Team Training Programs

Enterprise MEV Training Management System

import asyncio
import logging
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Callable, Any, Union
from datetime import datetime, timedelta
from enum import Enum
import json
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import seaborn as sns

class SkillLevel(Enum):
    BEGINNER = "beginner"
    INTERMEDIATE = "intermediate"
    ADVANCED = "advanced"
    EXPERT = "expert"

class TrainingFormat(Enum):
    INSTRUCTOR_LED = "instructor_led"
    ONLINE_SELF_PACED = "online_self_paced"
    BLENDED = "blended"
    HANDS_ON_WORKSHOP = "hands_on_workshop"
    MENTORING = "mentoring"
    SIMULATION = "simulation"

class AssessmentType(Enum):
    KNOWLEDGE_TEST = "knowledge_test"
    PRACTICAL_DEMONSTRATION = "practical_demonstration"
    SIMULATION_EXERCISE = "simulation_exercise"
    CASE_STUDY = "case_study"
    PEER_REVIEW = "peer_review"
    PORTFOLIO_REVIEW = "portfolio_review"

class TrainingStatus(Enum):
    NOT_STARTED = "not_started"
    IN_PROGRESS = "in_progress"
    COMPLETED = "completed"
    FAILED = "failed"
    CERTIFIED = "certified"

@dataclass
class EmployeeProfile:
    """Individual employee profile for training management"""
    employee_id: str
    name: str
    role: str
    department: str
    experience_level: int  # years
    current_skills: Dict[str, SkillLevel]
    learning_preferences: List[TrainingFormat]
    availability: Dict[str, Any]  # time slots, constraints
    performance_history: List[Dict]
    certification_status: Dict[str, bool]
    learning_path: Optional[str] = None
    mentor_assigned: Optional[str] = None

@dataclass
class SkillRequirement:
    """Skill requirement for a specific role"""
    role: str
    skill_category: str
    required_level: SkillLevel
    priority: int  # 1-5, 1 being highest
    assessment_method: AssessmentType
    certification_required: bool = False
    renewal_period_months: Optional[int] = None

@dataclass
class TrainingModule:
    """Individual training module"""
    module_id: str
    module_name: str
    category: str
    description: str
    target_audience: List[str]
    learning_objectives: List[str]
    prerequisites: List[str]
    duration_hours: float
    format: TrainingFormat
    difficulty_level: SkillLevel
    max_participants: int
    cost_per_participant: float
    success_criteria: Dict[str, Any]
    assessment_method: AssessmentType
    certification_available: bool
    renewal_required: bool = False

@dataclass
class TrainingSession:
    """Individual training session"""
    session_id: str
    module_id: str
    trainer: str
    start_date: datetime
    end_date: datetime
    participants: List[str]
    format: TrainingFormat
    location: Optional[str]
    virtual_link: Optional[str]
    capacity: int
    status: str
    materials: List[str]
    assessments: List[Dict]
    feedback: Optional[Dict] = None

@dataclass
class LearningPath:
    """Structured learning path for specific roles"""
    path_id: str
    path_name: str
    target_role: str
    description: str
    modules: List[str]  # module IDs in sequence
    estimated_duration_weeks: int
    competency_framework: Dict[str, SkillLevel]
    assessment_checkpoints: List[str]
    certification_requirements: List[str]

@dataclass
class TrainingOutcome:
    """Training outcome and results"""
    employee_id: str
    module_id: str
    session_id: str
    completion_date: datetime
    score: Optional[float]
    status: TrainingStatus
    feedback_score: Optional[float]
    skills_improved: List[str]
    certification_earned: Optional[str]
    practical_application_score: Optional[float]
    peer_review_score: Optional[float]

class MEVTrainingManager:
    """Comprehensive MEV team training management system"""
    
    def __init__(self, config: Dict):
        self.config = config
        self.logger = logging.getLogger(__name__)
        
        # Training data
        self.employees = {}
        self.training_modules = {}
        self.learning_paths = {}
        self.training_sessions = {}
        self.training_outcomes = {}
        
        # Skills framework
        self.skill_categories = {
            'technical': [
                'blockchain_fundamentals',
                'smart_contracts',
                'defi_protocols',
                'web3_development',
                'mev_extraction',
                'risk_management',
                'data_analysis'
            ],
            'quantitative': [
                'statistical_analysis',
                'mathematical_modeling',
                'backtesting',
                'portfolio_optimization',
                'algorithmic_trading',
                'market_microstructure'
            ],
            'operational': [
                'trading_systems',
                'execution_algorithms',
                'order_management',
                'position_tracking',
                'compliance_procedures',
                'incident_response'
            ],
            'business': [
                'market_analysis',
                'strategy_development',
                'project_management',
                'communication',
                'leadership',
                'stakeholder_management'
            ]
        }
        
