create-trading-strategies

name	create-trading-strategies
description	Generates trading strategy modules (momentum, mean-reversion, yield)

When to Use

• When asked to create logic for trade decisions
• When implementing or modifying individual trading strategies
• When adding new strategy modules to the agent

Instructions

1. Generate strategy files: momentum.py, mean_reversion.py, yield_optimizer.py
2. Each module must return a standardized signal format:

   {
       "signal": "BUY" | "SELL" | "HOLD",
       "confidence": 0.0 to 1.0,
       "metadata": {
           "indicators": {...},
           "reasoning": "..."
       }
   }
   

3. Ensure the AI predictor module (ai_predictor.py) can read outputs from all strategies and make weighted decisions
4. Include docstrings and inline comments explaining the trading logic
5. Use technical indicators from utils/indicators.py
6. All parameters must come from configuration — never hardcode thresholds

Strategy Details

Momentum (momentum.py)

• Core Logic: Moving average crossover (fast MA crosses above slow MA = BUY)
• Confirmation: Volume must be above average to confirm trend
• Indicators: SMA/EMA (short + long period), volume moving average
• Exit Signal: Fast MA crosses below slow MA, or trailing stop triggered

Mean Reversion (mean_reversion.py)

• Core Logic: Price deviation from mean using Bollinger Bands and z-score
• Entry: Buy when price drops below lower Bollinger Band (z-score < -2)
• Exit: Sell when price returns to mean or exceeds upper band
• Indicators: Bollinger Bands, z-score, RSI for oversold/overbought confirmation

Yield Optimizer (yield_optimizer.py)

• Core Logic: Evaluate yield/APY opportunities across pools
• Criteria: Risk-adjusted returns, liquidity depth, protocol safety
• Output: Allocation suggestions with expected yield
• Constraints: Max allocation per pool, minimum liquidity requirements

AI Predictor (ai_predictor.py)

• Core Logic: Weighted ensemble of all strategy signals
• Weighting: Based on recent strategy accuracy and market regime
• Output: Final prediction with aggregated confidence
• Explainability: Log which strategies contributed most to the decision

Example

Input: "Create momentum strategy for ETH/USDC"

Output: momentum.py with:

• Configurable moving average periods (default: fast=12, slow=26)
• Volume confirmation filter
• Signal generation returning standardized format
• Confidence score based on crossover strength and volume