[03] Designing a Personal Commitment Line — Two Loans, One Defense System

[03] Designing a Personal Commitment Line — Two Loans, One Defense System This is Part 3 of a 6-part series: Building Investment Systems with Python The Corporate Playbook Every major corporation maintains a revolving credit facility — a pre-arranged borrowing line they can draw from instantly during a crisis. They pay a commitment fee for the privilege of having this standby capacity, even when they don't use it. The logic is simple: the time you most need to borrow is exactly when borrowing becomes hardest. This applies to individuals too. A securities-backed loan shrinks when your portfolio drops. You need a second source — one that's independent of market conditions. Correlated vs. Orthogonal Defense This is the most important concept in the series. # defense_types.py from dataclasses import dataclass from typing import Optional @dataclass class Loan: name: str balance: float rate: float collateral_value: Optional[float] # None = unsecured freeze_ratio: Optional[float] call_ratio: Optional[float] liq_ratio: Optional[float] def available_at_drawdown(self, drawdown_pct: float) -> float: """How much of this loan's balance is still accessible after a drawdown?""" if self.collateral_value is None: # Unsecured — always available regardless of market return self.balance # Collateral-based — capacity shrinks with market new_collateral = self.collateral_value * (1 - drawdown_pct) if self.freeze_ratio and (self.balance / new_collateral) > self.freeze_ratio: return 0 # Frozen — can't borrow more return self.balance def repay_needed(self, drawdown_pct: float, target_ratio: float = 0.65) -> float: """How much must be repaid to restore target ratio after drawdown?""" if self.collateral_value is None: return 0 # No margin requirements new_collateral = self.collateral_value * (1 - drawdown_pct) target_balance = new_collateral * target_ratio return max(0, self.balance - target_balance) def compare_strategies(): portfolio = 125_000_000 # Strategy A: All margin, no orthogonal defense strategy_a = [ Loan("margin_only", 58_000_000, 0.02, portfolio, 0.60, 0.70, 0.85), ] # Strategy B: Lower margin + orthogonal credit line strategy_b = [ Loan("margin", 50_000_000, 0.02, portfolio, 0.60, 0.70, 0.85), Loan("credit_line", 8_000_000, 0.0225, None, None, None, None), ] print(f"{'Drawdown':>10} {'Strategy A (¥58M margin)':>28} {'Strategy B (¥50M + ¥8M credit)':>32}") print(f"{'':>10} {'Repay to survive':>28} {'Repay to survive':>32}") print("─" * 72) for drop in range(0, 70, 5): pct = drop / 100 # Strategy A a_collateral = portfolio * (1 - pct) a_ratio = 58_000_000 / a_collateral if a_collateral > 0 else float('inf') a_repay = max(0, 58_000_000 - a_collateral * 0.85) a_status = "💀" if a_repay > 0 and a_ratio > 0.85 else "✅" # Strategy B b_collateral = portfolio * (1 - pct) b_ratio = 50_000_000 / b_collateral if b_collateral > 0 else float('inf') b_repay = max(0, 50_000_000 - b_collateral * 0.85) b_defense = 8_000_000 # credit line available for repayment b_status = "✅" if b_repay <= b_defense else "💀" print(f" -{drop:>3}% ¥{a_repay:>12,.0f} {a_status:>6} ¥{b_repay:>12,.0f} {b_status:>6}") print() print("Same total borrowed (¥58M). Radically different outcomes.") print("The ¥8M credit line acts as an 'airbag' — orthogonal to the crash.") if __name__ == "__main__": compare_strategies() The Cost Optimizer The question isn't just "should I have a credit line?" — it's "what's the optimal split between margin debt and unsecured credit to minimize cost while maximizing survival?" # optimize_defense.py import sqlite3 from alm_schema import DB_PATH def optimize_loan_structure( total_needed: float, portfolio_value: float, margin_rate: float = 0.02, credit_rate: float = 0.0225, target_survival: float = 0.60, # survive a -60% drawdown ): """ Find the minimum-cost loan split that survives the target drawdown. The margin loan is cheaper but correlated with the portfolio. The credit line is slightly more expensive but orthogonal. """ results = [] # Try every possible split in ¥1M