asset-allocation
This skill teaches portfolio optimization from theory to implementation, covering Modern Portfolio Theory, Black-Litterman blending, risk budgeting, and all-weather allocation strategies. It includes five configurable optimizers—equal volatility, risk parity, mean-variance, maximum diversification, and turnover-aware—each suited to different market conditions and cost constraints.
Asset Allocation optimizes your portfolio weights using MPT, risk parity, and other proven frameworks.
AI-generated summary based on this skill's SKILL.md
Install
HKUDS/Vibe-Trading/asset-allocation · repository language: Python
git clone https://github.com/HKUDS/Vibe-Trading
cp -r Vibe-Trading/agent/src/skills/asset-allocation ~/.claude/skills/asset-allocationFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I build a balanced portfolio using asset-allocation?
asset-allocation teaches portfolio optimization from theory to implementation. Start by understanding your risk tolerance and investment horizon, then use the skill's five configurable optimizers—equal volatility, risk parity, mean-variance, maximum diversification, and turnover-aware—to construct allocations suited to your market conditions and cost constraints. The skill covers Modern Portfolio Theory fundamentals to guide weight selection across stocks, bonds, gold, and other assets.
What's the difference between risk parity vs mean variance in asset-allocation?
asset-allocation covers both frameworks. Mean-variance optimization (Markowitz) maximizes risk-adjusted returns by balancing expected returns against portfolio variance. Risk parity, by contrast, allocates capital so each asset contributes equally to total portfolio risk, often performing better in diversified, low-correlation environments. The skill lets you compare both approaches and choose based on your market outlook and constraints.
How often should I rebalance my portfolio using asset-allocation?
asset-allocation implements rebalancing triggers and frequency for asset allocation maintenance as a core feature. Rather than fixed schedules, the skill helps you configure trigger-based rebalancing—rebalancing when allocations drift beyond thresholds—and turnover-aware optimization that balances drift correction against transaction costs. This approach prevents excessive trading while maintaining your target allocation.
How does asset-allocation use the Black-Litterman model?
asset-allocation includes the Black-Litterman model for incorporating investor views into allocations. This advanced framework blends market-implied returns (from equilibrium pricing) with your own return forecasts, producing more stable and intuitive allocations than pure mean-variance optimization. The skill provides a tutorial on applying Black-Litterman to refine portfolio weights based on your market convictions.
What correlation structures does asset-allocation analyze?
asset-allocation teaches understanding correlation structures and diversification benefits across assets as a key principle. The skill helps you build correlation matrices for your asset mix, identify low-correlation asset combinations that reduce portfolio variance, and calculate diversification ratios to measure how effectively your allocation spreads risk. These insights guide construction of all-weather portfolios resilient across market regimes.
Which optimization algorithms does asset-allocation support?
asset-allocation configures and compares five optimization algorithms: equal volatility (equal risk contribution), risk parity (equal risk budgeting), mean-variance (Markowitz), maximum diversification (highest diversification ratio), and turnover-aware (minimizes rebalancing costs). Each suits different market conditions and constraints. The skill teaches you to select and tune the right optimizer for your portfolio goals and trading costs.
SKILL.md
rendered from the published skill — quoted content, verbatim
Asset Allocation and Portfolio Optimization
Overview
From asset allocation theory to practical implementation, this skill covers classical frameworks (MPT, BL, risk budgeting, all-weather) and the usage of the four optimizers built into this system. The output can be written directly into config.json.
Asset Allocation Theory
1. Modern Portfolio Theory (MPT, Markowitz)
Core idea: maximize expected return for a given level of risk (the efficient frontier).
Optimization problem:
min w'Σw (portfolio variance)
s.t. w'μ = target_return
Σw = 1
w ≥ 0 (no shorting)
| Advantages | Disadvantages |
|---|---|
| Mathematically rigorous | Extremely sensitive to inputs (garbage in, garbage out) |
| Efficient frontier is visualizable | Concentrated-allocation problem (often produces extreme weights) |
| Foundational framework | Assumes normality and ignores fat tails |
Practical advice: do
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agent/src/skills/asset-allocation/SKILL.md