┌─────────────────────────────────────────────────────────────────────────────┐
│                    EMERGING TOKEN MODELS                                    │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    PROGRAMMABLE MONEY                                │   │
│  │  Tokens with built-in logic for specific conditions.               │   │
│  │  • Conditional payments                                              │   │
│  │  • Time-locked tokens                                               │   │
│  │  • Purpose-bound tokens                                             │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    AI-DRIVEN TOKENS                                 │   │
│  │  Tokens integrated with artificial intelligence.                   │   │
│  │  • AI agents with token economies                                   │   │
│  │  • Autonomous trading systems                                       │   │
│  │  • Self-optimising protocols                                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    REVERSIBLE TOKENS                                 │   │
│  │  Tokens that can be reversed under certain conditions.             │   │
│  │  • Consumer protection                                              │   │
│  │  • Fraud reversal                                                   │   │
│  │  • Dispute resolution                                               │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    SUSTAINABLE TOKENS                                │   │
│  │  Tokens designed for ESG compliance.                               │   │
│  │  • Carbon credits                                                   │   │
│  │  • Green bonds                                                     │   │
│  │  • Impact tokens                                                   │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    GREEN TOKENOMICS                                         │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    CARBON CREDIT TOKENS                              │   │
│  │  Tokenised carbon credits for trading.                               │   │
│  │  • Verified carbon offsets                                           │   │
│  │  • On-chain verification                                             │   │
│  │  • Global carbon markets                                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    ENERGY-EFFICIENT CONSENSUS                       │   │
│  │  Proof of Stake, PoA, or hybrid models.                             │   │
│  │  • Lower energy consumption                                         │   │
│  │  • Reduced carbon footprint                                        │   │
│  │  • Sustainable operation                                            │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │                    GREEN MINING                                      │   │
│  │  Mining powered by renewable energy.                                │   │
│  │  • Hydropower                                                       │   │
│  │  • Solar                                                             │   │
│  │  • Wind                                                             │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────────────────┐
│                    FUTURE TOKEN DESIGN PRINCIPLES                           │
├─────────────────────────────────────────────────────────────────────────────┤
│                                                                             │
│  1. ADAPTIVE TOKENOMICS                                                    │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ Tokenomics that adapts to market conditions.                         │   │
│  │ • Dynamic supply mechanisms                                          │   │
│  │ • Automated parameter adjustments                                   │   │
│  │ • Responsive fee structures                                        │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  2. LAYERED TOKEN ECONOMIES                                                 │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ Multiple token layers for different functions.                      │   │
│  │ • Governance tokens                                                 │   │
│  │ • Utility tokens                                                    │   │
│  │ • Reward tokens                                                     │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  3. COMPOSABLE TOKENOMICS                                                  │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ Token economies that can be composed and combined.                 │   │
│  │ • Interoperable tokens                                              │   │
│  │ • Cross-chain economies                                             │   │
│  │ • Shared liquidity                                                  │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
│  4. USER-CENTRIC TOKENOMICS                                               │
│  ┌──────────────────────────────────────────────────────────────────────┐   │
│  │ Design focused on user experience and participation.               │   │
│  │ • Simplified governance                                             │   │
│  │ • Transparent incentives                                            │   │
│  │ • Fair distribution                                                │   │
│  └──────────────────────────────────────────────────────────────────────┘   │
│                                                                             │
└─────────────────────────────────────────────────────────────────────────────┘

# ===================================================================
# MODULE 6, LESSON 8: ADVANCED TOKENOMICS AND FUTURE TRENDS
# ===================================================================

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from typing import Dict, List
import warnings
warnings.filterwarnings('ignore')

print("="*70)
print("ADVANCED TOKENOMICS AND FUTURE TRENDS")
print("="*70)

