MaverickMCP

"""MCP Prompts for better tool discovery and usage guidance.""" from fastmcp import FastMCP def register_mcp_prompts(mcp: FastMCP): """Register MCP prompts to help clients understand how to use the tools.""" # Backtesting prompts @mcp.prompt() async def backtest_strategy_guide(): """Guide for running backtesting strategies.""" return """ # Backtesting Strategy Guide ## Available Strategies (15 total) ### Traditional Strategies (9): - `sma_cross`: Simple Moving Average Crossover - `rsi`: RSI Mean Reversion (oversold/overbought) - `macd`: MACD Signal Line Crossover - `bollinger`: Bollinger Bands (buy low, sell high) - `momentum`: Momentum-based trading - `ema_cross`: Exponential Moving Average Crossover - `mean_reversion`: Mean Reversion Strategy - `breakout`: Channel Breakout Strategy - `volume_momentum`: Volume-Weighted Momentum ### ML Strategies (6): - `online_learning`: Adaptive learning with dynamic thresholds - `regime_aware`: Market regime detection (trending vs ranging) - `ensemble`: Multiple strategy voting system ## Example Usage: ### Traditional Strategy: "Run a backtest on AAPL using the sma_cross strategy from 2024-01-01 to 2024-12-31" ### ML Strategy: "Test the online_learning strategy on TSLA for the past year with a learning rate of 0.01" ### Parameters: - Most strategies have default parameters that work well - You can customize: fast_period, slow_period, threshold, etc. """ @mcp.prompt() async def ml_strategy_examples(): """Examples of ML strategy usage.""" return """ # ML Strategy Examples ## 1. Online Learning Strategy "Run online_learning strategy on NVDA with parameters: - lookback: 20 days - learning_rate: 0.01 - start_date: 2024-01-01 - end_date: 2024-12-31" ## 2. Regime-Aware Strategy "Test regime_aware strategy on SPY to detect market regimes: - regime_window: 50 days - threshold: 0.02 - Adapts between trending and ranging markets" ## 3. Ensemble Strategy "Use ensemble strategy on AAPL combining multiple signals: - Combines SMA, RSI, and Momentum - Uses voting to generate signals - More robust than single strategies" ## Important Notes: - ML strategies work through the standard run_backtest tool - Use strategy_type parameter: "online_learning", "regime_aware", or "ensemble" - These are simplified ML strategies that don't require training """ @mcp.prompt() async def optimization_guide(): """Guide for parameter optimization.""" return """ # Parameter Optimization Guide ## How to Optimize Strategy Parameters ### Basic Optimization: "Optimize sma_cross parameters for MSFT over the past 6 months" This will test combinations like: - fast_period: [5, 10, 15, 20] - slow_period: [20, 30, 50, 100] ### Custom Parameter Ranges: "Optimize RSI strategy for TSLA with: - period: [7, 14, 21] - oversold: [20, 25, 30] - overbought: [70, 75, 80]" ### Optimization Metrics: - sharpe_ratio (default): Risk-adjusted returns - total_return: Raw returns - win_rate: Percentage of winning trades ## Results Include: - Best parameter combination - Performance metrics for top combinations - Comparison across all tested parameters """ @mcp.prompt() async def available_tools_summary(): """Summary of all available MCP tools.""" return """ # MaverickMCP Tools Summary ## 1. Backtesting Tools - `run_backtest`: Run any strategy (traditional or ML) - `optimize_parameters`: Find best parameters - `compare_strategies`: Compare multiple strategies - `get_strategy_info`: Get strategy details ## 2. Data Tools - `get_stock_data`: Historical price data - `get_stock_info`: Company information - `get_multiple_stocks_data`: Batch data fetching ## 3. Technical Analysis - `calculate_sma`, `calculate_ema`: Moving averages - `calculate_rsi`: Relative Strength Index - `calculate_macd`: MACD indicator - `calculate_bollinger_bands`: Bollinger Bands - `get_full_technical_analysis`: All indicators ## 4. Screening Tools - `get_maverick_recommendations`: Bullish stocks - `get_maverick_bear_recommendations`: Bearish setups - `get_trending_breakout_recommendations`: Breakout candidates ## 5. Portfolio Tools - `optimize_portfolio`: Portfolio optimization - `analyze_portfolio_risk`: Risk assessment - `calculate_correlation_matrix`: Asset correlations ## Usage Tips: - Start with simple strategies before trying ML - Use default parameters initially - Optimize parameters after initial testing - Compare multiple strategies