| Comprehensive Analysis of Token Projects: $AO, $PENDLE, and $UNI | |
| This report provides a detailed comparative summary of three successful token projects—$AO (low inflation supply), $PENDLE (yield farming), and $UNI (liquidity mining)—focusing on token distribution, utility mechanisms, and methods for price stability and liquidity. The analysis is tailored for a professional audience, including prospective employers and a knowledgeable crypto community, ensuring clarity and precision without overloading jargon. | |
| Token Distribution | |
| The distribution mechanisms of these tokens reflect their unique tokenomics models, influencing initial allocation, vesting schedules, and major holder dynamics. | |
| $AO (Low Inflation Supply) | |
| Total Supply and Current State: $AO, associated with the AO Computer on the Arweave network, has a total supply of 21 million tokens, modeled after Bitcoin, with a current supply of approximately 1.0387 million tokens as of June 2024 CoinGecko. | |
| Distribution Mechanism: The token distribution began wit |
| import requests | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| from sklearn.linear_model import LinearRegression | |
| # 1. Data Collection | |
| # Fetch historical price data for Bitcoin (last 365 days) | |
| url = "https://api.coingecko.com/api/v3/coins/bitcoin/market_chart" | |
| params = {"vs_currency": "usd", "days": "365"} | |
| data = requests.get(url, params=params).json() |
| #!/usr/bin/env python3 | |
| """ | |
| monitor.py | |
| A simple monitoring module for THORChain, Synapse, and Rubic. | |
| Performs checks on API response time, price anomalies, and liquidity levels. | |
| Alerts are sent via Telegram when critical thresholds are breached. | |
| Usage: | |
| python monitor.py |
| import pandas as pd | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| from datetime import datetime | |
| # Configuration | |
| ARBITRAGE_THRESHOLD = 2.0 # Minimum percentage price difference for trades | |
| MAX_POSITION_RISK = 0.02 # Maximum portfolio risk per trade (2%) | |
| INITIAL_CAPITAL = 10000 # Starting portfolio value in USD |
| import numpy as np | |
| import pandas as pd | |
| import datetime | |
| import json | |
| # (Module code goes here...) | |
| # Test Cases | |
| if __name__ == "__main__": | |
| print("Running Test Cases for Risk Management Module") |
| import time | |
| import requests | |
| # Configuration: Set your API keys for each protocol | |
| API_KEYS = { | |
| "THORChain": "your_thorchain_api_key", # Replace with your THORChain API key | |
| "Synapse": "your_synapse_api_key", # Replace with your Synapse API key | |
| "Rubic": "your_rubic_api_key" # Replace with your Rubic API key | |
| } |
| import openai | |
| import json | |
| import requests | |
| import time | |
| # OpenAI API Key | |
| openai.api_key = "your_openai_api_key" | |
| # Documentation URLs | |
| DOCUMENTATION_URLS = { |
This Python script monitors price discrepancies for assets across three DeFi protocols: THORChain, Synapse, and Rubic. It calculates potential arbitrage opportunities in real-time and outputs alerts when a predefined threshold is met.
requests library:
pip install requests