Bl View MCP
Portfolio Optimization MCP based on black-litterman model
Installation
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Black-Litterman Portfolio Optimization MCP Server
Black-Litterman portfolio optimization MCP server for AI agents
Works with Claude Desktop, Windsurf IDE, Google ADK, and any MCP-compatible AI
Features
- Portfolio Optimization - Black-Litterman model with sensitivity analysis
- Investor Views - Absolute/relative views with confidence levels
- Backtesting - Strategy comparison, drawdown analysis, timeseries
- Asset Analysis - Correlation matrix, VaR, per-asset statistics
- Dashboard Generation - Visualization hints for AI-generated charts
- Multiple Assets - S&P 500, NASDAQ 100, ETF, Crypto, custom data
Quick Start
Option 1: Smithery (Easiest - No Installation!) π
Install via Smithery in one command:
npx @smithery/cli install @irresi/bl-view-mcp --client claude
Or visit smithery.ai/server/@irresi/bl-view-mcp and click:
- "Add to Claude Desktop" - One-click setup
- "Add to ChatGPT" - Direct integration
- "Run" - Test in browser instantly
No Python/uv installation needed! Smithery hosts the server for you.
Option 2: Local Installation (uvx)
For offline use or development:
Step 1: Find uvx path
Run in terminal:
which uvx
# Example output: /Users/USERNAME/.local/bin/uvx
If uvx is not installed:
curl -LsSf https://astral.sh/uv/install.sh | sh
Step 2: Configure Claude Desktop
Config file location:
- macOS:
~/Library/Application Support/Claude/claude_desktop_config.json - Windows:
%APPDATA%\Claude\claude_desktop_config.json
File content (replace with your uvx path):
{
"mcpServers": {
"black-litterman": {
"command": "/Users/USERNAME/.local/bin/uvx",
"args": ["black-litterman-mcp"]
}
}
}
Step 3: Restart Claude Desktop
Cmd+Q (macOS) or fully quit and restart
Usage
Ask Claude:
"Optimize a portfolio with AAPL, MSFT, GOOGL. I think AAPL will return 10%."
First run: S&P 500 data auto-downloads (~30 seconds)
Tip: Want charts or dashboards? Just ask: "Show me a dashboard with the results" or "Create a visualization of the portfolio weights"
Example Use Cases
Try these prompts with Claude:
Note: Default period is 1 year for all tools. All returns are annualized - when you say "outperform by 40%", it means 40% annual return expectation.
Basic Optimization + Visualization
Optimize a portfolio with AAPL, MSFT, GOOGL, NVDA. I am confident that NVDA will outperform others by 40%. Show me a dashboard.
Backtesting with Benchmark
Backtest the above optimized portfolio for 3 years and compare with SPY.
Strategy Comparison
Compare buy_and_hold, passive_rebalance, and risk_managed strategies for this portfolio.
Correlation Analysis
Analyze the correlation between NVDA, AMD, and INTC.
Sensitivity Analysis
Create a portfolio with AAPL and MSFT. I expect AAPL to return 15%. Run sensitivity analysis with confidence levels 0.3, 0.5, 0.7, 0.9.
Demo Dashboards
Generated using the example prompts above with Claude Desktop:
Click images to view interactive HTML dashboards:
| Optimization | Backtest | Strategy |
|---|---|---|
![]() | ![]() | ![]() |
| Correlation | Sensitivity |
|---|---|
![]() | ![]() |
Other Installation Methods
pip (Python Package)
Install directly from PyPI:
pip install black-litterman-mcp
Then configure your MCP client to run:
black-litterman-mcp # or bl-view-mcp, bl-mcp
Requires Python 3.11+. Data auto-downloads on first use.