increments for margin_amount in range(0, int(total_needed) + 1, 1_000_000): credit_amount = total_needed - margin_amount # Annual cost annual_cost = margin_amount * margin_rate + credit_amount * credit_rate # Stress test: can this survive target_survival drawdown? stressed_collateral = portfolio_value * (1 - target_survival) # Margin ratio after drawdown if stressed_collateral <= 0: continue margin_ratio = margin_amount / stressed_collateral # Repayment needed to avoid forced liquidation (85%) repay_needed = max(0, margin_amount - stressed_collateral * 0.85) # Can the credit line cover the repayment? survives = repay_needed <= credit_amount # Margin ratio at rest (no drawdown) rest_ratio = margin_amount / portfolio_value results.append({ 'margin': margin_amount, 'credit': credit_amount, 'annual_cost': annual_cost, 'rest_ratio': rest_ratio, 'stressed_ratio': margin_ratio, 'repay_needed': repay_needed, 'survives': survives, }) # Filter survivors and sort by cost survivors = [r for r in results if r['survives']] if not survivors: print("No configuration survives the target drawdown.") return survivors.sort(key=lambda x: x['annual_cost']) print(f"Target: Survive -{target_survival:.0%} drawdown") print(f"Portfolio: ¥{portfolio_value:,.0f}") print(f"Total borrowing: ¥{total_needed:,.0f}") print(f"Margin rate: {margin_rate:.2%} | Credit rate: {credit_rate:.2%}") print() print(f"{'Margin':>12} {'Credit':>12} {'Cost/yr':>10} {'Rest %':>8} " f"{'Stress %':>10} {'Repay':>12}") print("─" * 70) for r in survivors[:10]: # Top 10 cheapest print(f"¥{r['margin']:>10,.0f} ¥{r['credit']:>10,.0f} " f"¥{r['annual_cost']:>8,.0f} {r['rest_ratio']:>7.1%} " f"{r['stressed_ratio']:>9.1%} ¥{r['repay_needed']:>10,.0f}") best = survivors[0] print() print(f"✅ Optimal: ¥{best['margin']:,.0f} margin + ¥{best['credit']:,.0f} credit") print(f" Annual cost: ¥{best['annual_cost']:,.0f}") print(f" Margin ratio at rest: {best['rest_ratio']:.1%}") if __name__ == "__main__": optimize_loan_structure( total_needed=58_000_000, portfolio_value=125_000_000, ) The Alert System Set up automatic alerts when your margin ratio approaches danger zones. # margin_alert.py import sqlite3 from datetime import date from alm_schema import DB_PATH THRESHOLDS = { 'green': 0.50, # comfortable 'yellow': 0.55, # attention 'orange': 0.60, # freeze imminent 'red': 0.70, # margin call territory } def check_margin_status(): conn = sqlite3.connect(DB_PATH) c = conn.cursor() c.execute(""" SELECT SUM(h.shares * p.close_price) FROM holdings h JOIN prices p ON h.ticker = p.ticker WHERE p.price_date = (SELECT MAX(price_date) FROM prices) """) portfolio = c.fetchone()[0] c.execute("SELECT SUM(balance) FROM loans WHERE collateral_type = 'portfolio'") margin_balance = c.fetchone()[0] conn.close() ratio = margin_balance / portfolio if ratio < THRESHOLDS['green']: status = "🟢 COMFORTABLE" action = "No action needed." elif ratio < THRESHOLDS['yellow']: status = "🟡 ATTENTION" action = "Monitor daily. Consider reducing if trend continues." elif ratio < THRESHOLDS['orange']: status = "🟠 WARNING" action = "Prepare cash for potential repayment. New borrowing may freeze." else: status = "🔴 CRITICAL" repay = margin_balance - portfolio * 0.60 action = f"Repay ¥{repay:,.0f} to restore to 60%. Consider using credit line." report = f""" [{date.today()}] MARGIN STATUS: {status} Portfolio: ¥{portfolio:>14,.0f} Margin Loan: ¥{margin_balance:>14,.0f} Ratio: {ratio:>12.1%} Action: {action} """ print(report) return ratio, status if __name__ == "__main__": check_margin_status() Wire this to cron. Get an alert every evening. Sleep well. What We Built A correlated vs. orthogonal defense comparison tool A cost optimizer that finds the cheapest loan split for your target survival rate An automated margin status alert system The conceptual framework: orthogonal defense > correlated defense Next week: [04] Dividend Snowball Simulator — "DOE: dividends that grow themselves." Series: Building Investment Systems with Python — Engineering financial independence with code.

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