add-strategy

name add-strategy description Create a new trading strategy for the ai-trader backtesting framework. Handles file creation, registration, docstrings, and backtest scaffolding. disable-model-invocation true argument-hint [strategy-type: classic|portfolio] allowed-tools Read, Write, Edit, Glob, Grep, Bash add-strategy Skill Create a new trading strategy for the ai-trader backtesting framework with automatic file generation, registration, and validation. Overview This skill automates the creation of trading strategies following established patterns in the ai-trader project. It handles: File creation with proper naming conventions (PascalCase class → snake_case filename) Comprehensive docstrings (module and class level) Parameter validation and defaults Automatic registration in __init__.py files Standalone backtest scaffolding Pre-flight validation for name conflicts and custom indicators Two strategy types are supported: Classic : Single-stock strategies (e.g., DoubleTopStrategy, BBandsStrategy) Portfolio : Multi-asset rotation strategies (e.g., ROCRotationStrategy, MultiBBandsRotationStrategy) Interactive Workflow The skill guides you through the following steps: Step 1: Strategy Type Confirm whether this is a classic (single-stock) or portfolio (multi-asset) strategy. Step 2: Name & Description Provide the strategy name in PascalCase (e.g., "MACDBBands") Provide a 1-2 sentence description The skill automatically converts to snake_case for the filename (macd_bbands.py) Step 3: Parameters Define parameters as comma-separated name=value pairs: Example: fast=12, slow=26, signal=9, bb_period=20 All parameters must have default values Parameter names must be valid Python identifiers Step 4: Entry & Exit Logic Describe the entry condition (buy signal) Describe the exit condition (sell signal) For portfolio strategies, describe rotation/rebalancing logic Step 5: Custom Indicators (Optional) If your strategy uses custom indicators from indicators.py , list them: Available: DoubleTop, RSRS, NormRSRS, RecentHigh, TripleRSI, etc. The skill verifies they exist and generates the import statement Step 6: Preview & Confirmation Review the generated file structure before creation: Target file path Class name and type Parameters Files to be modified ( init .py imports and all ) Step 7: Creation & Verification The skill creates: Strategy file with complete structure Updates imports and all list in init .py Validates syntax by attempting import Shows git status for verification Usage Examples Creating a Classic Strategy $ /add-strategy classic Creating a classic single-stock strategy. What should we name this strategy? (e.g., "BollingerBreakout" ) > MACDBBands Brief description (1-2 sentences)? > Combines MACD for trend and Bollinger Bands for entry timing Parameters with defaults? (e.g., "fast=12, slow=26, signal=9, bb_period=20" ) > fast=12, slow=26, signal=9, bb_period=20, bb_dev=2 Entry condition (buy signal)? > MACD crosses above signal line AND price below lower Bollinger Band Exit condition (sell signal)? > MACD crosses below signal line OR price above upper Bollinger Band Any custom indicators from indicators.py? (e.g., DoubleTop, RSRS) > No [Preview shown] Proceed with creation? ( yes /no) > yes ✓ Created macd_bbands.py ✓ Updated classic/__init__.py (added import) ✓ Updated classic/__init__.py (added to __all__) ✓ Verified file is importable Next steps: 1. Test standalone: python ai_trader/backtesting/strategies/classic/macd_bbands.py 2. Review generated code and refine logic 3. Test via CLI: ai-trader quick MACDBBandsStrategy your_data.csv 4. Check changes: git diff 5. Commit when ready Creating a Portfolio Strategy $ /add-strategy portfolio Creating a portfolio multi-asset strategy. What should we name this strategy? (e.g., "MomentumRotation" ) > TripleEMARotation Brief description? > Rotates portfolio to assets with strongest triple EMA alignment Parameters? > short_ema=10, med_ema=20, long_ema=50, top_k=5 Rotation/Rebalancing logic? > Rotate monthly to top-5 assets where all three EMAs are bullishly aligned Custom indicators? > No [Preview shown] Proceed with creation? ( yes /no) > yes ✓ Created triple_ema_rotation.py ✓ Updated portfolio/__init__.py (added import) ✓ Updated