longbridge-asset-allocation

name	longbridge-asset-allocation
description	Asset allocation and portfolio optimisation via Longbridge — efficient frontier (MPT), Black-Litterman model overview, risk parity / risk budgeting, all-weather strategy, and practical allocation recommendations based on the user's Longbridge account data. Triggers: "资产配置", "组合优化", "有效前沿", "Black-Litterman", "风险预算", "风险平价", "全天候策略", "大类资产", "資產配置", "組合優化", "有效前沿", "風險預算", "風險平價", "全天候策略", "大類資產", "asset allocation", "portfolio optimization", "efficient frontier", "Black-Litterman", "risk parity", "all-weather strategy", "mean-variance optimization", "strategic allocation".
license	MIT
metadata	{"author":"longbridge","version":"1.0.0","risk_level":"account_read","requires_login":false,"default_install":true,"requires_mcp":false,"tier":"analysis"}

longbridge-asset-allocation

Prompt-only analysis skill. Explains major asset-allocation frameworks (MPT efficient frontier, Black-Litterman, risk parity, all-weather) and, when the user is logged in, applies them to their actual Longbridge portfolio data.

Response language: match the user's input language — Simplified Chinese / Traditional Chinese / English.

When to use

• "帮我做资产配置分析" / "資產配置分析" / "help me with asset allocation"
• "什么是有效前沿" / "有效前沿" / "explain the efficient frontier"
• "Black-Litterman 模型怎么用" / "Black-Litterman model"
• "风险平价策略" / "風險平價策略" / "risk parity strategy"
• "全天候策略怎么配置" / "全天候策略" / "all-weather portfolio allocation"
• "帮我优化组合配置" / "optimize my portfolio allocation"

Workflow

1. Framework selection: identify which allocation approach the user wants (MPT / Black-Litterman / risk parity / all-weather / practical advice).
2. Account data (if logged in): fetch current positions and 252-day price history for each holding.
3. Explain the framework with the user's actual holdings as context.
4. Generate suggested target weights based on the chosen framework.
5. Present the allocation with rationale.

CLI

Run longbridge <subcommand> --help to verify exact flags before calling.

# Current holdings (if user is logged in)
longbridge portfolio --format json
longbridge positions --format json

# 252-day daily price history for each holding (run concurrently; ~1 year for covariance)
longbridge kline <SYMBOL> --period day --count 252 --format json

# Optional: valuation context
longbridge calc-index <SYMBOL> --format json

Framework Reference

MPT (Modern Portfolio Theory)

• Compute expected return (historical mean daily return × 252) and covariance matrix from 252-day returns.
• Find minimum-variance portfolio and tangency portfolio (max Sharpe).
• Caution: MPT is sensitive to input estimation error; treat outputs as directional, not prescriptive.

Black-Litterman

• Start from market-cap equilibrium weights (CAPM implied returns).
• Blend user's views (e.g. "I expect TSLA to outperform by 5%") via Bayesian update.
• Output: posterior expected returns + revised weights.
• Explain conceptually; provide numeric illustration when user supplies explicit views.

Risk Parity

• Allocate so each asset contributes equally to total portfolio volatility.
• Approximate weight ∝ 1 / volatility (simplified). For full risk parity use covariance.
• Result: typically overweights low-volatility assets (bonds, gold) vs equities.

All-Weather (Bridgewater style)

• 4 economic quadrants: growth up/down × inflation up/down.
• Suggested weight guidance: 30% equities, 40% long bonds, 15% intermediate bonds, 7.5% gold, 7.5% commodities.
• Map user's holdings to quadrant exposure; identify gaps.

Output template

Asset Allocation Analysis — Source: Longbridge Securities
Framework: <MPT / Black-Litterman / Risk Parity / All-Weather / Practical>
Date: <today>

[Current Portfolio]
Asset       Weight   Expected Return   Volatility (ann.)
<symbol>    <N>%     <N>%              <N>%
...

[Suggested Allocation — <Framework>]
Asset       Target Weight   Rationale
<symbol>    <N>%            <reason>
...

[Key Metrics]
- Portfolio expected return (ann.): N%
- Portfolio volatility (ann.): N%
- Sharpe ratio (rf=4%): N

[Caveats]
- Historical returns do not guarantee future results.
- Covariance estimates are noisy over short windows.
- <framework-specific caveats>

⚠️ 仅供参考，不构成投资建议。/ 僅供參考，不構成投資建議。/ For reference only. Not investment advice.

Error handling

Situation	简体回复	繁體回復	English reply
command not found: longbridge	回退到 MCP；若也不可用，请安装 longbridge-terminal	回退到 MCP；若也不可用，請安裝 longbridge-terminal	Fall back to MCP; if unavailable, install longbridge-terminal.
stderr not logged in	未登录时将使用用户指定的标的做示例分析	未登入時將使用用戶指定的標的做示例分析	Not logged in — will analyse user-specified symbols instead.
Price history < 60 days	数据不足，降级为简单波动率估算	數據不足，降級為簡單波動率估算	Insufficient history; degrade to simple volatility estimate.
No positions and no symbols given	请提供要分析的标的或登录账户	請提供要分析的標的或登入賬戶	Please provide symbols to analyse or log in to your account.

MCP fallback

If longbridge CLI is not installed, use MCP tools:

When the CLI is unavailable, fall back to the MCP server. Discover available tools from the MCP server's tool list at runtime — do not rely on hardcoded tool names.

MCP setup: claude mcp add --transport http longbridge https://openapi.longbridge.com/mcp (quote scope; trade_read for account data).

Related skills

• Rebalance to a new target → longbridge-portfolio-rebalance
• Portfolio health-check → longbridge-portfolio-diagnosis
• Risk metrics (VaR, drawdown) → longbridge-risk-analysis

File layout

longbridge-asset-allocation/
└── SKILL.md          # prompt-only, no scripts/