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Simulation charts

Interactive visualizations and charts for tokenomics simulation analysis. This document includes Mermaid diagrams and Python code for modeling token emissions, contributor growth, and economic sustainability.

By tieubao18 Jun 20255 min read

Words
983
Contents

1Chart 1: Token supply evolution

ICY supply growth over time

xychart-beta
    title "ICY Token Supply Evolution (24 Months)"
    x-axis [Month-0, Month-3, Month-6, Month-9, Month-12, Month-15, Month-18, Month-21, Month-24]
    y-axis "ICY Tokens (Millions)" 6 --> 8
    line "Circulating ICY" [6.0, 6.06, 6.13, 6.24, 6.36, 6.48, 6.60, 6.72, 6.84]
    line "Staked ICY" [0, 0.45, 0.90, 1.35, 1.80, 2.25, 2.70, 3.15, 3.60]
    line "Total Supply" [6.0, 6.51, 7.03, 7.59, 8.16, 8.73, 9.30, 9.87, 10.44]

DFG distribution progression

pie title DFG Token Distribution (Month 24)
    "Team Holdings" : 400000
    "Community Earned" : 2400
    "Reserved Pool" : 197600

2Chart 2: Contributor growth & activity

Contributors vs monthly ICY rewards

xychart-beta
    title "Contributors Growth vs ICY Rewards"
    x-axis [Month-0, Month-6, Month-12, Month-18, Month-24]
    y-axis "Count / ICY (Thousands)" 0 --> 100
    bar "Contributors" [40, 58, 76, 91, 105]
    line "Monthly ICY Rewards" [17.5, 26.0, 36.0, 46.0, 55.0]

Staking participation rate

xychart-beta
    title "ICY Staking Adoption Rate"
    x-axis [Month-0, Month-3, Month-6, Month-9, Month-12, Month-15, Month-18, Month-21, Month-24]
    y-axis "Staking Rate (%)" 0 --> 60
    line "Staking Participation" [0, 7.4, 14.7, 21.6, 28.3, 34.7, 40.9, 46.9, 52.6]

3Chart 3: Economic sustainability

Revenue vs token rewards cost

xychart-beta
    title "Economic Sustainability Analysis"
    x-axis [Month-0, Month-6, Month-12, Month-18, Month-24]
    y-axis "USD (Thousands)" 0 --> 200
    bar "Monthly Revenue" [50, 80, 110, 135, 150]
    line "Token Rewards Cost" [17.5, 26.0, 36.0, 46.0, 55.0]
    line "Sustainability Ratio" [2.86, 3.08, 3.06, 2.93, 2.73]

Treasury growth (BTC value)

xychart-beta
    title "Bitcoin Treasury Growth"
    x-axis [Month-0, Month-6, Month-12, Month-18, Month-24]
    y-axis "Treasury Value (USD Millions)" 2 --> 6
    line "BTC Treasury Value" [2.0, 2.41, 3.03, 4.03, 5.23]

4Chart 4: Staking dynamics

ICY staking rewards distribution

sankey-beta
    ICY Staked,6-Month Staking,1800000
    ICY Staked,12-Month Staking,1800000
    6-Month Staking,APY Rewards (5%),90000
    12-Month Staking,APY Rewards (7.5%),135000
    6-Month Staking,DFG Conversion,1800
    12-Month Staking,DFG Conversion,1800

DFG dividend flow

flowchart TD
    A[Quarterly Profits: $150K] --> B[Dividend Pool: $15K]
    B --> C[DFG Stakers: 240K tokens]
    C --> D[Average Dividend: $37.50]
    C --> E[Top 10% Dividend: $375]
    
    F[Staking Rewards] --> G[Enhanced Voting: 2x Weight]
    G --> H[Governance Participation: 60%]

5Python implementation

Data visualization code

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime, timedelta

# Simulation data
months = np.arange(0, 25)
contributors = np.linspace(40, 105, 25)
icy_circulating = 6000000 + np.cumsum(np.linspace(0, 838500, 25))
icy_staked = np.linspace(0, 3600000, 25)
dfg_earned = np.maximum(0, (icy_staked / 1000) - 150)  # 6-month delay
revenue = np.linspace(50000, 150000, 25)

# Create comprehensive dashboard
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))

# Chart 1: Token Supply Evolution
ax1.plot(months, icy_circulating/1e6, label='Circulating ICY', linewidth=2)
ax1.plot(months, icy_staked/1e6, label='Staked ICY', linewidth=2)
ax1.plot(months, (icy_circulating + icy_staked)/1e6, label='Total ICY', linewidth=2, linestyle='--')
ax1.set_title('ICY Token Supply Evolution', fontsize=14, fontweight='bold')
ax1.set_xlabel('Month')
ax1.set_ylabel('ICY Tokens (Millions)')
ax1.legend()
ax1.grid(True, alpha=0.3)

# Chart 2: Contributors vs Rewards
ax2_twin = ax2.twinx()
ax2.bar(months, contributors, alpha=0.6, label='Contributors', color='skyblue')
ax2_twin.plot(months, (contributors * 500 * (1 + 0.3 * months/24))/1000, 
              color='red', linewidth=2, label='Monthly ICY Rewards (K)')
ax2.set_title('Contributors Growth vs ICY Rewards', fontsize=14, fontweight='bold')
ax2.set_xlabel('Month')
ax2.set_ylabel('Contributors', color='blue')
ax2_twin.set_ylabel('ICY Rewards (Thousands)', color='red')
ax2.legend(loc='upper left')
ax2_twin.legend(loc='upper right')