        # Competency requirements by role
        self.role_competencies = self._define_role_competencies()
        
        # Training delivery platforms
        self.delivery_platforms = {
            'lms': 'corporate_learning_management_system',
            'virtual_classroom': 'zoom_teams',
            'simulation_platform': 'custom_mev_simulator',
            'hands_on_lab': 'cloud_based_trading_environment',
            'knowledge_base': 'confluence_notion'
        }
        
    async def initialize_system(self):
        """Initialize the training management system"""
        self.logger.info("Initializing MEV Training Management System...")
        
        # Load existing employee data
        await self._load_employee_data()
        
        # Set up training modules
        await self._initialize_training_modules()
        
        # Create learning paths
        await self._create_learning_paths()
        
        # Set up assessment framework
        await self._initialize_assessment_framework()
        
        # Initialize reporting systems
        await self._setup_reporting_systems()
        
        self.logger.info("Training management system initialized")
    
    async def assess_team_capabilities(self) -> Dict:
        """Comprehensive team capability assessment"""
        assessment_results = {
            'individual_assessments': {},
            'team_overview': {},
            'skill_gaps': [],
            'training_recommendations': [],
            'priority_areas': []
        }
        
        # Individual assessments
        for employee_id, employee in self.employees.items():
            individual_assessment = await self._assess_individual_capabilities(employee)
            assessment_results['individual_assessments'][employee_id] = individual_assessment
        
        # Team-level analysis
        team_analysis = await self._analyze_team_capabilities()
        assessment_results['team_overview'] = team_analysis
        
        # Identify skill gaps
        skill_gaps = await self._identify_skill_gaps()
        assessment_results['skill_gaps'] = skill_gaps
        
        # Generate training recommendations
        recommendations = await self._generate_training_recommendations()
        assessment_results['training_recommendations'] = recommendations
        
        # Prioritize training areas
        priority_areas = await self._prioritize_training_areas(skill_gaps, team_analysis)
        assessment_results['priority_areas'] = priority_areas
        
        return assessment_results
    
    async def _assess_individual_capabilities(self, employee: EmployeeProfile) -> Dict:
        """Assess individual employee capabilities"""
        # Get current skill levels
        current_skills = employee.current_skills
        
        # Determine required skills for role
        role_requirements = self.role_competencies.get(employee.role, {})
        
        # Calculate skill gaps
        skill_gaps = {}
        skill_strengths = {}
        
        for skill_category, required_level in role_requirements.items():
            current_level = current_skills.get(skill_category, SkillLevel.BEGINNER)
            
            # Map skill levels to numeric values for comparison
            level_values = {
                SkillLevel.BEGINNER: 1,
                SkillLevel.INTERMEDIATE: 2,
                SkillLevel.ADVANCED: 3,
                SkillLevel.EXPERT: 4
            }
            
            required_value = level_values[required_level]
            current_value = level_values[current_level]
            
            if current_value < required_value:
                skill_gaps[skill_category] = {
                    'current_level': current_level.value,
                    'required_level': required_level.value,
                    'gap_severity': required_value - current_value,
                    'priority': self._calculate_skill_priority(employee.role, skill_category)
                }
            elif current_value > required_value:
                skill_strengths[skill_category] = {
                    'current_level': current_level.value,
                    'expertise_level': current_value - required_value
                }
        
        # Calculate overall readiness score
        total_required_skills = len(role_requirements)
        skills_at_required_level = sum(1 for skill, req_level in role_requirements.items() 
                                     if current_skills.get(skill, SkillLevel.BEGINNER) >= req_level)
        readiness_score = skills_at_required_level / max(total_required_skills, 1)
        
        return {
            'employee_id': employee.employee_id,
            'role': employee.role,
            'experience_years': employee.experience_level,
            'current_skills': {k: v.value for k, v in current_skills.items()},
            'skill_gaps': skill_gaps,
            'skill_strengths': skill_strengths,
            'readiness_score': readiness_score,
            'learning_preferences': [pref.value for pref in employee.learning_preferences],
            'recommended_training': await self._recommend_training_for_employee(employee, skill_gaps)
        }
    
    async def _analyze_team_capabilities(self) -> Dict:
        """Analyze team-level capabilities"""
        team_skills = {category: [] for category in self.skill_categories.keys()}
        skill_distributions = {}
        team_readiness = []
        
        for employee in self.employees.values():
            role = employee.role
            skills = employee.current_skills
            
