feat: implement KJB (Kim Jong-bong) short-term trading strategy

Full system: signal generator, daily backtest engine, trading portfolio,
signal API, scheduler job, and frontend dashboard.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

backend/alembic/versions/6c09aa4368e5_add_signals_table.py

@@ -0,0 +1,47 @@
+"""add signals table
+
+Revision ID: 6c09aa4368e5
+Revises: 882512221354
+Create Date: 2026-02-19 12:00:00.000000
+
+"""
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '6c09aa4368e5'
+down_revision: Union[str, None] = '882512221354'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table('signals',
+        sa.Column('id', sa.Integer(), nullable=False),
+        sa.Column('date', sa.Date(), nullable=False),
+        sa.Column('ticker', sa.String(length=20), nullable=False),
+        sa.Column('name', sa.String(length=100), nullable=True),
+        sa.Column('signal_type', sa.Enum('BUY', 'SELL', 'PARTIAL_SELL', name='signaltype'), nullable=False),
+        sa.Column('entry_price', sa.Numeric(precision=12, scale=2), nullable=True),
+        sa.Column('target_price', sa.Numeric(precision=12, scale=2), nullable=True),
+        sa.Column('stop_loss_price', sa.Numeric(precision=12, scale=2), nullable=True),
+        sa.Column('reason', sa.String(length=200), nullable=True),
+        sa.Column('status', sa.Enum('ACTIVE', 'EXECUTED', 'EXPIRED', name='signalstatus'), nullable=True),
+        sa.Column('created_at', sa.DateTime(), nullable=True),
+        sa.PrimaryKeyConstraint('id'),
+    )
+    op.create_index(op.f('ix_signals_date'), 'signals', ['date'], unique=False)
+    op.create_index(op.f('ix_signals_id'), 'signals', ['id'], unique=False)
+    op.create_index(op.f('ix_signals_ticker'), 'signals', ['ticker'], unique=False)
+
+
+def downgrade() -> None:
+    op.drop_index(op.f('ix_signals_ticker'), table_name='signals')
+    op.drop_index(op.f('ix_signals_id'), table_name='signals')
+    op.drop_index(op.f('ix_signals_date'), table_name='signals')
+    op.drop_table('signals')
+    sa.Enum('BUY', 'SELL', 'PARTIAL_SELL', name='signaltype').drop(op.get_bind(), checkfirst=True)
+    sa.Enum('ACTIVE', 'EXECUTED', 'EXPIRED', name='signalstatus').drop(op.get_bind(), checkfirst=True)

backend/app/api/__init__.py

@@ -6,6 +6,7 @@ from app.api.market import router as market_router
 from app.api.backtest import router as backtest_router
 from app.api.snapshot import router as snapshot_router
 from app.api.data_explorer import router as data_explorer_router
+from app.api.signal import router as signal_router
 
 __all__ = [
     "auth_router",
@@ -16,4 +17,5 @@ __all__ = [
     "backtest_router",
     "snapshot_router",
     "data_explorer_router",
+    "signal_router",
 ]

backend/app/api/signal.py

@@ -0,0 +1,56 @@
+"""
+KJB Signal API endpoints.
+"""
+from datetime import date
+from typing import List, Optional
+
+from fastapi import APIRouter, Depends, Query
+from sqlalchemy.orm import Session
+
+from app.core.database import get_db
+from app.api.deps import CurrentUser
+from app.models.signal import Signal
+from app.schemas.signal import SignalResponse
+
+router = APIRouter(prefix="/api/signal", tags=["signal"])
+
+
+@router.get("/kjb/today", response_model=List[SignalResponse])
+async def get_today_signals(
+    current_user: CurrentUser,
+    db: Session = Depends(get_db),
+):
+    """Get today's KJB trading signals."""
+    today = date.today()
+    signals = (
+        db.query(Signal)
+        .filter(Signal.date == today)
+        .order_by(Signal.signal_type, Signal.ticker)
+        .all()
+    )
+    return signals
+
+
+@router.get("/kjb/history", response_model=List[SignalResponse])
+async def get_signal_history(
+    current_user: CurrentUser,
+    db: Session = Depends(get_db),
+    start_date: Optional[date] = Query(None),
+    end_date: Optional[date] = Query(None),
+    ticker: Optional[str] = Query(None),
+    limit: int = Query(100, ge=1, le=1000),
+):
+    """Get historical KJB signals."""
+    query = db.query(Signal)
+    if start_date:
+        query = query.filter(Signal.date >= start_date)
+    if end_date:
+        query = query.filter(Signal.date <= end_date)
+    if ticker:
+        query = query.filter(Signal.ticker == ticker)
+    signals = (
+        query.order_by(Signal.date.desc(), Signal.ticker)
+        .limit(limit)
+        .all()
+    )
+    return signals

backend/app/api/strategy.py

@@ -7,9 +7,9 @@ from sqlalchemy.orm import Session
 from app.core.database import get_db
 from app.api.deps import CurrentUser
 from app.schemas.strategy import (
-    MultiFactorRequest, QualityRequest, ValueMomentumRequest, StrategyResult,
+    MultiFactorRequest, QualityRequest, ValueMomentumRequest, KJBRequest, StrategyResult,
 )
-from app.services.strategy import MultiFactorStrategy, QualityStrategy, ValueMomentumStrategy
+from app.services.strategy import MultiFactorStrategy, QualityStrategy, ValueMomentumStrategy, KJBStrategy
 
 router = APIRouter(prefix="/api/strategy", tags=["strategy"])
 
@@ -61,3 +61,18 @@ async def run_value_momentum(
         value_weight=request.value_weight,
         momentum_weight=request.momentum_weight,
     )
+
+
+@router.post("/kjb", response_model=StrategyResult)
+async def run_kjb(
+    request: KJBRequest,
+    current_user: CurrentUser,
+    db: Session = Depends(get_db),
+):
+    """Run KJB strategy."""
+    strategy = KJBStrategy(db)
+    return strategy.run(
+        universe_filter=request.universe,
+        top_n=request.top_n,
+        base_date=request.base_date,
+    )

backend/app/main.py

@@ -10,6 +10,7 @@ from fastapi.middleware.cors import CORSMiddleware
 from app.api import (
     auth_router, admin_router, portfolio_router, strategy_router,
     market_router, backtest_router, snapshot_router, data_explorer_router,
+    signal_router,
 )
 
 # Configure logging
@@ -112,6 +113,7 @@ app.include_router(market_router)
 app.include_router(backtest_router)
 app.include_router(snapshot_router)
 app.include_router(data_explorer_router)
+app.include_router(signal_router)
 
 
 @app.get("/health")

backend/app/models/__init__.py

@@ -22,6 +22,7 @@ from app.models.stock import (
     AssetClass,
     JobLog,
 )
+from app.models.signal import Signal, SignalType, SignalStatus
 from app.models.backtest import (
     Backtest,
     BacktestStatus,
@@ -60,4 +61,7 @@ __all__ = [
     "BacktestEquityCurve",
     "BacktestHolding",
     "BacktestTransaction",
+    "Signal",
+    "SignalType",
+    "SignalStatus",
 ]

backend/app/models/signal.py

@@ -0,0 +1,40 @@
+"""
+Trading signal models.
+"""
+import enum
+from datetime import datetime
+
+from sqlalchemy import (
+    Column, Integer, String, Numeric, DateTime, Date,
+    Text, Enum as SQLEnum,
+)
+
+from app.core.database import Base
+
+
+class SignalType(str, enum.Enum):
+    BUY = "buy"
+    SELL = "sell"
+    PARTIAL_SELL = "partial_sell"
+
+
+class SignalStatus(str, enum.Enum):
+    ACTIVE = "active"
+    EXECUTED = "executed"
+    EXPIRED = "expired"
+
+
+class Signal(Base):
+    __tablename__ = "signals"
+
+    id = Column(Integer, primary_key=True, index=True)
+    date = Column(Date, nullable=False, index=True)
+    ticker = Column(String(20), nullable=False, index=True)
+    name = Column(String(100))
+    signal_type = Column(SQLEnum(SignalType), nullable=False)
+    entry_price = Column(Numeric(12, 2))
+    target_price = Column(Numeric(12, 2))
+    stop_loss_price = Column(Numeric(12, 2))
+    reason = Column(String(200))
+    status = Column(SQLEnum(SignalStatus), default=SignalStatus.ACTIVE)
+    created_at = Column(DateTime, default=datetime.utcnow)

backend/app/schemas/backtest.py

@@ -27,7 +27,7 @@ class BacktestStatus(str, Enum):
 
 class BacktestCreate(BaseModel):
     """Request to create a new backtest."""
-    strategy_type: str = Field(..., description="multi_factor, quality, or value_momentum")
+    strategy_type: str = Field(..., description="multi_factor, quality, value_momentum, or kjb")
     strategy_params: Dict[str, Any] = Field(default_factory=dict)
     start_date: date
     end_date: date

backend/app/schemas/signal.py

@@ -0,0 +1,53 @@
+"""
+Signal related Pydantic schemas.
+"""
+from datetime import date, datetime
+from decimal import Decimal
+from typing import Optional, List
+from enum import Enum
+
+from pydantic import BaseModel
+
+from app.schemas.portfolio import FloatDecimal
+
+
+class SignalType(str, Enum):
+    BUY = "buy"
+    SELL = "sell"
+    PARTIAL_SELL = "partial_sell"
+
+
+class SignalStatus(str, Enum):
+    ACTIVE = "active"
+    EXECUTED = "executed"
+    EXPIRED = "expired"
+
+
+class SignalResponse(BaseModel):
+    id: int
+    date: date
+    ticker: str
+    name: Optional[str] = None
+    signal_type: str
+    entry_price: Optional[FloatDecimal] = None
+    target_price: Optional[FloatDecimal] = None
+    stop_loss_price: Optional[FloatDecimal] = None
+    reason: Optional[str] = None
+    status: str
+    created_at: datetime
+
+    class Config:
+        from_attributes = True
+
+
+class ActivePosition(BaseModel):
+    ticker: str
+    name: Optional[str] = None
+    entry_date: date
+    entry_price: FloatDecimal
+    current_price: FloatDecimal
+    shares: int
+    stop_loss_price: FloatDecimal
+    target_price: FloatDecimal
+    pnl_percent: FloatDecimal
+    pnl_amount: FloatDecimal

backend/app/schemas/strategy.py

@@ -50,6 +50,11 @@ class ValueMomentumRequest(StrategyRequest):
     momentum_weight: FloatDecimal = Field(default=Decimal("0.5"), ge=0, le=1)
 