# Chart 3: Economic Sustainability
sustainability_ratio = revenue / (contributors * 500 * (1 + 0.3 * months/24))
ax3.bar(months, revenue/1000, alpha=0.6, label='Monthly Revenue ($K)', color='green')
ax3.plot(months, sustainability_ratio, color='red', linewidth=2, marker='o', 
         label='Sustainability Ratio')
ax3.axhline(y=2.0, color='red', linestyle='--', alpha=0.7, label='Target Ratio (2.0)')
ax3.set_title('Economic Sustainability Analysis', fontsize=14, fontweight='bold')
ax3.set_xlabel('Month')
ax3.set_ylabel('Revenue ($K) / Ratio')
ax3.legend()
ax3.grid(True, alpha=0.3)

# Chart 4: Staking Dynamics
staking_rate = 0.25 + 0.5 * months / 24
ax4.plot(months, staking_rate * 100, linewidth=3, color='purple', label='Staking Rate (%)')
ax4.fill_between(months, 0, staking_rate * 100, alpha=0.3, color='purple')
ax4.plot(months, dfg_earned/100, linewidth=2, color='gold', label='DFG Earned (Hundreds)')
ax4.set_title('Staking Adoption & DFG Earning', fontsize=14, fontweight='bold')
ax4.set_xlabel('Month')
ax4.set_ylabel('Percentage / DFG (Hundreds)')
ax4.legend()
ax4.grid(True, alpha=0.3)

plt.tight_layout()
plt.show()

# Advanced Analytics
def calculate_token_velocity(circulating, transaction_volume):
    """Calculate token velocity metric"""
    return transaction_volume / circulating

def sustainability_score(revenue, token_cost, treasury_growth):
    """Calculate overall sustainability score"""
    revenue_ratio = revenue / token_cost
    treasury_ratio = treasury_growth / revenue
    return (revenue_ratio * 0.6 + treasury_ratio * 0.4)

# Scenario modeling
scenarios = {
    'optimistic': {'growth_multiplier': 1.25, 'retention_rate': 0.9},
    'base': {'growth_multiplier': 1.0, 'retention_rate': 0.85},
    'conservative': {'growth_multiplier': 0.75, 'retention_rate': 0.8},
    'bear': {'growth_multiplier': 0.5, 'retention_rate': 0.7}
}

def run_scenario(scenario_params, months=24):
    """Run tokenomics simulation for different scenarios"""
    growth_mult = scenario_params['growth_multiplier']
    retention = scenario_params['retention_rate']
    
    contributors = np.minimum(40 * (1 + 0.08 * months * growth_mult)**np.arange(months+1), 
                             105 * growth_mult)
    revenue = 50000 * (1 + 0.1 * months * growth_mult)**np.arange(months+1)
    icy_rewards = contributors * 500 * (1 + 0.4 * np.arange(months+1)/24)
    
    return {
        'contributors': contributors,
        'revenue': revenue,
        'icy_rewards': icy_rewards,
        'sustainability': revenue / icy_rewards
    }

# Generate scenario comparison
fig, ax = plt.subplots(figsize=(12, 8))
colors = ['green', 'blue', 'orange', 'red']
for i, (name, params) in enumerate(scenarios.items()):
    result = run_scenario(params)
    ax.plot(result['sustainability'], label=f'{name.title()} Scenario', 
            color=colors[i], linewidth=2)

ax.axhline(y=2.0, color='black', linestyle='--', alpha=0.7, label='Target Ratio')
ax.set_title('Sustainability Ratio Across Scenarios', fontsize=16, fontweight='bold')
ax.set_xlabel('Month')
ax.set_ylabel('Sustainability Ratio')
ax.legend()
ax.grid(True, alpha=0.3)
plt.show()

Advanced metrics calculation

class TokenomicsSimulator:
    def __init__(self, initial_supply=10000000, initial_contributors=40):
        self.icy_supply = initial_supply
        self.dfg_supply = 1000000
        self.contributors = initial_contributors
        self.treasury_btc = 2000000
        
    def simulate_month(self, month):
        """Simulate one month of protocol activity"""
        # Calculate contributors growth
        new_contributors = min(40 + (65 * month / 24), 105)
        
        # Calculate ICY emissions
        avg_icy_per_contributor = 500 + (200 * month / 24)
        activity_multiplier = 1 + (0.3 * month / 24)
        monthly_icy = new_contributors * avg_icy_per_contributor * activity_multiplier
        
        # Calculate staking
        staking_rate = 0.25 + (0.5 * month / 24)
        staked_icy = self.icy_supply * staking_rate
        
        # Calculate DFG earned (6-month delay)
        dfg_earned = max(0, staked_icy / 1000) if month >= 6 else 0
        
        # Update supply and treasury based on calculations
        # For example, a simplified update to ICY supply:
        self.icy_supply += monthly_icy
        
        return {
            'month': month,
            'contributors': new_contributors,
            'monthly_icy_emissions': monthly_icy,
            'staked_icy': staked_icy,
            'dfg_earned': dfg_earned
        }

# Example usage:
simulator = TokenomicsSimulator()
simulation_results = []
for i in range(25):
    results = simulator.simulate_month(i)
    simulation_results.append(results)

# You can now process simulation_results to create more detailed plots or analyses.
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