# Basic farming rig specifications
MINIMUM_SPECS = {
    'cpu': 'Intel i5-8400 / Ryzen 5 2600',
    'ram': '16GB DDR4',
    'gpu': 'GTX 1060 / RX 580',
    'storage': '500GB SSD',
    'network': '100Mbps stable connection'
}

# Multi-account farming specifications
PROFESSIONAL_SPECS = {
    'cpu': 'Intel i9-12900K / Ryzen 9 5900X',
    'ram': '64GB DDR4-3600',
    'gpu': 'RTX 3070 (or multiple GPUs)',
    'storage': '2TB NVMe SSD + 4TB HDD',
    'network': '1Gbps fiber connection',
    'monitors': '4K monitor + 2-3 additional displays',
    'peripherals': 'Mechanical keyboard, gaming mouse, KVM switch'
}

class GrindingBot:
    def __init__(self, game_client):
        self.client = game_client
        self.routes = self.load_farming_routes()
        self.current_route = None

    def load_farming_routes(self):
        """Load optimized farming paths"""
        return {
            'low_level_grind': [
                {'x': 1234, 'y': 5678, 'action': 'kill_mobs'},
                {'x': 1235, 'y': 5679, 'action': 'loot_corpses'},
                {'x': 1236, 'y': 5680, 'action': 'vendor_items'}
            ],
            'herb_farming': [
                {'x': 2345, 'y': 6789, 'action': 'gather_herbs'},
                {'x': 2346, 'y': 6790, 'action': 'process_herbs'},
                {'x': 2347, 'y': 6791, 'action': 'sell_to_vendor'}
            ]
        }

    def execute_route(self, route_name):
        """Execute a farming route"""
        if route_name not in self.routes:
            raise ValueError(f"Route {route_name} not found")

        self.current_route = self.routes[route_name]

        for waypoint in self.current_route:
            self.move_to_position(waypoint['x'], waypoint['y'])
            self.perform_action(waypoint['action'])
            self.check_for_interruptions()

    def move_to_position(self, x, y):
        """Move character to coordinates"""
        # Implementation depends on game API
        pass

    def perform_action(self, action):
        """Execute farming action"""
        actions = {
            'kill_mobs': self.kill_nearby_mobs,
            'loot_corpses': self.loot_all_corpses,
            'gather_herbs': self.gather_resources,
            'sell_to_vendor': self.vendor_gray_items
        }

        if action in actions:
            actions[action]()

    def check_for_interruptions(self):
        """Check for game events requiring attention"""
        # Check for player proximity
        # Monitor health/mana levels
        # Handle random events
        pass

class GatheringBot:
    def __init__(self, game_client):
        self.client = game_client
        self.gathering_professions = ['mining', 'herbalism', 'fishing']
        self.current_profession = None

    def optimize_gathering_route(self, profession):
        """Calculate most efficient gathering path"""
        nodes = self.scan_for_resource_nodes(profession)
        optimized_path = self.calculate_tsp_path(nodes)

        return optimized_path

    def scan_for_resource_nodes(self, profession):
        """Scan area for gatherable resources"""
        # Use game API to detect resource nodes
        # Filter by profession and skill level
        # Return coordinates and resource quality
        pass

    def calculate_tsp_path(self, nodes):
        """Solve traveling salesman problem for optimal path"""
        # Implement TSP algorithm for efficient routing
        # Consider travel time, resource density, competition
        pass

    def execute_gathering_cycle(self):
        """Complete gathering cycle"""
        while self.should_continue_gathering():
            route = self.optimize_gathering_route(self.current_profession)
            self.follow_gathering_route(route)
            self.manage_inventory()
            self.handle_fatigue()

    def manage_inventory(self):
        """Handle inventory management"""
        # Sell low-value items
        # Store high-value materials
        # Use profession skills for processing
        pass

import pandas as pd
from datetime import datetime, timedelta
import numpy as np

class MarketBot:
    def __init__(self, game_client, api_key):
        self.client = game_client
        self.api_key = api_key
        self.market_data = {}
        self.trading_history = []

    def scan_market_data(self):
        """Collect market pricing data"""
        items_to_track = self.get_profitable_items()

        for item_id in items_to_track:
            market_info = self.client.get_market_data(item_id)
            self.market_data[item_id] = {
                'buyout_price': market_info['buyout_price'],
                'bid_price': market_info['bid_price'],
                'quantity': market_info['quantity'],
                'timestamp': datetime.now()
            }

    def analyze_market_trends(self):
        """Analyze price trends and opportunities"""
        df = pd.DataFrame.from_dict(self.market_data, orient='index')

