import os
import json
import numpy as np
from modules.services.ollama import OllamaManager
from modules.analysis.technical import TechnicalAnalyzer
from modules.analysis.deep_learning import PricePredictor

# [최적화] 워커 프로세스별 전역 변수 (LSTM 모델 캐싱)
_lstm_predictor = None

def get_predictor():
    """워커 프로세스 내에서 PricePredictor 인스턴스를 싱글톤으로 관리"""
    global _lstm_predictor
    if _lstm_predictor is None:
        print(f"🧩 [Worker {os.getpid()}] Initializing LSTM Predictor...")
        _lstm_predictor = PricePredictor()
    return _lstm_predictor

def analyze_stock_process(ticker, prices, news_items):
    """
    [CPU Intensive] 기술적 분석 및 AI 판단을 수행하는 함수
    (ProcessPoolExecutor에서 실행됨)
    """
    print(f"⚙️ [Bot Process] Analyzing {ticker} ({len(prices)} candles)...")
    
    # 1. 기술적 지표 계산
    current_price = prices[-1] if prices else 0
    # [수정] 변동성, 거래량 비율 반환 (거래량 데이터가 없으면 None 전달)
    tech_score, rsi, volatility, vol_ratio = TechnicalAnalyzer.get_technical_score(current_price, prices, volume_history=None)
    
    # 2. LSTM 주가 예측
    # [최적화] 전역 캐시된 Predictor 사용
    lstm_predictor = get_predictor()
    if lstm_predictor:
        lstm_predictor.training_status['current_ticker'] = ticker
    pred_result = lstm_predictor.train_and_predict(prices)
    
    lstm_score = 0.5 # 중립
    ai_confidence = 0.5
    ai_loss = 1.0

    if pred_result:
        ai_confidence = pred_result.get('confidence', 0.5)
        ai_loss = pred_result.get('loss', 1.0)
        
        # 상승/하락 예측에 따라 점수 조정 (신뢰도 반영)
        # 최대 5% 변동폭까지 반영
        change_magnitude = min(abs(pred_result['change_rate']), 5.0) / 5.0 
        
        if pred_result['trend'] == 'UP':
            # 상승 예측 시: 기본 0.5 + (강도 * 신뢰도 * 0.4) -> 최대 0.9
            lstm_score = 0.5 + (change_magnitude * ai_confidence * 0.4)
        else:
            # 하락 예측 시: 기본 0.5 - (강도 * 신뢰도 * 0.4) -> 최소 0.1
            lstm_score = 0.5 - (change_magnitude * ai_confidence * 0.4)
            
        lstm_score = max(0.0, min(1.0, lstm_score))

    # 3. AI 뉴스 분석 
    ollama = OllamaManager() 
    prompt = f"""
    [System Instruction]
    1. Role: You are a Expert Quant Trader with 20 years of experience.
    2. Market Data:
       - Technical Score: {tech_score:.2f} (RSI: {rsi:.2f})
       - AI Prediction: {pred_result['predicted']:.0f} KRW ({pred_result['change_rate']}%)
       - AI Confidence: {ai_confidence:.2f} (Loss: {ai_loss:.4f})
    3. Strategy:
       - If AI Confidence > 0.8 and Trend is UP -> Strong BUY signal.
       - If Tech Score > 0.7 -> BUY signal.
       - If Trend is DOWN -> SELL/AVOID.
    4. Task: Analyze the news and combine with market data to decide sentiment.
    
    News Data: {json.dumps(news_items, ensure_ascii=False)}
    
    Response (JSON):
    {{
        "sentiment_score": 0.8,
        "reason": "AI confidence is high and news supports the uptrend."
    }}
    """
    ai_resp = ollama.request_inference(prompt)
    sentiment_score = 0.5
    try:
        data = json.loads(ai_resp)
        sentiment_score = float(data.get("sentiment_score", 0.5))
    except:
        pass
        
    # 4. 통합 점수 (동적 가중치)
    # AI 신뢰도가 높으면 AI 비중을 대폭 상향
    if ai_confidence >= 0.85:
        w_tech, w_news, w_ai = 0.2, 0.2, 0.6
        print(f"   🤖 [High Confidence] AI Weight Boosted to 60%")
    else:
        w_tech, w_news, w_ai = 0.4, 0.3, 0.3
        
    total_score = (w_tech * tech_score) + (w_news * sentiment_score) + (w_ai * lstm_score)
    
    decision = "HOLD"
    
    # [신규] 강한 단일 신호 매수 로직 (기준 강화)
    strong_signal = False
    strong_reason = ""
    
    if tech_score >= 0.80:
        strong_signal = True
        strong_reason = "Super Strong Technical"
    elif lstm_score >= 0.80 and ai_confidence >= 0.8:
        strong_signal = True
        strong_reason = f"High Confidence AI Buy (Conf: {ai_confidence})"
    elif sentiment_score >= 0.85:
        strong_signal = True
        strong_reason = "Strong News Sentiment"
    
    if strong_signal:
        decision = "BUY"
        print(f"   🎯 [{strong_reason}] Overriding to BUY!")
    elif total_score >= 0.60: # (0.5 -> 0.6 상향 조정으로 보수적 접근)
        decision = "BUY"
    elif total_score <= 0.30:
        decision = "SELL"
    
    print(f"   └─ Scores: Tech={tech_score:.2f} News={sentiment_score:.2f} LSTM={lstm_score:.2f}(Conf:{ai_confidence:.2f}) → Total={total_score:.2f} [{decision}]")
        
    # [신규] 변동성(Volatility) 계산
    if len(prices) > 1:
        prices_np = np.array(prices)
        changes = np.diff(prices_np) / prices_np[:-1]
        volatility = np.std(changes) * 100 # 퍼센트 단위
    else:
        volatility = 0.0

    return {
        "ticker": ticker,
        "score": total_score,
        "tech": tech_score,
        "sentiment": sentiment_score,
        "lstm_score": lstm_score,
        "volatility": volatility,
        "volume_ratio": vol_ratio,
        "prediction": pred_result,
        "decision": decision,
        "current_price": current_price
    }