import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
import time
class FrameworkComparison:
    """Comprehensive comparison of backtesting frameworks."""
@staticmethod
    def backtrader_example():
        """Example implementation using Backtrader pattern."""
        try:
            import backtrader as bt
class SimpleStrategy(bt.Strategy):
                def __init__(self):
                    self.sma20 = bt.indicators.MovingAverage(
                        self.data.close, period=20
                    )
                    self.sma50 = bt.indicators.MovingAverage(
                        self.data.close, period=50
                    )
def next(self):
                    if self.sma20[0] > self.sma50[0]:
                        if not self.position:
                            self.buy()
                    elif self.position:
                        self.sell()
cerebro = bt.Cerebro()
            cerebro.broker.setcash(100000.0)
# Add data
            data = bt.feeds.PandasData(dataname=pd.DataFrame())
            cerebro.adddata(data)
# Add strategy
            cerebro.addstrategy(SimpleStrategy)
# Run
            cerebro.run()
except ImportError:
            print("Backtrader not installed")
@staticmethod
    def zipline_example():
        """Example implementation using Zipline pattern."""
        try:
            from zipline.api import order, record, symbol
            from zipline import run_algorithm
def initialize(context):
                context.i = 0
def handle_data(context, data):
                context.i += 1
if context.i % 100 == 0:
                    order(symbol('AAPL'), 10)
record(AAPL=data[symbol('AAPL')].close)
run_algorithm(
                start=pd.Timestamp('2015-01-01'),
                end=pd.Timestamp('2015-12-31'),
                initialize=initialize,
                handle_data=handle_data,
                capital_base=10000,
                data_frequency='daily'
            )
except ImportError:
            print("Zipline not installed")
@staticmethod
    def vectorbt_example():
        """Example implementation using VectorBT pattern."""
        try:
            import vectorbt as vbt
# Download data
            price = vbt.YFData.download('AAPL', start='2015-01-01',
                                       end='2015-12-31')['Close']
# Calculate moving averages
            ma20 = price.rolling(20).mean()
            ma50 = price.rolling(50).mean()
# Generate signals
            signals = (ma20 > ma50).astype(int)
# Create entries and exits
            entries = signals.diff() > 0
            exits = signals.diff() < 0
# Backtest portfolio
            portfolio = vbt.Portfolio.from_signals(
                close=price,
                entries=entries,
                exits=exits,
                init_cash=100000
            )
# Get results
            metrics = {
                'total_return': portfolio.total_return(),
                'sharpe_ratio': portfolio.sharpe_ratio(),
                'max_drawdown': portfolio.max_drawdown()
            }
return metrics
except ImportError:
            print("VectorBT not installed")
class DetailedComparison:
    """Detailed comparison metrics."""
@staticmethod
    def performance_benchmark() -> Dict:
        """Benchmark framework performance."""