        # Calculate moving averages
        df['ma_7'] = df['buyout_price'].rolling(window=7).mean()
        df['ma_30'] = df['buyout_price'].rolling(window=30).mean()

        # Identify undervalued items
        df['undervalued'] = df['buyout_price'] < (df['ma_30'] * 0.8)

        # Calculate profit margins
        df['profit_margin'] = (df['ma_30'] - df['buyout_price']) / df['buyout_price']

        return df[df['undervalued'] & (df['profit_margin'] > 0.1)]

    def execute_arbitrage_trade(self, item_id, buy_price, sell_price):
        """Execute profitable arbitrage trade"""
        # Buy low on one server
        self.client.buy_item(item_id, buy_price, server='low_price_server')

        # Sell high on another server
        self.client.sell_item(item_id, sell_price, server='high_price_server')

        # Record trade
        trade_record = {
            'item_id': item_id,
            'buy_price': buy_price,
            'sell_price': sell_price,
            'profit': sell_price - buy_price,
            'timestamp': datetime.now()
        }

        self.trading_history.append(trade_record)

    def manage_auction_house(self):
        """Manage auction house listings"""
        # Cancel unprofitable auctions
        # Relist expired items
        # Post new profitable items
        # Monitor competition
        pass

class CraftingBot:
    def __init__(self, game_client):
        self.client = game_client
        self.professions = ['blacksmithing', 'alchemy', 'enchanting']
        self.recipes = self.load_recipe_database()

    def load_recipe_database(self):
        """Load crafting recipes and requirements"""
        return {
            'blacksmithing': {
                'copper_boots': {
                    'materials': {'copper_bar': 6, 'coarse_thread': 2},
                    'skill_required': 1,
                    'profit_margin': 0.15
                },
                'iron_breastplate': {
                    'materials': {'iron_bar': 16, 'coarse_thread': 4},
                    'skill_required': 100,
                    'profit_margin': 0.25
                }
            }
        }

    def calculate_crafting_profitability(self):
        """Calculate most profitable crafting recipes"""
        profitability = {}

        for profession, recipes in self.recipes.items():
            for recipe_name, recipe_data in recipes.items():
                material_cost = self.calculate_material_cost(recipe_data['materials'])
                selling_price = self.estimate_selling_price(recipe_name)

                if material_cost > 0:
                    profit_margin = (selling_price - material_cost) / material_cost
                    profitability[recipe_name] = {
                        'profit_margin': profit_margin,
                        'daily_volume': self.estimate_daily_volume(recipe_name),
                        'skill_required': recipe_data['skill_required']
                    }

        return sorted(profitability.items(), key=lambda x: x[1]['profit_margin'], reverse=True)

    def execute_crafting_cycle(self, recipe_name):
        """Execute automated crafting cycle"""
        recipe = self.recipes[self.current_profession][recipe_name]

        while self.has_materials(recipe['materials']):
            # Craft item
            self.client.craft_item(recipe_name)

            # Update inventory
            self.consume_materials(recipe['materials'])
            self.add_to_inventory(recipe_name)

            # Check skill progression
            self.check_skill_up()

            # Handle anti-detection measures
            self.random_delay()

    def optimize_crafting_queue(self):
        """Optimize crafting queue for maximum profit"""
        profitable_recipes = self.calculate_crafting_profitability()

        # Consider material availability
        # Factor in crafting time
        # Account for skill requirements
        # Balance different professions

        optimized_queue = []
        available_materials = self.get_available_materials()

        for recipe_name, data in profitable_recipes:
            recipe = self.recipes[self.current_profession][recipe_name]

