Skill #84: Risk Parity Balancer

SkillCommerce & finance

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

Available today. Use it from your connected AI after setup.

Connect ahel once, and every AI you use reads what you have installed.

Then ask your AI: use the Skill #84: Risk Parity Balancer skill

About this capability

About AI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading

What this skill tells your AI

The instructions your AI receives, as published by signal-execution-labs/forex-trading-ai-agent in skills/risk-parity-balancer/SKILL.md and read by ahel’s review.

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

ModeDescriptionUse Case
inverse-vol1/volatility weightingSimple, correlation-agnostic
ercEqual Risk ContributionFull covariance optimization
hierarchicalHierarchical Risk Parity (HRP)Handles instability in correlation
minimum-varianceMin variance portfolioRisk minimization

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

ParameterDefaultDescription
--assets-Comma-separated asset list
--portfolio-Portfolio ID to rebalance
--modeercWeighting mode (inverse-vol, erc, hrp, min-var)
--lookback60dVolatility/correlation lookback period
--target-vol-Target annual volatility (enables leverage)
--max-leverage3.0Maximum leverage allowed
--min-weight0.02Minimum weight per asset (2%)
--max-weight0.40Maximum weight per asset (40%)
--rebalancemanualRebalance frequency (daily, weekly, monthly)
--correlationpearsonCorrelation method

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

Signals

GitHub stars
136
Forks
870
Last commit
Sep 2026
Advanced
Catalog kind
skill
Gateway key
risk-parity-balancer
Source
github.com/signal-execution-labs/forex-trading-ai-agent