 
+class KJBRequest(StrategyRequest):
+    """KJB strategy request."""
+    pass
+
+
 class StockFactor(BaseModel):
     """Factor scores for a single stock."""
     ticker: str

backend/app/services/backtest/__init__.py

@@ -2,6 +2,8 @@ from app.services.backtest.engine import BacktestEngine, DataValidationResult
 from app.services.backtest.portfolio import VirtualPortfolio, Transaction, HoldingInfo
 from app.services.backtest.metrics import MetricsCalculator, BacktestMetrics
 from app.services.backtest.worker import submit_backtest, get_executor_status
+from app.services.backtest.daily_engine import DailyBacktestEngine
+from app.services.backtest.trading_portfolio import TradingPortfolio, TradingTransaction
 
 __all__ = [
     "BacktestEngine",
@@ -13,4 +15,7 @@ __all__ = [
     "BacktestMetrics",
     "submit_backtest",
     "get_executor_status",
+    "DailyBacktestEngine",
+    "TradingPortfolio",
+    "TradingTransaction",
 ]

backend/app/services/backtest/daily_engine.py

@@ -0,0 +1,245 @@
+"""
+Daily simulation backtest engine for signal-based strategies (KJB).
+"""
+import logging
+from datetime import date
+from decimal import Decimal
+from typing import Dict, List
+
+import pandas as pd
+from sqlalchemy.orm import Session
+
+from app.models.backtest import (
+    Backtest, BacktestResult, BacktestEquityCurve,
+    BacktestTransaction,
+)
+from app.models.stock import Stock, Price
+from app.services.backtest.trading_portfolio import TradingPortfolio, TradingTransaction
+from app.services.backtest.metrics import MetricsCalculator
+from app.services.strategy.kjb import KJBSignalGenerator
+
+logger = logging.getLogger(__name__)
+
+
+class DailyBacktestEngine:
+    """
+    Backtest engine for KJB signal-based strategy.
+    Runs daily simulation with individual position management.
+    """
+
+    def __init__(self, db: Session):
+        self.db = db
+        self.signal_gen = KJBSignalGenerator()
+
+    def run(self, backtest_id: int) -> None:
+        backtest = self.db.query(Backtest).get(backtest_id)
+        if not backtest:
+            raise ValueError(f"Backtest {backtest_id} not found")
+
+        params = backtest.strategy_params or {}
+
+        portfolio = TradingPortfolio(
+            initial_capital=backtest.initial_capital,
+            max_positions=params.get("max_positions", 10),
+            cash_reserve_ratio=Decimal(str(params.get("cash_reserve_ratio", 0.3))),
+            stop_loss_pct=Decimal(str(params.get("stop_loss_pct", 0.03))),
+            target1_pct=Decimal(str(params.get("target1_pct", 0.05))),
+            target2_pct=Decimal(str(params.get("target2_pct", 0.10))),
+        )
+
+        trading_days = self._get_trading_days(backtest.start_date, backtest.end_date)
+        if not trading_days:
+            raise ValueError("No trading days found")
+
+        universe_tickers = self._get_universe_tickers()
+
+        # Load all data upfront for performance
+        all_prices = self._load_all_prices(universe_tickers, backtest.start_date, backtest.end_date)
+        stock_dfs = self._build_stock_dfs(all_prices, universe_tickers)
+        kospi_df = self._load_kospi_df(backtest.start_date, backtest.end_date)
+        benchmark_prices = self._load_benchmark_prices(backtest.benchmark, backtest.start_date, backtest.end_date)
+
+        # Build day -> prices lookup
+        day_prices_map: Dict[date, Dict[str, Decimal]] = {}
+        for p in all_prices:
+            if p.date not in day_prices_map:
+                day_prices_map[p.date] = {}
+            day_prices_map[p.date][p.ticker] = p.close
+
+        equity_curve_data: List[Dict] = []
+        all_transactions: List[tuple] = []
+
+        initial_benchmark = benchmark_prices.get(trading_days[0], Decimal("1"))
+        if initial_benchmark == 0:
+            initial_benchmark = Decimal("1")
+
+        for trading_date in trading_days:
+            day_prices = day_prices_map.get(trading_date, {})
+
+            # 1. Check exits first
+            exit_txns = portfolio.check_exits(
+                date=trading_date,
+                prices=day_prices,
+                commission_rate=backtest.commission_rate,
+                slippage_rate=backtest.slippage_rate,
+            )
+            for txn in exit_txns:
+                all_transactions.append((trading_date, txn))
+
+            # 2. Check entry signals
+            for ticker in universe_tickers:
+                if ticker in portfolio.positions:
+                    continue
+                if ticker not in stock_dfs or ticker not in day_prices:
+                    continue
+
+                stock_df = stock_dfs[ticker]
+                if trading_date not in stock_df.index:
+                    continue
+
+                hist = stock_df.loc[stock_df.index <= trading_date]
+                if len(hist) < 21:
+                    continue
+
+                kospi_hist = kospi_df.loc[kospi_df.index <= trading_date]
+                if len(kospi_hist) < 11:
+                    continue
+
+                signals = self.signal_gen.generate_signals(hist, kospi_hist)
+
+                if trading_date in signals.index and signals.loc[trading_date, "buy"]:
+                    txn = portfolio.enter_position(
+                        ticker=ticker,
+                        price=day_prices[ticker],
+                        date=trading_date,
+                        commission_rate=backtest.commission_rate,
+                        slippage_rate=backtest.slippage_rate,
+                    )
+                    if txn:
+                        all_transactions.append((trading_date, txn))
+
+            # 3. Record daily value
+            portfolio_value = portfolio.get_value(day_prices)
+            benchmark_value = benchmark_prices.get(trading_date, initial_benchmark)
+            normalized_benchmark = benchmark_value / initial_benchmark * backtest.initial_capital
+
+            equity_curve_data.append({
+                "date": trading_date,
+                "portfolio_value": portfolio_value,
+                "benchmark_value": normalized_benchmark,
+            })
+
+        # Calculate and save
+        portfolio_values = [Decimal(str(e["portfolio_value"])) for e in equity_curve_data]
+        benchmark_values = [Decimal(str(e["benchmark_value"])) for e in equity_curve_data]
+
+        metrics = MetricsCalculator.calculate_all(portfolio_values, benchmark_values)
+        drawdowns = MetricsCalculator.calculate_drawdown_series(portfolio_values)
+
+        self._save_results(backtest_id, metrics, equity_curve_data, drawdowns, all_transactions)
+
+    def _get_trading_days(self, start_date: date, end_date: date) -> List[date]:
+        prices = (
+            self.db.query(Price.date)
+            .filter(Price.date >= start_date, Price.date <= end_date)
+            .distinct()
+            .order_by(Price.date)
+            .all()
+        )
+        return [p[0] for p in prices]
+
+    def _get_universe_tickers(self) -> List[str]:
+        stocks = (
+            self.db.query(Stock)
+            .filter(Stock.market_cap.isnot(None))
+            .order_by(Stock.market_cap.desc())
+            .limit(30)
+            .all()
+        )
+        return [s.ticker for s in stocks]
+
+    def _load_all_prices(self, tickers: List[str], start_date: date, end_date: date) -> List:
+        return (
+            self.db.query(Price)
+            .filter(Price.ticker.in_(tickers))
+            .filter(Price.date >= start_date, Price.date <= end_date)
+            .all()
+        )
+
+    def _load_kospi_df(self, start_date: date, end_date: date) -> pd.DataFrame:
+        prices = (
+            self.db.query(Price)
+            .filter(Price.ticker == "069500")
+            .filter(Price.date >= start_date, Price.date <= end_date)
+            .order_by(Price.date)
+            .all()
+        )
+        if not prices:
+            return pd.DataFrame(columns=["close"])
+        data = [{"date": p.date, "close": float(p.close)} for p in prices]
+        return pd.DataFrame(data).set_index("date")
+
+    def _load_benchmark_prices(self, benchmark: str, start_date: date, end_date: date) -> Dict[date, Decimal]:
+        prices = (
+            self.db.query(Price)
+            .filter(Price.ticker == "069500")
+            .filter(Price.date >= start_date, Price.date <= end_date)
+            .all()
+        )
+        return {p.date: p.close for p in prices}
+
+    def _build_stock_dfs(self, price_data: List, tickers: List[str]) -> Dict[str, pd.DataFrame]:
+        ticker_rows: Dict[str, list] = {t: [] for t in tickers}
+        for p in price_data:
+            if p.ticker in ticker_rows:
+                ticker_rows[p.ticker].append({
+                    "date": p.date,
+                    "open": float(p.open),
+                    "high": float(p.high),
+                    "low": float(p.low),
+                    "close": float(p.close),
+                    "volume": int(p.volume),
+                })
+        result = {}
+        for ticker, rows in ticker_rows.items():
+            if rows:
+                df = pd.DataFrame(rows).set_index("date").sort_index()
+                result[ticker] = df
+        return result
+
+    def _save_results(self, backtest_id, metrics, equity_curve_data, drawdowns, transactions):
+        result = BacktestResult(
+            backtest_id=backtest_id,
+            total_return=metrics.total_return,
+            cagr=metrics.cagr,
+            mdd=metrics.mdd,
+            sharpe_ratio=metrics.sharpe_ratio,
+            volatility=metrics.volatility,
+            benchmark_return=metrics.benchmark_return,
+            excess_return=metrics.excess_return,
+        )
+        self.db.add(result)
+
+        for i, point in enumerate(equity_curve_data):
+            curve_point = BacktestEquityCurve(
+                backtest_id=backtest_id,
+                date=point["date"],
+                portfolio_value=point["portfolio_value"],
+                benchmark_value=point["benchmark_value"],
+                drawdown=drawdowns[i] if i < len(drawdowns) else Decimal("0"),
+            )
+            self.db.add(curve_point)
+
+        for trading_date, txn in transactions:
+            t = BacktestTransaction(
+                backtest_id=backtest_id,
+                date=trading_date,
+                ticker=txn.ticker,
+                action=txn.action,
+                shares=txn.shares,
+                price=txn.price,
+                commission=txn.commission,
+            )
+            self.db.add(t)
+
+        self.db.commit()