            # Check if we can craft this recipe
            if self.can_craft_recipe(recipe, available_materials):
                optimized_queue.append(recipe_name)

                # Update available materials
                for material, quantity in recipe['materials'].items():
                    available_materials[material] -= quantity

        return optimized_queue

import random
import time

class AntiDetectionBot:
    def __init__(self):
        self.action_patterns = self.load_human_patterns()
        self.last_action_time = time.time()

    def load_human_patterns(self):
        """Load realistic human behavior patterns"""
        return {
            'movement_delays': {'min': 0.5, 'max': 3.0},
            'action_variations': {'min': 0.1, 'max': 0.5},
            'pause_frequency': 0.15,  # 15% chance of random pause
            'pause_duration': {'min': 2, 'max': 10}
        }

    def humanize_action(self, action_func, *args, **kwargs):
        """Execute action with human-like timing"""
        # Add random delay before action
        delay = random.uniform(
            self.action_patterns['movement_delays']['min'],
            self.action_patterns['movement_delays']['max']
        )
        time.sleep(delay)

        # Execute action with slight variation
        variation = random.uniform(
            self.action_patterns['action_variations']['min'],
            self.action_patterns['action_variations']['max']
        )
        time.sleep(variation)

        # Execute the actual action
        result = action_func(*args, **kwargs)

        # Random pause after action
        if random.random() < self.action_patterns['pause_frequency']:
            pause_duration = random.uniform(
                self.action_patterns['pause_duration']['min'],
                self.action_patterns['pause_duration']['max']
            )
            time.sleep(pause_duration)

        self.last_action_time = time.time()
        return result

    def simulate_afk_behavior(self):
        """Simulate away-from-keyboard behavior"""
        afk_actions = [
            self.random_mouse_movement,
            self.random_camera_adjustment,
            self.periodic_health_check,
            self.inventory_organization
        ]

        # Randomly select and execute AFK-like actions
        action = random.choice(afk_actions)
        self.humanize_action(action)

class PatternDisruptor:
    def __init__(self):
        self.action_history = []
        self.pattern_threshold = 5  # Actions before pattern disruption

    def track_action(self, action_type, coordinates=None):
        """Track executed actions for pattern detection"""
        action_record = {
            'type': action_type,
            'coordinates': coordinates,
            'timestamp': time.time(),
            'sequence_number': len(self.action_history)
        }

        self.action_history.append(action_record)

        # Check for repetitive patterns
        if self.detect_repetitive_pattern():
            self.disrupt_pattern()

    def detect_repetitive_pattern(self):
        """Detect if current actions form a repetitive pattern"""
        if len(self.action_history) < self.pattern_threshold:
            return False

        recent_actions = self.action_history[-self.pattern_threshold:]

        # Check for identical action sequences
        action_sequence = [action['type'] for action in recent_actions]

        # Look for repeated patterns
        for i in range(2, len(action_sequence) // 2 + 1):
            pattern = action_sequence[:i]
            if action_sequence == pattern * (len(action_sequence) // len(pattern)):
                return True

        return False

    def disrupt_pattern(self):
        """Disrupt detected repetitive patterns"""
        disruption_methods = [
            self.insert_random_action,
            self.change_movement_path,
            self.vary_action_timing,
            self.simulate_human_error
        ]

        method = random.choice(disruption_methods)
        method()

    def insert_random_action(self):
        """Insert a random, non-productive action"""
        random_actions = [
            lambda: time.sleep(random.uniform(1, 3)),
            self.random_mouse_movement,
            self.open_random_menu
        ]

        action = random.choice(random_actions)
        action()

    def vary_action_timing(self):
        """Vary timing between actions"""
        # Increase randomness in delays
        self.action_patterns['movement_delays']['max'] *= 1.5
        self.action_patterns['action_variations']['max'] *= 1.2

class AccountManager:
    def __init__(self):
        self.accounts = self.load_account_database()
        self.active_sessions = {}
        self.security_measures = self.initialize_security()

    def load_account_database(self):
        """Load account credentials and settings"""
        # Securely load encrypted account data
        # Never store passwords in plain text
        pass

    def initialize_security(self):
        """Set up account security measures"""
        return {
            'ip_rotation': True,
            'vpn_required': True,
            'unique_hardware': True,
            'session_limits': {'max_per_ip': 3, 'max_concurrent': 5},
            'login_delays': {'min': 30, 'max': 300}  # seconds
        }

    def rotate_account(self, current_account):
        """Switch to different account to avoid detection"""
        # Close current session
        self.logout_account(current_account)

