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Portfolio-level risk controls, drawdown management, exposure limits, and circuit breakers for crypto trading
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Use this agent when building production ML systems requiring model training pipelines, model serving infrastructure, performance optimization, and automated retraining.
Use when building Python 3.11+ applications requiring type safety, async programming, or production-grade patterns. Invoke for type hints, pytest, async/await, dataclasses, mypy configuration.
Expert quantitative analyst specializing in financial modeling, algorithmic trading, and risk analytics. Masters statistical methods, derivatives pricing, and trading with focus on mathematical rigor, performance optimization, and profitable strategy development.
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
| name | risk-management |
| description | Portfolio-level risk controls, drawdown management, exposure limits, and circuit breakers for crypto trading |
Portfolio-level risk controls for crypto and Solana trading. This skill provides frameworks for drawdown management, exposure limits, circuit breakers, and crypto-specific risk considerations.
Every decision must respect this priority order:
Violating this hierarchy (chasing growth at the expense of survival) is the primary cause of account blowups.
Halt trading when portfolio drawdown from equity peak reaches a threshold:
| Account Type | Max Drawdown | Action |
|---|---|---|
| Conservative | -15% | Full stop, review all strategies |
| Moderate | -20% | Full stop, reduce to minimum size on recovery |
| Aggressive | -25% | Full stop, mandatory cooling period |
Recovery math makes this critical: a -20% drawdown requires +25% to recover. A -50% drawdown requires +100%. See references/drawdown_management.md for the full recovery table.
Stop opening new positions after daily P&L (realized + unrealized) hits:
Reset at midnight UTC. Three consecutive days hitting the daily limit triggers a weekly halt.
Reduce size or halt after weekly P&L reaches:
Maximum allocation to any single dimension:
| Dimension | Max Concentration |
|---|---|
| Single token (blue chip) | 10% of account |
| Single token (mid-cap) | 5% |
| Single token (small-cap) | 2% |
| Single token (PumpFun/micro) | 0.5% |
| Single sector/narrative | 30% |
| Single strategy | 40% |
Total deployed capital constraints:
Crypto assets correlate >0.7 during sell-offs. Effective diversification requires:
See references/exposure_limits.md for detailed limits by token type and strategy.
| 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 |
| 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.
Automated controls that restrict trading when conditions are met:
See references/circuit_breakers.md for implementation details.
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%
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
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
token-holder-analysis skill for red flagsslippage-modeling skill for detailed cost estimationPumpFun and similar meme token platforms require a distinct risk approach:
Treat every PumpFun trade as a potential 100% loss. Size accordingly.
position-sizing: Use risk limits from this skill to constrain position sizesexit-strategies: Circuit breakers override exit strategies (forced exits)portfolio-analytics: Feed portfolio metrics back for risk assessmentliquidity-analysis: Adjust position limits based on available liquidityslippage-modeling: Factor execution costs into risk calculationsreferences/drawdown_management.md — Drawdown math, response framework, causes, and remediationreferences/exposure_limits.md — Position limits by token type, portfolio limits, correlation managementreferences/circuit_breakers.md — Implementation details for all circuit breaker typesscripts/risk_dashboard.py — Portfolio risk dashboard with limit checking and color-coded statusscripts/drawdown_analyzer.py — Equity curve drawdown analysis with response recommendations# Run the risk dashboard with demo data
python scripts/risk_dashboard.py --demo
# Analyze drawdowns on a demo equity curve
python scripts/drawdown_analyzer.py --demo