backend/app/services/backtest/trading_portfolio.py

@@ -0,0 +1,219 @@
+"""
+Trading portfolio for signal-based strategies (KJB).
+Supports individual position management with stop-loss and trailing stops.
+"""
+from decimal import Decimal, ROUND_DOWN
+from typing import Dict, List, Optional
+from dataclasses import dataclass, field
+from datetime import date
+
+
+@dataclass
+class Position:
+    """An active trading position."""
+    ticker: str
+    shares: int
+    entry_price: Decimal
+    entry_date: date
+    stop_loss: Decimal
+    target1: Decimal
+    target2: Decimal
+    partial_sold: bool = False
+
+
+@dataclass
+class TradingTransaction:
+    """A single trading transaction."""
+    ticker: str
+    action: str  # 'buy', 'sell', 'partial_sell'
+    shares: int
+    price: Decimal
+    commission: Decimal
+    reason: str = ""
+
+
+class TradingPortfolio:
+    """
+    Portfolio for signal-based daily trading.
+    Individual position entry/exit with stop-loss, take-profit, trailing stop.
+    Cash reserve enforcement (30%).
+    Partial position exits (50% at +5%).
+    """
+
+    def __init__(
+        self,
+        initial_capital: Decimal,
+        max_positions: int = 10,
+        cash_reserve_ratio: Decimal = Decimal("0.3"),
+        stop_loss_pct: Decimal = Decimal("0.03"),
+        target1_pct: Decimal = Decimal("0.05"),
+        target2_pct: Decimal = Decimal("0.10"),
+    ):
+        self.initial_capital = initial_capital
+        self.cash = initial_capital
+        self.max_positions = max_positions
+        self.cash_reserve_ratio = cash_reserve_ratio
+        self.stop_loss_pct = stop_loss_pct
+        self.target1_pct = target1_pct
+        self.target2_pct = target2_pct
+        self.positions: Dict[str, Position] = {}
+
+    @property
+    def investable_capital(self) -> Decimal:
+        return self.initial_capital * (1 - self.cash_reserve_ratio)
+
+    @property
+    def position_size(self) -> Decimal:
+        return self.investable_capital / Decimal(str(self.max_positions))
+
+    def get_value(self, prices: Dict[str, Decimal]) -> Decimal:
+        holdings_value = sum(
+            Decimal(str(pos.shares)) * prices.get(pos.ticker, Decimal("0"))
+            for pos in self.positions.values()
+        )
+        return self.cash + holdings_value
+
+    def enter_position(
+        self,
+        ticker: str,
+        price: Decimal,
+        date: date,
+        commission_rate: Decimal,
+        slippage_rate: Decimal,
+    ) -> Optional[TradingTransaction]:
+        if len(self.positions) >= self.max_positions:
+            return None
+        if ticker in self.positions:
+            return None
+
+        buy_price = price * (1 + slippage_rate)
+        max_cost = self.position_size
+        available = self.cash - (self.initial_capital * self.cash_reserve_ratio)
+        if available <= 0:
+            return None
+        actual_cost = min(max_cost, available)
+
+        divisor = buy_price * (1 + commission_rate)
+        if divisor <= 0:
+            return None
+        shares = int((actual_cost / divisor).to_integral_value(rounding=ROUND_DOWN))
+
+        if shares <= 0:
+            return None
+
+        cost = Decimal(str(shares)) * buy_price
+        commission = cost * commission_rate
+        total_cost = cost + commission
+
+        self.cash -= total_cost
+
+        self.positions[ticker] = Position(
+            ticker=ticker,
+            shares=shares,
+            entry_price=price,
+            entry_date=date,
+            stop_loss=price * (1 - self.stop_loss_pct),
+            target1=price * (1 + self.target1_pct),
+            target2=price * (1 + self.target2_pct),
+        )
+
+        return TradingTransaction(
+            ticker=ticker,
+            action="buy",
+            shares=shares,
+            price=buy_price,
+            commission=commission,
+            reason="entry_signal",
+        )
+
+    def check_exits(
+        self,
+        date: date,
+        prices: Dict[str, Decimal],
+        commission_rate: Decimal,
+        slippage_rate: Decimal,
+    ) -> List[TradingTransaction]:
+        transactions = []
+
+        for ticker in list(self.positions.keys()):
+            pos = self.positions[ticker]
+            current_price = prices.get(ticker)
+            if current_price is None:
+                continue
+
+            sell_price = current_price * (1 - slippage_rate)
+
+            # 1. Stop-loss
+            if current_price <= pos.stop_loss:
+                txn = self._exit_full(ticker, sell_price, commission_rate, "stop_loss")
+                if txn:
+                    transactions.append(txn)
+                continue
+
+            # 2. Take-profit 2: +10% (full exit of remaining after partial)
+            if current_price >= pos.target2 and pos.partial_sold:
+                txn = self._exit_full(ticker, sell_price, commission_rate, "take_profit_2")
+                if txn:
+                    transactions.append(txn)
+                continue
+
+            # 3. Take-profit 1: +5% (partial exit 50%)
+            if current_price >= pos.target1 and not pos.partial_sold:
+                txn = self._exit_partial(ticker, sell_price, commission_rate)
+                if txn:
+                    transactions.append(txn)
+                    pos.stop_loss = pos.entry_price
+                continue
+
+            # 4. Trailing stop update
+            if pos.partial_sold:
+                gain_pct = (current_price - pos.entry_price) / pos.entry_price
+                if gain_pct >= self.target2_pct:
+                    new_stop = pos.entry_price * (1 + self.target1_pct)
+                    if new_stop > pos.stop_loss:
+                        pos.stop_loss = new_stop
+
+        return transactions
+
+    def _exit_full(
+        self, ticker: str, sell_price: Decimal,
+        commission_rate: Decimal, reason: str,
+    ) -> Optional[TradingTransaction]:
+        pos = self.positions.get(ticker)
+        if not pos or pos.shares <= 0:
+            return None
+
+        proceeds = Decimal(str(pos.shares)) * sell_price
+        commission = proceeds * commission_rate
+        self.cash += proceeds - commission
+
+        shares = pos.shares
+        del self.positions[ticker]
+
+        return TradingTransaction(
+            ticker=ticker, action="sell", shares=shares,
+            price=sell_price, commission=commission, reason=reason,
+        )
+
+    def _exit_partial(
+        self, ticker: str, sell_price: Decimal, commission_rate: Decimal,
+    ) -> Optional[TradingTransaction]:
+        pos = self.positions.get(ticker)
+        if not pos or pos.shares <= 0:
+            return None
+
+        sell_shares = pos.shares // 2
+        if sell_shares <= 0:
+            return None
+
+        proceeds = Decimal(str(sell_shares)) * sell_price
+        commission = proceeds * commission_rate
+        self.cash += proceeds - commission
+
+        pos.shares -= sell_shares
+        pos.partial_sold = True
+
+        return TradingTransaction(
+            ticker=ticker, action="partial_sell", shares=sell_shares,
+            price=sell_price, commission=commission, reason="take_profit_1",
+        )

backend/app/services/backtest/worker.py

@@ -44,7 +44,11 @@ def _run_backtest_job(backtest_id: int) -> None:
         db.commit()
         logger.info(f"Backtest {backtest_id} started")
 
-        # Run backtest
+        # Run backtest - route KJB to DailyBacktestEngine
+        if backtest.strategy_type == "kjb":
+            from app.services.backtest.daily_engine import DailyBacktestEngine
+            engine = DailyBacktestEngine(db)
+        else:
             engine = BacktestEngine(db)
         engine.run(backtest_id)
 

backend/app/services/strategy/__init__.py

@@ -2,5 +2,6 @@ from app.services.strategy.base import BaseStrategy
 from app.services.strategy.multi_factor import MultiFactorStrategy
 from app.services.strategy.quality import QualityStrategy
 from app.services.strategy.value_momentum import ValueMomentumStrategy
+from app.services.strategy.kjb import KJBStrategy, KJBSignalGenerator
 
-__all__ = ["BaseStrategy", "MultiFactorStrategy", "QualityStrategy", "ValueMomentumStrategy"]
+__all__ = ["BaseStrategy", "MultiFactorStrategy", "QualityStrategy", "ValueMomentumStrategy", "KJBStrategy", "KJBSignalGenerator"]

backend/app/services/strategy/kjb.py

@@ -0,0 +1,178 @@
+"""
+Kim Jong-bong (KJB) strategy implementation.
+
+Signal-based short-term trading strategy:
+- Universe: market cap top 30, daily trading value >= 200B KRW
+- Entry: relative strength > KOSPI + breakout or large candle
+- Exit: stop-loss -3%, take-profit +5%/+10%, trailing stop
+"""
+from datetime import date
+from decimal import Decimal
+from typing import Dict, List, Optional
+
+import pandas as pd
+from sqlalchemy.orm import Session
+
+from app.services.strategy.base import BaseStrategy
+from app.schemas.strategy import StockFactor, StrategyResult, UniverseFilter
+from app.services.factor_calculator import FactorCalculator
+
+
+class KJBSignalGenerator:
+    """
+    Generates daily buy/sell signals based on KJB rules.
+    Pure computation - no DB access. Takes DataFrames as input.
+    """
+
+    def calculate_relative_strength(
+        self,
+        stock_df: pd.DataFrame,
+        kospi_df: pd.DataFrame,
+        lookback: int = 10,
+    ) -> pd.Series:
+        """
+        RS = (stock return / market return) * 100
+        RS > 100 means stock outperforms market.
+        """
+        stock_ret = stock_df["close"].pct_change(lookback)
+        kospi_ret = kospi_df["close"].pct_change(lookback)
+
+        # Align on common index
+        aligned = pd.DataFrame({
+            "stock_ret": stock_ret,
+            "kospi_ret": kospi_ret,
+        }).dropna()
+
+        rs = pd.Series(dtype=float, index=stock_df.index)
+        for idx in aligned.index:
+            market_ret = aligned.loc[idx, "kospi_ret"]
+            stock_r = aligned.loc[idx, "stock_ret"]
+            if abs(market_ret) < 1e-10:
+                rs[idx] = 100.0 if abs(stock_r) < 1e-10 else (200.0 if stock_r > 0 else 0.0)
+            else:
+                rs[idx] = (stock_r / market_ret) * 100
+        return rs
+
+    def detect_breakout(
+        self,
+        stock_df: pd.DataFrame,
+        lookback: int = 20,
+    ) -> pd.Series:
+        """Close > highest high of previous lookback days."""
+        prev_high = stock_df["high"].rolling(lookback).max().shift(1)
+        return stock_df["close"] > prev_high
+
+    def detect_large_candle(
+        self,
+        stock_df: pd.DataFrame,
+        pct_threshold: float = 0.05,
+        vol_multiplier: float = 1.5,
+    ) -> pd.Series:
+        """
+        Daily return >= 5% AND volume >= 1.5x 20-day average.
+        """
+        daily_return = stock_df["close"].pct_change()
+        avg_volume = stock_df["volume"].rolling(20).mean()
+        volume_ratio = stock_df["volume"] / avg_volume
+        return (daily_return >= pct_threshold) & (volume_ratio >= vol_multiplier)
+
+    def generate_signals(
+        self,
+        stock_df: pd.DataFrame,
+        kospi_df: pd.DataFrame,
+        rs_lookback: int = 10,
+        breakout_lookback: int = 20,
+    ) -> pd.DataFrame:
+        """
+        Buy when: RS > 100 AND (breakout OR large candle)
+        """
+        rs = self.calculate_relative_strength(stock_df, kospi_df, rs_lookback)
+        breakout = self.detect_breakout(stock_df, breakout_lookback)
+        large_candle = self.detect_large_candle(stock_df)
+
+        signals = pd.DataFrame(index=stock_df.index)
+        signals["rs"] = rs
+        signals["breakout"] = breakout.fillna(False)
+        signals["large_candle"] = large_candle.fillna(False)
+        signals["buy"] = (rs > 100) & (breakout.fillna(False) | large_candle.fillna(False))
+        signals["buy"] = signals["buy"].fillna(False)
+        return signals
+
+
+class KJBStrategy(BaseStrategy):
+    """
+    KJB strategy for stock ranking.
+    Ranks stocks by relative strength and momentum.
+    Compatible with existing strategy pattern (returns StrategyResult).
+    """
+
+    strategy_name = "kjb"
+
+    def run(
+        self,
+        universe_filter: UniverseFilter,
+        top_n: int,
+        base_date: date = None,
+        **kwargs,
+    ) -> StrategyResult:
+        if base_date is None:
+            base_date = date.today()
+
+        # Get universe - filter to top 30 by market cap
+        stocks = self.get_universe(universe_filter)
+        stocks.sort(key=lambda s: s.market_cap or 0, reverse=True)
+        stocks = stocks[:30]
+
+        tickers = [s.ticker for s in stocks]
+        stock_map = {s.ticker: s for s in stocks}
+
+        if not tickers:
+            return StrategyResult(
+                strategy_name=self.strategy_name,
+                base_date=base_date,
+                universe_count=0,
+                result_count=0,
+                stocks=[],
+            )
+
+        # Get valuations and sectors
+        valuations = self.factor_calc.get_valuations(tickers, base_date)
+        sectors = self.factor_calc.get_sectors(tickers)
+
+        # Calculate 1-month momentum as ranking proxy
+        momentum = self.factor_calc.calculate_momentum(
+            tickers, base_date, months=1, skip_recent=0,
+        )
+
+        # Build results
+        results = []
+        for ticker in tickers:
+            stock = stock_map[ticker]
+            val = valuations.get(ticker)
+            mom = momentum.get(ticker, Decimal("0"))
+
+            results.append(StockFactor(
+                ticker=ticker,
+                name=stock.name,
+                market=stock.market,
+                sector_name=sectors.get(ticker),
+                market_cap=int(stock.market_cap / 100_000_000) if stock.market_cap else None,
+                close_price=Decimal(str(stock.close_price)) if stock.close_price else None,
+                per=Decimal(str(val.per)) if val and val.per else None,
+                pbr=Decimal(str(val.pbr)) if val and val.pbr else None,
+                dividend_yield=Decimal(str(val.dividend_yield)) if val and val.dividend_yield else None,
+                momentum_score=mom,
+                total_score=mom,
+            ))
+
+        results.sort(key=lambda x: x.total_score or Decimal("0"), reverse=True)
+        for i, r in enumerate(results[:top_n], 1):
+            r.rank = i
+
+        return StrategyResult(
+            strategy_name=self.strategy_name,
+            base_date=base_date,
+            universe_count=len(stocks),
+            result_count=min(top_n, len(results)),
+            stocks=results[:top_n],
+        )

