TIANHE
136 lines
5.0 KiB
Python
136 lines
5.0 KiB
Python
"""
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Trader agent.
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Synthesizes all analysis outputs and produces a final trading decision.
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"""
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import json
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from typing import Dict, Any
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from .base_agent import BaseAgent
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from app.services.llm import LLMService
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logger = __import__('app.utils.logger', fromlist=['get_logger']).get_logger(__name__)
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class TraderAgent(BaseAgent):
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"""Trader agent."""
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def __init__(self, memory=None):
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super().__init__("TraderAgent", memory)
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self.llm_service = LLMService()
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def analyze(self, context: Dict[str, Any]) -> Dict[str, Any]:
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"""Make a final trading decision."""
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market = context.get('market')
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symbol = context.get('symbol')
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language = context.get('language', 'zh-CN')
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model = context.get('model')
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# Inputs
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market_report = context.get('market_report', {})
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fundamental_report = context.get('fundamental_report', {})
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news_report = context.get('news_report', {})
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sentiment_report = context.get('sentiment_report', {})
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risk_report = context.get('risk_report', {})
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# Debate outputs
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bull_argument = context.get('bull_argument', {})
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bear_argument = context.get('bear_argument', {})
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research_decision = context.get('research_decision', '')
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# Memory
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situation = f"{market}:{symbol} trading decision"
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memory_meta = {
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"market": market,
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"symbol": symbol,
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"timeframe": context.get("timeframe"),
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"features": context.get("memory_features") or {},
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}
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memories = self.get_memories(situation, n_matches=None, metadata=memory_meta)
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memory_prompt = self.format_memories_for_prompt(memories)
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lang_instruction = self._get_language_instruction(language)
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system_prompt = f"""You are a Trader, needing to make a final trading decision based on all analysis results.
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{lang_instruction}
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Your tasks:
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1. Synthesize analysis results from all dimensions.
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2. Consider both bullish and bearish arguments.
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3. Make a clear trading decision: BUY, SELL, or HOLD.
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4. Provide a detailed trading plan.
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5. Learn from historical experience: {memory_prompt}
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6. **Confidence Score**: Evaluate your confidence in the decision (0-100). Be realistic. If the signals are mixed, confidence should be lower (e.g., 40-60). Only use high confidence (>80) for very clear strong signals. Do NOT default to 85.
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Please return in JSON format as follows:
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{{
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"decision": "BUY/SELL/HOLD",
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"confidence": 85,
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"reasoning": "Reason for decision...",
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"trading_plan": {{
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"entry_price": "Suggested entry price",
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"stop_loss": "Stop loss price",
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"take_profit": "Take profit price",
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"position_size": "Suggested position size"
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}},
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"report": "Detailed trading plan report..."
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}}"""
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user_prompt = f"""Based on all the following analyses, make a trading decision for {symbol} in {market} market:
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**Market Technical Analysis:**
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{json.dumps(market_report.get('data', {}), ensure_ascii=False, indent=2) if market_report else 'No Data'}
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**Fundamental Analysis:**
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{json.dumps(fundamental_report.get('data', {}), ensure_ascii=False, indent=2) if fundamental_report else 'No Data'}
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**News Analysis:**
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{json.dumps(news_report.get('data', {}), ensure_ascii=False, indent=2) if news_report else 'No Data'}
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**Sentiment Analysis:**
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{json.dumps(sentiment_report.get('data', {}), ensure_ascii=False, indent=2) if sentiment_report else 'No Data'}
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**Risk Analysis:**
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{json.dumps(risk_report.get('data', {}), ensure_ascii=False, indent=2) if risk_report else 'No Data'}
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**Bullish Argument:**
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{json.dumps(bull_argument.get('data', {}), ensure_ascii=False, indent=2) if bull_argument else 'No Data'}
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**Bearish Argument:**
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{json.dumps(bear_argument.get('data', {}), ensure_ascii=False, indent=2) if bear_argument else 'No Data'}
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**Research Manager Decision:**
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{research_decision if research_decision else 'No Data'}
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Please make a clear trading decision (BUY/SELL/HOLD) and provide a detailed trading plan."""
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result = self.llm_service.safe_call_llm(
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system_prompt,
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user_prompt,
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{
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"decision": "HOLD",
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"confidence": 50,
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"reasoning": "",
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"trading_plan": {},
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"report": "Failed to parse trader decision"
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},
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model=model
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)
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return {
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"type": "trader",
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"data": result
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}
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def _get_language_instruction(self, language: str) -> str:
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language_map = {
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'zh-CN': 'Answer in Simplified Chinese.',
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'zh-TW': 'Answer in Traditional Chinese.',
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'en-US': 'Answer in English.',
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'ja-JP': 'Answer in Japanese.',
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'ko-KR': 'Answer in Korean.',
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'vi-VN': 'Answer in Vietnamese.',
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'th-TH': 'Answer in Thai.',
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'ar-SA': 'Answer in Arabic.',
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'fr-FR': 'Answer in French.',
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'de-DE': 'Answer in German.'
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}
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return language_map.get(language, 'Answer in English.')
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