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asset-allocation

Generate portfolio allocation configurations using classical frameworks and built-in optimizers.

Updated Apr 14, 2026
Search Tags:#portfolio#optimizer#allocation#rebalancing#black-litterman#risk-budgeting#mpt
One-click install
npx skills add https://github.com/Liangwei-zhang/six-stock --skill asset-allocation-liangwei-zhang
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: asset-allocation
Source: https://github.com/Liangwei-zhang/six-stock/tree/main/Vibe-Trading/agent/src/skills/asset-allocation
Command: npx skills add https://github.com/Liangwei-zhang/six-stock --skill asset-allocation-liangwei-zhang

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill provides a structured framework to design robust asset allocations using Modern Portfolio Theory, Black-Litterman, risk budgeting, and the all-weather approach, turning theory into practical, backtest-ready configurations.

Core Features & Use Cases

  • Classical frameworks for portfolio construction and risk control (MPT, Black-Litterman, risk budgeting, all-weather)
  • Built-in optimizers: equal_volatility, risk_parity, mean_variance, max_diversification
  • Rebalancing rules, backtesting-ready configuration, and integration with config.json

Quick Start

Configure a basic 60-day lookback mean_variance optimizer and set monthly rebalancing to generate initial weights.

Frequently Asked Questions about asset-allocation

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the best way to optimize portfolio allocation using Modern Portfolio Theory?▼

Portfolio allocation using Modern Portfolio Theory is optimized by calculating expected returns and covariances to produce actionable target weights and rebalancing rules.

How do I apply Black-Litterman and risk budgeting to generate backtest-ready configurations?▼

Applying Black-Litterman and risk budgeting involves processing expected returns and covariances through built-in optimizers like mean_variance and risk_parity to output target weights and rebalancing triggers.

Can I use mean_variance and risk_parity optimizers for my rebalancing rules?▼

Mean_variance and risk_parity are supported as built-in optimizers alongside equal_volatility and max_diversification to calculate target weights and define rebalancing triggers.

When do I need to use the all-weather strategy versus max_diversification for asset allocation?▼

The all-weather strategy applies risk budgeting across asset classes for robustness across economic scenarios, while max_diversification maximizes the diversification ratio of the portfolio assets.

What inputs are required to start optimizing asset weights with these frameworks?▼

Optimizing asset weights requires inputs such as expected returns, asset covariances, and an eligible asset universe to configure the optimizer and generate target weights for a trading pipeline.

Does the portfolio optimizer output integrate directly into a backtesting pipeline?▼

The portfolio optimizer produces actionable configuration outputs like optimizer settings, target weights, and rebalancing triggers ready for direct integration into a trading or backtesting pipeline.

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