How do I use Portfolio Rotation MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@Portfolio Rotation MCP Server My portfolio is AAPL 20%, MSFT 15%, JPM 10%. Evaluate META and AVGO as swap candidates." That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

Portfolio Rotation MCP Server

by mothanaprime

Overview Schema Related Servers Score Discussions

Python

Remote

Portfolio Rotation MCP Server

PyPI Python

MCP server for portfolio rotation analysis. Score holdings and candidates across 5 dimensions, identify optimal swaps, validate with risk checks and backtests.

Works with any MCP client: Claude Desktop, ChatGPT, Gemini, LangChain, Cursor, Windsurf, VS Code, Ollama clients, and more.

What It Does

You give it a portfolio and candidate tickers. It returns:

ROTATION SCORECARD (GARP Style)
Ticker | Thesis | Valuation | Momentum | Catalyst | Technical | Composite | Action
META   |   75   |    80     |    78    |    85    |    74     |   78.4    | Strong Buy
AVGO   |   70   |    72     |    75    |    70    |    80     |   73.1    | Buy
AAPL   |   70   |    65     |    62    |    60    |    68     |   65.5    | Hold
MSFT   |   65   |    60     |    58    |    55    |    62     |   60.2    | Hold
JPM    |   50   |    55     |    45    |    40    |    42     |   47.4    | Watch

SWAP RECOMMENDATIONS
Sell JPM (47.4) → Buy META (78.4) | Delta: +31.0 | Strong Swap
Sell JPM (47.4) → Buy AVGO (73.1) | Delta: +25.7 | Strong Swap

RISK FLAGS
⚠️ Technology sector: 35% (>30% limit)

BACKTEST (2y)
Strategy: +42.3% | Benchmark (SPY): +28.1% | Sharpe: 1.24 | Max Drawdown: -14.2%

Related MCP server: portfolio-mcp

Quick Start

# Install from PyPI
pip install portfolio-rotation-mcp

# Or run directly (no install needed)
uvx portfolio-rotation-mcp

# Set API key (optional -- falls back to yfinance without it)
export FINANCIAL_DATASETS_API_KEY=your-key

Prerequisites

Python >= 3.10
Optional: financial-datasets.ai API key for premium data (without it, prices come from yfinance and financial statements are unavailable)

11 Tools

Tool	Description
`fetch_prices`	Historical OHLCV prices (API + yfinance fallback)
`fetch_financials`	Income/balance/cashflow statements
`fetch_ff_factors`	Fama-French 5-factor + momentum data
`score_tickers`	5-dimension scoring (auto + manual)
`analyze_risk`	Concentration, correlation, volatility
`compare_swaps`	Pairwise swap recommendations (delta >= 15)
`run_backtest`	Historical strategy simulation
`stress_test`	Scenario replay, Monte Carlo, factor decomposition
`compute_attribution`	Trade attribution and swap alpha
`run_pipeline`	Full 6-stage rotation analysis
`get_skill`	Retrieve domain knowledge (scoring rules, swap logic, risk thresholds)

Platform Setup

Claude Desktop

Add to your config file:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Linux: ~/.config/claude/claude_desktop_config.json

{
  "mcpServers": {
    "portfolio-rotation": {
      "command": "uvx",
      "args": ["portfolio-rotation-mcp"],
      "env": {
        "FINANCIAL_DATASETS_API_KEY": "your-key"
      }
    }
  }
}

Then in Claude Desktop, just say:

My portfolio is AAPL 20%, MSFT 15%, JPM 10%. Evaluate META and AVGO as swap candidates.

Claude will automatically call the MCP tools.