portfolio/__init__.py (added to __all__) ✓ Verified file is importable File Templates Classic Strategy Template """ [Strategy Name] [1-2 sentence description of what the strategy does and the market conditions it targets.] """ import backtrader as bt from ai_trader.backtesting.strategies.base import BaseStrategy # [Add custom indicator imports if needed] # from ai_trader.backtesting.strategies.indicators import CustomIndicator class [StrategyName]Strategy(BaseStrategy): """ [Strategy Name] - [One-line tagline describing the core approach]. [Detailed description paragraph explaining the trading logic, market conditions, and why this strategy works in those conditions.] Entry Logic (Buy): - Condition 1 - Condition 2 Exit Logic (Sell): - Condition 1 - Condition 2 Parameters: - param_name (type): Description [default: value] Notes: - Insight 1 - Insight 2 """ params = dict (param1=value1, param2=value2) def __init__ ( self ): """Initialize indicators and signals.""" super ().__init__() # Initialize indicators here # self.indicator = bt.indicators.SMA(self.data) def next ( self ): """Execute trading logic each bar.""" if self .position.size == 0 : # Check buy signal and enter pass else : # Check exit signal and close pass if __name__ == "__main__" : from ai_trader.utils.backtest import run_backtest # Run backtest with [StrategyName]Strategy results = run_backtest( strategy=[StrategyName]Strategy, data_source= None , # Use example data cash= 1000000 , commission= 0.001425 , ) print ( "Backtest completed! Use cerebro.plot() to visualize results." ) Portfolio Strategy Template """ [Strategy Name] [1-2 sentence description of the portfolio rotation strategy.] """ import backtrader as bt from ai_trader.backtesting.strategies.base import BaseStrategy # [Add custom indicator imports if needed] class [StrategyName]Strategy(BaseStrategy): """ [Strategy Name] - [One-line tagline]. [Detailed description of the rotation/rebalancing logic.] Entry Logic (Buy): - Condition 1 (applies to each asset in the portfolio) - Condition 2 Exit Logic (Sell): - Condition 1 - Asset no longer in top-k performers Parameters: - param_name (type): Description [default: value] Notes: - Rotates portfolio based on selection criteria - Equal-weight or custom allocation across selected assets - Rebalances when conditions change """ params = dict (param1=value1, top_k= 5 ) def __init__ ( self ): """Initialize indicators for all assets.""" super ().__init__() self .indicators = { data: bt.ind.SMA(data) for data in self .datas } self .top_k = self .params.top_k def next ( self ): """Execute portfolio rebalancing logic.""" # Get current holdings holding = [d for d, pos in self .getpositions().items() if pos] # Identify candidates and exits to_buy = [data for data in self .datas if self ._is_buy_signal(data)] to_close = [data for data in self .datas if self ._is_exit_signal(data)] # Close positions in assets with exit signals for data in to_close: if data in holding: self .order_target_percent(data=data, target= 0.0 ) self .log( f"Exit {data._name} " ) # Select top-k by performance portfolio = list ( set (to_buy + holding)) if not portfolio: return if len (portfolio) > self .top_k: # Rank by indicator and select top-k ranked = sorted ( [(d, self .indicators[d][ 0 ]) for d in portfolio], key= lambda x: x[ 1 ], reverse= True , ) portfolio = [d for d, _ in ranked[: self .top_k]] # Equal-weight allocation weight = 1 / len (portfolio) for data in portfolio: self .order_target_percent(data, target=weight * 0.95 ) def _is_buy_signal ( self, data ): """Check if data meets buy criteria.""" # Implement your entry logic return False def _is_exit_signal ( self, data ): """Check if data meets exit criteria.""" # Implement your exit logic return False if __name__ == "__main__" : from ai_trader.utils.backtest import run_backtest # Run backtest with [StrategyName]Strategy results = run_backtest( strategy=[StrategyName]Strategy, data_source= None , # Use example data cash= 1000000 , commission= 0.001425 , ) print ( "Backtest completed! Use cerebro.plot() to visualize results." ) Registration Logic When creating a strategy, the skill updates the appropriate __init__.py file: Example for classic/macd_bbands.py: # Before from ai_trader.backtesting.strategies.classic.bbands