AI-Powered Governance Voting: How to Never Miss Another DAO Vote (And Actually Make Smart Decisions)

import asyncio
import aiohttp
from web3 import Web3
from datetime import datetime

class DAOProposalMonitor:
    def __init__(self, dao_addresses, rpc_endpoint):
        self.dao_addresses = dao_addresses
        self.w3 = Web3(Web3.HTTPProvider(rpc_endpoint))
        self.active_proposals = {}
    
    async def monitor_proposals(self):
        """Continuously monitor for new governance proposals"""
        while True:
            for dao_address in self.dao_addresses:
                try:
                    # Check for new proposals via contract events
                    new_proposals = await self.fetch_new_proposals(dao_address)
                    
                    for proposal in new_proposals:
                        if proposal['id'] not in self.active_proposals:
                            # New proposal detected - analyze it
                            analysis = await self.analyze_proposal(proposal)
                            self.active_proposals[proposal['id']] = {
                                'proposal': proposal,
                                'analysis': analysis,
                                'status': 'pending_vote'
                            }
                            
                            print(f"New proposal detected: {proposal['title']}")
                            print(f"Recommendation: {analysis['recommendation']}")
                            
                except Exception as e:
                    print(f"Error monitoring {dao_address}: {e}")
            
            await asyncio.sleep(300)  # Check every 5 minutes
    
    async def fetch_new_proposals(self, dao_address):
        """Fetch proposals from DAO contract or API"""
        # Implementation depends on DAO type (Compound, Aragon, etc.)
        # This is a simplified example
        contract_abi = [...] # Your DAO's ABI
        contract = self.w3.eth.contract(address=dao_address, abi=contract_abi)
        
        # Get recent ProposalCreated events
        latest_block = self.w3.eth.block_number
        events = contract.events.ProposalCreated.get_logs(
            fromBlock=latest_block - 1000,
            toBlock='latest'
        )
        
        proposals = []
        for event in events:
            proposals.append({
                'id': event['args']['proposalId'],
                'title': event['args']['description'][:100],
                'description': event['args']['description'],
                'proposer': event['args']['proposer'],
                'created_block': event['blockNumber']
            })
        
        return proposals

import openai
import json
from typing import Dict, Any

class ProposalAnalyzer:
    def __init__(self, openai_api_key: str, voting_strategy: Dict):
        self.client = openai.OpenAI(api_key=openai_api_key)
        self.strategy = voting_strategy
    
    async def analyze_proposal(self, proposal: Dict[str, Any]) -> Dict:
        """Analyze a DAO proposal and generate voting recommendation"""
        
        # Extract key information
        analysis_prompt = f"""
        Analyze this DAO governance proposal and provide a structured recommendation:
        
        Title: {proposal['title']}
        Description: {proposal['description']}
        Proposer: {proposal['proposer']}
        
        Please analyze:
        1. Technical feasibility and risks
        2. Economic impact on token holders
        3. Alignment with DAO's stated goals
        4. Potential conflicts of interest
        5. Community sentiment indicators
        
        Provide a JSON response with:
        - recommendation: "FOR", "AGAINST", or "ABSTAIN"
        - confidence: 0-100 (how sure are you?)
        - risk_level: "LOW", "MEDIUM", or "HIGH"
        - key_factors: [list of main decision factors]
        - reasoning: detailed explanation
        """
        
        try:
            response = await self.client.chat.completions.acreate(
                model="gpt-4",
                messages=[
                    {"role": "system", "content": "You are an expert DAO governance analyst with deep knowledge of blockchain economics, smart contract security, and decentralized governance."},
                    {"role": "user", "content": analysis_prompt}
                ],
                response_format={"type": "json_object"}
            )
            
            analysis = json.loads(response.choices[0].message.content)
            
            # Apply user's voting strategy filters
            final_recommendation = self.apply_strategy_filters(analysis)
            
            return final_recommendation
            
        except Exception as e:
            print(f"Analysis failed: {e}")
            return {
                "recommendation": "ABSTAIN",
                "confidence": 0,
                "risk_level": "HIGH",
                "reasoning": f"Analysis failed due to error: {e}"
            }
    
    def apply_strategy_filters(self, analysis: Dict) -> Dict:
        """Apply user-defined voting strategy"""
        
        # Example strategy rules
        if analysis['risk_level'] == 'HIGH' and self.strategy.get('risk_averse', True):
            analysis['recommendation'] = 'AGAINST'
            analysis['reasoning'] += " [OVERRIDE: Risk-averse strategy activated]"
        
        if analysis['confidence'] < self.strategy.get('min_confidence', 70):
            analysis['recommendation'] = 'ABSTAIN'
            analysis['reasoning'] += f" [OVERRIDE: Confidence {analysis['confidence']}% below threshold]"
        
        return analysis

from web3 import Web3
from eth_account import Account
import json

class AutomatedVoter:
    def __init__(self, private_key: str, rpc_endpoint: str):
        self.w3 = Web3(Web3.HTTPProvider(rpc_endpoint))
        self.account = Account.from_key(private_key)
        self.w3.eth.default_account = self.account.address
    
    async def execute_vote(self, dao_address: str, proposal_id: int, 
                          vote_choice: str, analysis: Dict):
        """Execute a vote on a DAO proposal"""
        
        try:
            # Load DAO contract (example for Compound-style governance)
            contract_abi = self.load_dao_abi(dao_address)
            contract = self.w3.eth.contract(address=dao_address, abi=contract_abi)
            