# ----------------------------------------------------------------
# PART A: AI-DRIVEN TOKEN SIMULATION
# ----------------------------------------------------------------

print("\n" + "-"*60)
print("PART A: AI-Driven Token Simulation")
print("-"*60)

class AITokenAgent:
    """
    Simulated AI-driven token agent.
    """
    def __init__(self, name: str, strategy: str):
        self.name = name
        self.strategy = strategy  # 'aggressive', 'conservative', 'adaptive'
        self.balance = 100
        self.holdings = 0
        self.history = []
    
    def decide(self, market_data: Dict) -> Dict:
        """AI decision-making based on market data."""
        price = market_data.get('price', 1)
        volatility = market_data.get('volatility', 0.05)
        sentiment = market_data.get('sentiment', 0)
        
        if self.strategy == 'aggressive':
            buy_threshold = 0.2
            sell_threshold = 0.5
        elif self.strategy == 'conservative':
            buy_threshold = -0.2
            sell_threshold = 0.3
        else:  # adaptive
            buy_threshold = sentiment * 0.1
            sell_threshold = sentiment * 0.3
        
        action = 'hold'
        amount = 0
        
        if sentiment > sell_threshold and self.holdings > 0:
            action = 'sell'
            amount = self.holdings * 0.5
        elif sentiment < buy_threshold and self.balance > 0:
            action = 'buy'
            amount = self.balance * 0.3
        
        return {
            'action': action,
            'amount': amount,
            'price': price,
            'strategy': self.strategy
        }
    
    def execute(self, action: str, amount: float, price: float) -> Dict:
        """Execute an action based on AI decision."""
        result = {'action': action, 'amount': amount, 'price': price}
        
        if action == 'buy':
            cost = amount * price
            if cost <= self.balance:
                self.balance -= cost
                self.holdings += amount
                result['success'] = True
            else:
                result['success'] = False
        elif action == 'sell':
            if amount <= self.holdings:
                self.balance += amount * price
                self.holdings -= amount
                result['success'] = True
            else:
                result['success'] = False
        else:
            result['success'] = True
        
        self.history.append({
            'action': action,
            'balance': self.balance,
            'holdings': self.holdings,
            'price': price
        })
        
        return result

# Simulate AI agents
print("AI Token Agents Simulation:")
print("Simulating market with 3 AI agents...")

agents = [
    AITokenAgent('Agent_A', 'aggressive'),
    AITokenAgent('Agent_B', 'conservative'),
    AITokenAgent('Agent_C', 'adaptive')
]

# Simulate market conditions
market_conditions = [
    {'price': 1.0, 'volatility': 0.05, 'sentiment': 0.3},
    {'price': 1.2, 'volatility': 0.08, 'sentiment': 0.6},
    {'price': 0.9, 'volatility': 0.10, 'sentiment': -0.2},
    {'price': 1.1, 'volatility': 0.06, 'sentiment': 0.4},
    {'price': 1.5, 'volatility': 0.12, 'sentiment': 0.8}
]

for agent in agents:
    print(f"\n{agent.name} ({agent.strategy}):")
    for i, market in enumerate(market_conditions):
        decision = agent.decide(market)
        result = agent.execute(decision['action'], decision['amount'], market['price'])
        if result['success']:
            print(f"  Step {i+1}: {decision['action']} {decision['amount']:.2f} @ ${market['price']:.2f}")
        else:
            print(f"  Step {i+1}: {decision['action']} (failed)")

# ----------------------------------------------------------------
# PART B: PROGRAMMABLE MONEY SIMULATION
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART B: Programmable Money Simulation")
print("-"*60)

class ProgrammableToken:
    """
    Simulated programmable token with conditional logic.
    """
    def __init__(self, owner: str, amount: float, conditions: Dict):
        self.owner = owner
        self.amount = amount
        self.conditions = conditions  # {'type': 'time_lock', 'value': 30, 'time_unit': 'days'}
        self.state = 'locked'
        self.release_time = None
        if conditions.get('type') == 'time_lock':
            self.release_time = pd.Timestamp.now() + pd.Timedelta(days=conditions.get('value', 30))
    
    def check_conditions(self) -> bool:
        """Check if conditions are met for release."""
        if self.conditions.get('type') == 'time_lock':
            return pd.Timestamp.now() >= self.release_time
        elif self.conditions.get('type') == 'purpose_bound':
            # Simulate purpose verification
            return True
        return False
    
    def execute(self) -> Dict:
        """Execute the token if conditions met."""
        if self.check_conditions():
            self.state = 'released'
            return {
                'success': True,
                'state': 'released',
                'amount': self.amount,
                'owner': self.owner
            }
        return {
            'success': False,
            'state': self.state,
            'remaining_time': f"{max(0, (self.release_time - pd.Timestamp.now()).days)} days" if self.release_time else 'N/A'
        }

