tokenomics

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Token economics simulation and analysis. Supports supply modeling, staking mechanisms, liquidity mining, governance dynamics, agent-based simulations, and cadCAD integration.

AI & Automation 814 stars 53 forks Updated today MIT

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Skill Content

# Token Economics Modeling Skill Expert token economics simulation and analysis for protocol design. ## Capabilities - **Supply Modeling**: Token supply and distribution - **Staking Simulation**: Staking and reward mechanisms - **Liquidity Mining**: LP incentive programs - **Governance Dynamics**: Token governance modeling - **Agent-Based Simulation**: cadCAD economic models - **Inflation Analysis**: Inflation/deflation mechanisms - **LP Economics**: DEX liquidity and impermanent loss ## Supply Distribution Models ### Vesting Schedule ```python # vesting_model.py import numpy as np import pandas as pd class VestingSchedule: def __init__(self, total_supply: int = 1_000_000_000): self.total_supply = total_supply # Allocation percentages self.allocation = { 'team': 0.20, 'investors': 0.15, 'community': 0.30, 'treasury': 0.20, 'liquidity': 0.15 } # Vesting parameters (months) self.vesting = { 'team': {'cliff': 12, 'duration': 36}, 'investors': {'cliff': 6, 'duration': 24}, 'community': {'cliff': 0, 'duration': 48}, 'treasury': {'cliff': 0, 'duration': 60}, 'liquidity': {'cliff': 0, 'duration': 1} # TGE } def get_unlocked(self, month: int) -> dict: unlocked = {} for category, params in self.vesting.items(): allocation = self.total_supply * self.allocation[catego...

Details

Author
a5c-ai
Repository
a5c-ai/babysitter
Created
4 months ago
Last Updated
today
Language
JavaScript
License
MIT

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