# Simplified example of a backtesting strategy engine
class BacktestingEngine:
    def __init__(self, data):
        self.data = data  # Historical market data
        self.portfolio = Portfolio()  # Portfolio management
        self.strategy = None  # Trading strategy
        self.transaction_cost = 0.001  # Transaction cost
        self.slippage = 0.0005  # Slippage
        self.trades = []  # Trade records
        
    def set_strategy(self, strategy):
        self.strategy = strategy
        
    def run(self):
        for i in range(len(self.data)):
            # Get current market state
            current_data = self.data.iloc[i]
            
            # Strategy generates signals
            signals = self.strategy.generate_signals(current_data, self.portfolio)
            
            # Execute trades
            for signal in signals:
                # Consider transaction costs and slippage
                executed_price = self.calculate_executed_price(signal)
                
                # Record trade
                trade = self.execute_trade(signal, executed_price)
                self.trades.append(trade)
                
            # Update portfolio value
            self.portfolio.update_value(current_data.close)
        
    def calculate_executed_price(self, signal):
        # Simplified execution price calculation considering slippage
        if signal['type'] == 'buy':
            return signal['price'] * (1 + self.slippage)
        else:
            return signal['price'] * (1 - self.slippage)
        
    def execute_trade(self, signal, executed_price):
        # Execute trade and return trade record
        # ...trade execution logic...
        return trade_record

# Note: This is just a simplified example; actual backtesting systems are much more complex

# Parameter sensitivity analysis example
def parameter_sensitivity_analysis(engine, base_params, param_ranges):
    results = {}
    
    # Analyze each parameter
    for param_name, param_range in param_ranges.items():
        param_results = []
        
        # Iterate over different parameter values
        for param_value in param_range:
            # Set current parameter value, keep others unchanged
            current_params = base_params.copy()
            current_params[param_name] = param_value
            
            # Set strategy parameters
            engine.strategy.set_params(current_params)
            
            # Run backtest
            engine.run()
            
            # Get backtest results
            performance = engine.get_performance_metrics()
            param_results.append({
                'param_value': param_value,
                'performance': performance
            })
        
        results[param_name] = param_results
    
    return results

# Usage example
param_ranges = {
    'lookback_period': [10, 20, 30, 40, 50],
    'threshold': [0.01, 0.02, 0.03, 0.04, 0.05]
}

sensitivity_results = parameter_sensitivity_analysis(
    engine, base_params, param_ranges
)