[02] Stress Testing Your Life — What Happens at -30%, -50%, -60%?

This is Part 2 of a 6-part series: Building Investment Systems with Python

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Banks Do This Every Quarter. You Never Have.

After the 2008 financial crisis, regulators required banks to run stress tests — hypothetical scenarios where markets crash 30%, 40%, 60% — and prove they could survive.

Your personal balance sheet faces the same risks. If you hold a securities-backed loan, a market crash doesn't just reduce your wealth — it can trigger forced liquidation at the worst possible time.

Today we build the stress test engine. One function. Every scenario. The exact cash needed to survive each one.

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The Core Engine

This reads from the ALM database we built in [Episode 01] and runs every drop scenario from -5% to -70%.

# stress_test.py
import sqlite3
from alm_schema import DB_PATH

def run_stress_test(cash_reserves=4_500_000):
    conn = sqlite3.connect(DB_PATH)
    c = conn.cursor()

    # Current portfolio value
    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_value = c.fetchone()[0]

    # Margin loan details
    c.execute("""
        SELECT balance, freeze_pct, margin_call_pct, forced_liq_pct
        FROM loans WHERE collateral_type = 'portfolio'
    """)
    loan = c.fetchone()
    if not loan:
        print("No margin loan found.")
        return

    loan_balance, freeze_pct, call_pct, liq_pct = loan

    # Unsecured credit line
    c.execute("SELECT balance FROM loans WHERE collateral_type IS NULL")
    credit_row = c.fetchone()
    credit_available = credit_row[0] if credit_row else 0

    conn.close()

    total_defense = cash_reserves + credit_available

    print(f"Portfolio Value:    ¥{portfolio_value:>14,.0f}")
    print(f"Loan Balance:      ¥{loan_balance:>14,.0f}")
    print(f"Current Ratio:       {loan_balance/portfolio_value:>12.1%}")
    print(f"Cash Reserves:     ¥{cash_reserves:>14,.0f}")
    print(f"Credit Line:       ¥{credit_available:>14,.0f}")
    print(f"Total Defense:     ¥{total_defense:>14,.0f}")
    print()
    print("=" * 90)
    print(f"{'Drop':>6} {'Collateral':>14} {'Ratio':>8} {'Status':>16} "
          f"{'Repay to 65%':>14} {'Repay to 85%':>14} {'Survive?':>10}")
    print("=" * 90)

    for drop in range(5, 75, 5):
        pct = drop / 100
        collateral = portfolio_value * (1 - pct)
        ratio = loan_balance / collateral if collateral > 0 else float('inf')

        # Determine status
        if ratio > liq_pct:
            status = "🔴 FORCED LIQ"
        elif ratio > call_pct:
            status = "🟠 MARGIN CALL"
        elif ratio > freeze_pct:
            status = "🟡 FROZEN"
        else:
            status = "🟢 OK"

        # Cash needed to bring ratio back to safe levels
        repay_to_65 = max(0, loan_balance - collateral * 0.65)
        repay_to_85 = max(0, loan_balance - collateral * 0.85)

        # Can we survive with available cash?
        can_survive = "✅ YES" if repay_to_85 <= total_defense else "❌ NO"

        print(f"{drop:>5}% ¥{collateral:>13,.0f} {ratio:>7.1%} {status:>16} "
              f"¥{repay_to_65:>13,.0f} ¥{repay_to_85:>13,.0f} {can_survive:>10}")

    # Find maximum survivable drawdown
    print()
    print("─" * 90)
    for drop in range(1, 100):
        collateral = portfolio_value * (1 - drop/100)
        if collateral <= 0:
            break
        ratio = loan_balance / collateral
        repay_to_85 = max(0, loan_balance - collateral * 0.85)
        if repay_to_85 > total_defense:
            print(f"⚡ Maximum survivable drawdown: -{drop-1}%")
            print(f"   At -{drop}%, you need ¥{repay_to_85:,.0f} but only have ¥{total_defense:,.0f}")
            break

if __name__ == "__main__":
    run_stress_test()

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Sample Output

Portfolio Value:    ¥  125,135,000
Loan Balance:      ¥   50,000,000
Current Ratio:              39.9%
Cash Reserves:     ¥    4,500,000
Credit Line:       ¥    8,000,000
Total Defense:     ¥   12,500,000

==========================================================================================
  Drop     Collateral    Ratio           Status   Repay to 65%   Repay to 85%   Survive?
==========================================================================================
    5% ¥ 118,878,250    42.1%           🟢 OK  ¥            0  ¥            0     ✅ YES
   10% ¥ 112,621,500    44.4%           🟢 OK  ¥            0  ¥            0     ✅ YES
   15% ¥ 106,364,750    47.0%           🟢 OK  ¥            0  ¥            0     ✅ YES
   20% ¥ 100,108,000    49.9%           🟢 OK  ¥            0  ¥            0     ✅ YES
   25% ¥  93,851,250    53.3%           🟢 OK  ¥            0  ¥            0     ✅ YES
   30% ¥  87,594,500    57.1%           🟢 OK  ¥            0  ¥            0     ✅ YES
   35% ¥  81,337,750    61.5%        🟡 FROZEN  ¥    1,130,463  ¥            0     ✅ YES
   40% ¥  75,081,000    66.6%        🟡 FROZEN  ¥    1,197,350  ¥            0     ✅ YES
   45% ¥  68,824,250    72.6%     🟠 MARGIN CALL ¥    5,264,238  ¥            0     ✅ YES
   50% ¥  62,567,500    79.9%     🟠 MARGIN CALL ¥    9,331,125  ¥            0     ✅ YES
   55% ¥  56,310,750    88.8%     🔴 FORCED LIQ  ¥   13,397,963  ¥    2,135,863     ✅ YES
   60% ¥  50,054,000    99.9%     🔴 FORCED LIQ  ¥   17,464,900  ¥    7,454,100     ✅ YES
   65% ¥  43,797,250   114.2%     🔴 FORCED LIQ  ¥   21,531,788  ¥   12,772,338     ❌ NO
   70% ¥  37,540,500   133.2%     🔴 FORCED LIQ  ¥   25,598,675  ¥   18,090,575     ❌ NO