        # Select next account
        next_account = self.select_next_account(current_account)

        # Apply login delay
        delay = random.uniform(
            self.security_measures['login_delays']['min'],
            self.security_measures['login_delays']['max']
        )
        time.sleep(delay)

        # Login to new account
        self.login_account(next_account)

        return next_account

    def monitor_account_health(self):
        """Monitor account status and detect issues"""
        for account_id, account_data in self.accounts.items():
            # Check login status
            # Monitor for security alerts
            # Track playtime limits
            # Detect suspicious activity

            if self.account_flagged(account_id):
                self.handle_flagged_account(account_id)

    def handle_flagged_account(self, account_id):
        """Handle potentially compromised accounts"""
        # Temporarily disable account
        # Notify administrators
        # Implement additional security measures
        # Log incident for analysis
        pass

# Optimized Windows settings for gaming bots
OPTIMIZED_SETTINGS = {
    'power_plan': 'High Performance',
    'visual_effects': 'Adjust for best performance',
    'processor_scheduling': 'Programs',
    'virtual_memory': 'System managed',
    'network_throttling': 'Disabled',
    'game_mode': 'Enabled',
    'hardware_accelerated_gpu': 'Enabled'
}

class SystemOptimizer:
    def __init__(self):
        self.apply_optimizations()

    def apply_optimizations(self):
        """Apply system-wide performance optimizations"""
        # Disable unnecessary services
        self.disable_services([
            'SysMain',  # Superfetch
            'WSearch',  # Windows Search
            'Spooler'   # Print Spooler
        ])

        # Optimize network settings
        self.optimize_network()

        # Configure power settings
        self.set_power_plan('High Performance')

        # Disable visual effects
        self.disable_visual_effects()

    def optimize_network(self):
        """Optimize network settings for low latency"""
        # Disable Nagle's algorithm
        # Set TCP parameters for gaming
        # Configure QoS settings
        pass

    def monitor_performance(self):
        """Monitor system performance metrics"""
        import psutil

        metrics = {
            'cpu_usage': psutil.cpu_percent(interval=1),
            'memory_usage': psutil.virtual_memory().percent,
            'disk_usage': psutil.disk_usage('/').percent,
            'network_latency': self.measure_network_latency()
        }

        # Log metrics for analysis
        self.log_performance_metrics(metrics)

        # Take corrective actions if needed
        if metrics['cpu_usage'] > 90:
            self.reduce_bot_activity()

        return metrics

class MemoryOptimizer:
    def __init__(self, max_memory_gb=8):
        self.max_memory = max_memory_gb * 1024 * 1024 * 1024  # Convert to bytes
        self.monitor_memory_usage()

    def monitor_memory_usage(self):
        """Monitor and optimize memory usage"""
        import psutil
        import gc

        process = psutil.Process()
        memory_usage = process.memory_info().rss

        if memory_usage > self.max_memory * 0.8:  # 80% threshold
            # Force garbage collection
            gc.collect()