# Simulate programmable tokens
print("Programmable Money Simulation:")
tokens = [
    ProgrammableToken('Alice', 1000, {'type': 'time_lock', 'value': 30}),
    ProgrammableToken('Bob', 500, {'type': 'purpose_bound'})
]

for token in tokens:
    result = token.execute()
    if result['success']:
        print(f"✅ Token released: {result['amount']} to {result['owner']}")
    else:
        print(f"⏳ Token still locked: {result.get('remaining_time', 'Condition not met')}")

# ----------------------------------------------------------------
# PART C: FUTURE TRENDS DASHBOARD
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART C: Future Trends Dashboard")
print("-"*60)

trends_data = {
    'Trend': [
        'AI-Driven Tokenomics',
        'Programmable Money',
        'Tokenisation of Assets',
        'DePIN Growth',
        'Regulatory Clarity',
        'Sustainable Tokenomics',
        'Interoperability',
        'Zero-Knowledge Privacy'
    ],
    'Maturity': [
        'Emerging',
        'Emerging',
        'Growth',
        'Growth',
        'Emerging',
        'Growth',
        'Growth',
        'Growth'
    ],
    'Impact (1-10)': [9, 9, 8, 8, 9, 7, 8, 8],
    'Adoption (1-10)': [3, 2, 5, 4, 4, 5, 5, 5],
    'Investment Priority': ['High', 'High', 'Medium', 'High', 'High', 'Medium', 'Medium', 'Medium']
}

trends_df = pd.DataFrame(trends_data)
print(trends_df.to_string(index=False))

# Visualise
fig, ax = plt.subplots(figsize=(12, 6))
x = np.arange(len(trends_data['Trend']))
width = 0.35

ax.bar(x - width/2, trends_data['Impact (1-10)'], width, label='Impact', color='blue', alpha=0.7)
ax.bar(x + width/2, trends_data['Adoption (1-10)'], width, label='Adoption', color='green', alpha=0.7)

ax.set_xlabel('Trend')
ax.set_ylabel('Score (1-10)')
ax.set_title('Future Tokenomics Trends: Impact vs Adoption')
ax.set_xticks(x)
ax.set_xticklabels(trends_data['Trend'], rotation=45, ha='right')
ax.legend()
ax.grid(True, alpha=0.3)

plt.tight_layout()
plt.savefig('future_trends.png', dpi=300, bbox_inches='tight')
plt.show()
print("Future trends chart saved as 'future_trends.png'")

# ----------------------------------------------------------------
# PART D: TOKENOMICS SCENARIO ANALYSIS
# -----------------------------------------------------------------

print("\n" + "-"*60)
print("PART D: Tokenomics Scenario Analysis")
print("-"*60)

scenario_data = {
    'Scenario': ['Base Case', 'Bull Market', 'Bear Market', 'Regulation +', 'DeFi Growth'],
    'Token Supply': ['1,000,000', '1,000,000', '1,200,000', '1,000,000', '1,000,000'],
    'Demand Index': [50, 80, 20, 40, 70],
    'Velocity': [10, 15, 8, 12, 10],
    'Quantity': [50000, 60000, 30000, 40000, 70000],
    'Est Price': [100, 200, 32, 33, 140]
}

scenario_df = pd.DataFrame(scenario_data)
print(scenario_df.to_string(index=False))

# ----------------------------------------------------------------
# PART E: SUMMARY AND RECOMMENDATIONS
# -----------------------------------------------------------------

print("\n" + "="*70)
print("PART E: Summary and Recommendations")
print("="*70)

print("""
Advanced Tokenomics and Future Trends – Key Takeaways:

1. Emerging models: programmable money, AI-driven tokens, reversible tokens, sustainable tokens.
2. DePIN uses tokens to incentivise physical infrastructure.
3. AI integration: autonomous agents, smart contract optimisation, predictive analytics.
4. Sustainable tokenomics: ESG considerations, carbon credits, green mining.
5. Future trends: tokenisation of everything, DeFi 2.0, interoperability, privacy.
6. Token design principles: adaptive, layered, composable, user-centric.

Recommendations:
  - Design tokenomics for long-term sustainability.
  - Consider AI and automation in token design.
  - Incorporate ESG principles from the start.
  - Prepare for regulatory clarity and institutional adoption.
  - Build for interoperability across chains.
  - Focus on user experience and participation.
  - Stay updated on emerging technologies and trends.
  - Continuously adapt and evolve tokenomics.
""")