        num_bars = 5000
# Generate test data
        dates = pd.date_range('2015-01-01', periods=num_bars, freq='D')
        closes = 100 + np.cumsum(np.random.randn(num_bars) * 2)
df = pd.DataFrame({
            'open': closes  (1 + np.random.randn(num_bars)  0.001),
            'high': closes  (1 + abs(np.random.randn(num_bars)  0.005)),
            'low': closes  (1 - abs(np.random.randn(num_bars)  0.005)),
            'close': closes,
            'volume': np.random.randint(1000, 10000, num_bars)
        }, index=dates)
results = {}
# Vectorized approach (like VectorBT)
        start = time.perf_counter()
sma20 = df['close'].rolling(20).mean()
        sma50 = df['close'].rolling(50).mean()
        signals = (sma20 > sma50).astype(int)
# Calculate returns
        daily_returns = df['close'].pct_change()
        strategy_returns = signals.shift(1) * daily_returns
end = time.perf_counter()
        results['vectorized'] = end - start
# Event-driven approach (like Backtrader)
        start = time.perf_counter()
cash = 100000
        position = 0
        entry_price = 0
for i in range(50, len(df)):
            current_sma20 = sma20.iloc[i]
            current_sma50 = sma50.iloc[i]
            current_price = df['close'].iloc[i]
if current_sma20 > current_sma50:
                if position == 0:
                    position = int(cash / current_price)
                    cash -= position * current_price
                    entry_price = current_price
else:
                if position > 0:
                    cash += position * current_price
                    position = 0
end = time.perf_counter()
        results['event_driven'] = end - start
return {
            'vectorized_time': results['vectorized'],
            'event_driven_time': results['event_driven'],
            'speedup': results['event_driven'] / results['vectorized']
        }
Detailed Framework Comparison Table
comparison_data = {
    'Feature': [
        'Architecture',
        'Speed',
        'Realism',
        'Learning Curve',
        'Live Trading',
        'Data Handling',
        'Leverage Support',
        'Options Support',
        'Community',
        'Documentation',
        'Price Feed Integration',
        'Order Types',
        'Market Impact',
        'Slippage Modeling',
        'Multi-Asset',
        'Portfolio Rebalancing',
        'Monte Carlo',
        'Walk-Forward'
    ],
    'Backtrader': [
        'Event-driven',
        'Moderate',
        'High',
        'Moderate',
        'Good',
        'Pandas-based',
        'Yes (built-in)',
        'Limited',
        'Large',
        'Excellent',
        'Multiple',
        'Market, Limit, Stop',
        'Partial',
        'Yes',
        'Yes',
        'Yes',
        'Yes',
        'Yes'
    ],
    'Zipline': [
        'Event-driven',
        'Slow',
        'Very High',
        'Steep',
        'Minimal',
        'In-memory',
        'Limited',
        'No',
        'Medium',
        'Good',
        'Yahoo Finance',
        'Market, Limit',
        'Yes',
        'Yes',
        'Yes',
        'Limited',
        'No',
        'No'
    ],
    'VectorBT': [
        'Vectorized',
        'Very Fast',
        'Lower',
        'Steep',
        'No',
        'NumPy/Pandas',
        'Yes',
        'Limited',
        'Growing',
        'Good',
        'Yahoo Finance, Custom',
        'Limited',
        'Limited',
        'Simple',
        'Yes',
        'Yes',
        'Yes',
        'Yes'
    ]
}
def print_comparison():
    """Print comparison table."""
    comparison_df = pd.DataFrame(comparison_data)
    print(comparison_df.to_string(index=False))
class UseCaseRecommendations:
    """Recommendations by use case."""
recommendations = {
        'Quick Strategy Testing': {
            'framework': 'VectorBT',
            'reason': 'Ultra-fast backtesting for rapid parameter optimization',
            'code_example': '''
import vectorbt as vbt
Load data
price = vbt.YFData.download('SPY')['Close']
Test multiple parameters
sma_shorts = [10, 20, 30]
sma_longs = [50, 100, 200]
Vectorized optimization
results = {}
for short in sma_shorts:
    for long in sma_longs:
        ma_short = price.rolling(short).mean()
        ma_long = price.rolling(long).mean()
        # Backtest...
            '''
        },
        'Production Trading System': {
            'framework': 'Backtrader',
            'reason': 'Realistic order execution, live trading integration, extensive features',
            'code_example': '''
import backtrader as bt
class ProductionStrategy(bt.Strategy):
    def __init__(self):
        # Technical indicators
        self.sma = bt.indicators.MovingAverage(self.data.close, period=50)
        # Risk management
        self.stop_loss = None
def next(self):
        # Order execution logic with sophisticated risk management
        if not self.position:
            if self.data.close > self.sma:
                self.buy()
                self.stop_loss = self.data.close * 0.95
        else:
            if self.data.close < self.stop_loss:
                self.close()
            '''
        },
        'Academic Research': {
            'framework': 'Zipline',
            'reason': 'Institutional-grade accuracy, research-focused, handles edge cases',
            'code_example': '''
from zipline.api import symbol, order, record
def initialize(context):
    context.stock = symbol('SPY')
def handle_data(context, data):
