#!/bin/bash
# 장중 매매 모니터링 스크립트
# 사용법: bash backend/monitor_trading.sh

echo "══════════════════════════════════════════════════════"
echo " Auto-Stock 장중 모니터링 ($(date '+%Y-%m-%d %H:%M:%S'))"
echo "══════════════════════════════════════════════════════"

# 1. 적응형 모드
echo ""
echo "▶ 적응형 전략 모드"
API_KEY=$(docker exec autostock-backend python3 -c "from app.core.config import settings; print(settings.SECRET_KEY)" 2>/dev/null)
curl -s http://127.0.0.1:8000/api/v1/adaptive-strategy/status \
  -H "X-API-Key: $API_KEY" 2>/dev/null | python3 -c "
import json, sys
d = json.load(sys.stdin)
c = d.get('condition', {})
p = d.get('params', {})
print(f'  모드: {d.get(\"trading_mode\", \"?\")}'.upper())
print(f'  ATR: {c.get(\"atr_percentile\", 0):.0f}% | 5일: {c.get(\"recent_5d_return\", 0):+.2f}%')
print(f'  스윙: RSI<{p.get(\"rsi_entry\",0)} 포지션{p.get(\"position_size\",0)*100:.0f}%')
print(f'  데이트레이딩: {\"ON\" if p.get(\"daytrading_enabled\") else \"OFF\"}')
" 2>/dev/null

# 2. 오늘 매매
echo ""
echo "▶ 오늘 매매"
docker exec autostock-backend python3 -c "
import asyncio
from datetime import datetime, timedelta

async def run():
    from app.core.database import AsyncSessionLocal
    from app.models.models import TradeHistory
    from sqlalchemy import select

    async with AsyncSessionLocal() as db:
        today = datetime.utcnow().replace(hour=0, minute=0, second=0)
        result = await db.execute(
            select(TradeHistory).where(TradeHistory.created_at >= today).order_by(TradeHistory.created_at.desc())
        )
        trades = result.scalars().all()
        buys = [t for t in trades if t.order_type == 'BUY' and t.status == 'COMPLETED']
        sells = [t for t in trades if t.order_type == 'SELL' and t.status == 'COMPLETED']
        pnl = sum(float(t.net_profit_loss or 0) for t in sells)
        wins = sum(1 for t in sells if float(t.net_profit_loss or 0) > 0)
        losses = sum(1 for t in sells if float(t.net_profit_loss or 0) <= 0)

        print(f'  매수 {len(buys)}건 | 매도 {len(sells)}건 | 손익 {pnl:+,.0f}원 ({wins}승 {losses}패)')
        print()
        for t in trades[:10]:
            kst = t.created_at + timedelta(hours=9) if t.created_at else None
            time_str = kst.strftime('%H:%M') if kst else '??:??'
            pnl_str = f'{float(t.net_profit_loss or 0):>+8,.0f}원' if t.order_type == 'SELL' else '        '
            flag = '🟢' if t.order_type == 'BUY' else ('✅' if float(t.net_profit_loss or 0) > 0 else '❌')
            status = '' if t.status == 'COMPLETED' else f' [{t.status}]'
            print(f'  {flag} {time_str} {t.order_type:4s} {(t.stock_name or t.stock_code):10s} {t.order_quantity:3d}주 {pnl_str}{status}')

asyncio.run(run())
" 2>/dev/null

# 3. 포트폴리오
echo ""
echo "▶ 포트폴리오"
docker exec autostock-backend python3 -c "
import asyncio

async def run():
    from app.core.database import AsyncSessionLocal
    from app.models.models import Portfolio
    from sqlalchemy import select

    async with AsyncSessionLocal() as db:
        pf = await db.execute(select(Portfolio).where(Portfolio.quantity > 0))
        holdings = pf.scalars().all()
        total_val = sum(float(h.current_price or 0) * h.quantity for h in holdings)
        total_pnl = sum(float(h.profit_loss or 0) for h in holdings)
        print(f'  보유 {len(holdings)}종목 | 평가 {total_val:,.0f}원 | 손익 {total_pnl:+,.0f}원')
        for h in sorted(holdings, key=lambda x: float(x.profit_loss_rate or 0), reverse=True):
            rate = float(h.profit_loss_rate or 0)
            flag = '🟢' if rate > 0 else '🔴' if rate < -2 else '🟡'
            print(f'  {flag} {(h.stock_name or h.stock_code):10s} {h.quantity:3d}주 {float(h.current_price or 0):>10,.0f}원 ({rate:>+.2f}%)')

asyncio.run(run())
" 2>/dev/null

# 4. 최근 에러
echo ""
echo "▶ 최근 경고/에러"
docker logs autostock-backend --since 5m 2>&1 | grep -iE "ERROR|WARNING|❌|⚠️" | grep -v "sklearn\|LGBMClassifier\|feature names" | tail -5
echo ""
echo "══════════════════════════════════════════════════════"