            # Convert vote choice to contract format
            vote_value = {
                'FOR': 1,
                'AGAINST': 0,
                'ABSTAIN': 2
            }[vote_choice]
            
            # Check if we have voting power
            voting_power = contract.functions.getVotes(
                self.account.address, 
                proposal_id
            ).call()
            
            if voting_power == 0:
                print(f"No voting power for proposal {proposal_id}")
                return False
            
            # Build transaction
            vote_tx = contract.functions.castVote(
                proposal_id, 
                vote_value
            ).build_transaction({
                'from': self.account.address,
                'gas': 150000,  # Adjust based on network
                'gasPrice': self.w3.to_wei('20', 'gwei'),
                'nonce': self.w3.eth.get_transaction_count(self.account.address)
            })
            
            # Sign and send
            signed_tx = self.account.sign_transaction(vote_tx)
            tx_hash = self.w3.eth.send_raw_transaction(signed_tx.rawTransaction)
            
            # Wait for confirmation
            receipt = self.w3.eth.wait_for_transaction_receipt(tx_hash)
            
            print(f"Vote cast successfully!")
            print(f"Proposal: {proposal_id}")
            print(f"Vote: {vote_choice}")
            print(f"Confidence: {analysis['confidence']}%")
            print(f"Transaction: {receipt['transactionHash'].hex()}")
            
            return True
            
        except Exception as e:
            print(f"Voting failed: {e}")
            return False
    
    def load_dao_abi(self, dao_address: str) -> list:
        """Load ABI for the specific DAO contract"""
        # In practice, you'd store ABIs for different DAO types
        # or fetch them from Etherscan/block explorers
        with open(f"abis/{dao_address.lower()}.json", 'r') as f:
            return json.load(f)

{
  "voting_strategy": {
    "risk_tolerance": "medium",
    "min_confidence": 75,
    "auto_vote_threshold": 85,
    "focus_areas": ["technical_upgrades", "treasury_management"],
    "blacklisted_proposers": [],
    "gas_limit_per_vote": 50,
    "voting_delay_hours": 2
  },
  "dao_configs": [
    {
      "name": "Compound",
      "address": "0xc00e94cb662c3520282e6f5717214004a7f26888",
      "type": "compound_governor",
      "min_voting_power": 1000,
      "priority": "high"
    },
    {
      "name": "Uniswap",
      "address": "0x1f9840a85d5af5bf1d1762f925bdaddc4201f984",
      "type": "uniswap_governor",
      "min_voting_power": 500,
      "priority": "medium"
    }
  ]
}

async def main():
    # Load configuration
    with open('governance_config.json', 'r') as f:
        config = json.load(f)
    
    # Initialize components
    monitor = DAOProposalMonitor(
        dao_addresses=[dao['address'] for dao in config['dao_configs']],
        rpc_endpoint="https://your-ethereum-node.com"
    )
    
    analyzer = ProposalAnalyzer(
        openai_api_key="your-openai-key",
        voting_strategy=config['voting_strategy']
    )
    
    voter = AutomatedVoter(
        private_key="your-private-key",
        rpc_endpoint="https://your-ethereum-node.com"
    )
    
    # Start monitoring
    print("Starting AI governance assistant...")
    await monitor.monitor_proposals()

if __name__ == "__main__":
    asyncio.run(main())

class MultiChainGovernance:
    def __init__(self):
        self.chains = {
            'ethereum': Web3(Web3.HTTPProvider("https://eth-mainnet.g.alchemy.com/v2/your-key")),
            'polygon': Web3(Web3.HTTPProvider("https://polygon-rpc.com")),
            'arbitrum': Web3(Web3.HTTPProvider("https://arb1.arbitrum.io/rpc")),
        }
        
    async def monitor_all_chains(self):
        """Monitor governance proposals across multiple chains"""
        tasks = []
        for chain_name, w3 in self.chains.items():
            task = self.monitor_chain_governance(chain_name, w3)
            tasks.append(task)
        
        await asyncio.gather(*tasks)

import tweepy
import praw  # Reddit API

class SentimentAnalyzer:
    def __init__(self, twitter_api_key, reddit_client_id):
        self.twitter_api = tweepy.Client(twitter_api_key)
        self.reddit = praw.Reddit(client_id=reddit_client_id)
    
    async def analyze_community_sentiment(self, proposal_title: str) -> float:
        """Analyze social sentiment around a proposal"""
        
        # Twitter sentiment
        tweets = self.twitter_api.search_recent_tweets(
            query=f"{proposal_title} DAO governance",
            max_results=100
        )
        
        # Reddit sentiment  
        reddit_posts = self.reddit.subreddit("ethereum").search(
            proposal_title, 
            limit=50
        )
        