on same data """ @mcp.prompt() async def troubleshooting_guide(): """Troubleshooting common issues.""" return """ # Troubleshooting Guide ## Common Issues and Solutions ### 1. "Unknown strategy type" **Solution**: Use one of these exact strategy names: - Traditional: sma_cross, rsi, macd, bollinger, momentum, ema_cross, mean_reversion, breakout, volume_momentum - ML: online_learning, regime_aware, ensemble ### 2. "No data available" **Solution**: - Check date range (use past dates, not future) - Verify stock symbol (use standard tickers like AAPL, MSFT) - Try shorter date ranges (1 year or less) ### 3. ML Strategy Issues **Solution**: Use the standard run_backtest tool with: ``` strategy_type: "online_learning" # or "regime_aware", "ensemble" ``` Don't use the run_ml_backtest tool for these strategies. ### 4. Parameter Errors **Solution**: Start with no parameters (uses defaults): "Run backtest on AAPL using sma_cross strategy" Then customize if needed: "Run backtest on AAPL using sma_cross with fast_period=10 and slow_period=30" ### 5. Connection Issues **Solution**: - Restart Claude Desktop - Check server is running: The white circle should be blue - Try a simple test: "Get AAPL stock data" """ @mcp.prompt() async def quick_start(): """Quick start guide for new users.""" return """ # Quick Start Guide ## Test These Commands First: ### 1. Simple Backtest "Run a backtest on AAPL using the sma_cross strategy for 2024" ### 2. Get Stock Data "Get AAPL stock data for the last 3 months" ### 3. Technical Analysis "Show me technical analysis for MSFT" ### 4. Stock Screening "Show me bullish stock recommendations" ### 5. ML Strategy Test "Test the online_learning strategy on TSLA for the past 6 months" ## Next Steps: 1. Try different strategies on your favorite stocks 2. Optimize parameters for better performance 3. Compare multiple strategies 4. Build a portfolio with top performers ## Pro Tips: - Use 2024 dates for reliable data - Start with liquid stocks (AAPL, MSFT, GOOGL) - Default parameters usually work well - ML strategies are experimental but fun to try """ # Register a resources endpoint for better discovery @mcp.prompt() async def strategy_reference(): """Complete strategy reference with all parameters.""" strategies = { "sma_cross": { "description": "Buy when fast SMA crosses above slow SMA", "parameters": { "fast_period": "Fast moving average period (default: 10)", "slow_period": "Slow moving average period (default: 20)", }, "example": "run_backtest(symbol='AAPL', strategy_type='sma_cross', fast_period=10, slow_period=20)", }, "rsi": { "description": "Buy oversold (RSI < 30), sell overbought (RSI > 70)", "parameters": { "period": "RSI calculation period (default: 14)", "oversold": "Oversold threshold (default: 30)", "overbought": "Overbought threshold (default: 70)", }, "example": "run_backtest(symbol='MSFT', strategy_type='rsi', period=14, oversold=30)", }, "online_learning": { "description": "ML strategy with adaptive thresholds", "parameters": { "lookback": "Historical window (default: 20)", "learning_rate": "Adaptation rate (default: 0.01)", }, "example": "run_backtest(symbol='TSLA', strategy_type='online_learning', lookback=20)", }, "regime_aware": { "description": "Detects and adapts to market regimes", "parameters": { "regime_window": "Regime detection window (default: 50)", "threshold": "Regime change threshold (default: 0.02)", }, "example": "run_backtest(symbol='SPY', strategy_type='regime_aware', regime_window=50)", }, "ensemble": { "description": "Combines multiple strategies with voting", "parameters": { "fast_period": "Fast MA period (default: 10)", "slow_period": "Slow MA period (default: 20)", "rsi_period": "RSI period (default: 14)", }, "example": "run_backtest(symbol='NVDA', strategy_type='ensemble')", }, } import json return f""" # Complete Strategy Reference ## All Available Strategies with Parameters ```json {json.dumps(strategies, indent=2)} ``` ## Usage Pattern: All strategies use the same tool: `run_backtest` Parameters: - symbol: Stock ticker (required) - strategy_type: Strategy name (required) - start_date: YYYY-MM-DD format - end_date: YYYY-MM-DD format - initial_capital: Starting amount (default: 10000) - Additional strategy-specific parameters ## Testing Order: 1. Start with sma_cross (simplest) 2. Try rsi or macd (intermediate) 3. Test online_learning (ML strategy) 4. Compare all with compare_strategies tool """ # Register resources for better discovery @mcp.resource("strategies://list") def list_strategies_resource(): """List of all available backtesting strategies with parameters.""" return { "traditional_strategies": { "sma_cross": { "name": "Simple Moving Average Crossover", "parameters": ["fast_period", "slow_period"], "default_values": {"fast_period": 10, "slow_period": 20}, }, "rsi": { "name": "RSI Mean Reversion", "parameters": ["period", "oversold", "overbought"], "default_values": {"period": 14, "oversold": 30, "overbought": 70}, }, "macd": { "name": "MACD Signal Line Crossover", "parameters": ["fast_period", "slow_period", "signal_period"], "default_values": { "fast_period": 12, "slow_period": 26, "signal_period": 9, }, }, "bollinger": { "name": "Bollinger Bands", "parameters": ["period", "std_dev"], "default_values": {"period": 20, "std_dev": 2}, }, "momentum": { "name": "Momentum Trading", "parameters": ["period", "threshold"], "default_values": {"period": 10, "threshold": 0.02}, }, "ema_cross": { "name": "EMA Crossover", "parameters": ["fast_period", "slow_period"], "default_values": {"fast_period": 12, "slow_period": 26}, }, "mean_reversion": { "name": "Mean Reversion", "parameters": ["lookback", "entry_z", "exit_z"], "default_values": {"lookback": 20, "entry_z": -2, "exit_z": 0}, }, "breakout": { "name": "Channel Breakout", "parameters": ["lookback", "breakout_factor"], "default_values": {"lookback": 20, "breakout_factor": 1.5}, }, "volume_momentum": { "name": "Volume-Weighted Momentum", "parameters": ["period", "volume_factor"], "default_values": {"period": 10, "volume_factor": 1.5}, }, }, "ml_strategies": { "online_learning": { "name": "Online Learning Adaptive Strategy", "parameters": ["lookback", "learning_rate"], "default_values": {"lookback": 20, "learning_rate": 0.01}, }, "regime_aware": { "name": "Market Regime Detection", "parameters": ["regime_window", "threshold"], "default_values": {"regime_window": 50, "threshold": 0.02}, }, "ensemble": { "name": "Ensemble Voting Strategy", "parameters": ["fast_period", "slow_period", "rsi_period"], "default_values": { "fast_period": 10, "slow_period": 20, "rsi_period": 14, }, }, }, "total_strategies": 15, } @mcp.resource("tools://categories") def tool_categories_resource(): """Categorized list of all available MCP tools.""" return { "backtesting": [ "run_backtest", "optimize_parameters", "compare_strategies", "get_strategy_info", ], "data": ["get_stock_data", "get_stock_info", "get_multiple_stocks_data"], "technical_analysis": [ "calculate_sma", "calculate_ema", "calculate_rsi", "calculate_macd", "calculate_bollinger_bands", "get_full_technical_analysis", ], "screening": [ "get_maverick_recommendations", "get_maverick_bear_recommendations", "get_trending_breakout_recommendations", ], "portfolio": [ "optimize_portfolio", "analyze_portfolio_risk", "calculate_correlation_matrix", ], "research": [ "research_comprehensive", "research_company", "analyze_market_sentiment", "coordinate_agents", ], } @mcp.resource("examples://backtesting") def backtesting_examples_resource(): """Practical examples of using backtesting tools.""" return { "simple_backtest": { "description": "Basic backtest with default parameters", "example": "run_backtest(symbol='AAPL', strategy_type='sma_cross')", "expected_output": "Performance metrics including total return, sharpe ratio, win rate", }, "custom_parameters": { "description": "Backtest with custom strategy parameters", "example": "run_backtest(symbol='TSLA', strategy_type='rsi', period=21, oversold=25)", "expected_output": "Performance with adjusted RSI parameters", }, "ml_strategy": { "description": "Running ML-based strategy", "example": "run_backtest(symbol='NVDA', strategy_type='online_learning', lookback=30)", "expected_output": "Adaptive strategy performance with online learning", }, "optimization": { "description": "Optimize strategy parameters", "example": "optimize_parameters(symbol='MSFT', strategy_type='sma_cross')", "expected_output": "Best parameter combination and performance metrics", }, "comparison": { "description": "Compare multiple strategies", "example": "compare_strategies(symbol='SPY', strategies=['sma_cross', 'rsi', 'online_learning'])", "expected_output": "Side-by-side comparison of strategy performance", }, } return True