Yield Farming Strategy with Ollama: Liquidity Pool ROI Optimization Guide

# Install Ollama
curl -fsSL https://ollama.ai/install.sh | sh

# Pull the recommended model for financial analysis
ollama pull llama3.1:8b

# Install Python dependencies
pip install web3 pandas numpy requests python-dotenv

mkdir ollama-yield-farming
cd ollama-yield-farming
touch .env config.py main.py pool_analyzer.py

# .env file
WEB3_PROVIDER_URL=https://mainnet.infura.io/v3/YOUR_API_KEY
PRIVATE_KEY=your_wallet_private_key
OLLAMA_HOST=http://localhost:11434
MIN_ROI_THRESHOLD=15.0
MAX_POSITION_SIZE=1000

# pool_analyzer.py
import requests
import pandas as pd
from web3 import Web3
import json

class LiquidityPoolAnalyzer:
    def __init__(self, web3_provider, ollama_host):
        self.web3 = Web3(Web3.HTTPProvider(web3_provider))
        self.ollama_host = ollama_host
        
    def fetch_pool_data(self, pool_address):
        """Fetch real-time pool metrics"""
        # Connect to DEX APIs (Uniswap, SushiSwap, etc.)
        pool_data = {
            'tvl': self.get_total_value_locked(pool_address),
            'volume_24h': self.get_daily_volume(pool_address),
            'fees_24h': self.get_daily_fees(pool_address),
            'apy': self.calculate_apy(pool_address),
            'impermanent_loss_risk': self.assess_il_risk(pool_address)
        }
        return pool_data
    
    def calculate_roi_score(self, pool_data):
        """Calculate weighted ROI score"""
        apy_weight = 0.4
        volume_weight = 0.3
        tvl_weight = 0.2
        risk_weight = 0.1
        
        roi_score = (
            pool_data['apy'] * apy_weight +
            self.normalize_volume(pool_data['volume_24h']) * volume_weight +
            self.normalize_tvl(pool_data['tvl']) * tvl_weight -
            pool_data['impermanent_loss_risk'] * risk_weight
        )
        
        return roi_score

# ollama_strategy.py
import requests
import json

class OllamaStrategyEngine:
    def __init__(self, ollama_host="http://localhost:11434"):
        self.ollama_host = ollama_host
        
    def analyze_pool_strategy(self, pool_data, market_conditions):
        """Get AI-powered strategy recommendations"""
        prompt = f"""
        Analyze this liquidity pool for yield farming optimization:
        
        Pool Metrics:
        - TVL: ${pool_data['tvl']:,.2f}
        - 24h Volume: ${pool_data['volume_24h']:,.2f}
        - Current APY: {pool_data['apy']:.2f}%
        - Impermanent Loss Risk: {pool_data['impermanent_loss_risk']:.2f}%
        
        Market Conditions:
        - Volatility: {market_conditions['volatility']}
        - Trend: {market_conditions['trend']}
        - Gas Fees: {market_conditions['gas_price']} gwei
        
        Provide a JSON response with:
        1. Recommended position size (0-100%)
        2. Entry timing score (1-10)
        3. Risk assessment (low/medium/high)
        4. Expected ROI range
        5. Exit strategy
        """
        
        response = requests.post(
            f"{self.ollama_host}/api/generate",
            json={
                "model": "llama3.1:8b",
                "prompt": prompt,
                "stream": False
            }
        )
        
        return self.parse_strategy_response(response.json()['response'])
    
    def parse_strategy_response(self, response_text):
        """Extract structured data from Ollama response"""
        try:
            # Parse JSON from AI response
            strategy = json.loads(response_text)
            return strategy
        except json.JSONDecodeError:
            # Fallback parsing for non-JSON responses
            return self.extract_strategy_data(response_text)

