Portfolio Rebalancer

Portfolio Rebalancer

Calculate the exact trades needed to rebalance any portfolio back to target allocations. Works with stocks, crypto, ETFs, or any mix of assets.

Quick Rebalance

Simple portfolio rebalance

python3 -c "
def rebalance(holdings, targets, prices=None):
    '''
    holdings: dict of {asset: quantity}
    targets: dict of {asset: target_pct} (must sum to 1.0)
    prices: dict of {asset: price_per_unit} (optional, defaults to 1.0)
    '''
    if prices is None:
        prices = {a: 1.0 for a in holdings}

    current_values = {a: holdings.get(a, 0) * prices.get(a, 1) for a in targets}
    total = sum(current_values.values())

    if total <= 0:
        print('Portfolio value is zero or negative.')
        return

    print(f'Total Portfolio Value: \${total:,.2f}')
    print(f'{\"\":-<60}')
    print(f'{\"Asset\":<12} {\"Current\":>10} {\"Target\":>10} {\"Diff\":>10} {\"Trade\":>14}')
    print(f'{\"\":-<60}')

    for asset in sorted(targets.keys()):
        current_pct = current_values[asset] / total
        target_pct = targets[asset]
        diff_pct = target_pct - current_pct
        trade_value = diff_pct * total
        trade_qty = trade_value / prices.get(asset, 1)
        action = 'BUY' if trade_value > 0 else 'SELL'

        if abs(trade_value) < 0.01:
            action = '  OK'
            print(f'{asset:<12} {current_pct:>9.1%} {target_pct:>9.1%} {diff_pct:>+9.1%} {\"balanced\":>14}')
        else:
            print(f'{asset:<12} {current_pct:>9.1%} {target_pct:>9.1%} {diff_pct:>+9.1%} {action} {abs(trade_qty):>7.4f}')

# Example: Crypto portfolio
holdings = {'BTC': 0.5, 'ETH': 4.0, 'SOL': 100}
targets = {'BTC': 0.50, 'ETH': 0.30, 'SOL': 0.20}
prices = {'BTC': 95000, 'ETH': 3200, 'SOL': 180}

rebalance(holdings, targets, prices)
"

Rebalance with new deposit

python3 -c "
def rebalance_with_deposit(holdings, targets, prices, deposit=0):
    current_values = {a: holdings.get(a, 0) * prices.get(a, 1) for a in targets}
    total = sum(current_values.values()) + deposit

    print(f'Current Value: \${sum(current_values.values()):,.2f}')
    print(f'New Deposit:   \${deposit:,.2f}')
    print(f'New Total:     \${total:,.2f}')
    print(f'{\"\":-<65}')
    print(f'{\"Asset\":<10} {\"Now\":>10} {\"Target\":>10} {\"Action\":>8} {\"Qty\":>10} {\"Value\":>12}')
    print(f'{\"\":-<65}')

    for asset in sorted(targets.keys()):
        target_value = targets[asset] * total
        current_value = current_values.get(asset, 0)
        diff = target_value - current_value
        qty = diff / prices[asset]
        action = 'BUY' if diff > 0 else 'SELL' if diff < 0 else 'HOLD'
        pct_now = current_value / (total - deposit) * 100 if (total - deposit) > 0 else 0
        print(f'{asset:<10} {pct_now:>9.1f}% {targets[asset]*100:>9.1f}% {action:>8} {qty:>+10.4f} \${abs(diff):>10,.2f}')

# Example: Add \$5000 to a stock portfolio
holdings = {'VTI': 50, 'VXUS': 20, 'BND': 30, 'VTIP': 10}
targets = {'VTI': 0.50, 'VXUS': 0.20, 'BND': 0.20, 'VTIP': 0.10}
prices = {'VTI': 280, 'VXUS': 60, 'BND': 72, 'VTIP': 50}

rebalance_with_deposit(holdings, targets, prices, deposit=5000)
"

Drift Detection

Check if portfolio needs rebalancing

python3 -c "
def check_drift(holdings, targets, prices, threshold=0.05):
    '''Check if any asset has drifted beyond threshold from target.'''
    values = {a: holdings.get(a, 0) * prices.get(a, 1) for a in targets}
    total = sum(values.values())
    needs_rebalance = False

    print(f'Portfolio: \${total:,.2f} | Drift Threshold: {threshold:.0%}')
    print(f'{\"\":-<50}')

    for asset in sorted(targets.keys()):
        pct = values[asset] / total if total > 0 else 0
        drift = abs(pct - targets[asset])
        flag = ' ** REBALANCE' if drift > threshold else ''
        if drift > threshold:
            needs_rebalance = True
        print(f'{asset:<10} {pct:>8.1%} vs {targets[asset]:>6.1%}  drift: {drift:>5.1%}{flag}')

    print(f'{\"\":-<50}')
    if needs_rebalance:
        print('ACTION NEEDED: Portfolio has drifted beyond threshold.')
    else:
        print('Portfolio is within tolerance. No action needed.')

# Example
holdings = {'BTC': 0.8, 'ETH': 5.0, 'SOL': 50}
targets = {'BTC': 0.50, 'ETH': 0.30, 'SOL': 0.20}
prices = {'BTC': 97000, 'ETH': 3100, 'SOL': 175}

check_drift(holdings, targets, prices, threshold=0.05)
"

Tax-Aware Rebalancing

Minimize sells (buy-only rebalance using new cash)

python3 -c "
def buy_only_rebalance(holdings, targets, prices, cash):
    '''Rebalance by only buying - never selling (tax-efficient).'''
    values = {a: holdings.get(a, 0) * prices.get(a, 1) for a in targets}
    total = sum(values.values()) + cash

    print(f'Available Cash: \${cash:,.2f}')
    print(f'Post-Rebalance Total: \${total:,.2f}')
    print(f'{\"\":-<50}')

    buys = {}
    for asset in targets:
        target_value = targets[asset] * total
        current_value = values.get(asset, 0)
        deficit = max(0, target_value - current_value)
        buys[asset] = deficit

    buy_total = sum(buys.values())

    if buy_total > cash:
        scale = cash / buy_total
        buys = {a: v * scale for a, v in buys.items()}

    remaining = cash
    for asset in sorted(buys.keys(), key=lambda a: buys[a], reverse=True):
        buy_val = min(buys[asset], remaining)
        qty = buy_val / prices[asset]
        if buy_val > 0.01:
            print(f'BUY {qty:.4f} {asset:<8} (\${buy_val:,.2f})')
            remaining -= buy_val

    if remaining > 0.01:
        print(f'Remaining cash: \${remaining:,.2f}')

holdings = {'VTI': 100, 'VXUS': 30, 'BND': 40}
targets = {'VTI': 0.60, 'VXUS': 0.25, 'BND': 0.15}
prices = {'VTI': 280, 'VXUS': 60, 'BND': 72}
buy_only_rebalance(holdings, targets, prices, cash=3000)
"

Tips

1. Rebalance on a schedule (quarterly or when drift exceeds 5%)
2. Use new deposits to rebalance before selling (tax-efficient)
3. Consider transaction costs - skip tiny trades under $50
4. Keep target allocations simple - fewer assets = easier to maintain
5. Document your target allocation and the reasoning behind it

Common Allocations

Author

Created by Ian Alloway - Data Scientist specializing in AI/ML and portfolio optimization.

License

MIT License