Using this comprehensive and systematic approach gives us a great chance to move away from the old Excel and screen methods we’ve been using. This change not only offers a fresh start but also allows for better efficiency and teamwork. Just think about how much easier our tasks can be when we use modern tools that really suit our needs. This shift can greatly improve how we work with data, making our jobs simpler and more satisfying.

Coore Design Principles

1. Separation of Concerns: The Portfolio class is focused solely on portfolio management, while market data is provided by an external provider. This follows good software design principles by decoupling data acquisition from portfolio management.
2. Comprehensive State Management: The class maintains a complete record of the portfolio’s state including holdings, cash, transactions, and performance history, enabling accurate performance calculation and historical analysis.
3. Immutable Transaction History: All portfolio actions are recorded in a transaction history, creating an audit trail that can be used to reconstruct the portfolio state at any point in time.
4. Flexible Date Handling: Most methods accept optional date parameters, allowing for backdated transactions and historical analysis.

Instance Variables Design

1. Holdings Dictionary: Structured as ticker -> {quantity, cost_basis, entry_date, last_update} to efficiently track all relevant information about each position.
2. Cash Management: Separate tracking of cash balance enables accurate portfolio valuation and supports cash transactions.
3. Currency Support: The currency field allows for potential multi-currency portfolio support.
4. Benchmark Integration: Including a benchmark enables performance comparison against market indices.
5. Transaction History: Comprehensive transaction logging supports auditing, performance attribution, and historical analysis.

Method Design Philosophy

The methods follow a logical progression of portfolio management operations:

1. Core Transaction Methods: add_cash, add_position, and remove_position form the foundation of portfolio management, each with proper validation and transaction recording.
2. Valuation Methods: get_current_value, get_positions_value, and get_weights provide different perspectives on portfolio valuation.
3. Performance Analysis: calculate_returns and calculate_risk_metrics implement financial analytics for performance evaluation.
4. Persistence: save_to_file and load_from_file (class method) enable portfolio persistence with proper serialization/deserialization of complex types like datetime objects.
5. Portfolio Management: rebalance implements sophisticated portfolio rebalancing logic to match target allocations.

This integration enables unique analysis comparing financial performance with innovation metrics.

Error Handling and Logging

The design includes robust error handling throughout:

• Validation before executing transactions
• Try/except blocks for external data calls
• Comprehensive logging for debugging and auditing

Extensibility

The class is designed for extensibility:

• Methods return rich data structures that can be further processed
• Clear separation between data providers and portfolio logic
• Consistent parameter patterns across methods

This thoughtful design creates a foundation for a sophisticated portfolio analysis system that can be extended with additional features while maintaining a clean, maintainable codeba

def rebalance(self, target_weights: Dict[str, float]) -> bool:
    # validate target weights
    total_weight = sum(target_weights.values())
    if abs(total_weight - 100.0) > 0.01: 
        logger.error(f"target weights, got {total_weight}%")
        return False

    # get current value and weights
    total_value = self.get_current_value()
    current_weights = self.get_weights()

    # calculate target values
    target_value = {}
    for ticker, weight in target_weights.items():
        target_values[ticker] = (weight / 100.0) * total_value
        
    # handle cash
    if 'CASH' in target_weights:
        target_cash = target_values.pop('CASH')
    else:
        target_cash = 0.0
        
    # trades needed
    trades = []
    # sell positions that are overweight
    for ticker, details in list(self.holdings.items()):
        if ticker not in target_weights or target_values.get(ticker, 0) < (details['quantity'] * self.market_data.get_live_price(ticker)):
            current_value = details['quantity'] * self.market_data.get_live_price(ticker)
                target_value = target_values.get(ticker, 0)
                value_to_sell = current_value - target_value
                
                if value_to_sell > 0:
                    price = self.market_data.get_live_price(ticker)
                    quantity_to_sell = value_to_sell / price
                    
                    # Round to appropriate precision
                    if price > 1000:
                        quantity_to_sell = round(quantity_to_sell, 4)
                    else:
                        quantity_to_sell = round(quantity_to_sell, 2)
                        
                    # Ensure we don't sell more than we have
                    quantity_to_sell = min(quantity_to_sell, details['quantity'])
                    
                    if quantity_to_sell > 0:
                        trades.append({
                            'type': 'sell',
                            'ticker': ticker,
                            'quantity': quantity_to_sell,
                            'price': price
                        })
        
        # Execute sell trades first to free up cash
        for trade in [t for t in trades if t['type'] == 'sell']:
            self.remove_position(trade['ticker'], trade['quantity'], trade['price'])
            
        # Then buy positions that are underweight or not in portfolio
        for ticker, target_value in target_values.items():
            current_value = 0
            if ticker in self.holdings:
                current_value = self.holdings[ticker]['quantity'] * self.market_data.get_live_price(ticker)
                
            value_to_buy = target_value - current_value
            
            if value_to_buy > 0:
                price = self.market_data.get_live_price(ticker)
                quantity_to_buy = value_to_buy / price
                
                # Round to appropriate precision
                if price > 1000:
                    quantity_to_buy = round(quantity_to_buy, 4)
                else:
                    quantity_to_buy = round(quantity_to_buy, 2)
                    
                if quantity_to_buy > 0:
                    trades.append({
                        'type': 'buy',
                        'ticker': ticker,
                        'quantity': quantity_to_buy,
                        'price': price
                    })
        
        # Execute buy trades
        for trade in [t for t in trades if t['type'] == 'buy']:
            self.add_position(trade['ticker'], trade['quantity'], trade['price'])
            
        # Update last rebalance date
        self.last_rebalance = datetime.now()
        
        logger.info(f"Portfolio rebalanced with {len(trades)} trades")
        return True