# pool_selector.py
import pandas as pd
import numpy as np

class PoolSelector:
    def __init__(self, analyzer, strategy_engine):
        self.analyzer = analyzer
        self.strategy_engine = strategy_engine
        
    def rank_pools(self, pool_addresses, market_data):
        """Rank pools by optimization potential"""
        pool_scores = []
        
        for address in pool_addresses:
            # Fetch pool data
            pool_data = self.analyzer.fetch_pool_data(address)
            
            # Get AI strategy analysis
            strategy = self.strategy_engine.analyze_pool_strategy(
                pool_data, market_data
            )
            
            # Calculate composite score
            score = self.calculate_composite_score(pool_data, strategy)
            
            pool_scores.append({
                'address': address,
                'score': score,
                'apy': pool_data['apy'],
                'tvl': pool_data['tvl'],
                'risk_level': strategy['risk_assessment'],
                'recommended_allocation': strategy['position_size']
            })
        
        # Sort by score descending
        return sorted(pool_scores, key=lambda x: x['score'], reverse=True)
    
    def calculate_composite_score(self, pool_data, strategy):
        """Combine quantitative metrics with AI insights"""
        base_score = self.analyzer.calculate_roi_score(pool_data)
        ai_multiplier = strategy['entry_timing_score'] / 10
        risk_penalty = self.get_risk_penalty(strategy['risk_assessment'])
        
        return base_score * ai_multiplier * risk_penalty

# position_optimizer.py
class PositionOptimizer:
    def __init__(self, max_position_size, risk_tolerance):
        self.max_position_size = max_position_size
        self.risk_tolerance = risk_tolerance
        
    def optimize_allocation(self, top_pools, total_capital):
        """Calculate optimal position sizes"""
        allocations = {}
        remaining_capital = total_capital
        
        for pool in top_pools:
            # Calculate base allocation
            base_allocation = self.calculate_base_allocation(
                pool['score'], 
                pool['risk_level']
            )
            
            # Apply risk constraints
            max_allowed = min(
                remaining_capital * 0.3,  # Max 30% per pool
                self.max_position_size
            )
            
            position_size = min(base_allocation, max_allowed)
            
            allocations[pool['address']] = {
                'amount': position_size,
                'percentage': (position_size / total_capital) * 100,
                'expected_apy': pool['apy']
            }
            
            remaining_capital -= position_size
            
            if remaining_capital < 100:  # Minimum position threshold
                break
                
        return allocations

# monitor.py
import time
import asyncio
from datetime import datetime

class PerformanceMonitor:
    def __init__(self, analyzer, strategy_engine):
        self.analyzer = analyzer
        self.strategy_engine = strategy_engine
        self.positions = {}
        
    async def monitor_positions(self):
        """Continuously monitor position performance"""
        while True:
            for pool_address, position in self.positions.items():
                current_data = self.analyzer.fetch_pool_data(pool_address)
                
                # Calculate current ROI
                roi = self.calculate_position_roi(position, current_data)
                
                # Check rebalancing triggers
                if self.should_rebalance(roi, current_data):
                    await self.execute_rebalance(pool_address, current_data)
                
                # Update position tracking
                self.update_position_metrics(pool_address, current_data, roi)
            
            await asyncio.sleep(300)  # Check every 5 minutes
    
    def should_rebalance(self, current_roi, pool_data):
        """Determine if rebalancing is needed"""
        rebalance_triggers = [
            current_roi < -10,  # Stop loss at -10%
            pool_data['apy'] < 5,  # APY below threshold
            pool_data['impermanent_loss_risk'] > 25  # High IL risk
        ]
        
        return any(rebalance_triggers)

# rebalancer.py
class AutoRebalancer:
    def __init__(self, web3, strategy_engine, min_profit_threshold=2.0):
        self.web3 = web3
        self.strategy_engine = strategy_engine
        self.min_profit_threshold = min_profit_threshold
        
    def execute_rebalance(self, old_pool, new_pool, amount):
        """Execute position rebalancing"""
        # Check gas costs
        gas_cost = self.estimate_gas_cost()
        
        if gas_cost > amount * 0.02:  # Don't rebalance if gas > 2%
            return False
            
