# Risk Of Ruin > Risk of Ruin formula, Kelly Criterion (full and fractional), Monte Carlo survival simulation, and position sizing models (fixed fractional, volatility-adjusted, fixed ratio). Use for risk of ruin, Kelly criterion, Monte Carlo, position sizing, bankroll management, or any ruin/sizing calculation. - Skill: `mahmoud20138/risk-of-ruin` (Agent Skill) - Install (CLI): `npx skillmds@latest add mahmoud20138/risk-of-ruin` - Raw SKILL.md: https://api.skillmd.com/api/skills/mahmoud20138/risk-of-ruin/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: mahmoud20138 (https://skillmd.com/u/mahmoud20138) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/mahmoud20138/risk-of-ruin --- > **Skill:** Risk of Ruin & Bankroll Mathematics | **Domain:** trading/risk-and-portfolio | **Category:** risk | **Level:** advanced > **Tags:** `ruin`, `kelly`, `monte-carlo`, `bankroll`, `survival`, `sizing` # Risk of Ruin & Bankroll Mathematics ## 1. Risk of Ruin Formula ### Classical Formula ``` For a system with: Win rate: W Loss rate: L = 1 - W Win/Loss ratio: R = avg_win / avg_loss Risk per trade: f (fraction of bankroll) Risk of Ruin = ((1 - Edge) / (1 + Edge))^(Capital_Units) Where: Edge = W × R - L = W × R - (1 - W) Capital_Units = Account / Risk_Per_Trade ``` ### Practical Table ``` Win Rate: 55% | Win/Loss Ratio: 1.5:1 | Edge = 0.55×1.5 - 0.45 = 0.375 Risk/Trade Risk of Ruin Account Units ────────── ──────────── ───────────── 1% 0.001% 100 units 2% 0.1% 50 units 3% 1.2% 33 units 5% 12.4% 20 units 10% 52.1% 10 units 20% 89.3% 5 units Win Rate: 45% | Win/Loss Ratio: 2.0:1 | Edge = 0.45×2.0 - 0.55 = 0.35 Risk/Trade Risk of Ruin ────────── ──────────── 1% 0.01% 2% 0.3% 5% 18.7% 10% 61.2% ``` **Key insight:** Even with a positive edge, risking >5% per trade gives unacceptable ruin probability. ## 2. Kelly Criterion ### Full Kelly ``` f* = (W × R - L) / R = Edge / R Where: f* = optimal fraction of bankroll to risk W = win probability R = win/loss ratio L = 1 - W Example: W = 55%, R = 1.5 f* = (0.55 × 1.5 - 0.45) / 1.5 f* = 0.375 / 1.5 f* = 25% ← Full Kelly ``` ### Why Full Kelly Is Dangerous ``` Full Kelly: ✓ Maximizes long-term growth rate ✗ Produces stomach-churning drawdowns (50-85% is normal) ✗ Assumes known exact edge (you don't know this) ✗ Assumes infinite time horizon ✗ One estimation error can be fatal RULE: Use fractional Kelly Half Kelly (f*/2): 75% of the growth rate of full Kelly Dramatically lower drawdowns Much more robust to edge estimation errors Quarter Kelly (f*/4): 50% of growth rate Very smooth equity curve Recommended for uncertain edge ``` ### Practical Kelly Sizing ```python def kelly_size(win_rate: float, avg_win: float, avg_loss: float, kelly_fraction: float = 0.25) -> float: """ Quarter Kelly by default. Never go above half Kelly. Returns: fraction of account to risk per trade """ R = avg_win / abs(avg_loss) edge = win_rate * R - (1 - win_rate) if edge <= 0: return 0.0 # No edge = no trade full_kelly = edge / R return full_kelly * kelly_fraction # Example: # kelly_size(0.55, 150, 100, kelly_fraction=0.25) # → Full Kelly = 25%, Quarter Kelly = 6.25% # → Risk 6.25% of account per trade # # But cap at 2% maximum regardless of Kelly output ``` ## 3. Monte Carlo Survival Analysis ### Simulation Framework ```python import numpy as np def monte_carlo_survival( win_rate: float, avg_win_r: float, # in R-multiples avg_loss_r: float, # typically -1R