            # Aggregate skills by category
            for skill_category, skill_level in skills.items():
                if skill_category in team_skills:
                    team_skills[skill_category].append(skill_level.value)
            
            # Calculate individual readiness score
            role_requirements = self.role_competencies.get(role, {})
            if role_requirements:
                skills_at_required = sum(1 for skill, req_level in role_requirements.items() 
                                       if skills.get(skill, SkillLevel.BEGINNER) >= req_level)
                readiness = skills_at_required / len(role_requirements)
                team_readiness.append(readiness)
        
        # Calculate skill distributions
        for category, skill_levels in team_skills.items():
            if skill_levels:
                skill_distributions[category] = {
                    'mean_level': np.mean(skill_levels),
                    'median_level': np.median(skill_levels),
                    'std_deviation': np.std(skill_levels),
                    'distribution': {
                        'beginner': skill_levels.count('beginner'),
                        'intermediate': skill_levels.count('intermediate'),
                        'advanced': skill_levels.count('advanced'),
                        'expert': skill_levels.count('expert')
                    }
                }
        
        return {
            'team_size': len(self.employees),
            'average_readiness': np.mean(team_readiness) if team_readiness else 0,
            'readiness_std': np.std(team_readiness) if team_readiness else 0,
            'skill_distributions': skill_distributions,
            'team_diversity': self._calculate_team_diversity(),
            'experience_profile': self._calculate_experience_profile()
        }
    
    def _calculate_skill_priority(self, role: str, skill_category: str) -> int:
        """Calculate priority of skill for specific role"""
        # Define skill priorities by role
        skill_priorities = {
            'mev_trader': {
                'blockchain_fundamentals': 5,
                'mev_extraction': 5,
                'risk_management': 4,
                'data_analysis': 4,
                'execution_algorithms': 3
            },
            'quant_analyst': {
                'statistical_analysis': 5,
                'mathematical_modeling': 5,
                'data_analysis': 4,
                'backtesting': 4,
                'portfolio_optimization': 3
            },
            'risk_manager': {
                'risk_management': 5,
                'statistical_analysis': 4,
                'compliance_procedures': 4,
                'market_analysis': 3,
                'portfolio_optimization': 3
            },
            'system_engineer': {
                'trading_systems': 5,
                'smart_contracts': 4,
                'web3_development': 4,
                'order_management': 3,
                'defi_protocols': 3
            }
        }
        
        return skill_priorities.get(role, {}).get(skill_category, 3)
    
    async def design_learning_path(self, role: str, target_skill_level: SkillLevel = SkillLevel.ADVANCED) -> LearningPath:
        """Design comprehensive learning path for specific role"""
        # Get role requirements
        role_requirements = self.role_competencies.get(role, {})
        
        # Identify required modules
        required_modules = []
        current_skill_level = SkillLevel.BEGINNER
        
        # Create sequenced learning path
        for skill_category, required_level in role_requirements.items():
            if required_level.value <= target_skill_level.value:
                # Find appropriate training modules for this skill
                skill_modules = await self._find_modules_for_skill(skill_category, current_skill_level, required_level)
                required_modules.extend(skill_modules)
        
        # Estimate duration
        total_hours = sum(self.training_modules.get(mod_id, TrainingModule(
            '', '', '', '', [], [], [], 1, TrainingFormat.INSTRUCTOR_LED, SkillLevel.BEGINNER, 1, 0, {}, AssessmentType.KNOWLEDGE_TEST, False
        )).duration_hours for mod_id in required_modules)
        
        estimated_weeks = max(1, int(total_hours / (20 / 7)))  # Assuming 20 hours/week study time
        
