comprehensive_example.py

"""Comprehensive example demonstrating v3.1.0 features."""

import sys
import os
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from forexsmartbot.strategies import get_strategy
from forexsmartbot.optimization import (
    GeneticOptimizer, 
    HyperparameterOptimizer,
    WalkForwardAnalyzer,
    MonteCarloSimulator,
    ParameterSensitivityAnalyzer
)
from forexsmartbot.adapters.data import YFinanceProvider
from forexsmartbot.services.backtest import BacktestService
from forexsmartbot.services.enhanced_backtest import EnhancedBacktestService
from forexsmartbot.monitoring import StrategyMonitor, PerformanceTracker, HealthChecker
from forexsmartbot.marketplace import StrategyMarketplace, StrategyListing
from forexsmartbot.core.risk_engine import RiskConfig
from datetime import datetime


def main():
    """Comprehensive example of v3.1.0 features."""
    print("=" * 70)
    print("ForexSmartBot v3.1.0 - Comprehensive Feature Demonstration")
    print("=" * 70)
    
    # Initialize data provider
    data_provider = YFinanceProvider()
    
    # ========================================================================
    # 1. ML Strategy Usage
    # ========================================================================
    print("\n1. ML Strategy Usage")
    print("-" * 70)
    
    try:
        # Try to create an ML strategy
        ml_strategy = get_strategy('Ensemble_ML_Strategy', 
            lookback_period=50,
            n_estimators=50,
            max_depth=5
        )
        print(f"✓ Created ML strategy: {ml_strategy.name}")
    except Exception as e:
        print(f"✗ ML strategy not available: {e}")
        ml_strategy = get_strategy('SMA_Crossover')
        print(f"✓ Using fallback strategy: {ml_strategy.name}")
    
    # ========================================================================
    # 2. Strategy Optimization
    # ========================================================================
    print("\n2. Strategy Optimization")
    print("-" * 70)
    
    def fitness_function(params):
        """Fitness function for optimization."""
        try:
            strategy = get_strategy('SMA_Crossover', **params)
            backtest_service = BacktestService(data_provider)
            results = backtest_service.run_backtest(
                strategy=strategy,
                symbol='EURUSD=X',
                start_date='2023-01-01',
                end_date='2023-06-30',
                initial_balance=10000.0
            )
            if 'error' in results:
                return -100.0
            metrics = results.get('metrics', {})
            return metrics.get('sharpe_ratio', 0.0)
        except:
            return -100.0
    
    # Genetic Algorithm Optimization
    print("Running Genetic Algorithm optimization...")
    param_bounds = {
        'fast_period': (10, 30),
        'slow_period': (40, 80)
    }
    
    ga_optimizer = GeneticOptimizer(param_bounds, population_size=10, generations=5)
    best_params, best_fitness = ga_optimizer.optimize(fitness_function, verbose=False)
    print(f"✓ GA Best parameters: {best_params}")
    print(f"✓ GA Best fitness: {best_fitness:.4f}")
    
    # ========================================================================
    # 3. Parameter Sensitivity Analysis
    # ========================================================================
    print("\n3. Parameter Sensitivity Analysis")
    print("-" * 70)
    
    def strategy_factory(params):
        return get_strategy('SMA_Crossover', **params)
    
    def performance_function(strategy):
        backtest_service = BacktestService(data_provider)
        results = backtest_service.run_backtest(
            strategy=strategy,
            symbol='EURUSD=X',
            start_date='2023-01-01',
            end_date='2023-06-30',
            initial_balance=10000.0
        )
        if 'error' in results:
            return 0.0
        metrics = results.get('metrics', {})
        return metrics.get('total_return', 0.0)
    
    sensitivity_analyzer = ParameterSensitivityAnalyzer(n_points=5)
    base_params = {'fast_period': 20, 'slow_period': 50}
    param_ranges = {
        'fast_period': (15, 25),
        'slow_period': (45, 55)
    }
    
    sensitivity_results = sensitivity_analyzer.analyze(
        strategy_factory, base_params, param_ranges, performance_function
    )
    
    print("Sensitivity Analysis Results:")
    for param_name, result in sensitivity_results.items():
        print(f"  {param_name}:")
        print(f"    Sensitivity Score: {result.sensitivity_score:.4f}")
        print(f"    Optimal Value: {result.optimal_value:.2f}")
        print(f"    Impact Range: [{result.impact_range[0]:.4f}, {result.impact_range[1]:.4f}]")
    
    # ========================================================================
    # 4. Walk-Forward Analysis
    # ========================================================================
    print("\n4. Walk-Forward Analysis")
    print("-" * 70)
    
    # Get data for walk-forward
    df = data_provider.get_data('EURUSD=X', '2023-01-01', '2023-12-31', '1h')
    
    if not df.empty:
        walk_forward = WalkForwardAnalyzer(train_period=60, test_period=20, step_size=10)
        
        def optimize_function(train_data, initial_params):
            # Simple optimization: return base params
            return initial_params
        
        def strategy_factory_wf(params):
            return get_strategy('SMA_Crossover', **params)
        
        wf_results = walk_forward.analyze(
            df, strategy_factory_wf, optimize_function, base_params
        )
        
        if wf_results:
            print(f"✓ Walk-forward analysis completed")
            print(f"  Periods analyzed: {wf_results.get('num_periods', 0)}")
            print(f"  Average Sharpe: {wf_results.get('avg_sharpe', 0):.4f}")
            print(f"  Win Rate: {wf_results.get('win_rate', 0):.2%}")
    
