189 lines
7.6 KiB
Python
189 lines
7.6 KiB
Python
from google import genai
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from google.genai import types
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from config import settings
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from utils import _dumps
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from typing import Dict, Any, List
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import json
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import logging
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logger = logging.getLogger(__name__)
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class GeminiClient:
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"""Gemini AI 클라이언트 (ClaudeClient와 동일한 인터페이스)"""
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def __init__(self):
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self.client = genai.Client(api_key=settings.GEMINI_API_KEY)
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self.model_name = settings.GEMINI_MODEL
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self.generate_config = types.GenerateContentConfig(
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max_output_tokens=settings.MAX_TOKENS,
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temperature=settings.TEMPERATURE,
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)
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async def analyze(
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self,
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system_prompt: str,
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user_message: str,
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tools: List[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""Gemini를 통한 분석 실행"""
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try:
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response = await self.client.aio.models.generate_content(
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model=self.model_name,
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contents=f"{system_prompt}\n\n{user_message}",
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config=self.generate_config,
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)
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text = response.text if response.text else ""
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return {
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"content": [{"type": "text", "text": text}],
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"stop_reason": "end_turn",
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"usage": {
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"input_tokens": response.usage_metadata.prompt_token_count if response.usage_metadata else 0,
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"output_tokens": response.usage_metadata.candidates_token_count if response.usage_metadata else 0,
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}
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}
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except Exception as e:
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logger.error(f"Gemini analysis error: {e}")
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raise
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def _parse_json_response(self, response: Dict[str, Any], fallback: Dict[str, Any]) -> Dict[str, Any]:
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"""응답 텍스트에서 JSON 파싱 (공통 헬퍼)"""
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for content in response["content"]:
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if content["type"] == "text":
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try:
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text = content["text"]
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json_start = text.find('{')
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json_end = text.rfind('}') + 1
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if json_start != -1 and json_end > json_start:
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return json.loads(text[json_start:json_end])
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except (json.JSONDecodeError, Exception):
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return {**fallback, "analysis": content["text"]}
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return fallback
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async def validate_trading_rule(
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self,
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rule: Dict[str, Any],
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market_data: Dict[str, Any],
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news_data: List[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""트레이딩 규칙 검증"""
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system_prompt = """주식 트레이딩 규칙 검증 전문가. 규칙/시장/뉴스를 분석하여 JSON으로만 응답.
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필수 필드: decision(BUY/SELL/HOLD), confidence(0-1), risk_level, recommendation.
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악재(유상증자,감자,소송,실적악화) 시 confidence 낮추고 경고 포함. JSON 외 텍스트 금지."""
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user_message = f"규칙:{_dumps(rule)}\n시장:{_dumps(market_data)}"
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if news_data:
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user_message += f"\n뉴스:{_dumps(news_data)}"
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"decision": "HOLD", "confidence": 0.0, "analysis": "분석 실패"}
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)
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async def analyze_market(
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self,
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stock_code: str,
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market_data: Dict[str, Any],
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news: List[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""시장 분석"""
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system_prompt = """주식 시장 분석 전문가. JSON으로만 응답.
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필수 필드: trend(UP/DOWN/SIDEWAYS), sentiment(POSITIVE/NEGATIVE/NEUTRAL), recommendation(BUY/SELL/HOLD), confidence(0-1), analysis(간략설명). JSON 외 텍스트 금지."""
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news_limit = settings.NEWS_LIMIT if settings.TOKEN_SAVE_MODE else 5
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news_text = ""
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if news:
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news_text = "\n뉴스:" + ",".join([item.get('title', '') for item in news[:news_limit]])
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user_message = f"종목:{stock_code}\n시장:{_dumps(market_data)}{news_text}"
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"trend": "UNKNOWN", "sentiment": "NEUTRAL", "recommendation": "HOLD", "confidence": 0.0}
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)
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async def calculate_fair_price(
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self,
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stock_code: str,
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market_data: Dict[str, Any],
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financial_data: Dict[str, Any] = None
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) -> Dict[str, Any]:
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"""AI 기반 적정 매수/매도 가격 산출"""
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system_prompt = """주식 가격 전략 전문가. JSON으로만 응답.
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필수 필드: buy_prices(aggressive,neutral,conservative), sell_prices(target1,target2,target3), stop_loss, strategy, confidence(0-1). JSON 외 텍스트 금지."""
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user_message = (
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f"종목:{stock_code} 현재가:{market_data.get('current_price')} "
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f"고가:{market_data.get('high_price')} 저가:{market_data.get('low_price')} "
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f"거래량:{market_data.get('volume')} 지표:{_dumps(market_data.get('indicators', {}))}"
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)
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"error": "분석 실패", "confidence": 0.0}
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)
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async def assess_risk(
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self,
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portfolio: Dict[str, Any],
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trade_plan: Dict[str, Any]
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) -> Dict[str, Any]:
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"""리스크 평가"""
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system_prompt = """투자 리스크 관리 전문가. JSON으로만 응답.
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필수 필드: risk_level(LOW/MEDIUM/HIGH/CRITICAL), risk_factors, approval(APPROVED/REJECTED), recommendation. JSON 외 텍스트 금지."""
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user_message = f"포트폴리오:{_dumps(portfolio)}\n거래계획:{_dumps(trade_plan)}"
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"error": "Failed to assess risk"}
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)
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async def optimize_trading_rules(
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self,
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stock_code: str,
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market_data: Dict[str, Any],
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news_data: List[Dict[str, Any]] = None
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) -> Dict[str, Any]:
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"""시장 상황에 맞는 트레이딩 규칙 최적화"""
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system_prompt = """알고리즘 트레이딩 전략가. JSON으로만 응답.
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필수 필드: strategy_name, market_condition, parameters(buy_conditions,sell_conditions(take_profit_rate,stop_loss_rate,trailing_stop)), risk_assessment, confidence_score(0-100).
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악재시 보수적, 호재시 적극적 설정. JSON 외 텍스트 금지."""
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user_message = f"종목:{stock_code}\n시장:{_dumps(market_data)}"
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if news_data:
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user_message += f"\n뉴스:{_dumps(news_data)}"
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"error": "Failed to generate optimized rules"}
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)
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async def generate_trade_journal(
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self,
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trade_info: Dict[str, Any],
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market_data: Dict[str, Any]
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) -> Dict[str, Any]:
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"""매매일지 자동 작성"""
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system_prompt = """매매일지 작성 전문가. JSON으로만 응답.
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필수 필드: title, content(간략 Markdown), mood(평정심/흥분/공포/아쉬움/만족), tags(배열), score(0-100).
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매매근거, 잘한점/아쉬운점, 개선사항 포함. JSON 외 텍스트 금지."""
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user_message = f"거래:{_dumps(trade_info)}\n시장:{_dumps(market_data)}"
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response = await self.analyze(system_prompt, user_message)
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return self._parse_json_response(
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response,
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{"error": "Failed to generate journal"}
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)
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gemini_client = GeminiClient()
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