──────────────────────────────────────────────────────────────────────────────────────────
⚡ Maximum survivable drawdown: -62%
   At -63%, you need ¥13,101,398 but only have ¥12,500,000

One table. Your entire risk profile. The number that lets you sleep at night.

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Visualizing the Danger Zones

# stress_chart.py
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import sqlite3
from alm_schema import DB_PATH

def plot_stress_test():
    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 balance, freeze_pct, margin_call_pct, forced_liq_pct FROM loans WHERE collateral_type = 'portfolio'")
    balance, freeze, call, liq = c.fetchone()
    conn.close()

    drops = list(range(0, 75))
    ratios = []
    for d in drops:
        collateral = portfolio * (1 - d/100)
        ratios.append(balance / collateral * 100 if collateral > 0 else 100)

    fig, ax = plt.subplots(figsize=(12, 6))
    fig.patch.set_facecolor('#0a0a0a')
    ax.set_facecolor('#0a0a0a')

    # Danger zones
    ax.axhspan(0, freeze*100, alpha=0.15, color='#00ff88', label=f'Safe (<{freeze:.0%})')
    ax.axhspan(freeze*100, call*100, alpha=0.15, color='#ffaa00', label=f'Frozen ({freeze:.0%}-{call:.0%})')
    ax.axhspan(call*100, liq*100, alpha=0.15, color='#ff6600', label=f'Margin Call ({call:.0%}-{liq:.0%})')
    ax.axhspan(liq*100, 120, alpha=0.15, color='#ff0000', label=f'Forced Liquidation (>{liq:.0%})')

    # Ratio line
    ax.plot(drops, ratios, color='#00ff88', linewidth=2.5, zorder=5)

    # Threshold lines
    for pct, color, style in [(freeze*100, '#ffaa00', '--'), (call*100, '#ff6600', '--'), (liq*100, '#ff0000', '-')]:
        ax.axhline(y=pct, color=color, linestyle=style, alpha=0.7, linewidth=1)

    ax.set_xlabel('Portfolio Drawdown (%)', color='#888', fontsize=12)
    ax.set_ylabel('Margin Ratio (%)', color='#888', fontsize=12)
    ax.set_title('Stress Test: Margin Ratio vs. Drawdown', color='white', fontsize=14, pad=20)
    ax.tick_params(colors='#888')
    ax.spines['bottom'].set_color('#333')
    ax.spines['left'].set_color('#333')
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)
    ax.legend(loc='upper left', facecolor='#111', edgecolor='#333', labelcolor='#ccc')
    ax.set_xlim(0, 74)
    ax.set_ylim(30, 120)

    plt.tight_layout()
    plt.savefig('stress_test.png', dpi=150, facecolor='#0a0a0a')
    print("Chart saved: stress_test.png")

if __name__ == "__main__":
    plot_stress_test()

This produces a single chart that maps your margin ratio against every possible drawdown level, with color-coded danger zones.

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Historical Context

How realistic are these scenarios? Here's what the data says for the Nikkei 225:

Event	Period	Peak-to-Trough
Bubble Collapse	1989 → 2003	-81% (over 13 years)
IT Bubble + Banking Crisis	2000 → 2003	-64% (over 3 years)
Lehman Shock	2007 → 2008	-62% (over 17 months)
COVID Crash	Jan → Mar 2020	-31% (over 2 months)
Aug 2024 Crash	Jul → Aug 2024	-27% (over 3 weeks)

The -60% scenarios happened twice in modern history — and both took years, not days. That's critical: you have time to react. The stress test tells you the thresholds; the timeline tells you there's usually a window to repay.

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The Key Insight

The stress test reveals something counterintuitive:

A lower loan balance doesn't always make you safer — more available cash does.

Compare:

• Loan ¥40M, Cash ¥1M → forced liquidation at -55%, no cash to repay
• Loan ¥50M, Cash ¥12.5M → forced liquidation at -53%, but cash covers repayment through -62%

The second scenario has more debt but higher survivability because the defense fund is orthogonal to the collateral.

This is why the "just pay down debt" advice can be wrong. It depends on the structure.

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What We Built

• A stress test engine that models every drawdown from -5% to -70%
• Automatic calculation of repayment amounts for each danger zone
• Maximum survivable drawdown computation
• A matplotlib visualization of margin ratio vs. drawdown with danger zones

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Next week: [03] Designing a Personal Commitment Line — "Two loans, one defense system."

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Series: Building Investment Systems with Python — Engineering financial independence with code.