本模块聚焦AI Agent在量化金融领域的任务规划与执行，涵盖以下核心内容：
完成本课程后，学员将掌握：
典型案例："构建多因子量化投资组合"
任务分解框架：
graph TD
A[数据采集] --> B[因子计算]
B --> C[组合优化]
C --> D[风险控制]
D --> E[执行监控]
采用JSON Schema规范金融数据交互格式：
{
"portfolio_optimization": {
"timeframe": "2015-2023",
"assets": ["Equity", "Fixed Income", "Commodities"],
"constraints": {
"max_drawdown": 15%,
"sector_exposure": {
"Technology": 30%,
"Healthcare": 20%
}
},
"optimization_metrics": ["Sharpe Ratio", "Sortino Ratio"]
}
}
class QuantAgentEnum(str, Enum):
DataCollector = "data_collector"
FactorEngine = "factor_engine"
Backtester = "backtester"
Optimizer = "portfolio_optimizer"
RiskManager = "risk_controller"
class FinancialTask(BaseModel):
task_type: QuantAgentEnum
parameters: dict
dependencies: List[str]
# 初始化代理集群
agents = {
QuantAgentEnum.DataCollector: DataCollectorAgent(),
QuantAgentEnum.FactorEngine: FactorProcessingAgent(),
QuantAgentEnum.Backtester: BacktestingFramework(),
QuantAgentEnum.Optimizer: OptimizationEngine(),
QuantAgentEnum.RiskManager: RiskMonitoringSystem()
}
# 任务执行管道
def execute_pipeline(task_flow: List[FinancialTask]):
context = {}
for task in topological_sort(task_flow):
agent = agents[task.task_type]
context.update(agent.execute(task.parameters, context))
return generate_report(context)
sequenceDiagram
participant U as 市场数据流
participant D as 数据代理
participant M as 模型代理
participant R as 风控代理
U->>D: 实时行情数据
D->>M: 更新因子矩阵
M->>R: 风险价值计算
R-->>M: 调整优化约束
M->>U: 生成调仓指令
class MarketAnomalyHandler:
def handle(self, context: dict) -> AdjustmentPlan:
anomaly_type = detect_anomaly(context['market_data'])
handlers = {
'flash_crash': self.handle_liquidity_crisis,
'volatility_spike': self.adjust_risk_parameters,
'sector_rotation': self.rebalance_sectors
}
return handlers[anomaly_type](context)
def multi_horizon_optimization():
horizons = ['1D', '1W', '1M']
results = {}
for horizon in horizons:
data = DataCollector(horizon).get_market_data()
factors = FactorEngine(data).compute_factors()
backtest = Backtester(factors).run()
optimized = Optimizer(backtest).efficient_frontier()
results[horizon] = RiskManager(optimized).stress_test()
return reconcile_strategies(results)
class MLEnhancedOptimizer:
def __init__(self):
self.prediction_model = load_model('lgbm_factor_predictor')
self.optimizer = CVXOPT_Solver()
def optimize_portfolio(self, market_conditions):
predicted_returns = self.prediction_model.predict(market_conditions)
return self.optimizer.solve(predicted_returns)
实战建议：