        # Exit old position
        exit_tx = self.exit_position(old_pool, amount)
        
        if exit_tx['status'] == 1:
            # Enter new position
            entry_tx = self.enter_position(new_pool, amount)
            return entry_tx['status'] == 1
            
        return False
    
    def calculate_rebalance_benefit(self, current_pool, target_pool):
        """Calculate expected benefit from rebalancing"""
        current_apy = current_pool['apy']
        target_apy = target_pool['apy']
        
        gas_cost_percentage = self.estimate_gas_cost() / current_pool['tvl']
        net_benefit = (target_apy - current_apy) - gas_cost_percentage
        
        return net_benefit > self.min_profit_threshold

# compound_optimizer.py
class CompoundOptimizer:
    def __init__(self, analyzer, min_compound_amount=50):
        self.analyzer = analyzer
        self.min_compound_amount = min_compound_amount
        
    def calculate_optimal_compound_frequency(self, pool_data, position_size):
        """Determine optimal compounding schedule"""
        daily_yield = position_size * (pool_data['apy'] / 365 / 100)
        gas_cost = self.estimate_compound_gas_cost()
        
        # Find frequency where gas cost < 5% of yield
        optimal_days = max(1, int(gas_cost / (daily_yield * 0.05)))
        
        return optimal_days
    
    async def auto_compound(self, pool_address, position):
        """Execute automated compounding"""
        rewards = self.get_pending_rewards(pool_address)
        
        if rewards >= self.min_compound_amount:
            compound_tx = self.compound_rewards(pool_address, rewards)
            
            if compound_tx['status'] == 1:
                self.log_compound_event(pool_address, rewards)
                return True
                
        return False

# multi_chain_optimizer.py
class MultiChainOptimizer:
    def __init__(self):
        self.chains = {
            'ethereum': {'rpc': 'eth_rpc_url', 'gas_token': 'ETH'},
            'polygon': {'rpc': 'polygon_rpc_url', 'gas_token': 'MATIC'},
            'arbitrum': {'rpc': 'arbitrum_rpc_url', 'gas_token': 'ETH'},
            'optimism': {'rpc': 'optimism_rpc_url', 'gas_token': 'ETH'}
        }
        
    def find_cross_chain_arbitrage(self, token_pair):
        """Identify cross-chain yield opportunities"""
        opportunities = []
        
        for chain_name, chain_config in self.chains.items():
            pools = self.get_chain_pools(chain_name, token_pair)
            
            for pool in pools:
                opportunity = {
                    'chain': chain_name,
                    'pool': pool['address'],
                    'apy': pool['apy'],
                    'bridge_cost': self.calculate_bridge_cost(chain_name),
                    'net_apy': pool['apy'] - self.calculate_bridge_cost(chain_name)
                }
                opportunities.append(opportunity)
        
        return sorted(opportunities, key=lambda x: x['net_apy'], reverse=True)

# risk_manager.py
class RiskManager:
    def __init__(self, max_il_threshold=15.0):
        self.max_il_threshold = max_il_threshold
        
    def calculate_impermanent_loss(self, token_a_price_change, token_b_price_change):
        """Calculate potential impermanent loss"""
        if token_a_price_change == 0 or token_b_price_change == 0:
            return 0
            
        price_ratio = (1 + token_a_price_change) / (1 + token_b_price_change)
        
        # Impermanent loss formula
        il = 2 * (price_ratio ** 0.5) / (1 + price_ratio) - 1
        
        return abs(il) * 100  # Return as percentage
    
    def assess_position_risk(self, pool_data, market_volatility):
        """Comprehensive risk assessment"""
        risk_factors = {
            'impermanent_loss': self.calculate_impermanent_loss_risk(pool_data),
            'smart_contract': self.assess_contract_risk(pool_data['contract_address']),
            'liquidity': self.assess_liquidity_risk(pool_data['tvl']),
            'market_volatility': market_volatility
        }
        