            # Clear unused caches
            self.clear_caches()

            # Restart memory-intensive processes if needed
            if memory_usage > self.max_memory * 0.9:
                self.restart_processes()

    def clear_caches(self):
        """Clear application caches"""
        # Clear image caches
        # Reset data structures
        # Free unused memory
        pass

    def optimize_data_structures(self):
        """Optimize data structures for memory efficiency"""
        # Use __slots__ for classes
        # Implement weak references
        # Use memory-efficient data types
        pass

class RiskAssessor:
    def __init__(self):
        self.costs = self.calculate_operational_costs()
        self.revenues = self.project_revenues()
        self.risks = self.assess_risks()

    def calculate_operational_costs(self):
        """Calculate total operational costs"""
        monthly_costs = {
            'hardware': 500,  # Server/hardware costs
            'software': 100,  # Bot licenses, VPNs
            'power': 150,     # Electricity
            'internet': 100,  # Bandwidth
            'accounts': 200,  # Game subscriptions
            'labor': 800      # Monitoring/maintenance
        }

        return sum(monthly_costs.values())

    def project_revenues(self):
        """Project potential revenues"""
        # Conservative estimates
        revenue_streams = {
            'gold_sales': 2000,    # Monthly gold sales
            'item_trading': 800,   # Item flipping profits
            'account_services': 600,  # Premium services
            'consulting': 400      # Bot setup consulting
        }

        return sum(revenue_streams.values())

    def assess_risks(self):
        """Assess operational risks"""
        risks = {
            'account_ban': {
                'probability': 0.3,  # 30% chance per month
                'impact': 500,       # Cost of replacement accounts
                'mitigation': 'Use VPNs, vary patterns'
            },
            'detection': {
                'probability': 0.2,
                'impact': 2000,      # Lost revenue + legal costs
                'mitigation': 'Advanced anti-detection'
            },
            'market_crash': {
                'probability': 0.1,
                'impact': 1500,      # Lost trading profits
                'mitigation': 'Diversify income streams'
            }
        }

        return risks

    def calculate_roi(self):
        """Calculate return on investment"""
        net_profit = self.revenues - self.costs
        total_investment = 5000  # Initial setup costs

        monthly_roi = (net_profit / total_investment) * 100

        # Account for risks
        risk_adjusted_roi = monthly_roi
        for risk in self.risks.values():
            risk_adjusted_roi -= (risk['probability'] * risk['impact'] / total_investment) * 100

        return {
            'monthly_profit': net_profit,
            'monthly_roi': monthly_roi,
            'risk_adjusted_roi': risk_adjusted_roi,
            'break_even_months': total_investment / net_profit if net_profit > 0 else float('inf')
        }

class ContingencyPlanner:
    def __init__(self):
        self.contingency_plans = self.define_contingency_plans()

    def define_contingency_plans(self):
        """Define contingency plans for various scenarios"""
        return {
            'mass_account_ban': {
                'trigger': 'ban_rate > 0.5',  # 50% of accounts banned
                'response': [
                    'Immediately suspend all operations',
                    'Switch to backup accounts',
                    'Implement enhanced security measures',
                    'Contact affected customers'
                ],
                'recovery_time': '2-4 weeks'
            },
            'game_update_breakage': {
                'trigger': 'bot_failure_rate > 0.8',
                'response': [
                    'Pause operations during update',
                    'Update bot software',
                    'Test in staging environment',
                    'Gradual rollout to production'
                ],
                'recovery_time': '1-3 days'
            },
            'market_crash': {
                'trigger': 'revenue_drop > 50%',
                'response': [
                    'Reduce operational costs',
                    'Diversify revenue streams',
                    'Liquidate non-essential assets',
                    'Seek alternative markets'
                ],
                'recovery_time': '1-2 months'
            }
        }

    def monitor_triggers(self):
        """Monitor for contingency triggers"""
        current_metrics = self.get_current_metrics()

        for scenario, plan in self.contingency_plans.items():
            trigger_condition = plan['trigger']

            if self.evaluate_trigger(trigger_condition, current_metrics):
                self.execute_contingency_plan(scenario, plan)

    def execute_contingency_plan(self, scenario, plan):
        """Execute appropriate contingency plan"""
        print(f"Executing contingency plan for: {scenario}")

        for action in plan['response']:
            print(f"Action: {action}")
            self.execute_action(action)

        # Log incident
        self.log_incident(scenario, plan)

    def evaluate_trigger(self, condition, metrics):
        """Evaluate if trigger condition is met"""
        # Parse condition (e.g., "ban_rate > 0.5")
        # Compare with current metrics
        # Return True if condition met
        pass