get_portfolio_strategy
Generate portfolio-aware trading signals by analyzing market questions with position context and correlation analysis to inform strategy decisions.
Instructions
Get a portfolio-aware strategy signal with position context and correlation analysis.
Args: market_query: Description of the bet or market question to analyze. portfolio: Optional list of current positions (dicts with ticker, side, size_usd). bankroll_usd: Total bankroll in USD for position sizing. max_position_pct: Maximum fraction of bankroll per position.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| market_query | Yes | ||
| portfolio | No | ||
| bankroll_usd | No | ||
| max_position_pct | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Implementation Reference
- src/rekko_mcp/server.py:273-295 (handler)The 'get_portfolio_strategy' function is a tool handler that constructs a request body with portfolio and market parameters, then makes an asynchronous POST request to the '/v1/signals/portfolio' endpoint to retrieve a strategy signal.
@mcp.tool() async def get_portfolio_strategy( market_query: str, portfolio: list[dict] | None = None, bankroll_usd: float = 10000.0, max_position_pct: float = 0.05, ) -> str: """Get a portfolio-aware strategy signal with position context and correlation analysis. Args: market_query: Description of the bet or market question to analyze. portfolio: Optional list of current positions (dicts with ticker, side, size_usd). bankroll_usd: Total bankroll in USD for position sizing. max_position_pct: Maximum fraction of bankroll per position. """ body: dict = { "market_query": market_query, "bankroll_usd": bankroll_usd, "max_position_pct": max_position_pct, } if portfolio: body["portfolio"] = portfolio return await _request("POST", "/v1/signals/portfolio", json=body)