{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-quantconnect","slug":"quantconnect","name":"QuantConnect","type":"mcp","url":"https://github.com/QuantConnect/mcp-server","page_url":"https://unfragile.ai/quantconnect","categories":["mcp-servers","deployment-infra"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-quantconnect__cap_0","uri":"capability://tool.use.integration.llm.autonomous.strategy.project.creation.via.mcp","name":"llm-autonomous strategy project creation via mcp","description":"Exposes QuantConnect project creation as an MCP tool that LLMs can invoke directly, allowing Claude or o3 Pro to programmatically scaffold new algorithmic trading projects with boilerplate code, asset classes, and data feeds pre-configured. The MCP server translates natural language intent (e.g., 'create a momentum strategy for SPY') into QuantConnect API calls that initialize project structure, set resolution/universe parameters, and wire up data subscriptions without manual UI interaction.","intents":["I want Claude to autonomously create a new trading strategy project without me logging into QuantConnect","I need to scaffold multiple strategy variants programmatically with different asset universes","I want to generate boilerplate trading code with proper QuantConnect imports and class structure automatically"],"best_for":["Quant researchers building LLM-driven strategy discovery workflows","Algorithmic traders automating project initialization at scale","Teams integrating QuantConnect into AI-native trading pipelines"],"limitations":["MCP tool invocation is synchronous — project creation latency (typically 2-5s) blocks LLM reasoning until completion","No built-in project templating beyond default boilerplate — custom strategy patterns require post-creation code edits","LLM must understand QuantConnect API constraints (e.g., valid asset classes, resolution limits) to avoid invalid project configs"],"requires":["QuantConnect API key with project creation permissions","MCP client supporting tool_use (Claude 3.5+, o3 Pro, or compatible LLM)","Docker runtime for MCP server deployment","Network access to QuantConnect API endpoints"],"input_types":["natural language instruction (e.g., 'create a mean-reversion strategy for crypto')","structured JSON with project parameters (name, asset class, resolution, universe)"],"output_types":["project ID and metadata (name, creation timestamp, initial config)","boilerplate Python code with QuantConnect Algorithm class scaffold"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_1","uri":"capability://automation.workflow.backtesting.strategy.execution.with.live.parameter.sweeping","name":"backtesting strategy execution with live parameter sweeping","description":"Allows LLMs to submit strategy code and parameter ranges to QuantConnect's backtesting engine via MCP, receiving backtest results (Sharpe ratio, max drawdown, returns) that feed back into LLM reasoning loops for iterative optimization. The server handles code submission, job queuing, result polling, and JSON parsing of backtest metrics, enabling the LLM to autonomously evaluate strategy variants without manual result inspection.","intents":["I want Claude to backtest a strategy variant and get performance metrics to decide if it's worth deploying","I need to run 50 parameter combinations and have Claude pick the best one based on risk-adjusted returns","I want to iteratively refine a strategy based on backtest feedback without leaving the LLM conversation"],"best_for":["Quant researchers using LLMs for strategy optimization loops","Traders prototyping strategy ideas with rapid backtest feedback","Teams building autonomous strategy discovery agents"],"limitations":["Backtest execution is asynchronous with polling overhead — typical latency 30-120s per backtest depending on data range and complexity","Parameter sweeping requires sequential backtest submissions; no built-in parallel batch execution means 50 variants = 50 sequential API calls","Backtest results are historical performance only — no forward-testing or live paper trading feedback in this capability","LLM must interpret raw backtest metrics (Sharpe, drawdown) without built-in risk analysis or statistical significance testing"],"requires":["QuantConnect API key with backtesting permissions","Strategy code in QuantConnect-compatible Python (QCAlgorithm subclass)","Historical data access for backtest date range (included with QuantConnect subscription)","MCP client with async/polling support for long-running jobs"],"input_types":["Python strategy code (string or file path)","parameter ranges (JSON: {param_name: [min, max, step]})","backtest config (start date, end date, initial capital, benchmark)"],"output_types":["backtest job ID and status","performance metrics JSON (total return, Sharpe