Portfolio allocation and rebalancing optimizer. Manages asset allocation across stocks/cash/bonds, performs periodic rebalancing, and ensures diversification according to market regime and risk tolerance.
포트폴리오의 자산 배분, 리밸런싱, 다각화를 관리하여 위험 대비 수익을 최적화합니다.
# 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
}
Tech: 최대 40%
Finance: 최대 30%
Healthcare: 최대 25%
Other sectors: 최대 20% each
IF deviation > 5%:
→ Rebalance recommended
Example:
Target: Stocks 70%
Current: Stocks 76%
Deviation: +6% → REBALANCE
IF deviation > 10%:
→ Urgent rebalance
→ Immediate notification
# 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%
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
{
"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"
}
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
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"
# }
# }
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
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
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)
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
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"
# 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