: Dict) -> Dict:
"""评估风险收益平衡"""
expected_return = decision.get("take_profit", 0.1)
expected_risk = risk_metrics.get("overall_risk", 0.5)
# 夏普比率估算
risk_free_rate = 0.02 # 假设无风险利率
excess_return = expected_return - risk_free_rate
sharpe_ratio = excess_return / max(expected_risk, 0.01)
# 风险调整后收益
risk_adjusted_return = expected_return / max(expected_risk, 0.01)
# 平衡评分
balance_score = self._calculate_balance_score(sharpe_ratio, risk_adjusted_return)
return {
"sharpe_ratio": sharpe_ratio,
"risk_adjusted_return": risk_adjusted_return,
"balance_score": balance_score,
"balance_assessment": self._assess_balance_quality(balance_score),
"optimization_suggestions": self._suggest_balance_optimization(
expected_return, expected_risk, balance_score
)
}
class RiskDecisionFramework:
"""风险决策框架"""
def make_risk_decision(self, risk_assessments: Dict, trading_decision: Dict) -> Dict:
"""制定风险管理决策"""
# 1. 风险共识分析
risk_consensus = self._analyze_risk_consensus(risk_assessments)
# 2. 风险等级确定
final_risk_level = self._determine_final_risk_level(risk_assessments, risk_consensus)
# 3. 决策建议生成
decision_recommendation = self._generate_decision_recommendation(
final_risk_level, risk_consensus, trading_decision
)
# 4. 风险控制措施
risk_controls = self._design_risk_controls(final_risk_level, trading_decision)
# 5. 监控要求
monitoring_requirements = self._define_monitoring_requirements(
final_risk_level, trading_decision
)
return {
"risk_consensus": risk_consensus,
"final_risk_level": final_risk_level,
"decision": decision_recommendation,
"risk_controls": risk_controls,
"monitoring": monitoring_requirements,
"approval_status": self._determine_final_approval(decision_recommendation)
}
def _analyze_risk_consensus(self, assessments: Dict) -> Dict:
"""分析风险评估共识"""
risk_scores = [
assessments["aggressive"]["risk_score"],
assessments["conservative"]["risk_score"],
assessments["neutral"]["comprehensive_risk"]["overall_risk"]
]
# 计算共识指标
avg_risk = sum(risk_scores) / len(risk_scores)
risk_std = np.std(risk_scores)
consensus_level = 1.0 - min(risk_std / avg_risk, 1.0) if avg_risk > 0 else 0.0
# 分析分歧点
disagreement_areas = self._identify_disagreement_areas(assessments)
return {
"average_risk_score": avg_risk,
"risk_score_std": risk_std,
"consensus_level": consensus_level,
"disagreement_areas": disagreement_areas,
"consensus_quality": "高" if consensus_level > 0.8 else "中" if consensus_level > 0.6 else "低"
}
def _generate_decision_recommendation(self, risk_level: str, consensus: Dict, decision: Dict) -> Dict:
"""生成决策建议"""
consensus_level = consensus["consensus_level"]
avg_risk = consensus["average_risk_score"]
if risk_level == "low" and consensus_level > 0.7:
recommendation = "approve"
confidence = 0.9
elif risk_level == "medium" and consensus_level > 0.6:
recommendation = "approve_with_conditions"
confidence = 0.7
elif risk_level == "high" and consensus_level > 0.8:
recommendation = "approve_with_strict_controls"
confidence = 0.6
else:
recommendation = "reject"
confidence = 0.8
return {
"recommendation": recommendation,
"confidence": confidence,
"reasoning": self._explain_recommendation_reasoning(
risk_level, consensus_level, avg_risk, recommendation
),
"conditions": self._define_approval_conditions(recommendation, risk_level)
}
class RiskMonitor:
"""风险监控系统"""
def __init__(self):
self.active_positions = {}
self.risk_alerts = []
self.monitoring_rules = self._setup_monitoring_rules()
def monitor_position_risk(self, position_id: str, current_data: Dict) -> Dict:
"""监控仓位风险"""
position = self.active_positions.get(position_id)
if not position:
return {"status": "position_not_found"}
# 实时风险计算
current_risk = self._calculate_current_risk(position, current_data)
# 风险阈值检查
risk_alerts = self._check_risk_thresholds(position, current_risk)
# 止损止盈检查
exit_signals = self._check_exit_conditions(position, current_data)
# 更新监控状态
monitoring_status = self._update_monitoring_status(
position, current_risk, risk_alerts, exit_signals
)
return {
"position_id": position_id,
"current_risk": current_risk,
"risk_alerts": risk_alerts,
"exit_signals": exit_signals,
"monitoring_status": monitoring_status,
"recommended_actions": self._recommend_risk_actions(
current_risk, risk_alerts, exit_signals
)
}
风险管理智能体通过多角度评估、严格控制和持续监控，确保交易活动在可控的风险范围内进行，保护投资组合的安全。