        # Define competency checkpoints
        checkpoints = []
        checkpoint_modules = required_modules[:len(required_modules)//3]  # 3 checkpoints
        for mod_id in checkpoint_modules:
            checkpoints.append(f"checkpoint_{mod_id}")
        
        learning_path = LearningPath(
            path_id=f"{role}_{target_skill_level.value}",
            path_name=f"{role.title()} - {target_skill_level.value.title()} Level",
            target_role=role,
            description=f"Comprehensive training path for {role} to achieve {target_skill_level.value} level competency",
            modules=required_modules,
            estimated_duration_weeks=estimated_weeks,
            competency_framework=role_requirements,
            assessment_checkpoints=checkpoints,
            certification_requirements=[f"certification_{skill}" for skill in role_requirements.keys()]
        )
        
        return learning_path
    
    async def implement_training_program(self, learning_path: LearningPath, cohort_size: int = 10) -> Dict:
        """Implement comprehensive training program"""
        implementation_plan = {
            'program_overview': {
                'learning_path': learning_path.path_name,
                'target_cohort_size': cohort_size,
                'estimated_duration_weeks': learning_path.estimated_duration_weeks,
                'total_participants': len(self.employees),
                'required_resources': await self._calculate_resource_requirements(learning_path, cohort_size)
            },
            'cohort_planning': await self._plan_training_cohorts(learning_path, cohort_size),
            'schedule_development': await self._develop_training_schedule(learning_path),
            'assessment_strategy': await self._design_assessment_strategy(learning_path),
            'support_structure': await self._create_support_structure(learning_path),
            'success_metrics': await self._define_success_metrics(learning_path),
            'budget_estimation': await self._estimate_training_budget(learning_path, cohort_size)
        }
        
        return implementation_plan
    
    async def _plan_training_cohorts(self, learning_path: LearningPath, cohort_size: int) -> Dict:
        """Plan training cohorts based on employee availability and requirements"""
        # Get employees eligible for this training path
        eligible_employees = []
        for employee in self.employees.values():
            if employee.role == learning_path.target_role:
                # Check if employee meets prerequisites
                prerequisites_met = True
                for prereq in learning_path.modules:
                    module = self.training_modules.get(prereq)
                    if module and module.prerequisites:
                        for prereq_skill in module.prerequisites:
                            if employee.current_skills.get(prereq_skill, SkillLevel.BEGINNER) < SkillLevel.INTERMEDIATE:
                                prerequisites_met = False
                                break
                    if not prerequisites_met:
                        break
                
                if prerequisites_met:
                    eligible_employees.append(employee)
        
        # Plan cohorts
        cohorts = []
        remaining_employees = eligible_employees.copy()
        
        cohort_number = 1
        while remaining_employees:
            # Create cohort
            cohort_employees = remaining_employees[:cohort_size]
            remaining_employees = remaining_employees[cohort_size:]
            
            # Consider availability and learning preferences
            optimized_cohort = await self._optimize_cohort_composition(cohort_employees, learning_path)
            
            cohort_plan = {
                'cohort_id': f"cohort_{cohort_number}",
                'participants': [emp.employee_id for emp in optimized_cohort],
                'start_date': await self._schedule_cohort_start(cohort_number, learning_path),
                'estimated_completion': None,  # Will be calculated based on schedule
                'training_formats': await self._recommend_cohort_formats(optimized_cohort),
                'special_considerations': await self._identify_cohort_considerations(optimized_cohort)
            }
            
            cohorts.append(cohort_plan)
            cohort_number += 1
        
        return {
            'total_cohorts': len(cohorts),
            'cohorts': cohorts,
            'scheduling_constraints': await self._analyze_scheduling_constraints(eligible_employees),
            'resource_allocation': await self._plan_resource_allocation(cohorts)
        }
    
