MCP Integration

Phandas provides MCP (Model Context Protocol) integration, allowing AI IDEs (like Cursor) to directly call Phandas operators and backtesting functions.

What is MCP?

MCP is a standard protocol that lets AI assistants access external tools and data sources. Through MCP, AI in Cursor can:

  • Directly fetch cryptocurrency market data

  • Browse all 50+ factor operators

  • View function source code

  • Execute factor backtests

Installation Steps

1. Install Phandas

pip install phandas

2. Configure Cursor

  1. Open Cursor

  2. Go to Settings → Tools & MCP → New MCP Server

  3. Paste the following JSON configuration:

{
  "mcpServers": {
    "phandas": {
      "command": "python",
      "args": ["-m", "phandas.mcp_server"]
    }
  }
}

  1. Save and restart Cursor

Verify Installation

After restarting Cursor, ask the AI in chat:

List all available phandas operators

If the AI responds with a list of operators, MCP configuration is successful.

Available Tools

The MCP server provides 4 tool functions:

fetch_market_data

Fetch cryptocurrency OHLCV data.

Parameters:

  • symbols: List of trading pairs (e.g., [‘BTC’, ‘ETH’])

  • timeframe: Time interval (‘1d’, ‘1h’, ‘15m’, etc.)

  • limit: Return last N data points (default: 5)

  • start_date: Start date (YYYY-MM-DD)

  • end_date: End date (YYYY-MM-DD)

  • sources: Data sources (default: [‘binance’])

Example:

Fetch the last 10 days of daily data for ETH and SOL

list_operators

List all available factor operators.

Returns names, function signatures, and documentation for all operators.

Example:

List all time series operators

read_source

View source code for any Phandas function or class.

Parameters:

  • object_path: Object path (e.g., ‘phandas.operators.ts_mean’)

Example:

Show the source code for ts_mean function

execute_factor_backtest

Execute custom factor backtests.

Parameters:

  • factor_code: Python code to calculate factor

  • symbols: List of trading tokens (default: [‘ETH’,’SOL’,’ARB’,’OP’,’POL’,’SUI’])

  • start_date: Start date (default: ‘2022-01-01’)

  • transaction_cost: Transaction fee rate (default: 0.0003 = 0.03%)

  • full_rebalance: Whether to fully rebalance (default: False)

Pre-defined variables:

  • close, open, high, low, volume

  • All Phandas operators (ts_rank(), ts_mean(), log(), rank(), vector_neut(), etc.)

Note: Code must assign result to variable named factor

Example:

Backtest a 20-day momentum factor neutralized against volume

Usage Examples

Common Use Cases

Query operators

Ask AI to list all available time series operators. AI will call list_operators() and filter relevant results.

Fetch market data

Request historical data for specific tokens. AI will call fetch_market_data() and return OHLCV data.

Execute factor backtest

Describe strategy logic. AI will auto-generate factor code and call execute_factor_backtest() for backtesting.

View source code

Ask about implementation details of specific functions. AI will use read_source() to display source code.

Benefits

Benefits of using MCP integration:

  • No coding required: Describe strategies in natural language, AI auto-generates code

  • Fast iteration: Quickly test different factor combinations

  • Learning tool: View source code to learn operator implementations

  • Data exploration: Easily fetch and analyze market data

Next Steps