Chart 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
Chart 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]
Chart 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]
Chart 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: $375]
F[Staking Rewards] --> G[Enhanced Voting: 2x Weight]
G --> H[Governance Participation: 60%]
Python implementation
Data visualization code
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
months = np.arange(0, 25)
contributors = np.linspace(40, 105, 25)
icy_circulating = 6000000 + np.cumsum(np.linspace(0, 838500, 25))
icy_staked = np.linspace(0, 3600000, 25)
dfg_earned = np.maximum(0, (icy_staked / 1000) - 150)
revenue = np.linspace(50000, 150000, 25)
fig, ((ax1, ax2), (ax3, ax4)) = plt.subplots(2, 2, figsize=(15, 12))
ax1.plot(months, icy_circulating/1e6, label='Circulating ICY', linewidth=2)
ax1.plot(months, icy_staked/1e6, label='Staked ICY', linewidth=2)
ax1.plot(months, (icy_circulating + icy_staked)/1e6, label='Total ICY', linewidth=2, linestyle='--')
ax1.set_title('ICY Token Supply Evolution', fontsize=14, fontweight='bold')
ax1.set_xlabel('Month')
ax1.set_ylabel('ICY Tokens (Millions)')
ax1.legend()
ax1.grid(True, alpha=0.3)
ax2_twin = ax2.twinx()
ax2.bar(months, contributors, alpha=0.6, label='Contributors', color='skyblue')
ax2_twin.plot(months, (contributors * 500 * (1 + 0.3 * months/24))/1000,
color='red', linewidth=2, label='Monthly ICY Rewards (K)')
ax2.set_title('Contributors Growth vs ICY Rewards', fontsize=14, fontweight='bold')
ax2.set_xlabel('Month')
ax2.set_ylabel('Contributors', color='blue')
ax2_twin.set_ylabel('ICY Rewards (Thousands)', color='red')
ax2.legend(loc='upper left')
ax2_twin.legend(loc='upper right')
sustainability_ratio = revenue / (contributors * 500 * (1 + 0.3 * months/24))
ax3.bar(months, revenue/1000, alpha=0.6, label='Monthly Revenue ($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)
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()
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)
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
}
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"""
new_contributors = min(40 + (65 * month / 24), 105)
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
staking_rate = 0.25 + (0.5 * month / 24)
staked_icy = self.icy_supply * staking_rate
dfg_earned = max(0, staked_icy / 1000) if month >= 6 else 0
self.icy_supply += monthly_icy
return {
'month': month,
'contributors': new_contributors,
'monthly_icy_emissions': monthly_icy,
'staked_icy': staked_icy,
'dfg_earned': dfg_earned
}
simulator = TokenomicsSimulator()
simulation_results = []
for i in range(25):
results = simulator.simulate_month(i)
simulation_results.append(results)