    async def _develop_performance_evaluation_system(self) -> Dict:
        """Develop comprehensive performance evaluation system"""
        evaluation_system = {
            'competency_framework': {
                'technical_competencies': {
                    'blockchain_knowledge': {
                        'levels': ['basic', 'intermediate', 'advanced', 'expert'],
                        'assessment_methods': ['knowledge_test', 'practical_demo', 'peer_review'],
                        'weight': 0.25
                    },
                    'mev_extraction_skills': {
                        'levels': ['novice', 'competent', 'proficient', 'expert'],
                        'assessment_methods': ['simulation_exercise', 'case_study', 'portfolio_review'],
                        'weight': 0.30
                    },
                    'risk_management': {
                        'levels': ['beginner', 'intermediate', 'advanced', 'expert'],
                        'assessment_methods': ['knowledge_test', 'practical_demo', 'simulation'],
                        'weight': 0.20
                    }
                },
                'quantitative_competencies': {
                    'statistical_analysis': {
                        'levels': ['basic', 'intermediate', 'advanced', 'expert'],
                        'assessment_methods': ['knowledge_test', 'practical_demo', 'case_study'],
                        'weight': 0.15
                    },
                    'mathematical_modeling': {
                        'levels': ['novice', 'competent', 'proficient', 'expert'],
                        'assessment_methods': ['knowledge_test', 'practical_demo', 'portfolio_review'],
                        'weight': 0.10
                    }
                }
            },
            'assessment_procedures': {
                'initial_assessment': {
                    'purpose': 'baseline capability measurement',
                    'methods': ['comprehensive_skill_inventory', 'practical_assessment', 'knowledge_test'],
                    'time_requirement': '4 hours',
                    'frequency': 'once per employee'
                },
                'ongoing_assessment': {
                    'purpose': 'progress tracking and skill development',
                    'methods': ['module_assessments', 'project_based_assessment', 'peer_feedback'],
                    'time_requirement': '2 hours per month',
                    'frequency': 'monthly'
                },
                'certification_assessment': {
                    'purpose': 'competency certification for role advancement',
                    'methods': ['comprehensive_examination', 'practical_simulation', 'portfolio_defense'],
                    'time_requirement': '8 hours',
                    'frequency': 'quarterly'
                }
            },
            'evaluation_criteria': {
                'knowledge_mastery': {
                    'weight': 0.30,
                    'measures': ['test_scores', 'quiz_performance', 'reading_comprehension']
                },
                'practical_application': {
                    'weight': 0.40,
                    'measures': ['simulation_performance', 'hands_on_exercises', 'project_deliverables']
                },
                'professional_skills': {
                    'weight': 0.20,
                    'measures': ['communication', 'teamwork', 'problem_solving', 'leadership']
                },
                'continuous_improvement': {
                    'weight': 0.10,
                    'measures': ['self_assessment', 'learning_engagement', 'feedback_incorporation']
                }
            },
            'certification_levels': {
                'foundation': {
                    'description': 'Basic competency in MEV fundamentals',
                    'requirements': ['completion_of_core_modules', 'passing_score_70_percent'],
                    'validity_period': '2_years'
                },
                'practitioner': {
                    'description': 'Demonstrated practical MEV skills',
                    'requirements': ['foundation_certification', 'practical_assessment_pass', 'portfolio_review'],
                    'validity_period': '3_years'
                },
                'specialist': {
                    'description': 'Advanced expertise in specific MEV domain',
                    'requirements': ['practitioner_certification', 'specialization_module_completion', 'expert_assessment'],
                    'validity_period': '3_years'
                },
                'expert': {
                    'description': 'Thought leadership and mentoring capability',
                    'requirements': ['specialist_certification', 'knowledge_contribution', 'mentoring_experience'],
                    'validity_period': '5_years'
                }
            },
            'feedback_mechanisms': {
                '360_degree_feedback': {
                    'participants': ['manager', 'peers', 'subordinates', 'self'],
                    'frequency': 'semiannual',
                    'focus_areas': ['competency_application', 'professional_behavior', 'leadership_skills']
                },
                'continuous_feedback': {
                    'mechanism': 'real_time_feedback_platform',
                    'frequency': 'ongoing',
                    'categories': ['performance', 'development', 'recognition']
                },
                'assessment_feedback': {
                    'timing': 'within_48_hours_of_assessment',
                    'content': ['performance_summary', 'improvement_areas', 'development_recommendations'],
                    'format': ['written_report', 'one_on_one_discussion', 'development_plan']
                }
            }
        }
        
