multi-asset

name	multi-asset
description	Trade and track stocks, ETFs, commodities, bonds, and forex. Unified portfolio across all asset classes.
metadata	{"openclaw":{"emoji":"🌍","requires":{"bins":["python3"],"pip":["yfinance","pandas","requests","alpha_vantage"]}}}

Multi-Asset

Vollständige Abdeckung aller Asset-Klassen in einem System.

Overview

• Stocks - US, EU, Emerging Markets
• ETFs - Index, Sector, Thematic
• Bonds - Government, Corporate
• Commodities - Gold, Silver, Oil
• Forex - Major pairs

🤖 AUTO-PILOT MODE

# ~/.kit/config/multi-asset.json
{
  "auto_pilot": {
    "enabled": true,
    "brokers": {
      "interactive_brokers": {"enabled": true, "account": "U1234567"},
      "trade_republic": {"enabled": true},
      "scalable": {"enabled": true}
    },
    "strategies": {
      "dca": {
        "enabled": true,
        "schedule": "weekly",
        "day": "monday",
        "investments": [
          {"symbol": "VTI", "amount_eur": 200},
          {"symbol": "VXUS", "amount_eur": 100},
          {"symbol": "BND", "amount_eur": 50}
        ]
      },
      "value_averaging": {
        "enabled": false,
        "target_growth_pct": 0.5
      },
      "rebalancing": {
        "enabled": true,
        "trigger": "quarterly"
      }
    },
    "alerts": {
      "price_target": true,
      "earnings": true,
      "dividend_ex_date": true,
      "52w_high_low": true
    },
    "require_approval": {
      "trades_above_eur": 1000,
      "new_positions": true
    }
  },
  "target_allocation": {
    "us_stocks": 35,
    "intl_stocks": 25,
    "bonds": 20,
    "commodities": 10,
    "crypto": 10
  }
}

Supported Brokers

Broker	Region	Features
Interactive Brokers	Global	Full API, all assets
Trade Republic	EU	Stocks, ETFs, Crypto
Scalable Capital	EU	ETFs, Stocks
Degiro	EU	Low cost stocks
Alpaca	US	Commission-free API

Commands

Full Portfolio Overview

python3 -c "
import yfinance as yf

portfolio = {
    'stocks': [
        {'symbol': 'AAPL', 'shares': 50, 'cost': 150},
        {'symbol': 'MSFT', 'shares': 30, 'cost': 280},
        {'symbol': 'GOOGL', 'shares': 20, 'cost': 120},
    ],
    'etfs': [
        {'symbol': 'VTI', 'shares': 100, 'cost': 200},
        {'symbol': 'VXUS', 'shares': 80, 'cost': 55},
        {'symbol': 'BND', 'shares': 50, 'cost': 75},
    ],
    'commodities': [
        {'symbol': 'GLD', 'shares': 25, 'cost': 170},
    ]
}

print('🌍 MULTI-ASSET PORTFOLIO')
print('=' * 80)

total_value = 0
total_cost = 0
by_class = {}

for asset_class, positions in portfolio.items():
    class_value = 0
    print(f'\\n📁 {asset_class.upper()}')
    print('-' * 80)
    
    for pos in positions:
        try:
            stock = yf.Ticker(pos['symbol'])
            price = stock.info.get('currentPrice', stock.info.get('regularMarketPrice', 0))
            value = pos['shares'] * price
            cost = pos['shares'] * pos['cost']
            pnl = value - cost
            pnl_pct = (pnl / cost * 100) if cost > 0 else 0
            
            emoji = '🟢' if pnl >= 0 else '🔴'
            print(f\"{pos['symbol']:8} {pos['shares']:>6} @ \${price:>8.2f} = \${value:>10,.2f} {emoji} {pnl_pct:>+6.1f}%\")
            
            class_value += value
            total_cost += cost
        except Exception as e:
            print(f\"{pos['symbol']:8} Error: {e}\")
    
