feat: initial commit - EvoTraders project

量化交易多智能体系统，包含：
- 分析师、投资组合经理、风险经理等智能体
- 股票分析、投资组合管理、风险控制工具
- React 前端界面
- FastAPI 后端服务

Co-Authored-By: Claude <noreply@anthropic.com>

backend/agents/risk_manager.py

@@ -0,0 +1,88 @@
+# -*- coding: utf-8 -*-
+"""
+Risk Manager Agent - Based on AgentScope ReActAgent
+Uses LLM for risk assessment
+"""
+from typing import Any, Dict, Optional
+
+from agentscope.agent import ReActAgent
+from agentscope.memory import InMemoryMemory, LongTermMemoryBase
+from agentscope.message import Msg
+from agentscope.tool import Toolkit
+
+from ..utils.progress import progress
+from .prompt_loader import PromptLoader
+
+_prompt_loader = PromptLoader()
+
+
+class RiskAgent(ReActAgent):
+    """
+    Risk Manager Agent - Uses LLM for risk assessment
+    Inherits from AgentScope's ReActAgent
+    """
+
+    def __init__(
+        self,
+        model: Any,
+        formatter: Any,
+        name: str = "risk_manager",
+        config: Optional[Dict[str, Any]] = None,
+        long_term_memory: Optional[LongTermMemoryBase] = None,
+    ):
+        """
+        Initialize Risk Manager Agent
+
+        Args:
+            model: LLM model instance
+            formatter: Message formatter instance
+            name: Agent name
+            config: Configuration dictionary
+            long_term_memory: Optional ReMeTaskLongTermMemory instance
+        """
+        self.config = config or {}
+
+        sys_prompt = self._load_system_prompt()
+
+        # Create dedicated toolkit for this agent
+        toolkit = Toolkit()
+
+        kwargs = {
+            "name": name,
+            "sys_prompt": sys_prompt,
+            "model": model,
+            "formatter": formatter,
+            "toolkit": toolkit,
+            "memory": InMemoryMemory(),
+            "max_iters": 10,
+        }
+        if long_term_memory:
+            kwargs["long_term_memory"] = long_term_memory
+            kwargs["long_term_memory_mode"] = "static_control"
+
+        super().__init__(**kwargs)
+
+    def _load_system_prompt(self) -> str:
+        """Load system prompt for risk manager"""
+        return _prompt_loader.load_prompt(
+            "risk_manager",
+            "system",
+        )
+
+    async def reply(self, x: Msg = None) -> Msg:
+        """
+        Provide risk assessment
+
+        Args:
+            x: Input message (content must be str)
+
+        Returns:
+            Msg with risk warnings (content is str)
+        """
+        progress.update_status(self.name, None, "Assessing risk")
+
+        result = await super().reply(x)
+
+        progress.update_status(self.name, None, "Risk assessment completed")
+
+        return result