ratio, max drawdown, win rate, etc.)","equity curve data (optional, for visualization)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_2","uri":"capability://automation.workflow.live.trading.deployment.with.strategy.code.push.and.execution","name":"live trading deployment with strategy code push and execution","description":"Enables LLMs to deploy backtested strategies to QuantConnect's live trading environment by pushing strategy code, configuring live parameters (broker, account, position sizing), and triggering execution via MCP tools. The server handles code validation, live algorithm instantiation, and order routing setup, allowing autonomous agents to move from backtest → live trading without manual deployment steps.","intents":["I want to deploy a backtested strategy to live trading after Claude confirms it meets risk criteria","I need Claude to autonomously push updated strategy code to a live algorithm without stopping the current deployment","I want to scale a strategy across multiple accounts by having Claude deploy variants with different position sizing"],"best_for":["Quantitative traders automating strategy deployment workflows","Hedge funds running autonomous strategy discovery → deployment pipelines","Risk-managed teams with strict deployment approval gates that LLMs can enforce"],"limitations":["Live deployment requires explicit QuantConnect live trading subscription and broker integration (Interactive Brokers, etc.) — not available on paper trading accounts","Code validation is basic (syntax check, import validation) — runtime errors in live trading are not caught until execution","No built-in circuit breaker or kill-switch in this capability — LLM must implement position limits and drawdown checks separately","Deployment is one-way; rolling back a live algorithm requires a separate stop/restart cycle","Order execution latency depends on broker and market conditions — LLM cannot guarantee fill prices or execution timing"],"requires":["QuantConnect live trading subscription with active broker connection","Broker API credentials (Interactive Brokers, Alpaca, etc.) configured in QuantConnect","Strategy code validated and backtested (no syntax errors)","Sufficient account capital for position sizing and margin requirements","MCP client with deployment permission scope"],"input_types":["Python strategy code (QCAlgorithm subclass)","live trading config (broker, account ID, initial capital, position limits)","deployment metadata (strategy name, version, approval status)"],"output_types":["deployment status (success/failure)","live algorithm ID and execution start timestamp","order routing confirmation and initial position state"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_3","uri":"capability://data.processing.analysis.real.time.portfolio.monitoring.and.position.tracking","name":"real-time portfolio monitoring and position tracking","description":"Exposes live portfolio state (positions, P&L, Greeks for options, margin utilization) as MCP tools that LLMs can query to make real-time trading decisions. The server polls QuantConnect's live trading API and caches portfolio snapshots, allowing LLMs to reason about current market exposure, hedge requirements, and rebalancing needs without manual dashboard inspection.","intents":["I want Claude to check my current portfolio P&L and decide if we need to hedge based on market conditions","I need real-time position tracking to trigger rebalancing when allocations drift beyond thresholds","I want Claude to monitor margin utilization and alert me if leverage is approaching dangerous levels"],"best_for":["Active traders using LLMs for real-time portfolio management","Hedge funds automating position monitoring and rebalancing","Risk managers building autonomous portfolio oversight agents"],"limitations":["Portfolio snapshots are eventually consistent — latency between live market state and MCP query result is typically 1-5 seconds","Greeks calculations (delta, gamma, vega) are approximations based on cached option data; real-time Greeks require separate option pricing integration","Margin utilization is broker-specific and may not be available for all brokers or account types","No built-in alerting — LLM must explicitly check portfolio state; missed checks = missed alerts","Position tracking is limited to QuantConnect-managed accounts; external positions (e.g., in other brokers) are invisible"],"requires":["Active live trading algorithm running on QuantConnect","Broker connection with real-time position data (Interactive Brokers, Alpaca, etc.)","MCP client with polling or subscription support for frequent queries","Network connectivity to QuantConnect live trading API"],"input_types":["portfolio query parameters (account ID, position filter, time range)","optional: specific symbol or