import BBandsStrategy from ai_trader.backtesting.strategies.classic.double_top import DoubleTopStrategy __all__ = [ "BBandsStrategy" , "DoubleTopStrategy" , # ... ] # After from ai_trader.backtesting.strategies.classic.bbands import BBandsStrategy from ai_trader.backtesting.strategies.classic.double_top import DoubleTopStrategy from ai_trader.backtesting.strategies.classic.macd_bbands import MACDBBandsStrategy __all__ = [ "BBandsStrategy" , "DoubleTopStrategy" , "MACDBBandsStrategy" , # ... ] Key points: Imports are added in alphabetical order by filename __all__ list is maintained in alphabetical order Existing structure and formatting are preserved Validation Pre-Creation Checks Name Validation: Class name must be PascalCase (e.g., MACDBBands) Converts to snake_case for filename (macd_bbands.py) No file exists at target path Class name not already in __all__ Type Validation: Strategy type is "classic" or "portfolio" Target directory exists Parameter Validation: All parameters have default values Parameter names are valid Python identifiers No reserved Python keywords as parameter names Indicator Validation: If custom indicators mentioned, verify they exist in indicators.py Generate correct import statements Post-Creation Verification File created successfully Imports added to __init__.py Name added to __all__ list in correct position File is syntactically valid (test import) Git recognizes new/modified files Next Steps After Creation Review Generated Code: The generated template provides the structure; you'll add the specific trading logic Implement next() method with your trading signals For portfolio strategies, implement _is_buy_signal() and _is_exit_signal() Test Standalone: python ai_trader/backtesting/strategies/classic/your_strategy.py Refine Parameters: Update default values based on backtesting results Add parameter optimization later if needed Integration Testing: ai-trader quick YourStrategyName your_data.csv Commit Changes: git add ai_trader/backtesting/strategies/classic/your_strategy.py git add ai_trader/backtesting/strategies/classic/__init__.py git commit -m "Add YourStrategyName strategy" Related Documentation PATTERNS.md - Complete examples of implemented strategies with annotations CONVENTIONS.md - Naming rules, import ordering, parameter format, docstring structure VALIDATION.md - Detailed validation rules and error handling Tips & Best Practices Inherit from BaseStrategy: Always inherit from BaseStrategy, not directly from bt.Strategy Provides automatic parameter logging via __init__() Provides self.log() for consistent date-formatted logging Provides notify_order() and notify_trade() implementations Use Backtrader Indicators: Use bt.ind.* for standard indicators (SMA, EMA, Bollinger Bands, MACD, RSI, etc.) Custom Indicators: Only create custom indicators if they're not available in backtrader Check indicators.py first Custom indicators should be reusable across multiple strategies Parameter Naming: Use descriptive, lowercase names with underscores Good: sma_short , bb_period , rsi_threshold Avoid: x , n , val Docstrings: Comprehensive docstrings help future maintenance Module level: Brief purpose and what the strategy does Class level: Entry/Exit/Parameters/Notes sections Helps other developers understand your strategy at a glance Testing: Always test with the __main__ block before committing Ensures the strategy initializes correctly Catches syntax errors early Portfolio Strategies: Use self.datas to iterate over all assets Use self.getpositions() to check current holdings Use self.order_target_percent() for position sizing Rebalance systematically (monthly, when signals change, etc.) Error Messages & Troubleshooting Error Cause Solution File already exists A strategy with that name already exists Choose a different name Class name already in __all__ Name conflict in registration Check existing strategies Invalid parameter name Parameter name is not a valid Python identifier Use alphanumeric + underscores only Custom indicator not found Indicator doesn't exist in indicators.py Use standard backtrader indicators or create a custom one Import failed Syntax error in generated file Review the generated code and fix issues Generated with Claude Code