    # Sophisticated analysis and ordering
    current_price = data[context.stock].close
    # Execute trades...
            '''
        },
        'Parameter Optimization': {
            'framework': 'VectorBT',
            'reason': 'Grid search across parameters in seconds',
            'code_example': '''
Vectorized parameter optimization

results = vbt.Portfolio.from_signals(
    close=price,
    entries=entries,
    exits=exits,
    init_cash=100000
)
            '''
        },
        'Complex Orders': {
            'framework': 'Backtrader',
            'reason': 'Support for advanced order types and management'
        },
        'High-Frequency': {
            'framework': 'Custom Event-Driven',
            'reason': 'Existing frameworks may be too slow for HFT'
        }
    }

@classmethod
    def get_recommendation(cls, use_case: str) -> str:
        """Get framework recommendation for use case."""
        if use_case in cls.recommendations:
            rec = cls.recommendations[use_case]
            return f"{rec['framework']}: {rec['reason']}"
        return "Unknown use case"
class MigrationGuide:
    """Guide for migrating between frameworks."""
@staticmethod
    def backtrader_to_vectorbt():
        """Convert Backtrader strategy to VectorBT."""
        migration_steps = {
            1: 'Extract trading logic from bt.Strategy.next()',
            2: 'Convert to vectorized numpy operations',
            3: 'Replace bt.buy() with signal generation',
            4: 'Use vbt.Portfolio.from_signals()',
            5: 'Verify results match original backtests'
        }
        return migration_steps
@staticmethod
    def zipline_to_backtrader():
        """Convert Zipline to Backtrader."""
        migration_steps = {
            1: 'Convert handle_data() to bt.Strategy.next()',
            2: 'Replace order() calls with self.buy()/self.sell()',
            3: 'Migrate record() to manual tracking',
            4: 'Port commission/slippage settings',
            5: 'Test with same data for validation'
        }
        return migration_steps
Hybrid Approach Example
class HybridBacktester:
    """Combine frameworks for optimal results."""
def __init__(self):
        self.vectorbt_for_exploration = True
        self.backtrader_for_validation = True
def optimize(self, param_ranges: Dict) -> Tuple:
        """
        Stage 1: Vectorized optimization
        Stage 2: Event-driven validation
        """
        # Stage 1: Fast parameter search with VectorBT
        print("Stage 1: Vectorized optimization...")
        best_params = self._vectorized_search(param_ranges)
# Stage 2: Detailed validation with Backtrader
        print("Stage 2: Event-driven validation...")
        detailed_metrics = self._backtrader_validation(best_params)
return best_params, detailed_metrics
def _vectorized_search(self, param_ranges: Dict) -> Dict:
        """Quick parameter exploration."""
        # Simulated: would use VectorBT for grid search
        return {'param1': 20, 'param2': 50}
def _backtrader_validation(self, params: Dict) -> Dict:
        """Detailed validation with Backtrader."""
        # Simulated: would use Backtrader for detailed metrics
        return {'sharpe': 1.5, 'max_dd': 0.15}
Example usage
if __name__ == "__main__":
    # Print comparison
    print("=== Framework Comparison ===\n")
    print_comparison()
# Performance benchmark
    print("\n=== Performance Benchmark ===")
    benchmark = DetailedComparison.performance_benchmark()
    print(f"Vectorized: {benchmark['vectorized_time']:.4f}s")
    print(f"Event-driven: {benchmark['event_driven_time']:.4f}s")
    print(f"Speedup: {benchmark['speedup']:.1f}x")
# Recommendations
    print("\n=== Use Case Recommendations ===")
    for use_case in ['Quick Strategy Testing', 'Production Trading System',
                    'Parameter Optimization']:
        print(f"{use_case}: {UseCaseRecommendations.get_recommendation(use_case)}")