Windsurf IDE
.windsurf/mcp_config.json:
{
"mcpServers": {
"black-litterman": {
"command": "/Users/USERNAME/.local/bin/uvx",
"args": ["black-litterman-mcp"]
}
}
}
From Source (Developers)
git clone https://github.com/irresi/bl-view-mcp.git
cd bl-view-mcp
make install
make download-data # S&P 500 data
make test-simple
Docker
docker build -t bl-mcp .
docker run -p 5000:5000 -v $(pwd)/data:/app/data bl-mcp
Google ADK Web UI
Test with Google ADK (Agent Development Kit):
# Terminal 1: Start MCP HTTP server
make server-http # localhost:5000
# Terminal 2: Start ADK Web UI
make web-ui # localhost:8000
Open http://localhost:8000 in browser
Requires
make install(includes google-adk dependency)
Supported Datasets
| Dataset | Tickers | Description |
|---|---|---|
snp500 | ~500 | S&P 500 constituents (default) |
nasdaq100 | ~100 | NASDAQ 100 constituents |
etf | ~130 | Popular ETFs |
crypto | ~100 | Cryptocurrencies |
custom | - | User-uploaded data |
PyPI install: S&P 500 data auto-downloads on first run
Source install: Download additional datasets manually
make download-data # S&P 500 (default)
make download-nasdaq100 # NASDAQ 100
make download-etf # ETF
make download-crypto # Crypto
MCP Tools
optimize_portfolio_bl
Calculate optimal portfolio weights using Black-Litterman model.
optimize_portfolio_bl(
tickers=["AAPL", "MSFT", "GOOGL"],
period="1Y",
views={"P": [{"AAPL": 1}], "Q": [0.10]}, # AAPL expected 10% return
confidence=0.7,
investment_style="balanced" # aggressive / balanced / conservative
)
Views examples:
# Absolute view: "AAPL will return 10%"
views = {"P": [{"AAPL": 1}], "Q": [0.10]}
# Relative view: "NVDA will outperform AAPL by 20%"
views = {"P": [{"NVDA": 1, "AAPL": -1}], "Q": [0.20]}
VaR Warning: When predicted returns exceed 40%, EGARCH-based VaR analysis is automatically included in the warnings field.
backtest_portfolio
Validate portfolio strategy with historical data.
backtest_portfolio(
tickers=["AAPL", "MSFT", "GOOGL"],
weights={"AAPL": 0.4, "MSFT": 0.35, "GOOGL": 0.25},
period="3Y",
strategy="passive_rebalance", # buy_and_hold / passive_rebalance / risk_managed
benchmark="SPY"
)
get_asset_stats
Get asset statistics including VaR, correlation matrix, and covariance matrix.
get_asset_stats(
tickers=["AAPL", "MSFT", "GOOGL"],
period="1Y",
include_var=True # Set False for faster response (skips EGARCH VaR)
)
# Returns: assets (price, return, volatility, sharpe, var_95, percentile_95),
# correlation_matrix, covariance_matrix
upload_price_data
Upload external data (international stocks, custom assets, etc.).
# Direct upload (small data)
upload_price_data(
ticker="005930.KS", # Samsung Electronics
prices=[
{"date": "2024-01-02", "close": 78000.0},
{"date": "2024-01-03", "close": 78500.0},
...
],
source="custom"
)
# Or load from file (large data)
upload_price_data(
ticker="CUSTOM_INDEX",
file_path="/path/to/data.csv",
date_column="Date",
close_column="Close"
)
list_available_tickers
Query available tickers.
list_available_tickers(search="AAPL") # Search
list_available_tickers(dataset="snp500") # S&P 500 only
list_available_tickers(dataset="custom") # Custom data
Documentation
| Document | Description |
|---|---|
| docs/TESTING.md | Testing guide |
| docs/ARCHITECTURE.md | Technical architecture |
Tech Stack
- MCP Server: FastMCP
- Optimization: PyPortfolioOpt
- Risk Model: arch (EGARCH)
- Data: yfinance, ccxt (crypto)
License
MIT License - LICENSE
Troubleshooting
"spawn uvx ENOENT" / "uv binary not found"
Claude Desktop may not recognize system PATH. Use absolute path:
which uvx
# Use the output path in config
"Data file not found"
Source install:
make download-data
PyPI install: Auto-downloads on first run (~30 seconds).
"uv: command not found"
curl -LsSf https://astral.sh/uv/install.sh | sh