Claude Code (CLI)

claude mcp add portfolio-rotation -- uvx portfolio-rotation-mcp

Cursor / Windsurf / VS Code

Add to your MCP settings (.cursor/mcp.json, .windsurf/mcp.json, or VS Code MCP config):

{
  "mcpServers": {
    "portfolio-rotation": {
      "command": "uvx",
      "args": ["portfolio-rotation-mcp"],
      "env": {
        "FINANCIAL_DATASETS_API_KEY": "your-key"
      }
    }
  }
}

LangChain (any model: DeepSeek, GPT, Llama, etc.)

from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI

# Use any model -- DeepSeek, GPT, Llama, etc.
llm = ChatOpenAI(
    model="deepseek-chat",  # or "gpt-4o", etc.
    base_url="https://api.deepseek.com/v1",
    api_key="sk-...",
)

async with MultiServerMCPClient({
    "portfolio-rotation": {
        "command": "uvx",
        "args": ["portfolio-rotation-mcp"],
        "env": {"FINANCIAL_DATASETS_API_KEY": "your-key"},
    }
}) as client:
    tools = client.get_tools()
    # Create agent with tools and invoke

OpenAI Agents SDK

from agents import Agent
from agents.mcp import MCPServerStdio

async with MCPServerStdio(
    command="uvx",
    args=["portfolio-rotation-mcp"],
) as server:
    tools = await server.list_tools()
    agent = Agent(name="Rotation Analyst", tools=tools)

Ollama + Continue / LibreChat

Configure in the MCP settings of your Ollama frontend:

{
  "command": "uvx",
  "args": ["portfolio-rotation-mcp"],
  "env": {
    "FINANCIAL_DATASETS_API_KEY": "your-key"
  }
}

Usage Examples

Quick: Full Pipeline (one tool call)

Ask your AI agent:

Analyze my portfolio: AAPL 20% (Technology), MSFT 15% (Technology), JPM 10% (Financials). Candidates: META, AVGO. Use GARP style.

The agent will call run_pipeline which runs all 6 stages automatically: data fetch -> scoring -> risk check -> swap comparison -> backtest -> report.

Targeted: Score Specific Tickers

Score AAPL, META, and AVGO. My thesis score for META is 80 and catalyst is 85.

The agent will call fetch_prices, then score_tickers with your manual overrides.

Deep Dive: Stress Test

Stress test my portfolio under a 2008-style crash scenario. Include Monte Carlo simulation.

The agent will call fetch_prices, fetch_ff_factors, then stress_test.

Post-Trade: Attribution

I sold INTC and bought NVDA on Jan 15 at $120. How did that swap perform?

The agent will call fetch_prices, then compute_attribution to measure swap alpha.

Development

# Clone and install in development mode
git clone git@github.com:mothanaprime/Rebalance-MCP.git
cd Rebalance-MCP
pip install -e .

# Run the server
portfolio-rotation-mcp

# Test with MCP inspector
mcp dev src/portfolio_rotation/server.py

Scoring Framework

5 dimensions, 0-100 each, weighted by investment style:

Dimension	GARP Weight	Auto?
Thesis Integrity	25%	Manual (via overrides)
Valuation Attractiveness	25%	Auto (needs financials)
Fundamental Momentum	20%	Auto (from prices)
Catalyst Proximity	15%	Manual (via overrides)
Technical Trend	15%	Auto (MA/RSI/relative strength)

Swap threshold: Buy Score - Hold Score >= 15

Style presets: garp (default), value, growth, momentum, event_driven -- each has different dimension weights.

See docs/scoring-framework.md for full details.

Agent Prompt

See docs/agent-prompt.md for a model-agnostic system prompt you can use to configure any AI agent for rotation analysis.

Environment Variables

Variable	Required	Default	Description
`FINANCIAL_DATASETS_API_KEY`	No	--	API key for financial-datasets.ai. Without it, prices fall back to yfinance and financials are unavailable.
`PORTFOLIO_ROTATION_SOURCE`	No	`auto`	Data source: `auto` (API first, yfinance fallback), `api`, `financial-datasets`, or `yfinance`. Can be overridden per-call.

License

MIT

Install Server

license - permissive license

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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