backend/jobs/__init__.py

@@ -3,8 +3,10 @@ Background jobs module.
 """
 from jobs.scheduler import scheduler, start_scheduler, stop_scheduler
 from jobs.collection_job import run_daily_collection, run_backfill
+from jobs.kjb_signal_job import run_kjb_signals
 
 __all__ = [
     "scheduler", "start_scheduler", "stop_scheduler",
     "run_daily_collection", "run_backfill",
+    "run_kjb_signals",
 ]

backend/jobs/kjb_signal_job.py

@@ -0,0 +1,109 @@
+"""
+Daily KJB signal generation job.
+"""
+import logging
+from datetime import date, timedelta
+
+import pandas as pd
+
+from app.core.database import SessionLocal
+from app.models.stock import Stock, Price
+from app.models.signal import Signal, SignalType, SignalStatus
+from app.services.strategy.kjb import KJBSignalGenerator
+
+logger = logging.getLogger(__name__)
+
+
+def run_kjb_signals():
+    """
+    Generate KJB trading signals for today.
+    Called by scheduler at 18:15 KST (after price collection).
+    """
+    logger.info("Starting KJB signal generation")
+    db = SessionLocal()
+
+    try:
+        today = date.today()
+        signal_gen = KJBSignalGenerator()
+
+        stocks = (
+            db.query(Stock)
+            .filter(Stock.market_cap.isnot(None))
+            .order_by(Stock.market_cap.desc())
+            .limit(30)
+            .all()
+        )
+        tickers = [s.ticker for s in stocks]
+        name_map = {s.ticker: s.name for s in stocks}
+
+        lookback_start = today - timedelta(days=90)
+        kospi_prices = (
+            db.query(Price)
+            .filter(Price.ticker == "069500")
+            .filter(Price.date >= lookback_start, Price.date <= today)
+            .order_by(Price.date)
+            .all()
+        )
+        if not kospi_prices:
+            logger.warning("No KOSPI data available for signal generation")
+            return
+
+        kospi_df = pd.DataFrame([
+            {"date": p.date, "close": float(p.close)}
+            for p in kospi_prices
+        ]).set_index("date")
+
+        signals_created = 0
+
+        for ticker in tickers:
+            stock_prices = (
+                db.query(Price)
+                .filter(Price.ticker == ticker)
+                .filter(Price.date >= lookback_start, Price.date <= today)
+                .order_by(Price.date)
+                .all()
+            )
+
+            if len(stock_prices) < 21:
+                continue
+
+            stock_df = pd.DataFrame([{
+                "date": p.date,
+                "open": float(p.open),
+                "high": float(p.high),
+                "low": float(p.low),
+                "close": float(p.close),
+                "volume": int(p.volume),
+            } for p in stock_prices]).set_index("date")
+
+            signals = signal_gen.generate_signals(stock_df, kospi_df)
+
+            if today in signals.index and signals.loc[today, "buy"]:
+                close_price = stock_df.loc[today, "close"]
+                reason_parts = []
+                if signals.loc[today, "breakout"]:
+                    reason_parts.append("breakout")
+                if signals.loc[today, "large_candle"]:
+                    reason_parts.append("large_candle")
+
+                signal = Signal(
+                    date=today,
+                    ticker=ticker,
+                    name=name_map.get(ticker),
+                    signal_type=SignalType.BUY,
+                    entry_price=close_price,
+                    target_price=round(close_price * 1.05, 2),
+                    stop_loss_price=round(close_price * 0.97, 2),
+                    reason=", ".join(reason_parts),
+                    status=SignalStatus.ACTIVE,
+                )
+                db.add(signal)
+                signals_created += 1
+
+        db.commit()
+        logger.info(f"KJB signal generation complete: {signals_created} buy signals")
+
+    except Exception as e:
+        logger.exception(f"KJB signal generation failed: {e}")
+    finally:
+        db.close()

backend/jobs/scheduler.py

@@ -11,6 +11,7 @@ KST = ZoneInfo("Asia/Seoul")
 
 from jobs.snapshot_job import create_daily_snapshots
 from jobs.collection_job import run_daily_collection, run_financial_collection
+from jobs.kjb_signal_job import run_kjb_signals
 
 logger = logging.getLogger(__name__)
 
@@ -66,6 +67,21 @@ def configure_jobs():
     )
     logger.info("Configured daily_snapshots job at 18:30 KST")
 
+    # KJB signal generation at 18:15 (after daily collection, before snapshot)
+    scheduler.add_job(
+        run_kjb_signals,
+        trigger=CronTrigger(
+            hour=18,
+            minute=15,
+            day_of_week='mon-fri',
+            timezone=KST,
+        ),
+        id='kjb_daily_signals',
+        name='Generate KJB trading signals',
+        replace_existing=True,
+    )
+    logger.info("Configured kjb_daily_signals job at 18:15 KST")
+
 
 def start_scheduler():
     """Start the scheduler."""

backend/tests/e2e/test_kjb_flow.py

@@ -0,0 +1,74 @@
+"""
+E2E tests for KJB strategy flow.
+"""
+from fastapi.testclient import TestClient
+
+
+def test_kjb_strategy_endpoint(client: TestClient, auth_headers):
+    """Test KJB strategy ranking endpoint."""
+    response = client.post(
+        "/api/strategy/kjb",
+        json={
+            "universe": {"markets": ["KOSPI"]},
+            "top_n": 10,
+        },
+        headers=auth_headers,
+    )
+    assert response.status_code in [200, 400, 500]
+    if response.status_code == 200:
+        data = response.json()
+        assert data["strategy_name"] == "kjb"
+        assert "stocks" in data
+
+
+def test_kjb_backtest_creation(client: TestClient, auth_headers):
+    """Test creating a KJB backtest."""
+    response = client.post(
+        "/api/backtest",
+        json={
+            "strategy_type": "kjb",
+            "strategy_params": {
+                "max_positions": 10,
+                "cash_reserve_ratio": 0.3,
+                "stop_loss_pct": 0.03,
+                "target1_pct": 0.05,
+                "target2_pct": 0.10,
+            },
+            "start_date": "2023-01-01",
+            "end_date": "2023-12-31",
+            "initial_capital": 10000000,
+            "commission_rate": 0.00015,
+            "slippage_rate": 0.001,
+            "benchmark": "KOSPI",
+            "top_n": 30,
+        },
+        headers=auth_headers,
+    )
+    assert response.status_code == 200
+    data = response.json()
+    assert "id" in data
+    assert data["status"] == "pending"
+
+
+def test_signal_today_endpoint(client: TestClient, auth_headers):
+    """Test today's signals endpoint."""
+    response = client.get("/api/signal/kjb/today", headers=auth_headers)
+    assert response.status_code == 200
+    assert isinstance(response.json(), list)
+
+
+def test_signal_history_endpoint(client: TestClient, auth_headers):
+    """Test signal history endpoint."""
+    response = client.get(
+        "/api/signal/kjb/history",
+        params={"limit": 10},
+        headers=auth_headers,
+    )
+    assert response.status_code == 200
+    assert isinstance(response.json(), list)
+
+
+def test_signal_requires_auth(client: TestClient):
+    """Test that signal endpoints require authentication."""
+    response = client.get("/api/signal/kjb/today")
+    assert response.status_code == 401

