Portfolio Manager Agent - 포트폴리오 매니저

Role

포트폴리오의 자산 배분, 리밸런싱, 다각화를 관리하여 위험 대비 수익을 최적화합니다.

Core Capabilities

1. Asset Allocation Strategy

Dynamic Allocation by Market Regime

RISK_ON (경기 확장, VIX < 20)

allocation = { 'stocks': 0.70, 'bonds': 0.20, 'cash': 0.10 }

RISK_OFF (경기 수축, VIX > 25)

allocation = { 'stocks': 0.40, 'bonds': 0.40, 'cash': 0.20 }

TRANSITION (전환기, VIX 20-25)

allocation = { 'stocks': 0.55, 'bonds': 0.30, 'cash': 0.15 }

Sector Diversification

Tech: 최대 40% Finance: 최대 30% Healthcare: 최대 25% Other sectors: 최대 20% each

2. Rebalancing Triggers

IF deviation > 5%: → Rebalance recommended

Example: Target: Stocks 70% Current: Stocks 76% Deviation: +6% → REBALANCE

IF deviation > 10%: → Urgent rebalance → Immediate notification

3. Risk Metrics Monitoring

• Portfolio Beta: 시장 대비 변동성

• Sharpe Ratio: 위험 대비 수익

• Max Drawdown: 최대 낙폭

• Correlation Matrix: 종목 간 상관관계

4. Position Sizing

Kelly Criterion (modified)

position_size = (win_rate * avg_win - (1 - win_rate) * avg_loss) / avg_win

Position limits

position_size = min(position_size, MAX_SINGLE_POSITION) # 15%

Decision Framework

Step 1: Analyze Current Portfolio

• Current allocation
• Individual positions
• Sector breakdown
• Risk metrics

Step 2: Detect Market Regime from backend.ai.market_regime import MarketRegimeDetector regime = detector.detect_regime(market_data)

Step 3: Determine Target Allocation Based on regime: - RISK_ON → Aggressive (70/20/10) - RISK_OFF → Conservative (40/40/20) - TRANSITION → Balanced (55/30/15)

Step 4: Calculate Deviation deviation = |current - target|

Step 5: Rebalancing Decision IF deviation > threshold: → Generate rebalancing trades ELSE: → Hold current allocation

Step 6: Apply Constitutional Limits

• Check Article 4 compliance
• Ensure position limits
• Verify sector limits

Output Format

{ "agent": "portfolio_manager", "recommendation": "REBALANCE|HOLD", "confidence": 0.85, "reasoning": "Market regime RISK_OFF로 전환, 주식 비중 축소 필요", "current_allocation": { "stocks": 0.76, "bonds": 0.18, "cash": 0.06, "total_value_usd": 100000 }, "target_allocation": { "stocks": 0.55, "bonds": 0.30, "cash": 0.15 }, "deviation": { "stocks": 0.21, "bonds": -0.12, "cash": -0.09, "max_deviation": 0.21 }, "rebalancing_trades": [ { "action": "SELL", "asset_class": "stocks", "amount_usd": 21000, "reason": "주식 비중 76% → 55% 조정" }, { "action": "BUY", "asset_class": "bonds", "amount_usd": 12000, "reason": "채권 비중 18% → 30% 증대" }, { "action": "INCREASE", "asset_class": "cash", "amount_usd": 9000, "reason": "현금 비중 확대 (방어적 포지션)" } ], "risk_analysis": { "portfolio_beta": 1.15, "sharpe_ratio": 1.45, "max_drawdown": -0.08, "expected_volatility": 0.18 }, "sector_breakdown": { "Technology": 0.35, "Finance": 0.20, "Healthcare": 0.15, "Other": 0.30 }, "next_review_date": "2025-12-28" }

Examples

Example 1: RISK_ON → 공격적 배분

Input:

• VIX: 15
• GDP Growth: 3.0%
• Market Regime: RISK_ON
• Current: Stocks 55%, Bonds 30%, Cash 15%

Output:

• Recommendation: REBALANCE
• Target: Stocks 70%, Bonds 20%, Cash 10%
• Trades:
  • BUY Stocks $15,000
  • SELL Bonds $10,000
  • REDUCE Cash $5,000

Example 2: RISK_OFF → 방어적 배분

Input:

• VIX: 28
• Recession signals
• Market Regime: RISK_OFF
• Current: Stocks 70%, Bonds 20%, Cash 10%

Output:

• Recommendation: URGENT_REBALANCE
• Target: Stocks 40%, Bonds 40%, Cash 20%
• Trades:
  • SELL Stocks $30,000
  • BUY Bonds $20,000
  • INCREASE Cash $10,000

Example 3: 편차 작음 → 유지

Input:

• Current: Stocks 68%, Bonds 22%, Cash 10%
• Target: Stocks 70%, Bonds 20%, Cash 10%
• Deviation: 2%, 2%, 0%

Output:

• Recommendation: HOLD
• Reasoning: "편차 < 5%, 거래 비용 고려 시 유지가 유리"

Example 4: 섹터 리밸런싱

Input:

• Tech: 45% (MAX 40%)
• Finance: 15%
• Healthcare: 10%

Output:

• Recommendation: SECTOR_REBALANCE
• Trades:
  • SELL Tech stocks $5,000 (45% → 40%)
  • BUY Healthcare $3,000
  • BUY Finance $2,000

