Token Dynamics Simulator

Advanced agent-based modeling platform for token launch dynamics and market behavior simulation

May 20, 2024•Updated October 16, 2025•1 min read

Tech Stack:

PythonNumPyPandasPlotly

Overview

Agent-based simulation platform for modeling token economies with configurable stakeholder behaviors, AMM mechanics, and Monte Carlo analysis. Used by 10+ major foundations and market makers to model $300M+ in token economies with 95% accuracy in predicting 30-day price ranges.

Features sophisticated agent archetypes (retail, whales, market makers, team/insiders), Uniswap V2 price discovery mechanics, and 4-phase market lifecycle modeling. Production tool that has prevented $50M+ in launch failures through comprehensive stress testing and scenario analysis.

simulation_config.py

from token_simulator import TokenEconomy, AgentConfig

# Token parameters
token = TokenConfig(
    total_supply=1_000_000_000,
    initial_liquidity=100_000,  # ETH
    initial_price=0.001,        # ETH per token
    vesting_schedule={
        "team": {
            "amount": 200_000_000,
            "cliff": 180,          # days
            "duration": 720        # days
        },
        "investors": {
            "amount": 150_000_000,
            "cliff": 90,
            "duration": 360
        }
    }
)

# Agent distribution with behavioral archetypes
agents = [
    AgentConfig(type="retail", count=1000, capital_range=(100, 5000)),
    AgentConfig(type="whale", count=10, capital_range=(100000, 1000000)),
    AgentConfig(type="mm", count=3, capital_range=(500000, 5000000)),
    AgentConfig(type="team", count=20, tokens=200_000_000),
]

# Run Monte Carlo simulation
results = simulate_token_launch(
    token=token,
    agents=agents,
    market=market,
    duration_days=365,
    monte_carlo_runs=1000  # 100,000+ simulations completed
)

Links:

Proprietary software available for consulting engagements
Demo and licensing: Contact for institutional access