        return evaluation_system
    
    async def create_knowledge_management_system(self) -> Dict:
        """Create comprehensive knowledge management system"""
        knowledge_system = {
            'knowledge_capture': {
                'documentation_standards': {
                    'technical_documentation': {
                        'format': 'structured_markdown',
                        'required_sections': ['overview', 'technical_details', 'examples', 'troubleshooting'],
                        'review_process': 'peer_review_then_manager_approval',
                        'version_control': 'git_based',
                        'access_control': 'role_based'
                    },
                    'procedural_documentation': {
                        'format': 'step_by_step_instructions',
                        'required_sections': ['objective', 'prerequisites', 'steps', 'validation', 'rollback'],
                        'review_process': 'subject_matter_expert_review',
                        'update_frequency': 'quarterly_or_as_needed'
                    },
                    'lesson_learned_documentation': {
                        'format': 'incident_based_template',
                        'required_sections': ['situation', 'action_taken', 'outcome', 'lessons_learned', 'recommendations'],
                        'review_process': 'team_review_session',
                        'sharing_mechanism': 'knowledge_base_and_monthly_meetings'
                    }
                },
                'capture_mechanisms': {
                    'after_action_reviews': {
                        'trigger': 'significant_events_or_projects',
                        'participants': 'full_project_team',
                        'facilitator': 'trained_facilitator',
                        'output': 'comprehensive_lessons_learned_report'
                    },
                    'peer_to_peer_knowledge_sharing': {
                        'format': 'informal_knowledge_sessions',
                        'frequency': 'weekly_or_biweekly',
                        'platform': 'collaboration_tools',
                        'recording': 'video_and_transcript'
                    },
                    'expert_interviews': {
                        'frequency': 'monthly',
                        'participants': 'subject_matter_experts',
                        'format': 'structured_interview',
                        'output': 'expert_knowledge_base_entries'
                    }
                }
            },
            'knowledge_organization': {
                'taxonomy_structure': {
                    'primary_categories': [
                        'technical_knowledge',
                        'procedural_knowledge',
                        'market_knowledge',
                        'organizational_knowledge',
                        'regulatory_knowledge'
                    ],
                    'secondary_categories': {
                        'technical_knowledge': [
                            'blockchain_technology',
                            'defi_protocols',
                            'mev_extraction',
                            'risk_management',
                            'system_architecture'
                        ],
                        'procedural_knowledge': [
                            'trading_procedures',
                            'risk_procedures',
                            'compliance_procedures',
                            'incident_response',
                            'emergency_procedures'
                        ]
                    }
                },
                'tagging_system': {
                    'mandatory_tags': ['content_type', 'expertise_level', 'last_updated', 'author'],
                    'optional_tags': ['related_protocols', 'risk_level', 'business_impact', 'urgency'],
                    'tag_governance': 'centralized_tag_management_with_user_suggestions'
                },
                'search_functionality': {
                    'search_engine': 'elasticsearch',
                    'search_capabilities': ['full_text', 'metadata', 'tag_based', 'semantic'],
                    'relevance_ranking': 'weighted_by_expertise_and_recency',
                    'search_analytics': 'track_search_patterns_and_improve_results'
                }
            },
            'knowledge_sharing': {
                'communities_of_practice': {
                    'mev_extraction_community': {
                        'members': 'mev_traders_and_analysts',
                        'meeting_frequency': 'weekly',
                        'activities': ['case_study_reviews', 'technique_sharing', 'strategy_discussions'],
                        'knowledge_sharing_platform': 'dedicated_slack_channel_and_monthly_meetings'
                    },
                    'risk_management_community': {
                        'members': 'risk_managers_and_analysts',
                        'meeting_frequency': 'biweekly',
                        'activities': ['risk_case_studies', 'regulatory_updates', 'methodology_sharing'],
                        'knowledge_sharing_platform': 'monthly_webinars_and_documentation_sharing'
                    }
                },
                'mentoring_program': {
                    'structure': 'formal_mentor_mentee_pairings',
                    'duration': '6_month_minimum_commitment',
                    'meeting_frequency': 'biweekly_formal_meetings_plus_as_needed',
                    'mentor_training': 'mentoring_skills_workshop',
                    'progress_tracking': 'regular_check_ins_and_feedback_sessions'
                },
                'learning_resources': {
                    'library_maintenance': 'curated_and_continuously_updated',
                    'resource_categorization': 'by_skill_level_and_topic',
                    'access_management': 'appropriate_permissions_for_different_levels',
                    'feedback_collection': 'user_ratings_and_comments_for_all_resources'
                }
            },
            'knowledge_retention': {
                'succession_planning': {
                    'critical_role_identification': 'roles_with_high_business_impact',
                    'knowledge_mapping': 'document_what_each_person_knows',
                    'cross_training': 'systematic_knowledge_transfer_between_employees',
                    'backup_personnel': 'identified_successors_for_all_critical_roles'
                },
                'retirement_preparation': {
                    'knowledge_transfer_timeline': '6_months_before_retirement',
                    'documentation_requirements': 'comprehensive_handover_documentation',
                    'shadow_period': '3_month_overlap_with_replacement',
                    'validation_process': 'knowledge_verification_through_assessments'
                },
                'turnover_management': {
                    'exit_interviews': 'structured_knowledge_capture_interviews',
                    'knowledge_capture_sessions': 'formal_sessions_with_team_members',
                    'documentation_deadlines': '30_day_completion_requirement',
                    'knowledge_continuity': 'immediate_transfer_to_designated_backup_personnel'
                }
            },
            'technology_platform': {
                'knowledge_base': {
                    'platform': 'enterprise_wiki_or_confluence',
                    'features': ['collaborative_editing', 'version_control', 'search', 'access_control'],
                    'integration': 'single_sign_on_and_ldap_integration',
                    'mobile_access': 'responsive_design_and_mobile_app'
                },
                'learning_management': {
                    'platform': 'corporate_lms',
                    'features': ['course_delivery', 'assessment_tracking', 'certification_management'],
                    'analytics': 'learning_analytics_and_progress_tracking',
                    'mobile_support': 'mobile_app_for_on_the_go_learning'
                },
                'collaboration_tools': {
                    'platform': 'slack_or_teams',
                    'features': ['channels', 'direct_messages', 'file_sharing', 'video_calls'],
                    'integrations': 'connect_with_knowledge_base_and_lms',
                    'notification_management': 'intelligent_notification_filtering'
                }
            }
        }
        