    # ========================================================================
    # 5. Monte Carlo Simulation
    # ========================================================================
    print("\n5. Monte Carlo Simulation")
    print("-" * 70)
    
    if not df.empty:
        returns = df['Close'].pct_change().dropna()
        mc_simulator = MonteCarloSimulator(n_simulations=100, confidence_level=0.95)
        mc_results = mc_simulator.simulate(returns, initial_balance=10000.0, n_periods=252)
        
        print(f"✓ Monte Carlo simulation completed")
        print(f"  Mean Return: {mc_results['mean_return']:.4f}")
        print(f"  VaR (95%): {mc_results['var']:.4f}")
        print(f"  CVaR (95%): {mc_results['cvar']:.4f}")
        print(f"  Probability of Profit: {mc_results['probability_of_profit']:.2%}")
    
    # ========================================================================
    # 6. Strategy Monitoring
    # ========================================================================
    print("\n6. Strategy Monitoring")
    print("-" * 70)
    
    monitor = StrategyMonitor()
    monitor.register_strategy("DemoStrategy")
    
    # Simulate some signals
    for i in range(5):
        monitor.record_signal("DemoStrategy", execution_time=0.05 + i * 0.01)
    
    health = monitor.get_health("DemoStrategy")
    print(f"✓ Strategy monitoring active")
    print(f"  Status: {health.status.value}")
    print(f"  Signal Count: {health.signal_count}")
    print(f"  Avg Execution Time: {health.execution_time_avg:.3f}s")
    
    # Health check
    health_checker = HealthChecker(monitor)
    check_result = health_checker.check("DemoStrategy")
    print(f"  Health Check: {check_result['status']}")
    
    # ========================================================================
    # 7. Performance Tracking
    # ========================================================================
    print("\n7. Performance Tracking")
    print("-" * 70)
    
    tracker = PerformanceTracker()
    
    # Simulate some trades
    for i in range(10):
        profit = (i % 3 - 1) * 50.0  # Mix of wins and losses
        tracker.record_trade("DemoStrategy", {
            'profit': profit,
            'entry_price': 1.1000,
            'exit_price': 1.1000 + profit / 10000,
            'entry_time': datetime.now(),
            'exit_time': datetime.now()
        })
        tracker.record_equity("DemoStrategy", 10000.0 + i * 10.0)
    
    metrics = tracker.calculate_metrics("DemoStrategy")
    if metrics:
        print(f"✓ Performance tracking active")
        print(f"  Total Return: {metrics.total_return:.2%}")
        print(f"  Sharpe Ratio: {metrics.sharpe_ratio:.4f}")
        print(f"  Win Rate: {metrics.win_rate:.2%}")
        print(f"  Total Trades: {metrics.total_trades}")
    
    # ========================================================================
    # 8. Strategy Marketplace
    # ========================================================================
    print("\n8. Strategy Marketplace")
    print("-" * 70)
    
    marketplace = StrategyMarketplace(storage_path="marketplace_demo")
    
    # Create a listing
    listing = StrategyListing(
        strategy_id="demo_strategy_001",
        name="Demo SMA Crossover",
        description="A simple SMA crossover strategy for demonstration",
        author="Demo User",
        version="1.0.0",
        category="Trend Following",
        tags=["SMA", "Crossover", "Trend"],
        created_at=datetime.now(),
        updated_at=datetime.now()
    )
    
    marketplace.add_listing(listing)
    print(f"✓ Strategy listed in marketplace")
    
    # Search listings
    results = marketplace.search_listings(query="SMA", min_rating=0.0)
    print(f"✓ Found {len(results)} strategies matching 'SMA'")
    
    # ========================================================================
    # 9. Enhanced Backtesting
    # ========================================================================
    print("\n9. Enhanced Backtesting")
    print("-" * 70)
    
    enhanced_service = EnhancedBacktestService(data_provider, use_parallel=False)
    
    strategy = get_strategy('SMA_Crossover', fast_period=20, slow_period=50)
    results = enhanced_service.run_backtest(
        strategy=strategy,
        symbol='EURUSD=X',
        start_date='2023-01-01',
        end_date='2023-06-30',
        initial_balance=10000.0,
        enable_logging=False
    )
    
    if 'error' not in results:
        print(f"✓ Enhanced backtest completed")
        print(f"  Total Return: {results.get('total_return', 0):.2%}")
        print(f"  Total Trades: {results.get('total_trades', 0)}")
        print(f"  Error Count: {results.get('error_count', 0)}")
    else:
        print(f"✗ Backtest error: {results.get('error')}")
    
    # ========================================================================
    # Summary
    # ========================================================================
    print("\n" + "=" * 70)
    print("Summary")
    print("=" * 70)
    print("✓ All v3.1.0 features demonstrated successfully!")
    print("\nFeatures tested:")
    print("  - ML Strategies")
    print("  - Strategy Optimization (GA, Optuna)")
    print("  - Parameter Sensitivity Analysis")
    print("  - Walk-Forward Analysis")
    print("  - Monte Carlo Simulation")
    print("  - Strategy Monitoring")
    print("  - Performance Tracking")
    print("  - Strategy Marketplace")
    print("  - Enhanced Backtesting")
    print("\n" + "=" * 70)


if __name__ == "__main__":
    main()