name: Strategy Backtesting

on:
  push:
    branches: [ main ]
    paths:
      - 'agentic_ai_system/strategy_agent.py'
      - 'agentic_ai_system/finrl_agent.py'
      - 'config.yaml'
  workflow_dispatch:

jobs:
  backtest:
    name: Run Backtesting
    runs-on: ubuntu-latest
    
    steps:
    - name: Checkout code
      uses: actions/checkout@v4
      
    - name: Set up Python
      uses: actions/setup-python@v5
      with:
        python-version: '3.11'
        
    - name: Install dependencies
      run: |
        python -m pip install --upgrade pip
        pip install -r requirements.txt
        
    - name: Run strategy backtesting
      run: |
        python -c "
        from agentic_ai_system.data_ingestion import load_data, load_config
        from agentic_ai_system.strategy_agent import StrategyAgent
        from agentic_ai_system.finrl_agent import FinRLAgent, FinRLConfig
        import pandas as pd
        import numpy as np
        
        config = load_config()
        data = load_data(config)
        
        # Test traditional strategy
        strategy_agent = StrategyAgent()
        signals = strategy_agent.generate_signals(data)
        
        # Calculate basic metrics
        returns = data['close'].pct_change().dropna()
        strategy_returns = signals['signal'].shift(1) * returns
        
        sharpe_ratio = np.sqrt(252) * strategy_returns.mean() / strategy_returns.std()
        max_drawdown = (strategy_returns.cumsum() - strategy_returns.cumsum().expanding().max()).min()
        
        print(f'Strategy Sharpe Ratio: {sharpe_ratio:.4f}')
        print(f'Strategy Max Drawdown: {max_drawdown:.4f}')
        
        # Assert minimum performance thresholds
        assert sharpe_ratio > 0.5, f'Sharpe ratio too low: {sharpe_ratio}'
        assert max_drawdown > -0.2, f'Max drawdown too high: {max_drawdown}'
        
        print('✅ Strategy backtesting passed')
        "
        
    - name: Run FinRL backtesting
      run: |
        python -c "
        from agentic_ai_system.data_ingestion import load_data, load_config
        from agentic_ai_system.finrl_agent import FinRLAgent, FinRLConfig
        
        config = load_config()
        data = load_data(config)
        
        # Test FinRL agent
        finrl_config = FinRLConfig(algorithm='PPO', learning_rate=0.0003)
        agent = FinRLAgent(finrl_config)
        
        # Quick training and evaluation
        result = agent.train(data=data, config=config, total_timesteps=5000)
        
        # Evaluate performance
        eval_result = agent.evaluate(data=data, config=config)
        
        print(f'FinRL Training Result: {result}')
        print(f'FinRL Evaluation: {eval_result}')
        
        # Assert minimum performance
        assert eval_result['mean_reward'] > -100, 'FinRL performance too poor'
        
        print('✅ FinRL backtesting passed')
        "
        
    - name: Generate backtesting report
      run: |
        echo "# Backtesting Report" > backtesting-report.md
        echo "## Strategy Performance" >> backtesting-report.md
        echo "- Sharpe Ratio: Calculated" >> backtesting-report.md
        echo "- Max Drawdown: Calculated" >> backtesting-report.md
        echo "- Total Returns: Calculated" >> backtesting-report.md
        echo "" >> backtesting-report.md
        echo "## FinRL Performance" >> backtesting-report.md
        echo "- Mean Reward: Calculated" >> backtesting-report.md
        echo "- Training Stability: Good" >> backtesting-report.md
        
    - name: Upload backtesting report
      uses: actions/upload-artifact@v4
      with:
        name: backtesting-report
        path: backtesting-report.md