Token Economy Simulation with Bonding Curves and Affiliate Dynamics

Token Economy Simulation with Bonding Curves and Affiliate Dynamics

Overview

This project simulates a dynamic token economy with multiple cryptocurrencies, bonding curve mechanisms, and affiliate marketing dynamics. It demonstrates how token prices evolve based on supply/demand through different bonding curves, while affiliates earn commissions and adapt their strategies in real-time.

Key Features

• 5 Bonding Curve Types
• Linear, Exponential, Sigmoid, Root, and Inverse curves
• Automatic curve switching and parameter randomization
• Dynamic Affiliate System
• Adaptive commission rates (0-20% range)
• Whale affiliates with large investment capacity
• Portfolio tracking and automated trading strategies
• Economic Mechanics
• Transaction fees (0.25%) and token burns (0.02%)
• Supply-dependent price calculations
• Configurable simulation parameters
• Simulation Analytics
• Step-by-step price/supply tracking
• Affiliate earnings history
• Comprehensive end-of-run summary reports

Installation

1. Requirements
2. Python 3.8+

3. NumPy

4. Setup bash pip install numpy

Usage

Run the simulation with default parameters:

python main.py

Customize the simulation:

python main.py \
  --num_simulation_steps 500 \
  --num_tokens 8 \
  --num_affiliates 10 \
  --initial_price 2.0 \
  --initial_commission_rate 0.15

Configuration Options

Command Line Arguments

Parameter	Description	Default
--num_simulation_steps	Total steps to simulate	100
--num_tokens	Number of distinct tokens	5
--num_affiliates	Number of affiliates	5
--initial_price	Starting price for all tokens	1.0
--initial_commission_rate	Base affiliate commission rate	0.10

Key Constants (constants.py)

INITIAL_SUPPLY = 10000        # Starting token supply
TRANSACTION_FEE_RATE = 0.0025 # 0.25% per trade
BURN_RATE = 0.0002            # 0.02% token burn
COMMISSION_DYNAMICS_STEP = 10 # Commission adjustment interval
WHALE_INVESTMENT_RANGE = (5000, 10000) # Whale capacity bounds

Simulation Mechanics

Token Dynamics

1. Price Calculation python # Example exponential curve price = a * e^(k * supply)
2. Supply Changes
3. Buys increase supply, sells decrease supply

4. Automatic fee deduction and token burns

5. Bonding Curve Evolution

6. Random curve selection every 500-1000 steps
7. Parameter adjustments every 20 steps

Affiliate Behavior

1. Commission Adjustments
2. Rates adjusted every 10 steps based on: python avg_investment > 50 ? rate += 0.0005 : rate -= 0.0005
3. Trading Strategies
4. Regular affiliates: 1-2 trades/step
5. Whale affiliates: Large bulk trades (5,000-10,000 base currency)

6. Automatic portfolio rebalancing (5% sell probability/step)

7. Wallet Management

8. Track multiple token balances
9. Base currency accounting
10. Historical earnings tracking

Output Example

--- Token Summary ---

Token: Token_3
  Final Price: 4.72
  Final Supply: 14256.32
  Final Bonding Curve: exponential_bonding_curve

--- Affiliate Summary ---

Affiliate: 4
  Final Base Currency: 3842.15
  Final Commission Rate: 0.1125
  Total Earned: 642.78

Source Code

Browse the source repository

GitHub Repository