risk_per_trade: float, # fraction of current bankroll num_trades: int = 500, num_sims: int = 10000, ruin_threshold: float = 0.5, # 50% drawdown = ruin ) -> dict: """ Simulate N trading paths and measure survival. """ ruin_count = 0 max_dds = [] final_balances = [] for _ in range(num_sims): balance = 1.0 # normalized peak = 1.0 max_dd = 0.0 ruined = False for _ in range(num_trades): if np.random.random() < win_rate: pnl = balance * risk_per_trade * avg_win_r else: pnl = balance * risk_per_trade * avg_loss_r balance += pnl peak = max(peak, balance) dd = (peak - balance) / peak max_dd = max(max_dd, dd) if balance <= (1.0 - ruin_threshold): ruined = True ruin_count += 1 break max_dds.append(max_dd) final_balances.append(balance) return { 'ruin_probability': ruin_count / num_sims, 'median_max_dd': np.median(max_dds), 'p95_max_dd': np.percentile(max_dds, 95), 'median_final_balance': np.median(final_balances), 'p5_final_balance': np.percentile(final_balances, 5), 'p95_final_balance': np.percentile(final_balances, 95), } # Run with YOUR actual stats: # result = monte_carlo_survival( # win_rate=0.52, avg_win_r=1.8, avg_loss_r=-1.0, # risk_per_trade=0.02, num_trades=500 # ) ``` ### Interpreting Results ``` ACCEPTABLE: Ruin probability: < 1% P95 max drawdown: < 30% P5 final balance: > starting balance Median final balance: significantly above starting WARNING: Ruin probability: 1-5% P95 max drawdown: 30-50% → Reduce risk_per_trade UNACCEPTABLE: Ruin probability: > 5% → System is not viable at this risk level → Either improve edge or reduce risk until ruin < 1% ``` ## 4. Position Sizing Models ### Fixed Fractional ``` Risk per trade = Account × Fixed_Percent Pro: Simple, anti-martingale (bet less after losses) Con: Can be slow to grow small accounts Best for: Most traders, most of the time Typical: 1-2% per trade ``` ### Volatility-Adjusted (ATR-Based) ``` Position Size = (Account × Risk%) / (ATR × ATR_Multiple) Pro: Automatically adjusts for market volatility Con: Requires reliable ATR calculation Best for: Multi-instrument portfolios Example: Account: $100,000 Risk: 1% = $1,000 EURUSD ATR(14): 80 pips ATR Multiple: 2 (stop at 2× ATR = 160 pips) Position: $1,000 / 160 pips = 6.25 per pip ≈ 0.6 lots ``` ### Fixed Ratio (Ryan Jones) ``` Next level increase when: Profits ≥ Delta × Current_Units Delta = chosen profit threshold per unit increase Lower delta = more aggressive scaling Higher delta = more conservative scaling Pro: Geometric growth potential Con: Complex, requires tracking Best for: Scaling up proven strategies ``` ## 5. Survival Rules ``` 1. NEVER risk more than 2% per trade. Period. 2. NEVER have more than 6% total open risk. (3 positions × 2% each, or 6 × 1%) 3. DAILY loss limit: 4% of account. Hit it → done for the day. No exceptions. 4. WEEKLY loss limit: 8% of account. Hit it → paper trade rest of week. 5. MONTHLY loss limit: 12% of account. Hit it → full stop, review everything. 6. NEVER add to a losing position. Average down = accelerate ruin. 7. Know your edge BEFORE you size. If you can't state your win rate and average R, you're gambling, not trading. 8. Run Monte Carlo quarterly. Your actual stats change. Your sizing should adapt. ``` --- ## Related Skills - [Risk And Portfolio](risk-and-portfolio.md) - [Drawdown Playbook](drawdown-playbook.md) - [Portfolio Optimization](portfolio-optimization.md) - [Real-Time Risk Monitor](real-time-risk-monitor.md)