backend/tests/unit/test_kjb_signal.py

@@ -0,0 +1,100 @@
+"""
+Unit tests for KJB signal generator.
+"""
+import pandas as pd
+from datetime import date, timedelta
+
+from app.services.strategy.kjb import KJBSignalGenerator
+
+
+def _make_price_df(closes, volumes=None, start_date=date(2024, 1, 2)):
+    dates = pd.bdate_range(start=start_date, periods=len(closes))
+    if volumes is None:
+        volumes = [1000000] * len(closes)
+    highs = [c * 1.01 for c in closes]
+    lows = [c * 0.99 for c in closes]
+    opens = closes.copy()
+    return pd.DataFrame({
+        "open": opens,
+        "high": highs,
+        "low": lows,
+        "close": closes,
+        "volume": volumes,
+    }, index=dates)
+
+
+def _make_kospi_df(closes, start_date=date(2024, 1, 2)):
+    dates = pd.bdate_range(start=start_date, periods=len(closes))
+    return pd.DataFrame({"close": closes}, index=dates)
+
+
+def test_relative_strength_above_market():
+    gen = KJBSignalGenerator()
+    stock_closes = [100 + i for i in range(25)]
+    kospi_closes = [100 + i * 0.5 for i in range(25)]
+    stock_df = _make_price_df(stock_closes)
+    kospi_df = _make_kospi_df(kospi_closes)
+    rs = gen.calculate_relative_strength(stock_df, kospi_df, lookback=10)
+    assert rs.dropna().iloc[-1] > 100
+
+
+def test_relative_strength_below_market():
+    gen = KJBSignalGenerator()
+    stock_closes = [100 + i * 0.3 for i in range(25)]
+    kospi_closes = [100 + i for i in range(25)]
+    stock_df = _make_price_df(stock_closes)
+    kospi_df = _make_kospi_df(kospi_closes)
+    rs = gen.calculate_relative_strength(stock_df, kospi_df, lookback=10)
+    assert rs.dropna().iloc[-1] < 100
+
+
+def test_detect_breakout():
+    gen = KJBSignalGenerator()
+    closes = [100.0] * 20 + [105.0]
+    stock_df = _make_price_df(closes)
+    breakouts = gen.detect_breakout(stock_df, lookback=20)
+    assert breakouts.iloc[-1] == True
+    assert breakouts.iloc[-2] == False
+
+
+def test_detect_large_candle():
+    gen = KJBSignalGenerator()
+    closes = [100.0] * 21 + [106.0]
+    volumes = [1000000] * 21 + [3000000]
+    stock_df = _make_price_df(closes, volumes)
+    large = gen.detect_large_candle(stock_df, pct_threshold=0.05, vol_multiplier=1.5)
+    assert large.iloc[-1] == True
+    assert large.iloc[-2] == False
+
+
+def test_no_large_candle_low_volume():
+    gen = KJBSignalGenerator()
+    closes = [100.0] * 21 + [106.0]
+    volumes = [1000000] * 22
+    stock_df = _make_price_df(closes, volumes)
+    large = gen.detect_large_candle(stock_df)
+    assert large.iloc[-1] == False
+
+
+def test_generate_buy_signal():
+    gen = KJBSignalGenerator()
+    closes = [100.0] * 20 + [106.0]
+    volumes = [1000000] * 20 + [3000000]
+    kospi_closes = [100.0 + i * 0.1 for i in range(21)]
+
+    stock_df = _make_price_df(closes, volumes)
+    kospi_df = _make_kospi_df(kospi_closes)
+
+    signals = gen.generate_signals(stock_df, kospi_df)
+    assert signals["buy"].iloc[-1] == True
+
+
+def test_no_signal_weak_stock():
+    gen = KJBSignalGenerator()
+    # Stock underperforms market
+    closes = [100.0 + i * 0.1 for i in range(25)]
+    kospi_closes = [100 + i for i in range(25)]
+    stock_df = _make_price_df(closes)
+    kospi_df = _make_kospi_df(kospi_closes)
+    signals = gen.generate_signals(stock_df, kospi_df)
+    assert signals["buy"].iloc[-1] == False

backend/tests/unit/test_trading_portfolio.py

@@ -0,0 +1,135 @@
+"""
+Unit tests for TradingPortfolio.
+"""
+from decimal import Decimal
+from datetime import date
+
+from app.services.backtest.trading_portfolio import TradingPortfolio
+
+
+def test_initial_state():
+    tp = TradingPortfolio(Decimal("10000000"))
+    assert tp.cash == Decimal("10000000")
+    assert tp.investable_capital == Decimal("7000000")
+    assert len(tp.positions) == 0
+
+
+def test_enter_position():
+    tp = TradingPortfolio(Decimal("10000000"))
+    txn = tp.enter_position(
+        ticker="005930",
+        price=Decimal("70000"),
+        date=date(2024, 1, 2),
+        commission_rate=Decimal("0.00015"),
+        slippage_rate=Decimal("0.001"),
+    )
+    assert txn is not None
+    assert txn.action == "buy"
+    assert "005930" in tp.positions
+    pos = tp.positions["005930"]
+    assert pos.entry_price == Decimal("70000")
+    assert pos.stop_loss == Decimal("67900")  # -3%
+    assert pos.target1 == Decimal("73500")  # +5%
+    assert pos.target2 == Decimal("77000")  # +10%
+
+
+def test_max_positions():
+    tp = TradingPortfolio(Decimal("10000000"), max_positions=2)
+    tp.enter_position("A", Decimal("1000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    tp.enter_position("B", Decimal("1000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    txn = tp.enter_position("C", Decimal("1000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    assert txn is None
+    assert len(tp.positions) == 2
+
+
+def test_stop_loss_exit():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    exits = tp.check_exits(
+        date=date(2024, 1, 3),
+        prices={"005930": Decimal("67000")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    assert len(exits) == 1
+    assert exits[0].action == "sell"
+    assert "005930" not in tp.positions
+
+
+def test_partial_take_profit():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    pos = tp.positions["005930"]
+    initial_shares = pos.shares
+
+    exits = tp.check_exits(
+        date=date(2024, 1, 10),
+        prices={"005930": Decimal("73500")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    assert len(exits) == 1
+    assert exits[0].action == "partial_sell"
+    assert exits[0].shares == initial_shares // 2
+    assert "005930" in tp.positions
+    assert tp.positions["005930"].stop_loss == Decimal("70000")
+
+
+def test_full_take_profit():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    tp.check_exits(
+        date=date(2024, 1, 10),
+        prices={"005930": Decimal("73500")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    exits = tp.check_exits(
+        date=date(2024, 1, 15),
+        prices={"005930": Decimal("77000")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    assert len(exits) == 1
+    assert exits[0].action == "sell"
+    assert "005930" not in tp.positions
+
+
+def test_trailing_stop_after_partial():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    tp.check_exits(
+        date=date(2024, 1, 10),
+        prices={"005930": Decimal("73500")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    # After partial sell, stop should be at entry (70000)
+    assert tp.positions["005930"].stop_loss == Decimal("70000")
+    # Price rises to +8%, stop stays at entry
+    tp.check_exits(
+        date=date(2024, 1, 12),
+        prices={"005930": Decimal("75600")},
+        commission_rate=Decimal("0"),
+        slippage_rate=Decimal("0"),
+    )
+    assert tp.positions["005930"].stop_loss == Decimal("70000")
+
+
+def test_cash_reserve():
+    tp = TradingPortfolio(Decimal("10000000"), cash_reserve_ratio=Decimal("0.3"))
+    assert tp.investable_capital == Decimal("7000000")
+
+
+def test_get_portfolio_value():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    value = tp.get_value({"005930": Decimal("75000")})
+    assert value > Decimal("10000000")
+
+
+def test_duplicate_entry_rejected():
+    tp = TradingPortfolio(Decimal("10000000"))
+    tp.enter_position("005930", Decimal("70000"), date(2024, 1, 2), Decimal("0"), Decimal("0"))
+    txn = tp.enter_position("005930", Decimal("70000"), date(2024, 1, 3), Decimal("0"), Decimal("0"))
+    assert txn is None

docs/plans/2026-02-19-kjb-strategy-design.md

@@ -0,0 +1,122 @@
+# Kim Jong-bong (KJB) Strategy - Full System Design
+
+**Date**: 2026-02-19
+**Status**: Approved
+
+## Overview
+
+Implement the Kim Jong-bong short-term trading strategy from `quant.md` as a full system:
+backtesting, daily signal generation, portfolio management, and frontend dashboard.
+
+This strategy is fundamentally different from existing strategies (Quality, MultiFactor,
+ValueMomentum) which are periodic-rebalancing based. KJB is signal-based daily trading
+with individual position management, stop-losses, and trailing stops.
+
+## Trading Rules (from quant.md)
+
+### Universe
+- Market cap rank: 1-30 (large caps)
+- Daily trading value >= 200 billion KRW
+
+### Entry Signals (all must be true)
+- Relative Strength vs KOSPI > 100 (stock outperforming market over 10 trading days)
+- AND at least one of:
+  - Box breakout (close > 20-day high)
+  - Large bullish candle (daily return >= 5% AND volume >= 1.5x 20-day average)
+
+### Exit Rules
+- Stop-loss: -3% from entry price
+- Take-profit 1: +5% → sell 50% of position
+- Take-profit 2: +10% → sell remaining
+- Trailing stop: when +5%, move stop to entry price; when +10%, move stop to +5%
+
+### Portfolio Rules
+- Cash reserve: 30% (investable = total capital x 0.7)
+- Max positions: 10
+- Position size: equal weight (investable / max_positions)
+
+## Architecture
+
+### 1. Strategy Layer
+
+**`KJBStrategy(BaseStrategy)`** - `services/strategy/kjb.py`
+- Compatible with existing strategy pattern (returns `StrategyResult`)
+- Used for stock ranking by KJB criteria
+- Scores based on: relative strength, breakout proximity, volume trend
+
+**`KJBSignalGenerator`** - `services/strategy/kjb.py`
+- Separate class for daily signal generation
+- Input: stock ticker + price data
+- Output: buy/sell signals with entry, target, stop-loss prices
+- Used by: DailyBacktestEngine, daily scheduler job
+
+### 2. Daily Backtest Engine
+
+**`DailyBacktestEngine`** - `services/backtest/daily_engine.py`
+- New engine alongside existing `BacktestEngine`
+- Daily simulation loop (not rebalance-based)
+- Each day: check entry signals for universe, check exit conditions for positions
+- Uses `TradingPortfolio` for position management
+
+**`TradingPortfolio`** - `services/backtest/trading_portfolio.py`
+- Separate class from existing `VirtualPortfolio`
+- Supports: `enter_position()`, `exit_position()`, `partial_exit()`
+- Per-position tracking: stop_loss, target_price, trailing_stop
+- Cash reserve enforcement (30%)
+- Transaction recording with reasons
+
+### 3. Signal System
+
+**DB Model: `Signal`** - `models/signal.py`
+- Fields: date, ticker, signal_type (buy/sell/partial_sell), entry_price,
+  target_price, stop_loss_price, reason, status (active/executed/expired)
+
+**Scheduler Job: `kjb_daily_signal_job`** - `jobs/kjb_signal_job.py`
+- Runs daily at 18:00 KST (after price data collection)
+- Generates signals for all universe stocks
+- Saves to DB
+
+### 4. API Endpoints
+
+**Signal API** - `api/signal.py`
+- `GET /api/signal/kjb/today` - today's buy/sell signals
+- `GET /api/signal/kjb/history` - historical signals
+- `GET /api/signal/kjb/positions` - active positions with P&L
+
+**Strategy API additions:**
+- `POST /api/strategy/kjb` - run KJB ranking
+
+**Backtest additions:**
+- Existing backtest API works with strategy_type="kjb"
+
+### 5. Frontend
+
+- KJB signal dashboard page
+- Today's signals cards (buy/sell recommendations)
+- Active positions table (entry price, current price, P&L, stop-loss level)
+- Portfolio value chart
+
+## Files to Create
+
+| File | Purpose |
+|------|---------|
+| `services/strategy/kjb.py` | KJBStrategy + KJBSignalGenerator |
+| `services/backtest/daily_engine.py` | DailyBacktestEngine |
+| `services/backtest/trading_portfolio.py` | TradingPortfolio |
+| `models/signal.py` | Signal DB model |
+| `schemas/signal.py` | Signal Pydantic schemas |
+| `api/signal.py` | Signal API endpoints |
+| `jobs/kjb_signal_job.py` | Daily signal scheduler job |
+| Alembic migration | signals table |
+| Frontend pages | KJB dashboard |
+
+## Files to Modify
+
+| File | Change |
+|------|--------|
+| `services/backtest/engine.py` | Add "kjb" to `_create_strategy()` |
+| `models/__init__.py` | Register Signal model |
+| `api/__init__.py` | Register signal router |
+| `schemas/backtest.py` | Add "kjb" to strategy_type enum |
+| `jobs/scheduler.py` | Register KJB signal job |
+| `api/strategy.py` | Add KJB strategy endpoint |