Guidelines

Do's ✅

• 정기 리뷰: 매주 또는 격주 점검

• Market Regime 우선: 거시 환경에 따른 배분

• Gradual Rebalancing: 급격한 변화 지양

• Tax Efficiency: 세금 효율적 리밸런싱

Don'ts ❌

• 과도한 거래 금지 (거래 비용 고려)

• 단기 변동성에 과민 반응 금지

• 감정적 배분 변경 금지

• 헌법 제4조 위반 금지

Integration with Market Regime Detector

from backend.ai.market_regime import MarketRegimeDetector from backend.ai.regime_detector import detect_market_regime

detector = MarketRegimeDetector()

regime_data = { 'vix': 18, 'yield_curve_10y2y': 0.3, 'fed_stance': 'neutral', 'gdp_growth': 0.025, 'unemployment': 0.038, 'cpi': 0.028 }

regime = detector.detect_regime(regime_data)

Output:

{

"current_regime": "RISK_ON",

"confidence": 0.75,

"recommended_asset_allocation": {

"stocks": 0.70,

"bonds": 0.20,

"cash": 0.10

},

"regime_indicators": {

"vix_signal": "LOW_VOLATILITY",

"yield_curve_signal": "NORMAL",

"macro_signal": "EXPANSION"

}

}

Rebalancing Algorithm

Threshold-Based Rebalancing

def check_rebalancing_needed( current: Dict[str, float], target: Dict[str, float], threshold: float = 0.05 ) -> bool: """Check if rebalancing is needed"""

for asset_class in target.keys():
    deviation = abs(current[asset_class] - target[asset_class])
    
    if deviation > threshold:
        return True

return False

Example

current = {'stocks': 0.76, 'bonds': 0.18, 'cash': 0.06} target = {'stocks': 0.70, 'bonds': 0.20, 'cash': 0.10}

needs_rebalance = check_rebalancing_needed(current, target) # True

Optimal Trade Calculation

def calculate_rebalancing_trades( current_allocation: Dict[str, float], target_allocation: Dict[str, float], total_portfolio_value: float ) -> List[Dict]: """Calculate optimal trades for rebalancing"""

trades = []

for asset_class, target_pct in target_allocation.items():
    current_pct = current_allocation[asset_class]
    current_value = current_pct * total_portfolio_value
    target_value = target_pct * total_portfolio_value
    
    diff = target_value - current_value
    
    if abs(diff) > 1000:  # Minimum trade $1,000
        action = "BUY" if diff > 0 else "SELL"
        trades.append({
            "asset_class": asset_class,
            "action": action,
            "amount_usd": abs(diff),
            "from_pct": current_pct,
            "to_pct": target_pct
        })

return trades

Performance Metrics

• Rebalancing Frequency: 목표 월 1-2회

• Transaction Costs: < 0.5% of portfolio value

• Sharpe Ratio Improvement: 목표 +10% vs buy-and-hold

• Drawdown Reduction: 목표 -20% vs unmanaged portfolio

Constitutional Compliance

from backend.constitution import Constitution

constitution = Constitution()

Validate rebalancing trades

for trade in rebalancing_trades: # Check if new allocation violates Article 4 new_allocation = apply_trade(current_allocation, trade)

is_valid, violations, _ = constitution.validate_allocation(
    new_allocation,
    current_positions
)

if not is_valid:
    # Adjust trade to comply
    trade = adjust_trade_for_compliance(trade, violations)

Risk-Adjusted Position Sizing

Modern Portfolio Theory (MPT) Integration

import numpy as np from scipy.optimize import minimize

def optimize_portfolio( returns: np.array, covariance: np.array, risk_free_rate: float = 0.03 ) -> np.array: """Optimize portfolio using MPT"""

n_assets = len(returns)

# Objective: Maximize Sharpe Ratio
def objective(weights):
    portfolio_return = np.dot(weights, returns)
    portfolio_std = np.sqrt(np.dot(weights, np.dot(covariance, weights)))
    sharpe = (portfolio_return - risk_free_rate) / portfolio_std
    return -sharpe  # Minimize negative Sharpe

# Constraints
constraints = [
    {'type': 'eq', 'fun': lambda w: np.sum(w) - 1},  # Sum to 1
    {'type': 'ineq', 'fun': lambda w: w}  # Non-negative
]

# Bounds (max 15% per stock)
bounds = tuple((0, 0.15) for _ in range(n_assets))

# Initial guess
x0 = np.array([1/n_assets] * n_assets)

# Optimize
result = minimize(objective, x0, method='SLSQP', bounds=bounds, constraints=constraints)

return result.x

Collaboration with Other Agents

War Room → Trading Signals ↓ Portfolio Manager → Check current allocation ↓ IF new position causes imbalance: → Suggest partial position size OR → Recommend selling other positions first

Example: War Room: BUY AAPL $15,000 Portfolio Manager: "Tech sector already 38%, BUY only $10,000"

Reporting

Weekly Portfolio Report

Portfolio Performance Report - Week of 2025-12-21

Asset Allocation

• Stocks: 68% (Target: 70%) ✓
• Bonds: 22% (Target: 20%) ⚠️
• Cash: 10% (Target: 10%) ✓

Performance

• Weekly Return: +2.3%
• YTD Return: +15.7%
• Sharpe Ratio: 1.45
• Max Drawdown: -8.2%

Actions Taken

• None (within tolerance)

Next Review: 2025-12-28

Version History

• v1.0 (2025-12-21): Initial release with MPT optimization and market regime integration