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

bash

$ /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

bash

$ /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

python

"""
[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

python

"""
[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:

python

# 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:

bash

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:

bash

ai-trader quick YourStrategyName your_data.csv

•

Commit Changes:

bash

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"

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

add-strategy Skill

Overview

Interactive Workflow

Step 1: Strategy Type

Step 2: Name & Description

Step 3: Parameters

Step 4: Entry & Exit Logic

Step 5: Custom Indicators (Optional)

Step 6: Preview & Confirmation

Step 7: Creation & Verification

Usage Examples

Creating a Classic Strategy

Creating a Portfolio Strategy

File Templates

Classic Strategy Template

Portfolio Strategy Template

Registration Logic

Validation

Pre-Creation Checks

Post-Creation Verification

Next Steps After Creation

Related Documentation

Tips & Best Practices

Error Messages & Troubleshooting