frontend/src/app/signals/page.tsx

@@ -0,0 +1,360 @@
+'use client';
+
+import { useEffect, useState } from 'react';
+import { useRouter } from 'next/navigation';
+import { DashboardLayout } from '@/components/layout/dashboard-layout';
+import { Card, CardContent, CardHeader, CardTitle } from '@/components/ui/card';
+import { Badge } from '@/components/ui/badge';
+import { Skeleton } from '@/components/ui/skeleton';
+import { Button } from '@/components/ui/button';
+import { Input } from '@/components/ui/input';
+import { Label } from '@/components/ui/label';
+import { api } from '@/lib/api';
+import { Radio, History, RefreshCw, ArrowUpCircle, ArrowDownCircle, MinusCircle } from 'lucide-react';
+
+interface Signal {
+  id: number;
+  date: string;
+  ticker: string;
+  name: string | null;
+  signal_type: string;
+  entry_price: number | null;
+  target_price: number | null;
+  stop_loss_price: number | null;
+  reason: string | null;
+  status: string;
+  created_at: string;
+}
+
+const signalTypeConfig: Record<string, { label: string; style: string; icon: typeof ArrowUpCircle }> = {
+  buy: {
+    label: '매수',
+    style: 'bg-green-100 text-green-800 dark:bg-green-900 dark:text-green-200',
+    icon: ArrowUpCircle,
+  },
+  sell: {
+    label: '매도',
+    style: 'bg-red-100 text-red-800 dark:bg-red-900 dark:text-red-200',
+    icon: ArrowDownCircle,
+  },
+  partial_sell: {
+    label: '부분매도',
+    style: 'bg-orange-100 text-orange-800 dark:bg-orange-900 dark:text-orange-200',
+    icon: MinusCircle,
+  },
+};
+
+const statusConfig: Record<string, { label: string; style: string }> = {
+  active: {
+    label: '활성',
+    style: 'bg-blue-100 text-blue-800 dark:bg-blue-900 dark:text-blue-200',
+  },
+  executed: {
+    label: '실행됨',
+    style: 'bg-green-100 text-green-800 dark:bg-green-900 dark:text-green-200',
+  },
+  expired: {
+    label: '만료',
+    style: 'bg-gray-100 text-gray-800 dark:bg-gray-900 dark:text-gray-200',
+  },
+};
+
+const formatPrice = (value: number | null | undefined) => {
+  if (value === null || value === undefined) return '-';
+  return new Intl.NumberFormat('ko-KR').format(value);
+};
+
+export default function SignalsPage() {
+  const router = useRouter();
+  const [loading, setLoading] = useState(true);
+  const [todaySignals, setTodaySignals] = useState<Signal[]>([]);
+  const [historySignals, setHistorySignals] = useState<Signal[]>([]);
+  const [showHistory, setShowHistory] = useState(false);
+  const [refreshing, setRefreshing] = useState(false);
+
+  // History filter state
+  const [filterTicker, setFilterTicker] = useState('');
+  const [filterStartDate, setFilterStartDate] = useState('');
+  const [filterEndDate, setFilterEndDate] = useState('');
+
+  useEffect(() => {
+    const init = async () => {
+      try {
+        await api.getCurrentUser();
+        await fetchTodaySignals();
+      } catch {
+        router.push('/login');
+      } finally {
+        setLoading(false);
+      }
+    };
+    init();
+  }, [router]);
+
+  const fetchTodaySignals = async () => {
+    try {
+      const data = await api.get<Signal[]>('/api/signal/kjb/today');
+      setTodaySignals(data);
+    } catch (err) {
+      console.error('Failed to fetch today signals:', err);
+    }
+  };
+
+  const fetchHistorySignals = async () => {
+    try {
+      const params = new URLSearchParams();
+      if (filterStartDate) params.set('start_date', filterStartDate);
+      if (filterEndDate) params.set('end_date', filterEndDate);
+      if (filterTicker) params.set('ticker', filterTicker);
+      const query = params.toString();
+      const url = `/api/signal/kjb/history${query ? `?${query}` : ''}`;
+      const data = await api.get<Signal[]>(url);
+      setHistorySignals(data);
+    } catch (err) {
+      console.error('Failed to fetch signal history:', err);
+    }
+  };
+
+  const handleRefresh = async () => {
+    setRefreshing(true);
+    try {
+      if (showHistory) {
+        await fetchHistorySignals();
+      } else {
+        await fetchTodaySignals();
+      }
+    } finally {
+      setRefreshing(false);
+    }
+  };
+
+  const handleShowHistory = async () => {
+    if (!showHistory && historySignals.length === 0) {
+      await fetchHistorySignals();
+    }
+    setShowHistory(!showHistory);
+  };
+
+  const handleFilterSubmit = async (e: React.FormEvent) => {
+    e.preventDefault();
+    await fetchHistorySignals();
+  };
+
+  const renderSignalTable = (signals: Signal[]) => (
+    <div className="overflow-x-auto">
+      <table className="w-full">
+        <thead className="bg-muted">
+          <tr>
+            <th scope="col" className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">날짜</th>
+            <th scope="col" className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">종목코드</th>
+            <th scope="col" className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">종목명</th>
+            <th scope="col" className="px-4 py-3 text-center text-sm font-medium text-muted-foreground">신호</th>
+            <th scope="col" className="px-4 py-3 text-right text-sm font-medium text-muted-foreground">진입가</th>
+            <th scope="col" className="px-4 py-3 text-right text-sm font-medium text-muted-foreground">목표가</th>
+            <th scope="col" className="px-4 py-3 text-right text-sm font-medium text-muted-foreground">손절가</th>
+            <th scope="col" className="px-4 py-3 text-left text-sm font-medium text-muted-foreground">사유</th>
+            <th scope="col" className="px-4 py-3 text-center text-sm font-medium text-muted-foreground">상태</th>
+          </tr>
+        </thead>
+        <tbody className="divide-y divide-border">
+          {signals.map((signal) => {
+            const typeConf = signalTypeConfig[signal.signal_type] || {
+              label: signal.signal_type,
+              style: 'bg-muted',
+              icon: MinusCircle,
+            };
+            const statConf = statusConfig[signal.status] || {
+              label: signal.status,
+              style: 'bg-muted',
+            };
+            const TypeIcon = typeConf.icon;
+
+            return (
+              <tr key={signal.id} className="hover:bg-muted/50">
+                <td className="px-4 py-3 text-sm">{signal.date}</td>
+                <td className="px-4 py-3 text-sm font-mono">{signal.ticker}</td>
+                <td className="px-4 py-3 text-sm">{signal.name || '-'}</td>
+                <td className="px-4 py-3 text-center">
+                  <Badge className={typeConf.style}>
+                    <TypeIcon className="h-3 w-3 mr-1" />
+                    {typeConf.label}
+                  </Badge>
+                </td>
+                <td className="px-4 py-3 text-sm text-right font-mono">{formatPrice(signal.entry_price)}</td>
+                <td className="px-4 py-3 text-sm text-right font-mono text-green-600">{formatPrice(signal.target_price)}</td>
+                <td className="px-4 py-3 text-sm text-right font-mono text-red-600">{formatPrice(signal.stop_loss_price)}</td>
+                <td className="px-4 py-3 text-sm max-w-xs truncate" title={signal.reason || ''}>
+                  {signal.reason || '-'}
+                </td>
+                <td className="px-4 py-3 text-center">
+                  <Badge className={statConf.style}>{statConf.label}</Badge>
+                </td>
+              </tr>
+            );
+          })}
+          {signals.length === 0 && (
+            <tr>
+              <td colSpan={9} className="px-4 py-8 text-center text-muted-foreground">
+                신호가 없습니다.
+              </td>
+            </tr>
+          )}
+        </tbody>
+      </table>
+    </div>
+  );
+
+  if (loading) {
+    return (
+      <DashboardLayout>
+        <div className="space-y-6">
+          <Skeleton className="h-8 w-48" />
+          <Skeleton className="h-[400px]" />
+        </div>
+      </DashboardLayout>
+    );
+  }
+
+  return (
+    <DashboardLayout>
+      <div className="mb-6 flex items-center justify-between">
+        <div>
+          <h1 className="text-2xl font-bold text-foreground">KJB 매매 신호</h1>
+          <p className="mt-1 text-muted-foreground">
+            KJB 전략 기반 매매 신호를 확인하세요
+          </p>
+        </div>
+        <div className="flex items-center gap-2">
+          <Button
+            variant="outline"
+            size="icon"
+            onClick={handleRefresh}
+            disabled={refreshing}
+          >
+            <RefreshCw className={`h-4 w-4 ${refreshing ? 'animate-spin' : ''}`} />
+          </Button>
+          <Button
+            variant="outline"
+            onClick={handleShowHistory}
+          >
+            {showHistory ? (
+              <>
+                <Radio className="mr-2 h-4 w-4" />
+                오늘의 신호
+              </>
+            ) : (
+              <>
+                <History className="mr-2 h-4 w-4" />
+                신호 이력
+              </>
+            )}
+          </Button>
+        </div>
+      </div>
+
+      {/* Summary Cards */}
+      {!showHistory && (
+        <div className="grid grid-cols-1 md:grid-cols-3 gap-4 mb-6">
+          <Card>
+            <CardContent className="p-4">
+              <div className="flex items-center gap-2 text-muted-foreground mb-1">
+                <ArrowUpCircle className="h-4 w-4 text-green-600" />
+                <span className="text-xs font-medium">매수 신호</span>
+              </div>
+              <p className="text-2xl font-bold">
+                {todaySignals.filter((s) => s.signal_type === 'buy').length}
+              </p>
+            </CardContent>
+          </Card>
+          <Card>
+            <CardContent className="p-4">
+              <div className="flex items-center gap-2 text-muted-foreground mb-1">
+                <ArrowDownCircle className="h-4 w-4 text-red-600" />
+                <span className="text-xs font-medium">매도 신호</span>
+              </div>
+              <p className="text-2xl font-bold">
+                {todaySignals.filter((s) => s.signal_type === 'sell').length}
+              </p>
+            </CardContent>
+          </Card>
+          <Card>
+            <CardContent className="p-4">
+              <div className="flex items-center gap-2 text-muted-foreground mb-1">
+                <MinusCircle className="h-4 w-4 text-orange-600" />
+                <span className="text-xs font-medium">부분매도 신호</span>
+              </div>
+              <p className="text-2xl font-bold">
+                {todaySignals.filter((s) => s.signal_type === 'partial_sell').length}
+              </p>
+            </CardContent>
+          </Card>
+        </div>
+      )}
+
+      {showHistory ? (
+        <div className="space-y-4">
+          {/* History Filters */}
+          <Card>
+            <CardContent className="p-4">
+              <form onSubmit={handleFilterSubmit} className="flex flex-wrap items-end gap-4">
+                <div className="space-y-2">
+                  <Label htmlFor="filter-start-date">시작일</Label>
+                  <Input
+                    id="filter-start-date"
+                    type="date"
+                    value={filterStartDate}
+                    onChange={(e) => setFilterStartDate(e.target.value)}
+                    className="w-40"
+                  />
+                </div>
+                <div className="space-y-2">
+                  <Label htmlFor="filter-end-date">종료일</Label>
+                  <Input
+                    id="filter-end-date"
+                    type="date"
+                    value={filterEndDate}
+                    onChange={(e) => setFilterEndDate(e.target.value)}
+                    className="w-40"
+                  />
+                </div>
+                <div className="space-y-2">
+                  <Label htmlFor="filter-ticker">종목코드</Label>
+                  <Input
+                    id="filter-ticker"
+                    type="text"
+                    placeholder="예: 005930"
+                    value={filterTicker}
+                    onChange={(e) => setFilterTicker(e.target.value)}
+                    className="w-36"
+                  />
+                </div>
+                <Button type="submit" variant="outline">
+                  조회
+                </Button>
+              </form>
+            </CardContent>
+          </Card>
+
+          {/* History Table */}
+          <Card>
+            <CardHeader>
+              <CardTitle>신호 이력</CardTitle>
+            </CardHeader>
+            <CardContent className="p-0">
+              {renderSignalTable(historySignals)}
+            </CardContent>
+          </Card>
+        </div>
+      ) : (
+        <Card>
+          <CardHeader>
+            <CardTitle>오늘의 매매 신호</CardTitle>
+          </CardHeader>
+          <CardContent className="p-0">
+            {renderSignalTable(todaySignals)}
+          </CardContent>
+        </Card>
+      )}
+    </DashboardLayout>
+  );
+}

