QuantConnect MCP Server

Perform sparse optimization algorithm with Huber Downward Risk minimization. Args: portfolio_symbols: List of symbols for the portfolio benchmark_symbol: Benchmark symbol to track start_date: Start date in YYYY-MM-DD format end_date: End date in YYYY-MM-DD format max_iterations: Maximum iterations for optimization tolerance: Convergence tolerance max_weight: Maximum weight per asset penalty_param: Penalty parameter (p) huber_param: Huber statistics M-value lambda_param: Penalty weight (l) instance_name: QuantBook instance name Returns: Dictionary containing optimized portfolio weights and performance

{ "properties": { "benchmark_symbol": { "title": "Benchmark Symbol", "type": "string" }, "end_date": { "title": "End Date", "type": "string" }, "huber_param": { "default": 0.0001, "title": "Huber Param", "type": "number" }, "instance_name": { "default": "default", "title": "Instance Name", "type": "string" }, "lambda_param": { "default": 0.01, "title": "Lambda Param", "type": "number" }, "max_iterations": { "default": 20, "title": "Max Iterations", "type": "integer" }, "max_weight": { "default": 0.1, "title": "Max Weight", "type": "number" }, "penalty_param": { "default": 0.5, "title": "Penalty Param", "type": "number" }, "portfolio_symbols": { "items": { "type": "string" }, "title": "Portfolio Symbols", "type": "array" }, "start_date": { "title": "Start Date", "type": "string" }, "tolerance": { "default": 0.001, "title": "Tolerance", "type": "number" } }, "required": [ "portfolio_symbols", "benchmark_symbol", "start_date", "end_date" ], "type": "object" }