        return knowledge_system
    
    # Helper methods
    def _define_role_competencies(self) -> Dict[str, Dict[str, SkillLevel]]:
        """Define competency requirements for each role"""
        return {
            'mev_trader': {
                'blockchain_fundamentals': SkillLevel.ADVANCED,
                'mev_extraction': SkillLevel.EXPERT,
                'risk_management': SkillLevel.ADVANCED,
                'data_analysis': SkillLevel.INTERMEDIATE,
                'execution_algorithms': SkillLevel.ADVANCED,
                'market_microstructure': SkillLevel.INTERMEDIATE
            },
            'quant_analyst': {
                'statistical_analysis': SkillLevel.EXPERT,
                'mathematical_modeling': SkillLevel.EXPERT,
                'data_analysis': SkillLevel.ADVANCED,
                'backtesting': SkillLevel.ADVANCED,
                'portfolio_optimization': SkillLevel.ADVANCED,
                'blockchain_fundamentals': SkillLevel.INTERMEDIATE
            },
            'risk_manager': {
                'risk_management': SkillLevel.EXPERT,
                'statistical_analysis': SkillLevel.ADVANCED,
                'compliance_procedures': SkillLevel.ADVANCED,
                'market_analysis': SkillLevel.INTERMEDIATE,
                'portfolio_optimization': SkillLevel.INTERMEDIATE,
                'regulatory_knowledge': SkillLevel.ADVANCED
            },
            'system_engineer': {
                'trading_systems': SkillLevel.EXPERT,
                'smart_contracts': SkillLevel.ADVANCED,
                'web3_development': SkillLevel.ADVANCED,
                'order_management': SkillLevel.ADVANCED,
                'defi_protocols': SkillLevel.INTERMEDIATE,
                'blockchain_fundamentals': SkillLevel.INTERMEDIATE
            }
        }
    
    async def _load_employee_data(self):
        """Load employee data (placeholder implementation)"""
        # This would load from HR system or database
        pass
    
    async def _initialize_training_modules(self):
        """Initialize training modules (placeholder implementation)"""
        # This would load from training database
        pass
    
    async def _create_learning_paths(self):
        """Create learning paths (placeholder implementation)"""
        # This would create based on role requirements
        pass
    
    async def _initialize_assessment_framework(self):
        """Initialize assessment framework (placeholder implementation)"""
        pass
    
    async def _setup_reporting_systems(self):
        """Set up reporting systems (placeholder implementation)"""
        pass
    
    async def _find_modules_for_skill(self, skill_category: str, current_level: SkillLevel, target_level: SkillLevel) -> List[str]:
        """Find appropriate training modules for skill development"""
        # Placeholder implementation
        return [f"module_{skill_category}_{target_level.value}"]
    
    def _calculate_team_diversity(self) -> Dict:
        """Calculate team diversity metrics"""
        return {
            'skill_diversity_index': 0.8,  # Placeholder
            'experience_diversity': 'well_distributed',
            'learning_style_diversity': 'balanced'
        }
    
    def _calculate_experience_profile(self) -> Dict:
        """Calculate team experience profile"""
        experiences = [emp.experience_level for emp in self.employees.values()]
        return {
            'mean_experience': np.mean(experiences),
            'experience_range': f"{min(experiences)}-{max(experiences)} years",
            'senior_staff_ratio': len([e for e in experiences if e >= 5]) / len(experiences)
        }
    
    async def _recommend_training_for_employee(self, employee: EmployeeProfile, skill_gaps: Dict) -> List[str]:
        """Recommend training modules for specific employee"""
        # Placeholder implementation
        return [f"training_{gap}" for gap in skill_gaps.keys()]
    
    async def _identify_skill_gaps(self) -> List[Dict]:
        """Identify organization-wide skill gaps"""
        # Placeholder implementation
        return []
    
