risk-management Checked

| Drawdown | Status | Response |

|---|---|---|

| 0–5% | Normal | Continue trading at full size |

| 5–10% | Caution | Reduce position sizes by 25–50% |

| 10–15% | Warning | Minimum position sizes only |

| 15–20% | Critical | Halt new trades, manage existing positions only |

| >20% | Emergency | Full stop, review everything before resuming |

### Recovery Requirements

| Loss | Required Gain to Recover |

|---|---|

| -5% | +5.3% |

| -10% | +11.1% |

| -15% | +17.6% |

| -20% | +25.0% |

| -30% | +42.9% |

| -40% | +66.7% |

| -50% | +100.0% |

The asymmetry accelerates rapidly. Managing small drawdowns prevents them from becoming catastrophic. See references/drawdown_management.md for the full framework.

Circuit Breakers

Automated controls that restrict trading when conditions are met:

### Time-Based

• No trading for 24 hours after hitting daily loss limit
• 48-hour cooling period after weekly loss limit
• Mandatory weekly review day (no new positions)

### Loss-Based

• 3 consecutive losses → reduce size 50%
• 5 consecutive losses → minimum size only
• 7 consecutive losses → halt 24 hours, full review

### Volatility-Based

• Portfolio volatility >2× rolling average → reduce exposure 50%
• Market-wide liquidation events → pause all new entries
• Individual token volatility spike → exit or tighten stops

### Emotional (Self-Assessed)

• Recognize tilt: anger after losses, urge to "make it back"
• FOMO: rushing entries without proper analysis
• Overconfidence: increasing size after a win streak without justification

See references/circuit_breakers.md for implementation details.

Risk Metrics

### Value at Risk (VaR)

95th-percentile daily loss estimate using historical returns:

import numpy as np

def historical_var(returns: list[float], confidence: float = 0.95) -> float:
    """Calculate historical VaR at given confidence level."""
    sorted_returns = sorted(returns)
    index = int((1 - confidence) * len(sorted_returns))
    return abs(sorted_returns[index])

# Example: 95% VaR of 3.2% means on 95% of days, loss won't exceed 3.2%

### Expected Shortfall (CVaR)

Average loss in the worst (1 - confidence)% of scenarios:

def expected_shortfall(returns: list[float], confidence: float = 0.95) -> float:
    """Average loss beyond VaR threshold."""
    sorted_returns = sorted(returns)
    index = int((1 - confidence) * len(sorted_returns))
    tail = sorted_returns[:index]
    return abs(sum(tail) / len(tail)) if tail else 0.0

### Maximum Drawdown

def max_drawdown(equity_curve: list[float]) -> float:
    """Peak-to-trough decline as a fraction."""
    peak = equity_curve[0]
    max_dd = 0.0
    for value in equity_curve:
        peak = max(peak, value)
        dd = (peak - value) / peak
        max_dd = max(max_dd, dd)
    return max_dd

### Additional Metrics

• **Win/loss streak tracking**: Detect hot/cold streaks for circuit breaker logic
• **Rolling Sharpe ratio**: 30-day rolling risk-adjusted returns
• **Calmar ratio**: Annualized return / max drawdown
• **Sortino ratio**: Return / downside deviation (penalizes only negative volatility)

Crypto-Specific Risks

### Smart Contract Risk

• Never allocate >5% of account to a single unaudited protocol
• Diversify across audited protocols for yield strategies
• Monitor exploit databases and social channels for emerging threats

### Rug Pull Risk

• Size inversely with token age: newer tokens get smaller positions
• Verify: locked liquidity, renounced mint authority, holder distribution
• Cross-reference with token-holder-analysis skill for red flags

### Bridge and Custody Risk

• Don't hold >20% on any single platform or bridge
• Self-custody the majority of trading capital
• Budget for bridge fees and delays in execution planning

### MEV and Execution Risk

• Budget 1–3% for MEV/slippage on Solana DEX trades
• Use priority fees during congestion
• See slippage-modeling skill for detailed cost estimation

### Correlation Spikes

• In crashes, crypto correlations approach 1.0
• Your "diversified" portfolio may behave as one position
• Stress-test portfolio assuming all positions drop simultaneously

PumpFun Risk Framework

PumpFun and similar meme token platforms require a distinct risk approach:

### Core Principle

Treat every PumpFun trade as a potential 100% loss. Size accordingly.

### Position Limits

• **Per-token maximum**: 0.1–0.5 SOL
• **Daily PumpFun budget**: Fixed allocation (e.g., 2 SOL/day)
• **Never exceed budget**: When daily allocation is gone, stop

### Tracking

• Track PumpFun P&L separately from main portfolio
• Calculate PumpFun win rate and expectancy independently
• Don't let PumpFun losses affect main portfolio risk limits

### Risk Adjustments

• No stop-losses on PumpFun (assume 100% loss at entry)
• Take profits aggressively: 2×, 3×, 5× partial exits
• Time-based exit: close within hours, not days

Integration with Other Skills

• **position-sizing**: Use risk limits from this skill to constrain position sizes
• **exit-strategies**: Circuit breakers override exit strategies (forced exits)
• **`portfo