    by_class[asset_class] = class_value
    total_value += class_value

print()
print('=' * 80)
print('SUMMARY BY CLASS:')
for cls, val in by_class.items():
    pct = (val / total_value * 100) if total_value > 0 else 0
    print(f'  {cls:15} \${val:>12,.2f} ({pct:5.1f}%)')

print()
total_pnl = total_value - total_cost
total_pnl_pct = (total_pnl / total_cost * 100) if total_cost > 0 else 0
print(f'TOTAL VALUE: \${total_value:,.2f}')
print(f'TOTAL P&L:   \${total_pnl:+,.2f} ({total_pnl_pct:+.1f}%)')
"

Dollar-Cost Averaging (DCA) Execution

python3 -c "
import yfinance as yf
from datetime import datetime

# Weekly DCA plan
dca_plan = [
    {'symbol': 'VTI', 'amount_eur': 200, 'name': 'US Total Market'},
    {'symbol': 'VXUS', 'amount_eur': 100, 'name': 'International'},
    {'symbol': 'BND', 'amount_eur': 50, 'name': 'Bonds'},
]

eur_usd = 1.08  # Exchange rate

print('💰 DCA EXECUTION')
print('=' * 60)
print(f'Date: {datetime.now().strftime(\"%Y-%m-%d\")}')
print(f'EUR/USD: {eur_usd}')
print()

total_invested = 0

for plan in dca_plan:
    try:
        stock = yf.Ticker(plan['symbol'])
        price = stock.info.get('currentPrice', 100)
        
        amount_usd = plan['amount_eur'] * eur_usd
        shares = amount_usd / price
        
        print(f\"{plan['symbol']:6} ({plan['name']})\")
        print(f\"  Budget: €{plan['amount_eur']} = \${amount_usd:.2f}\")
        print(f\"  Price:  \${price:.2f}\")
        print(f\"  Shares: {shares:.4f}\")
        print()
        
        total_invested += plan['amount_eur']
        
        # Execute order:
        # broker.buy(plan['symbol'], shares)
        
    except Exception as e:
        print(f\"{plan['symbol']}: Error - {e}\")

print(f'Total Invested: €{total_invested}')
print()
print('⚠️ DRY RUN - Enable auto_pilot to execute')
"

Sector Analysis

python3 -c "
import yfinance as yf

# Sector ETFs
sectors = {
    'Technology': 'XLK',
    'Healthcare': 'XLV',
    'Financials': 'XLF',
    'Consumer Disc.': 'XLY',
    'Industrials': 'XLI',
    'Energy': 'XLE',
    'Utilities': 'XLU',
    'Materials': 'XLB',
    'Real Estate': 'XLRE',
    'Comm. Services': 'XLC',
    'Cons. Staples': 'XLP',
}

print('📊 SECTOR PERFORMANCE')
print('=' * 60)

performances = []

for name, symbol in sectors.items():
    try:
        etf = yf.Ticker(symbol)
        hist = etf.history(period='1mo')
        
        if len(hist) > 1:
            start = hist['Close'].iloc[0]
            end = hist['Close'].iloc[-1]
            change = ((end - start) / start) * 100
            performances.append((name, change))
    except:
        pass

# Sort by performance
performances.sort(key=lambda x: x[1], reverse=True)

for name, change in performances:
    emoji = '🟢' if change >= 0 else '🔴'
    bar = '█' * int(abs(change))
    print(f'{emoji} {name:18} {change:>+6.1f}% {bar}')
"

