creat

Signed-off-by: TIANHE <TIANHE@GMAIL.COM>

backend_api_python/app/services/agents/risk_agents.py

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+"""
+Risk debate agents.
+Includes: aggressive / neutral / conservative risk analysts.
+"""
+import json
+from typing import Dict, Any
+from .base_agent import BaseAgent
+from app.services.llm import LLMService
+
+logger = __import__('app.utils.logger', fromlist=['get_logger']).get_logger(__name__)
+
+
+class RiskyAnalyst(BaseAgent):
+    """Aggressive risk analyst."""
+    
+    def __init__(self, memory=None):
+        super().__init__("RiskyAnalyst", memory)
+        self.llm_service = LLMService()
+    
+    def analyze(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze risk from an aggressive perspective."""
+        market = context.get('market')
+        symbol = context.get('symbol')
+        language = context.get('language', 'zh-CN')
+        model = context.get('model')
+        trader_plan = context.get('trader_plan', {})
+        
+        lang_instruction = self._get_language_instruction(language)
+        system_prompt = f"""You are an Aggressive Risk Analyst. You tend to:
+{lang_instruction}
+1. Emphasize high return potential, even with higher risks.
+2. Believe current risks are controllable and worth taking.
+3. Support aggressive trading strategies.
+
+Please return in JSON format as follows:
+{{
+  "argument": "Aggressive risk analysis argument...",
+  "risk_assessment": "Risk controllable, high return potential",
+  "recommendation": "Support trading plan"
+}}"""
+
+        user_prompt = f"""Perform aggressive risk analysis for {symbol} in {market} market.
+
+**Trading Plan:**
+{json.dumps(trader_plan, ensure_ascii=False, indent=2) if trader_plan else 'No Data'}
+
+Please analyze risk from an aggressive perspective, emphasizing return potential."""
+
+        result = self.llm_service.safe_call_llm(
+            system_prompt,
+            user_prompt,
+            {"argument": "", "risk_assessment": "", "recommendation": ""},
+            model=model
+        )
+        
+        return {
+            "type": "risky",
+            "data": result
+        }
+    
+    def _get_language_instruction(self, language: str) -> str:
+        language_map = {
+            'zh-CN': 'Answer in Simplified Chinese.',
+            'zh-TW': 'Answer in Traditional Chinese.',
+            'en-US': 'Answer in English.',
+            'ja-JP': 'Answer in Japanese.',
+            'ko-KR': 'Answer in Korean.',
+            'vi-VN': 'Answer in Vietnamese.',
+            'th-TH': 'Answer in Thai.',
+            'ar-SA': 'Answer in Arabic.',
+            'fr-FR': 'Answer in French.',
+            'de-DE': 'Answer in German.'
+        }
+        return language_map.get(language, 'Answer in English.')
+
+
+class NeutralAnalyst(BaseAgent):
+    """Neutral risk analyst."""
+    
+    def __init__(self, memory=None):
+        super().__init__("NeutralAnalyst", memory)
+        self.llm_service = LLMService()
+    
+    def analyze(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze risk from a neutral perspective."""
+        market = context.get('market')
+        symbol = context.get('symbol')
+        language = context.get('language', 'zh-CN')
+        model = context.get('model')
+        trader_plan = context.get('trader_plan', {})
+        
+        lang_instruction = self._get_language_instruction(language)
+        system_prompt = f"""You are a Neutral Risk Analyst. You tend to:
+{lang_instruction}
+1. Balance risk and return.
+2. Objectively evaluate various possibilities.
+3. Provide neutral risk advice.
+
+Please return in JSON format as follows:
+{{
+  "argument": "Neutral risk analysis argument...",
+  "risk_assessment": "Balance between risk and return",
+  "recommendation": "Cautiously execute trading plan"
+}}"""
+
+        user_prompt = f"""Perform neutral risk analysis for {symbol} in {market} market.
+
+**Trading Plan:**
+{json.dumps(trader_plan, ensure_ascii=False, indent=2) if trader_plan else 'No Data'}
+
+Please analyze risk from a neutral perspective, balancing risk and return."""
+
+        result = self.llm_service.safe_call_llm(
+            system_prompt,
+            user_prompt,
+            {"argument": "", "risk_assessment": "", "recommendation": ""},
+            model=model
+        )
+        
+        return {
+            "type": "neutral",
+            "data": result
+        }
+    
+    def _get_language_instruction(self, language: str) -> str:
+        language_map = {
+            'zh-CN': 'Answer in Simplified Chinese.',
+            'zh-TW': 'Answer in Traditional Chinese.',
+            'en-US': 'Answer in English.',
+            'ja-JP': 'Answer in Japanese.',
+            'ko-KR': 'Answer in Korean.',
+            'vi-VN': 'Answer in Vietnamese.',
+            'th-TH': 'Answer in Thai.',
+            'ar-SA': 'Answer in Arabic.',
+            'fr-FR': 'Answer in French.',
+            'de-DE': 'Answer in German.'
+        }
+        return language_map.get(language, 'Answer in English.')
+
+
+class SafeAnalyst(BaseAgent):
+    """Conservative risk analyst."""
+    
+    def __init__(self, memory=None):
+        super().__init__("SafeAnalyst", memory)
+        self.llm_service = LLMService()
+    
+    def analyze(self, context: Dict[str, Any]) -> Dict[str, Any]:
+        """Analyze risk from a conservative perspective."""
+        market = context.get('market')
+        symbol = context.get('symbol')
+        language = context.get('language', 'zh-CN')
+        model = context.get('model')
+        trader_plan = context.get('trader_plan', {})
+        risk_report = context.get('risk_report', {})
+        
+        lang_instruction = self._get_language_instruction(language)
+        system_prompt = f"""You are a Conservative Risk Analyst. You tend to:
+{lang_instruction}
+1. Emphasize risk control, prioritizing capital protection.
+2. Identify potential risk points.
+3. Suggest cautious or conservative trading strategies.
+
+Please return in JSON format as follows:
+{{
+  "argument": "Conservative risk analysis argument...",
+  "risk_assessment": "High risk exists, suggest caution",
+  "recommendation": "Suggest reducing position or suspending trading"
+}}"""
+
+        user_prompt = f"""Perform conservative risk analysis for {symbol} in {market} market.
+
+**Trading Plan:**
+{json.dumps(trader_plan, ensure_ascii=False, indent=2) if trader_plan else 'No Data'}
+
+**Risk Analysis Report:**
+{json.dumps(risk_report.get('data', {}), ensure_ascii=False, indent=2) if risk_report else 'No Data'}
+
+Please analyze risk from a conservative perspective, emphasizing risk control."""
+
+        result = self.llm_service.safe_call_llm(
+            system_prompt,
+            user_prompt,
+            {"argument": "", "risk_assessment": "", "recommendation": ""},
+            model=model
+        )
+        
+        return {
+            "type": "safe",
+            "data": result
+        }
+    
+    def _get_language_instruction(self, language: str) -> str:
+        language_map = {
+            'zh-CN': 'Answer in Simplified Chinese.',
+            'zh-TW': 'Answer in Traditional Chinese.',
+            'en-US': 'Answer in English.',
+            'ja-JP': 'Answer in Japanese.',
+            'ko-KR': 'Answer in Korean.',
+            'vi-VN': 'Answer in Vietnamese.',
+            'th-TH': 'Answer in Thai.',
+            'ar-SA': 'Answer in Arabic.',
+            'fr-FR': 'Answer in French.',
+            'de-DE': 'Answer in German.'
+        }
+        return language_map.get(language, 'Answer in English.')