strategy filter"],"output_types":["position list with entry price, current price, P&L, quantity, Greeks (if options)","portfolio summary (total value, cash, margin utilization, total P&L)","timestamp of last portfolio update"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_4","uri":"capability://code.generation.editing.strategy.code.analysis.and.optimization.suggestions","name":"strategy code analysis and optimization suggestions","description":"Analyzes submitted strategy code for performance bottlenecks, risk violations, and optimization opportunities using static analysis and backtest metrics. The MCP server parses Python code, identifies common anti-patterns (e.g., look-ahead bias, excessive rebalancing), and suggests refactorings that improve Sharpe ratio or reduce drawdown based on historical performance data.","intents":["I want Claude to review my strategy code and suggest optimizations to improve risk-adjusted returns","I need to detect if my strategy has look-ahead bias or other common backtesting pitfalls","I want Claude to identify expensive operations (e.g., daily rebalancing) and suggest more efficient alternatives"],"best_for":["Quant researchers learning strategy development best practices","Teams enforcing code quality standards for trading strategies","Traders optimizing existing strategies for better performance"],"limitations":["Static analysis detects only common patterns; sophisticated bugs (e.g., subtle data leakage) may be missed","Optimization suggestions are heuristic-based and may not apply to all market regimes or asset classes","No dynamic profiling — cannot measure actual execution time or memory usage without running backtest","Suggestions are generic; domain-specific optimizations (e.g., for crypto vs equities) require manual interpretation"],"requires":["Strategy code in QuantConnect-compatible Python","Optional: backtest results for performance-based suggestions","MCP client with code parsing support"],"input_types":["Python strategy code (string or file path)","optional: backtest metrics (Sharpe, drawdown, returns)"],"output_types":["list of detected issues (look-ahead bias, excessive rebalancing, etc.)","optimization suggestions with estimated impact (e.g., 'reduce rebalancing frequency to improve Sharpe by 0.2')","refactored code snippets (optional)"],"categories":["code-generation-editing","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_5","uri":"capability://automation.workflow.multi.strategy.portfolio.composition.and.rebalancing","name":"multi-strategy portfolio composition and rebalancing","description":"Allows LLMs to compose portfolios from multiple backtested strategies, allocate capital across them, and trigger rebalancing based on performance drift or market conditions. The MCP server manages strategy weights, tracks composite portfolio metrics, and executes rebalancing orders across all deployed strategies simultaneously, enabling autonomous multi-strategy portfolio management.","intents":["I want Claude to allocate capital across 5 different strategies based on their Sharpe ratios and correlation","I need to rebalance a multi-strategy portfolio when allocations drift beyond target weights","I want Claude to dynamically adjust strategy weights based on market regime changes (volatility, correlation shifts)"],"best_for":["Hedge funds managing diversified strategy portfolios","Quant teams automating multi-strategy allocation and rebalancing","Risk managers building autonomous portfolio composition agents"],"limitations":["Rebalancing is synchronous across all strategies — execution latency scales with number of strategies (typically 5-30s for 5+ strategies)","Correlation estimates are based on historical backtest data; regime changes may invalidate allocations quickly","No built-in transaction cost modeling — rebalancing frequency must be manually tuned to avoid excessive slippage","Strategy weights are static between rebalancing events; dynamic weight adjustment requires separate capability","Cross-strategy hedging (e.g., offsetting positions) is not automated — must be implemented in individual strategies"],"requires":["Multiple backtested strategies deployed on QuantConnect","Backtest data for correlation and performance analysis","Live trading account with sufficient capital for all strategies","MCP client with portfolio composition and rebalancing support"],"input_types":["list of strategy IDs and target allocations (weights or dollar amounts)","rebalancing trigger (time-based, drift-based, or manual)","optional: correlation matrix and risk constraints"],"output_types":["composite portfolio metrics (total return, Sharpe, max drawdown, correlation matrix)","rebalancing orders and execution status","updated strategy weights and capital