    async def _generate_training_recommendations(self) -> List[Dict]:
        """Generate training recommendations"""
        # Placeholder implementation
        return []
    
    async def _prioritize_training_areas(self, skill_gaps: List, team_analysis: Dict) -> List[Dict]:
        """Prioritize training areas based on business impact"""
        # Placeholder implementation
        return []
    
    async def _calculate_resource_requirements(self, learning_path: LearningPath, cohort_size: int) -> Dict:
        """Calculate resource requirements for training program"""
        # Placeholder implementation
        return {}
    
    async def _schedule_cohort_start(self, cohort_number: int, learning_path: LearningPath) -> datetime:
        """Schedule cohort start date"""
        # Placeholder implementation
        return datetime.now() + timedelta(weeks=cohort_number * 4)
    
    async def _optimize_cohort_composition(self, employees: List[EmployeeProfile], learning_path: LearningPath) -> List[EmployeeProfile]:
        """Optimize cohort composition"""
        # Placeholder implementation
        return employees[:min(len(employees), 10)]
    
    async def _recommend_cohort_formats(self, employees: List[EmployeeProfile]) -> List[TrainingFormat]:
        """Recommend training formats for cohort"""
        # Analyze employee preferences
        preferences = {}
        for emp in employees:
            for pref in emp.learning_preferences:
                preferences[pref] = preferences.get(pref, 0) + 1
        
        # Return most preferred formats
        sorted_prefs = sorted(preferences.items(), key=lambda x: x[1], reverse=True)
        return [pref[0] for pref in sorted_prefs[:3]]
    
    async def _identify_cohort_considerations(self, employees: List[EmployeeProfile]) -> List[str]:
        """Identify special considerations for cohort"""
        considerations = []
        
        # Check for similar experience levels
        experiences = [emp.experience_level for emp in employees]
        if max(experiences) - min(experiences) > 5:
            considerations.append("High experience diversity - consider mixed-level approach")
        
        # Check for learning preferences
        preference_diversity = len(set(emp.learning_preferences for emp in employees))
        if preference_diversity > 3:
            considerations.append("Diverse learning preferences - use blended approach")
        
        return considerations
    
    async def _analyze_scheduling_constraints(self, employees: List[EmployeeProfile]) -> Dict:
        """Analyze scheduling constraints"""
        # Placeholder implementation
        return {'constraints': 'minimal'}
    
    async def _plan_resource_allocation(self, cohorts: List[Dict]) -> Dict:
        """Plan resource allocation across cohorts"""
        # Placeholder implementation
        return {'resources': 'adequately_planned'}
    
    async def _estimate_training_budget(self, learning_path: LearningPath, cohort_size: int) -> Dict:
        """Estimate training budget"""
        # Placeholder implementation
        return {
            'total_estimated_cost': 50000,
            'cost_breakdown': {
                'instructor_costs': 20000,
                'platform_costs': 5000,
                'material_costs': 10000,
                'participant_time': 15000
            }
        }

# Example usage
async def main():
    # Initialize training manager
    config = {
        'organization_size': 50,
        'training_budget': 500000,
        'certification_requirements': ['sox', 'gdpr'],
        'learning_preferences': ['blended', 'hands_on', 'peer_learning']
    }
    
    manager = MEVTrainingManager(config)
    await manager.initialize_system()
    
    # Assess team capabilities
    assessment = await manager.assess_team_capabilities()
    print(f"Team readiness score: {assessment['team_overview']['average_readiness']:.2%}")
    print(f"Priority training areas: {len(assessment['priority_areas'])}")
    
    # Design learning path
    learning_path = await manager.design_learning_path('mev_trader', SkillLevel.ADVANCED)
    print(f"Learning path created: {learning_path.path_name}")
    print(f"Duration: {learning_path.estimated_duration_weeks} weeks")
    
    # Implement training program
    implementation = await manager.implement_training_program(learning_path)
    print(f"Training program planned: {implementation['program_overview']['total_cohorts']} cohorts")
    
    # Develop performance evaluation system
    evaluation_system = await manager._develop_performance_evaluation_system()
    print(f"Performance evaluation system ready")
    print(f"Certification levels: {len(evaluation_system['certification_levels'])}")
    
    # Create knowledge management system
    knowledge_system = await manager.create_knowledge_management_system()
    print(f"Knowledge management system created")
    print(f"Communities of practice: {len(knowledge_system['knowledge_sharing']['communities_of_practice'])}")

if __name__ == "__main__":
    asyncio.run(main())