""" Module: pricing.py Description: This module provides a pricing optimization functionality based on market data. It uses a simple optimization algorithm and incorporates error handling, logging, and type hints. """ import logging import json from typing import Dict, Union, Optional import os import pandas as pd # Using pandas for more robust data handling # Initialize logging logging.basicConfig( level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s' ) # Default configuration file path (relative to the script's location) DEFAULT_CONFIG_PATH = "/mnt/e/genesis-system/core/revenue/config/pricing_config.json" class PricingOptimizer: """ A class that optimizes pricing based on market data. """ def __init__(self, config_path: str = DEFAULT_CONFIG_PATH) -> None: """ Initializes the PricingOptimizer with a configuration file. Args: config_path (str): Path to the configuration file (JSON). Defaults to DEFAULT_CONFIG_PATH. """ self.config = self._load_config(config_path) self.market_data: Optional[pd.DataFrame] = None # Store market data def _load_config(self, config_path: str) -> Dict: """ Loads the configuration from a JSON file. Args: config_path (str): Path to the configuration file. Returns: Dict: A dictionary containing the configuration. Raises: FileNotFoundError: If the configuration file is not found. json.JSONDecodeError: If the configuration file is invalid JSON. """ try: with open(config_path, 'r') as f: config = json.load(f) logging.info(f"Configuration loaded from {config_path}") return config except FileNotFoundError: logging.error(f"Configuration file not found: {config_path}") raise FileNotFoundError(f"Configuration file not found: {config_path}") except json.JSONDecodeError as e: logging.error(f"Invalid JSON in configuration file: {config_path} - {e}") raise json.JSONDecodeError(f"Invalid JSON in configuration file: {config_path}", e.doc, e.pos) def load_market_data(self, data_path: str, file_type: str = 'csv') -> None: """ Loads market data from a CSV or JSON file into a pandas DataFrame. Args: data_path (str): Path to the market data file. file_type (str, optional): The file type ('csv' or 'json'). Defaults to 'csv'. Raises: FileNotFoundError: If the data file is not found. ValueError: If the file type is not supported. """ try: if file_type == 'csv': self.market_data = pd.read_csv(data_path) elif file_type == 'json': self.market_data = pd.read_json(data_path) else: raise ValueError(f"Unsupported file type: {file_type}") logging.info(f"Market data loaded from {data_path} (type: {file_type})") except FileNotFoundError: logging.error(f"Market data file not found: {data_path}") raise FileNotFoundError(f"Market data file not found: {data_path}") except Exception as e: logging.error(f"Error loading market data from {data_path}: {e}") raise def optimize_price(self, base_price: float) -> float: """ Optimizes the price based on loaded market data and configured parameters. Args: base_price (float): The initial base price. Returns: float: The optimized price. Returns the base price if market data is missing. """ if self.market_data is None: logging.warning("Market data not loaded. Returning base price.") return base_price # Placeholder optimization logic. Replace with actual algorithm. # This example adjusts the price based on average demand. try: demand_column = self.config.get("demand_column", "demand") # Get demand column from config. Default to "demand" demand_data = self.market_data[demand_column] average_demand = demand_data.mean() sensitivity = self.config.get("price_sensitivity", 0.05) # Get price sensitivity from config. Default to 0.05 optimized_price = base_price * (1 + sensitivity * (average_demand - self.config.get("target_demand", 100))) #Assume target demand is 100 if not set. min_price = self.config.get("min_price", 0.0) #Get min price from config, default to 0 max_price = self.config.get("max_price", 1000.0) #Get max price from config, default to 1000 optimized_price = max(min_price, min(optimized_price, max_price)) #Clamp within bounds. logging.info(f"Optimized price from {base_price} to {optimized_price} (avg demand: {average_demand})") return optimized_price except KeyError as e: logging.error(f"Column not found in market data: {e}") return base_price except Exception as e: logging.error(f"Error optimizing price: {e}") return base_price def get_current_market_summary(self) -> Union[Dict, str]: """ Returns a summary of the current market data, if available. Returns: Union[Dict, str]: A dictionary containing market summary statistics, or a message indicating that data is not loaded. """ if self.market_data is None: return "Market data not loaded." try: summary = self.market_data.describe().to_dict() return summary except Exception as e: logging.error(f"Error generating market summary: {e}") return f"Error generating market summary: {e}" # Example usage (for testing/demonstration purposes) if __name__ == '__main__': # Create a dummy market data file dummy_data = { 'date': ['2023-01-01', '2023-01-02', '2023-01-03'], 'demand': [90, 110, 100], 'price': [20, 22, 21] } dummy_df = pd.DataFrame(dummy_data) dummy_csv_path = "/tmp/dummy_market_data.csv" # Changed to /tmp since we don't have write access dummy_df.to_csv(dummy_csv_path, index=False) # Create a dummy configuration file dummy_config = { "demand_column": "demand", "price_sensitivity": 0.05, "target_demand": 100, "min_price": 10, "max_price": 30 } dummy_config_path = "/tmp/dummy_pricing_config.json" with open(dummy_config_path, 'w') as f: json.dump(dummy_config, f) try: optimizer = PricingOptimizer(config_path=dummy_config_path) optimizer.load_market_data(data_path=dummy_csv_path) initial_price = 20.0 optimized_price = optimizer.optimize_price(initial_price) print(f"Initial Price: {initial_price}, Optimized Price: {optimized_price}") market_summary = optimizer.get_current_market_summary() print("Market Summary:") print(market_summary) except Exception as e: print(f"An error occurred: {e}") finally: # Clean up dummy files (important!) if os.path.exists(dummy_csv_path): os.remove(dummy_csv_path) if os.path.exists(dummy_config_path): os.remove(dummy_config_path)