Bond Ladder Builder

python3 -c "
# Bond ladder for stable income
ladder = [
    {'maturity': '1Y', 'etf': 'SHY', 'allocation': 20, 'yield': 4.8},
    {'maturity': '3Y', 'etf': 'IEI', 'allocation': 20, 'yield': 4.2},
    {'maturity': '7Y', 'etf': 'IEF', 'allocation': 20, 'yield': 4.0},
    {'maturity': '10Y', 'etf': 'TLH', 'allocation': 20, 'yield': 4.3},
    {'maturity': '20Y', 'etf': 'TLT', 'allocation': 20, 'yield': 4.5},
]

total_investment = 50000

print('🪜 BOND LADDER')
print('=' * 60)
print(f'Total Investment: \${total_investment:,}')
print()
print(f'{\"Maturity\":10} {\"ETF\":6} {\"Amount\":>12} {\"Yield\":>8} {\"Income\":>10}')
print('-' * 60)

total_income = 0

for rung in ladder:
    amount = total_investment * (rung['allocation'] / 100)
    income = amount * (rung['yield'] / 100)
    total_income += income
    
    print(f\"{rung['maturity']:10} {rung['etf']:6} \${amount:>11,.0f} {rung['yield']:>7.1f}% \${income:>9,.0f}\")

print('-' * 60)
avg_yield = (total_income / total_investment) * 100
print(f'{\"TOTAL\":10} {\"\":6} \${total_investment:>11,} {avg_yield:>7.1f}% \${total_income:>9,.0f}')
print()
print(f'Monthly Income: \${total_income/12:,.0f}')
"

Commodity Exposure

python3 -c "
import yfinance as yf

commodities = {
    'Gold': 'GLD',
    'Silver': 'SLV',
    'Oil': 'USO',
    'Natural Gas': 'UNG',
    'Agriculture': 'DBA',
    'Copper': 'CPER',
}

print('🪙 COMMODITY PRICES')
print('=' * 50)

for name, symbol in commodities.items():
    try:
        etf = yf.Ticker(symbol)
        hist = etf.history(period='5d')
        
        if len(hist) > 0:
            price = hist['Close'].iloc[-1]
            prev = hist['Close'].iloc[0]
            change = ((price - prev) / prev) * 100
            emoji = '🟢' if change >= 0 else '🔴'
            print(f'{name:15} \${price:>8.2f} {emoji} {change:>+5.1f}%')
    except Exception as e:
        print(f'{name:15} Error')
"

Auto-Pilot: Full Automation

python3 -c "
from datetime import datetime

print('🤖 MULTI-ASSET AUTO-PILOT')
print('=' * 50)
print(f'Running: {datetime.now().isoformat()}')
print()

# Check what day it is for DCA
day = datetime.now().strftime('%A')

tasks = [
    (f'📅 Check DCA schedule (Today: {day})', 'DCA due: Monday'),
    ('💰 Execute weekly DCA', 'Pending approval'),
    ('📊 Rebalance check', 'Within tolerance'),
    ('🔔 Earnings calendar', 'AAPL reports in 5 days'),
    ('💸 Dividend tracker', 'MSFT ex-date tomorrow'),
    ('📈 Performance update', 'Portfolio +2.3% MTD'),
]

for task, status in tasks:
    print(f'{task}')
    print(f'  → {status}')
    print()

# Pending actions requiring approval
print('📋 PENDING APPROVALS:')
print('  1. DCA: Buy €350 worth of VTI, VXUS, BND')
print('     Reply \"APPROVE DCA\" to execute')
print()
print('Next check: Tomorrow 09:00')
"

Workflow

Asset Class Roles

Class	Role	Target %
US Stocks	Growth	35%
Intl Stocks	Diversification	25%
Bonds	Stability, Income	20%
Commodities	Inflation Hedge	10%
Crypto	High Growth	10%

DCA Best Practices

1. Fixed schedule - Same day each week/month
2. Ignore prices - Invest regardless of market
3. Automate - Remove emotion
4. Rebalance - Quarterly or threshold-based

Tax-Efficient Placement

Account Type	Best Assets
Taxable	Index ETFs (low turnover)
Tax-Deferred (401k)	Bonds, REITs
Tax-Free (Roth)	High growth stocks