Risk Parity Balancer

Skill #84: Risk Parity Balancer

Allocates portfolio weights based on risk contribution rather than capital weights. Each asset contributes equally to total portfolio risk.

Why Risk Parity?

Traditional portfolios (60/40 stocks/bonds) are dominated by stock risk:

• 60% stocks = ~90% of portfolio risk
• 40% bonds = ~10% of portfolio risk

Risk parity ensures each asset contributes equally to volatility.

Features

Core Functionality

• Equal Risk Contribution (ERC): Each asset contributes 1/N of total risk
• Inverse Volatility: Weight inversely proportional to volatility
• Correlation-Aware: Accounts for asset correlations via covariance matrix
• Leverage Targeting: Optional leverage to hit target volatility (e.g., 10%)

Supported Modes

Risk Metrics Calculated

• Individual asset volatility (rolling window)
• Correlation matrix (Pearson, Spearman, or shrunk)
• Marginal Risk Contribution (MRC) per asset
• Risk Contribution (RC) as percentage
• Portfolio Sharpe ratio (pre/post rebalance)

Usage

kit skill risk-parity-balancer --assets BTC,ETH,SOL,USDC --mode erc
kit skill risk-parity-balancer --portfolio my-crypto --target-vol 12
kit skill risk-parity-balancer --lookback 90d --rebalance weekly

Parameters

Example Output

🎯 Risk Parity Analysis

Assets: BTC, ETH, SOL, USDC
Mode: Equal Risk Contribution (ERC)
Lookback: 60 days

📊 Current Allocation:
Asset   Weight   Vol(ann)   Risk Contrib
BTC     40.0%    65%        58.2%  ⚠️
ETH     30.0%    75%        31.5%
SOL     20.0%    95%        9.8%
USDC    10.0%    0.1%       0.5%

📐 Risk Parity Weights:
Asset   New Weight   Risk Contrib   Change
BTC     18.5%        25.0%          -21.5%
ETH     15.2%        25.0%          -14.8%
SOL     8.3%         25.0%          -11.7%
USDC    58.0%        25.0%          +48.0%

📈 Portfolio Impact:
Before: 52% annual vol, Sharpe 0.85
After:  15% annual vol, Sharpe 1.42 (+67%)

Algorithm

Equal Risk Contribution (ERC)

Objective: Minimize difference in risk contributions:

min Σᵢ Σⱼ (wᵢ(Σw)ᵢ - wⱼ(Σw)ⱼ)²

Subject to:
- Σwᵢ = 1 (or leverage target)
- wᵢ ≥ min_weight
- wᵢ ≤ max_weight

Where:

• wᵢ = weight of asset i
• Σ = covariance matrix
• (Σw)ᵢ = marginal risk contribution of asset i

Hierarchical Risk Parity (HRP)

1. Calculate correlation matrix
2. Apply hierarchical clustering (dendrogram)
3. Quasi-diagonalize matrix
4. Recursive bisection for weights
5. Apply constraints

Integration

With Other Skills

• #43 Momentum Ranking: Filter assets before risk parity
• #51 Trailing Grid: Use risk parity for grid sizing
• #56 Tax Calculator: Optimize for tax-efficient rebalancing
• #21 DeFi Yield: Include yield in return estimates

Auto-Rebalance Hook

// hooks/risk-parity-rebalance.js
module.exports = {
  name: 'risk-parity-rebalance',
  events: ['scheduler.daily'],
  async handler(event, context) {
    const weights = await context.skill('risk-parity-balancer', {
      portfolio: 'main',
      mode: 'erc',
      threshold: 0.05  // 5% drift trigger
    });
    
    if (weights.needsRebalance) {
      await context.trading.rebalance(weights.orders);
      await context.notify(`Rebalanced: ${weights.summary}`);
    }
  }
};

References

• Maillard, Roncalli, Teïletche (2010): "The Properties of Equally Weighted Risk Contribution Portfolios"
• De Prado (2016): "Building Diversified Portfolios that Outperform Out of Sample"
• Roncalli (2013): "Introduction to Risk Parity and Budgeting"

Related Skills

• #15 Risk Manager: Sets overall risk limits
• #17 Portfolio Allocator: Capital-weighted allocation
• #44 Correlation Analyzer: Deep dive into correlations
• #83 Deal Manager: Manages individual position sizing