longbridge-asset-allocation

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

longbridge <subcommand> --help

# 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

command not found: longbridge

not logged in

MCP fallback

longbridge

mcp__longbridge__portfolio

longbridge portfolio

mcp__longbridge__positions

longbridge positions

mcp__longbridge__history_candlesticks

longbridge kline --period day

mcp__longbridge__calc_indexes

longbridge calc-index

claude mcp add --transport http longbridge https://openapi.longbridge.com/mcp

quote

trade_read

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/