        # Use AI to analyze sentiment
        sentiment_score = await self.calculate_sentiment(tweets, reddit_posts)
        return sentiment_score

class GasOptimizer:
    def __init__(self, w3: Web3):
        self.w3 = w3
        self.gas_tracker = {}
    
    async def optimal_voting_time(self, proposal_end_time: int) -> int:
        """Calculate optimal time to vote based on gas prices"""
        
        current_time = int(time.time())
        time_remaining = proposal_end_time - current_time
        
        if time_remaining < 3600:  # Less than 1 hour left
            return current_time  # Vote immediately
        
        # Predict gas price trends
        gas_predictions = await self.predict_gas_prices(time_remaining)
        optimal_time = min(gas_predictions, key=gas_predictions.get)
        
        return optimal_time
    
    async def batch_votes(self, votes: List[Dict]) -> str:
        """Batch multiple votes into single transaction"""
        # Implementation for multicall or similar batching
        pass

import os
from cryptography.fernet import Fernet
from getpass import getpass

class SecureKeyManager:
    def __init__(self):
        self.encryption_key = os.environ.get('GOVERNANCE_KEY')
        if not self.encryption_key:
            self.encryption_key = getpass("Enter encryption key: ")
        
        self.fernet = Fernet(self.encryption_key)
    
    def encrypt_private_key(self, private_key: str) -> str:
        """Encrypt private key for storage"""
        return self.fernet.encrypt(private_key.encode()).decode()
    
    def decrypt_private_key(self, encrypted_key: str) -> str:
        """Decrypt private key for use"""
        return self.fernet.decrypt(encrypted_key.encode()).decode()

class VoteValidator:
    def __init__(self, governance_history_db):
        self.history_db = governance_history_db
    
    def validate_vote(self, proposal: Dict, analysis: Dict) -> bool:
        """Validate vote before execution"""
        
        # Check for similar historical proposals
        similar_proposals = self.find_similar_proposals(proposal)
        
        # Verify consistency with past votes
        if not self.check_consistency(similar_proposals, analysis):
            print("Warning: Vote inconsistent with historical pattern")
            return False
        
        # Verify economic impact estimates
        if analysis['economic_impact'] > self.max_acceptable_impact:
            print("Warning: Economic impact exceeds threshold")
            return False
        
        return True
    
    def emergency_abstain(self, proposal_id: int, reason: str):
        """Emergency abstain if something goes wrong"""
        # Implementation for emergency stops
        pass

class GovernanceMetrics:
    def __init__(self):
        self.vote_history = []
        self.token_price_data = {}
    
    def calculate_governance_roi(self, time_period: int) -> Dict:
        """Calculate ROI from governance participation"""
        
        # Voting rewards earned
        rewards_earned = sum(vote['rewards'] for vote in self.vote_history[-time_period:])
        
        # Gas costs for voting
        gas_costs = sum(vote['gas_cost'] for vote in self.vote_history[-time_period:])
        
        # Token price impact from successful proposals
        price_impact = self.calculate_price_impact(time_period)
        
        total_roi = (rewards_earned + price_impact - gas_costs) / gas_costs
        
        return {
            'roi_percentage': total_roi * 100,
            'rewards_earned': rewards_earned,
            'gas_costs': gas_costs,
            'price_impact': price_impact,
            'votes_cast': len(self.vote_history[-time_period:])
        }

import streamlit as st
import plotly.graph_objects as go

def create_governance_dashboard():
    st.title("AI Governance Assistant Dashboard")
    
    # Recent votes
    st.subheader("Recent Voting Activity")
    recent_votes = get_recent_votes()  # Your implementation
    st.dataframe(recent_votes)
    
    # Success metrics
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Voting Success Rate", "87%", "+5%")
    
    with col2:
        st.metric("Gas Savings", "$2,341", "+$143")
    
    with col3:
        st.metric("Proposals Analyzed", "156", "+12")
    
    # ROI chart
    roi_data = get_monthly_roi_data()  # Your implementation
    fig = go.Figure(data=go.Scatter(x=roi_data['months'], y=roi_data['roi']))
    fig.update_layout(title="Governance ROI Over Time")
    st.plotly_chart(fig)

if __name__ == "__main__":
    create_governance_dashboard()

class VotingDiversityChecker:
    def __init__(self):
        self.vote_history = []
    
    def check_voting_diversity(self) -> bool:
        """Ensure AI isn't just voting YES on everything"""
        recent_votes = self.vote_history[-50:]  # Last 50 votes
        yes_percentage = sum(1 for vote in recent_votes if vote == 'FOR') / len(recent_votes)
        
        if yes_percentage > 0.9:  # More than 90% YES votes
            print("Warning: AI showing excessive approval bias")
            return False
        
        return True

def should_vote_given_gas_price(proposal_impact: float, current_gas_price: int) -> bool:
    """Determine if proposal impact justifies gas cost"""
    
    # Estimate voting cost in USD
    vote_cost_usd = (current_gas_price * 150000 * eth_price_usd) / 10**18
    
    # Only vote if expected impact > 5x gas cost
    min_impact_threshold = vote_cost_usd * 5
    
    return proposal_impact > min_impact_threshold