backtrader
Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers,…
Portfolio-level risk controls, drawdown management, exposure limits, and circuit breakers for crypto trading
$ npx -y skills add agiprolabs/claude-trading-skills --skill risk-management --agent claude-codeHow it fires
How this skill gets triggered: by you, by Claude, or both.
/risk-managementContext preview
The summary Claude sees to decide when to auto-load this skill.
Portfolio-level risk controls, drawdown management, exposure limits, and circuit breakers for crypto trading
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:
1. **Survival** — Never risk account ruin. No single trade, day, or week should threaten your ability to continue trading. 2. **Capital preservation** — Protect what you have. Losses compound geometrically; recovery requires outsized gains. 3. **Growth** — Only after survival and preservation are secured, pursue returns.
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 0.0def 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_ddA comprehensive collection of 68 ready-to-use trading, DeFi, and quantitative finance Agent Skills. Works with Claude Code, Cursor, Codex, Gemini CLI, and 30+ other tools.
Repo: agiprolabs/claude-trading-skills
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