allocation"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_6","uri":"capability://data.processing.analysis.historical.backtest.data.retrieval.and.analysis","name":"historical backtest data retrieval and analysis","description":"Provides LLMs with access to historical backtest results, equity curves, and trade logs for strategies, enabling post-hoc analysis and comparison. The MCP server queries QuantConnect's backtest archive, parses results, and surfaces key metrics (Sharpe, drawdown, trade statistics) that LLMs can use to reason about strategy performance across different time periods or market conditions.","intents":["I want Claude to compare backtest results across 10 different strategy variants and pick the best one","I need to analyze why a strategy underperformed in a specific market regime (e.g., 2020 COVID crash)","I want Claude to extract trade-level statistics (win rate, avg profit per trade) to validate strategy logic"],"best_for":["Quant researchers analyzing strategy performance across time periods","Traders debugging strategy underperformance with historical data","Teams building automated strategy selection pipelines"],"limitations":["Backtest data is read-only; no ability to re-run backtests or modify historical results","Trade logs are limited to QuantConnect's recorded trades; slippage and commission assumptions may not match live execution","Equity curve granularity depends on backtest resolution (daily, hourly, etc.); intraday drawdowns may be missed","No built-in statistical significance testing — LLM must manually assess if performance differences are meaningful","Historical data is limited to QuantConnect's supported asset classes and date ranges"],"requires":["Completed backtest results stored in QuantConnect","QuantConnect API key with backtest history access","MCP client with data retrieval support"],"input_types":["backtest ID or strategy name","optional: date range filter, metric filter (e.g., 'Sharpe > 1.5')"],"output_types":["backtest summary (total return, Sharpe, max drawdown, win rate, etc.)","equity curve (daily/hourly values)","trade log (entry price, exit price, P&L, duration)","drawdown analysis (peak-to-trough, recovery time)"],"categories":["data-processing-analysis","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_7","uri":"capability://safety.moderation.risk.constraint.enforcement.and.position.limit.management","name":"risk constraint enforcement and position limit management","description":"Enforces user-defined risk constraints (max drawdown, max leverage, sector concentration limits) on live trading algorithms by intercepting orders and rejecting those that violate thresholds. The MCP server maintains a risk model that tracks current exposure, calculates constraint violations, and provides LLMs with real-time feedback on whether proposed trades are allowed.","intents":["I want to prevent my strategy from exceeding 2x leverage even if the algorithm tries to","I need to enforce sector concentration limits (e.g., max 20% in tech) across all positions","I want Claude to check if a proposed trade would violate my risk constraints before executing"],"best_for":["Risk managers enforcing firm-wide trading constraints","Traders protecting against algorithmic runaway scenarios","Teams with strict regulatory or internal risk limits"],"limitations":["Constraint enforcement is post-order (after order submission) — cannot prevent market impact from rejected orders","Risk model is based on cached position data; real-time market moves may invalidate constraints before enforcement","Sector/industry classification is static; dynamic sector definitions (e.g., ESG reclassification) require manual updates","No built-in constraint optimization — if a trade violates constraints, no automatic sizing-down or alternative suggestion","Constraints are algorithm-level; cross-algorithm risk aggregation requires separate capability"],"requires":["Risk constraint definitions (JSON or config file)","Live trading algorithm with order submission hooks","MCP client with constraint enforcement support","Real-time position and market data for constraint calculation"],"input_types":["risk constraints (max leverage, max drawdown, sector limits, etc.)","proposed order (symbol, quantity, direction)","current portfolio state (positions, cash, margin)"],"output_types":["constraint check result (allowed/rejected)","violation details if rejected (which constraint, by how much)","suggested order sizing to comply with constraints (optional)"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-quantconnect__cap_8","uri":"capability://automation.workflow.automated.strategy.discovery.via.parameter.grid.search","name":"automated strategy discovery via parameter grid search","description":"Enables LLMs to define parameter search spaces (e.g., moving average windows, momentum thresholds) and