frontend/src/components/layout/new-sidebar.tsx

@@ -10,6 +10,7 @@ import {
   FlaskConical,
   Database,
   Search,
+  Radio,
   ChevronLeft,
   ChevronRight,
 } from 'lucide-react';
@@ -28,6 +29,7 @@ const navItems = [
   { href: '/portfolio', label: '포트폴리오', icon: Briefcase },
   { href: '/strategy', label: '전략', icon: TrendingUp },
   { href: '/backtest', label: '백테스트', icon: FlaskConical },
+  { href: '/signals', label: '매매 신호', icon: Radio },
   { href: '/admin/data', label: '데이터 수집', icon: Database },
   { href: '/admin/data/explorer', label: '데이터 탐색', icon: Search },
 ];

quant.md

@@ -0,0 +1,439 @@
+# 김종봉 주식 전략 퀀트 구현 상세 가이드
+
+## 📌 전략 개요
+
+### 출처
+- **영상 1**: "이 방법만 알고 있으면 종잣돈 천만원으로 금방 1억 법니다" (GZw3wTgMTwE, 2023-08-09)
+- **영상 2**: "이 방법만 알고 있다면 종잣돈 천만원으로 금방 1억 법니다" (Ps9tzVhHpu0, 2024-01-30)
+- **채널**: 월급쟁이부자들TV
+- **전략가**: 김종봉
+
+### 핵심 철학
+- **목표 수익률**: 월 10% (연 약 120%)
+- **자금 증대 목표**: 1,000만원 → 1억원 (1년 내)
+- **투자 기간**: 단기~중기 (보유 기간 수일~수주)
+- **핵심 원칙**: "시장(코스피)을 이기는 종목"에만 투자
+
+---
+
+## 🎯 구체적 매매 규칙
+
+### 1. 초기 자금 및 포트폴리오 구성
+
+| 항목 | 기준 |
+|------|------|
+| **시드 머니** | 1,000만원 (초보자는 100만원 연습 추천) |
+| **분산 종목 수** | 5~10개 |
+| **종목당 투자금** | 100만원 (1,000만원 기준) 또는 5~10만원 (100만원 기준) |
+| **현금 비중** | 약 30% (하락장 대비) |
+| **리밸런싱 주기** | 3개월 (분기별 성과 검증) |
+
+### 2. 종목 선정 기준 (필수 필터)
+
+#### A. 유동성 필터
+```
+✅ 일 거래대금 >= 2,000억원 (2조 아님, 2천억)
+```
+
+**대상 종목 예시**:
+- 삼성전자
+- SK하이닉스
+- LG전자
+- LG이노텍
+- 네이버
+- 카카오
+- LG에너지솔루션
+
+#### B. 시가총액 필터
+```
+✅ 시가총액 순위: 1~30위 (대형주 중심)
+```
+
+#### C. 상대강도 필터 (핵심)
+```
+✅ 종목 수익률 > 코스피 수익률 (최근 2주~1개월)
+✅ 특히 삼성전자 > 코스피 일 때 시장 강세 신호
+```
+
+**구현 방법**:
+```python
+# 상대강도 지수 (Relative Strength Index vs Market)
+RS = (종목 수익률 / 코스피 수익률) * 100
+# RS > 100이면 시장 대비 강함
+```
+
+#### D. 차트 패턴 필터
+```
+✅ 장대양봉 출현 (전일 대비 5% 이상 상승 + 거래량 폭증)
+✅ 박스권 돌파 (직전 고점 갱신)
+✅ 상승 추세 중 조정 후 지지선 터치
+```
+
+**장대양봉 정의**:
+- 종가 >= 시가 + (고가 - 저가) × 0.7
+- 거래대금 >= 직전 20일 평균 × 1.5
+
+### 3. 진입 규칙
+
+#### 시나리오 작성 (필수)
+매매 전 반드시 기록:
+```
+1. 진입가: [예시] 삼성전자 72,000원
+2. 목표가: [예시] 75,600원 (+5%)
+3. 손절가: [예시] 69,840원 (-3%)
+4. 근거: 코스피 지지선 반등 + 삼성전자 박스권 상단 돌파
+```
+
+#### 진입 타이밍
+| 조건 | 설명 |
+|------|------|
+| **조건 1** | 코스피 3선(지지·저항·추세) 차트에서 지지선 근처 |
+| **조건 2** | 박스권 상단 돌파 후 재진입 (돌파 확인 1~2일 후) |
+| **조건 3** | 장대양봉 발생 다음날 시초가 근처 |
+| **진입 방식** | 지정가 주문 (고점 근처에서 대기) |
+
+### 4. 청산 규칙
+
+#### A. 익절 (Profit Taking)
+```
+✅ 1차 익절: +5% 도달 시 50% 물량 청산
+✅ 2차 익절: +10% 도달 시 나머지 50% 청산
+✅ 목표: 주당 5% 수익 (주 1~2회 매매)
+```
+
+#### B. 손절 (Stop Loss)
+```
+❌ 손절 기준: -3% ~ -10%
+❌ 원칙: 시나리오 이탈 시 즉시 손절
+❌ 예시: 진입가 72,000원 → 손절가 69,840원 (-3%)
+```
+
+#### C. 트레일링 스톱
+```
+- 5% 이상 수익 발생 시 손절선을 진입가(본전)로 상향
+- 10% 이상 수익 시 손절선을 +5% 지점으로 상향
+```
+
+### 5. 리스크 관리
+
+| 항목 | 기준 |
+|------|------|
+| **종목당 최대 손실** | -3% (초기 자본의 0.3%) |
+| **포트폴리오 최대 손실** | -10% (초기 자본 기준) |
+| **승률 목표** | 60% 이상 |
+| **손익비** | 1:1.5 이상 (손실 3% vs 수익 5%) |
+| **월간 목표 수익률** | 10% |
+| **연간 목표 수익률** | 120% (복리 고려 시 약 113%) |
+
+---
+
+## 🔧 퀀트 구현 상세
+
+### 필요 데이터
+
+#### 1. 시장 데이터
+- **코스피 지수**: 일봉 OHLCV (최소 1년 이상)
+- **삼성전자**: 일봉 OHLCV (선행지표로 활용)
+
+#### 2. 종목 데이터
+```python
+필수 컬럼:
+- 날짜 (Date)
+- 시가 (Open)
+- 고가 (High)
+- 저가 (Low)
+- 종가 (Close)
+- 거래량 (Volume)
+- 거래대금 (Value = Close × Volume)
+- 시가총액 (Market Cap)
+```
+
+#### 3. 데이터 소스
+- **한국거래소 API** (공식, 실시간)
+- **FinanceDataReader** (무료, Python 라이브러리)
+- **증권사 API** (키움, 이베스트 등, 실전 매매 시)
+- **야후 파이낸스** (yfinance, 해외 주식 포함)
+
+### 구현 단계
+
+#### Phase 1: 데이터 수집 및 전처리
+```python
+import pandas as pd
+import FinanceDataReader as fdr
+
+# 1. 코스피 지수
+kospi = fdr.DataReader('KS11', '2020-01-01')
+
+# 2. 시가총액 상위 30개 종목
+krx = fdr.StockListing('KRX')
+top30 = krx.nlargest(30, 'Marcap')
+
+# 3. 개별 종목 데이터
+stocks = {}
+for code in top30['Code']:
+    stocks[code] = fdr.DataReader(code, '2020-01-01')
+```
+
+#### Phase 2: 필터링 로직
+```python
+def filter_liquidity(df, threshold=200_000_000_000):
+    """거래대금 2000억 이상 필터"""
+    df['Value'] = df['Close'] * df['Volume']
+    return df[df['Value'] >= threshold]
+
+def relative_strength(stock_return, market_return):
+    """상대강도 계산"""
+    return (stock_return / market_return) * 100
+
+def detect_breakout(df, lookback=20):
+    """박스권 돌파 감지"""
+    df['Highest'] = df['High'].rolling(lookback).max()
+    df['Breakout'] = df['Close'] > df['Highest'].shift(1)
+    return df
+
+def detect_large_candle(df, threshold=0.05):
+    """장대양봉 감지 (5% 이상)"""
+    df['Daily_Return'] = df['Close'].pct_change()
+    df['Volume_Ratio'] = df['Volume'] / df['Volume'].rolling(20).mean()
+    df['Large_Candle'] = (df['Daily_Return'] >= threshold) & (df['Volume_Ratio'] >= 1.5)
+    return df
+```
+
+#### Phase 3: 신호 생성
+```python
+def generate_signals(stock_df, kospi_df):
+    """매수/매도 신호 생성"""
+    signals = pd.DataFrame(index=stock_df.index)
+    
+    # 상대강도
+    stock_ret = stock_df['Close'].pct_change(10)  # 2주
+    market_ret = kospi_df['Close'].pct_change(10)
+    signals['RS'] = (stock_ret / market_ret) * 100
+    
+    # 매수 신호
+    signals['Buy'] = (
+        (signals['RS'] > 100) &  # 시장 대비 강함
+        (stock_df['Breakout']) &  # 박스권 돌파
+        (stock_df['Large_Candle'])  # 장대양봉
+    )
+    
+    return signals
+```
+
+#### Phase 4: 백테스팅 엔진
+```python
+class KimJongBongStrategy:
+    def __init__(self, initial_capital=10_000_000):
+        self.capital = initial_capital
+        self.cash = initial_capital * 0.7  # 30% 현금 보유
+        self.positions = {}
+        self.max_stocks = 10
+        self.position_size = initial_capital / self.max_stocks
+        
+    def enter_position(self, code, price, date):
+        """진입"""
+        shares = int(self.position_size / price)
+        cost = shares * price
+        if cost <= self.cash:
+            self.positions[code] = {
+                'shares': shares,
+                'entry_price': price,
+                'entry_date': date,
+                'stop_loss': price * 0.97,  # -3%
+                'target': price * 1.05  # +5%
+            }
+            self.cash -= cost
+            return True
+        return False
+    
+    def exit_position(self, code, price, date, reason):
+        """청산"""
+        if code in self.positions:
+            pos = self.positions[code]
+            proceeds = pos['shares'] * price
+            self.cash += proceeds
+            profit = (price / pos['entry_price'] - 1) * 100
+            del self.positions[code]
+            return profit
+        return 0
+    
+    def check_exits(self, date, prices):
+        """익절/손절 체크"""
+        for code in list(self.positions.keys()):
+            pos = self.positions[code]
+            current_price = prices[code]
+            
+            # 손절
+            if current_price <= pos['stop_loss']:
+                self.exit_position(code, current_price, date, 'stop_loss')
+            
+            # 익절
+            elif current_price >= pos['target']:
+                self.exit_position(code, current_price, date, 'take_profit')
+```
+
+#### Phase 5: 성과 평가
+```python
+def calculate_metrics(returns):
+    """성과 지표"""
+    total_return = (returns + 1).prod() - 1
+    sharpe = returns.mean() / returns.std() * (252 ** 0.5)
+    max_dd = (returns.cumsum() - returns.cumsum().cummax()).min()
+    
+    return {
+        'Total Return': f"{total_return:.2%}",
+        'Sharpe Ratio': f"{sharpe:.2f}",
+        'Max Drawdown': f"{max_dd:.2%}",
+        'Win Rate': f"{(returns > 0).sum() / len(returns):.2%}"
+    }
+```
+
+---
+
+## 📊 백테스팅 파라미터
+
+### 권장 설정
+| 파라미터 | 값 | 설명 |
+|----------|-----|------|
+| **백테스팅 기간** | 2020-01-01 ~ 2024-12-31 | 최소 3년 이상 |
+| **초기 자본** | 10,000,000원 | 영상 기준 |
+| **리밸런싱** | 분기별 (3개월) | 성과 검증 및 종목 교체 |
+| **수수료** | 0.015% (매수) + 0.25% (매도, 세금 포함) | 실전 반영 |
+| **슬리피지** | 0.1% | 체결가 불리함 |
+| **벤치마크** | 코스피 지수 (KS11) | 비교 대상 |
+
+### 최적화 대상
+1. **상대강도 기간**: 10일 (2주) vs 20일 (1개월) vs 60일 (3개월)
+2. **손절 비율**: -3% vs -5% vs -10%
+3. **익절 비율**: +5% vs +7% vs +10%
+4. **종목 수**: 5개 vs 10개 vs 15개
+5. **현금 비중**: 20% vs 30% vs 50%
+
+---
+
+## 🚀 실전 적용 로드맵
+
+### Step 1: 백테스팅 (1~2주)
+- 과거 데이터로 전략 검증
+- 연평균 수익률, 최대 낙폭, 승률 확인
+- 벤치마크 대비 초과 수익 여부 평가
+
+### Step 2: 페이퍼 트레이딩 (1개월)
+- 실시간 데이터로 모의 투자
+- 신호 발생 빈도 및 정확도 체크
+- 감정 개입 없이 기계적 실행 연습
+
+### Step 3: 소액 실전 (3개월)
+- 100만원으로 시작
+- 5~10개 종목, 종목당 10~20만원
+- 매매 일지 기록 (진입/청산 근거, 수익률)
+
+### Step 4: 본격 운용 (6개월 이후)
+- 1,000만원 투입
+- 분기별 성과 리뷰
+- 전략 개선 (필터 추가, 파라미터 최적화)
+
+---
+
+## ⚠️ 주의사항
+
+### 전략의 한계
+1. **백테스팅 없음**: 영상은 실제 통계 검증 없이 경험 기반
+2. **생존 편향**: 성공 사례 중심, 실패 사례 미공개
+3. **시장 환경 의존**: 상승장에서 유리, 하락장에서 취약
+4. **거래 비용**: 잦은 매매 시 수수료+세금 누적
+
+### 리스크 관리 필수
+- **과최적화 주의**: 과거 데이터에만 맞춘 전략은 미래 실패
+- **레버리지 금지**: 신용/미수 사용 시 손실 확대
+- **분산 투자**: 한 종목에 30% 이상 투자 금지
+- **감정 통제**: 손절/익절 원칙 엄수
+
+---
+
+## 📦 필요 기술 스택
+
+### Python 라이브러리
+```bash
+pip install pandas numpy yfinance FinanceDataReader
+pip install backtrader vectorbt ta-lib
+pip install matplotlib seaborn plotly
+```
+
+### 개발 환경
+- **로컬**: Jupyter Notebook / VS Code
+- **클라우드**: Google Colab (무료) / AWS SageMaker
+- **데이터베이스**: SQLite (소규모) / PostgreSQL (대규모)
+
+### 자동매매 연동 (선택)
+- **증권사 API**: 키움증권 Open API, 이베스트투자증권 xingAPI
+- **프레임워크**: PyQt5 (키움), gRPC (이베스트)
+
+---
+
+## 📚 참고 자료
+
+### 추가 학습
+1. **책**: "퀀트 투자 무작정 따라하기", "파이썬을 이용한 금융 데이터 분석"
+2. **강의**: 패스트캠퍼스 퀀트 트레이딩, 인프런 주식 자동매매
+3. **커뮤니티**: 네이버 카페 "시스템트레이딩", GitHub quant 저장소
+
+### 관련 전략
+- **모멘텀 전략**: 과거 수익률 상위 종목 매수
+- **브레이크아웃**: 52주 신고가 돌파
+- **평균회귀**: 과매도 구간 매수
+
+---
+
+## ✅ 체크리스트
+
+### 구현 전 확인사항
+- [ ] 데이터 소스 확보 (최소 3년 일봉)
+- [ ] 백테스팅 환경 구축 (Python + 라이브러리)
+- [ ] 초기 자본 및 목표 수익률 설정
+- [ ] 리스크 허용 범위 결정 (최대 손실 -10%?)
+- [ ] 매매 일지 양식 작성
+
+### 실전 전 확인사항
+- [ ] 백테스팅 결과 만족 (연 10% 이상, 샤프 1 이상)
+- [ ] 페이퍼 트레이딩 1개월 이상
+- [ ] 손절 원칙 테스트 (감정 개입 없이 실행 가능?)
+- [ ] 증권사 계좌 개설 (API 사용 가능 여부 확인)
+- [ ] 세금 및 수수료 계산 완료
+
+---
+
+## 🎯 최종 목표
+
+| 기간 | 시드 | 목표 | 월 수익률 | 비고 |
+|------|------|------|-----------|------|
+| **0개월** | 1,000만원 | - | - | 시작 |
+| **3개월** | 1,000만원 | 1,300만원 | 10% | 1분기 |
+| **6개월** | 1,300만원 | 1,700만원 | 10% | 2분기 |
+| **9개월** | 1,700만원 | 2,200만원 | 10% | 3분기 |
+| **12개월** | 2,200만원 | **1억원** | 10% | 목표 달성 |
+
+**복리 효과 시뮬레이션**:
+```
+월 10% × 12개월 = 약 213% (단리)
+월 10% 복리 = (1.1)^12 - 1 = 약 213.8%
+1,000만원 × 3.138 = 3,138만원 (현실적 목표)
+```
+
+---
+
+## 💬 추가 문의사항
+
+구현 과정에서 필요한 사항을 알려주시면:
+1. **전체 Python 코드** (백테스팅 시스템)
+2. **단계별 모듈** (데이터 수집 → 신호 생성 → 백테스팅)
+3. **Jupyter Notebook** (인터랙티브 실습)
+4. **자동매매 연동** (증권사 API 통합)
+
+맞춤형으로 제공해 드리겠습니다.
+
+---
+
+**작성일**: 2026-02-19  
+**버전**: 1.0  
+**출처**: 김종봉 전략 유튜브 영상 (월급쟁이부자들TV)