automatically generate and backtest strategy variants across the grid. The MCP server orchestrates parallel backtest submissions, collects results, and ranks strategies by performance metrics, allowing LLMs to discover optimal parameters without manual iteration.","intents":["I want Claude to find the best moving average window (5-200 days) for a momentum strategy by testing all combinations","I need to optimize a strategy's entry/exit thresholds across a 2D parameter grid (momentum, volatility)","I want to discover which asset universe (top 50 vs top 100 stocks) performs best for a given strategy"],"best_for":["Quant researchers optimizing strategy parameters at scale","Traders automating parameter tuning workflows","Teams building autonomous strategy discovery pipelines"],"limitations":["Grid search is combinatorially expensive — 10 parameters × 10 values each = 10 billion backtest combinations; practical limits are ~100-1000 variants","No built-in overfitting detection — best-performing parameters may be overfit to historical data and fail in live trading","Parameter interactions are not explored — grid search assumes parameters are independent","Backtest latency scales linearly with grid size; 1000 variants = 1000 × 30-120s = 8-33 hours of total backtest time","Results are ranked by single metric (e.g., Sharpe); multi-objective optimization (Sharpe vs drawdown) requires manual post-processing"],"requires":["Base strategy code (parameterized with variable placeholders)","Parameter search space definition (ranges, step sizes)","QuantConnect API key with backtesting permissions","MCP client with grid search orchestration support","Sufficient QuantConnect backtest quota for all variants"],"input_types":["base strategy code with parameter placeholders","parameter grid (param_name: {min, max, step})","backtest config (date range, initial capital, benchmark)"],"output_types":["ranked list of strategy variants with performance metrics","best-performing parameters and their Sharpe/drawdown/returns","optional: heatmap of parameter performance (for 2D grids)"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["QuantConnect API key with project creation permissions","MCP client supporting tool_use (Claude 3.5+, o3 Pro, or compatible LLM)","Docker runtime for MCP server deployment","Network access to QuantConnect API endpoints","QuantConnect API key with backtesting permissions","Strategy code in QuantConnect-compatible Python (QCAlgorithm subclass)","Historical data access for backtest date range (included with QuantConnect subscription)","MCP client with async/polling support for long-running jobs","QuantConnect live trading subscription with active broker connection","Broker API credentials (Interactive Brokers, Alpaca, etc.) configured in QuantConnect"],"failure_modes":["MCP tool invocation is synchronous — project creation latency (typically 2-5s) blocks LLM reasoning until completion","No built-in project templating beyond default boilerplate — custom strategy patterns require post-creation code edits","LLM must understand QuantConnect API constraints (e.g., valid asset classes, resolution limits) to avoid invalid project configs","Backtest execution is asynchronous with polling overhead — typical latency 30-120s per backtest depending on data range and complexity","Parameter sweeping requires sequential backtest submissions; no built-in parallel batch execution means 50 variants = 50 sequential API calls","Backtest results are historical performance only — no forward-testing or live paper trading feedback in this capability","LLM must interpret raw backtest metrics (Sharpe, drawdown) without built-in risk analysis or statistical significance testing","Live deployment requires explicit QuantConnect live trading subscription and broker integration (Interactive Brokers, etc.) — not available on paper trading accounts","Code validation is basic (syntax check, import validation) — runtime errors in live trading are not caught until execution","No built-in circuit breaker or kill-switch in this capability — LLM must implement position limits and drawdown checks separately","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.43,"ecosystem":0.49999999999999994,"match_graph":0.25,"freshness":0.6,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.048Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=quantconnect","compare_url":"https://unfragile.ai/compare?artifact=quantconnect"}},"signature":"XlE4tDoMbYIxYLedkB2z5m50+u25/jbwWs95zrH3uvk2W6eI66rX2li/66oT/qBQAibFGAMUvzq2C2IaUefUDA==","signedAt":"2026-06-22T05:09:03.211Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/quantconnect","artifact":"https://unfragile.ai/quantconnect","verify":"https://unfragile.ai/api/v1/verify?slug=quantconnect","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}