        # Calculate composite risk score
        risk_score = sum(risk_factors.values()) / len(risk_factors)
        
        return {
            'score': risk_score,
            'level': self.categorize_risk(risk_score),
            'factors': risk_factors
        }

# emergency_exit.py
class EmergencyExitManager:
    def __init__(self, stop_loss_threshold=-20.0):
        self.stop_loss_threshold = stop_loss_threshold
        self.emergency_active = False
        
    def monitor_market_conditions(self, market_data):
        """Monitor for emergency exit conditions"""
        emergency_triggers = [
            market_data['btc_price_change_24h'] < -15,  # BTC down >15%
            market_data['defi_tvl_change'] < -25,       # DeFi TVL down >25%
            market_data['network_congestion'] > 80      # High gas fees
        ]
        
        if any(emergency_triggers) and not self.emergency_active:
            self.trigger_emergency_exit()
    
    def trigger_emergency_exit(self):
        """Execute emergency exit from all positions"""
        self.emergency_active = True
        
        # Exit positions in order of risk (highest first)
        for position in self.get_positions_by_risk():
            try:
                self.exit_position_emergency(position)
            except Exception as e:
                self.log_exit_error(position, e)
        
        self.emergency_active = False

# backtester.py
import pandas as pd
from datetime import datetime, timedelta

class StrategyBacktester:
    def __init__(self, strategy_engine, start_date, end_date):
        self.strategy_engine = strategy_engine
        self.start_date = start_date
        self.end_date = end_date
        
    def run_backtest(self, initial_capital=10000):
        """Run comprehensive strategy backtest"""
        results = {
            'total_return': 0,
            'max_drawdown': 0,
            'sharpe_ratio': 0,
            'win_rate': 0,
            'trades': []
        }
        
        current_date = self.start_date
        portfolio_value = initial_capital
        peak_value = initial_capital
        
        while current_date <= self.end_date:
            # Get historical market data
            market_data = self.get_historical_data(current_date)
            
            # Run strategy for this day
            strategy_results = self.strategy_engine.execute_strategy(
                market_data, portfolio_value
            )
            
            # Update portfolio
            portfolio_value = strategy_results['new_portfolio_value']
            
            # Track performance metrics
            if portfolio_value > peak_value:
                peak_value = portfolio_value
            
            drawdown = (peak_value - portfolio_value) / peak_value
            results['max_drawdown'] = max(results['max_drawdown'], drawdown)
            
            current_date += timedelta(days=1)
        
        # Calculate final metrics
        results['total_return'] = (portfolio_value - initial_capital) / initial_capital
        results['annualized_return'] = self.calculate_annualized_return(
            results['total_return'], 
            (self.end_date - self.start_date).days
        )
        
        return results

# docker-compose.yml
version: '3.8'
services:
  ollama-yield-farmer:
    build: .
    environment:
      - WEB3_PROVIDER_URL=${WEB3_PROVIDER_URL}
      - PRIVATE_KEY=${PRIVATE_KEY}
      - OLLAMA_HOST=http://ollama:11434
    depends_on:
      - ollama
      - redis
    
  ollama:
    image: ollama/ollama
    ports:
      - "11434:11434"
    volumes:
      - ollama_data:/root/.ollama
    
  redis:
    image: redis:alpine
    ports:
      - "6379:6379"
      
  prometheus:
    image: prom/prometheus
    ports:
      - "9090:9090"
    volumes:
      - ./prometheus.yml:/etc/prometheus/prometheus.yml

volumes:
  ollama_data:

# monitoring.py
import logging
from prometheus_client import start_http_server, Gauge, Counter

class ProductionMonitor:
    def __init__(self):
        # Prometheus metrics
        self.portfolio_value = Gauge('portfolio_value_usd', 'Current portfolio value')
        self.active_positions = Gauge('active_positions', 'Number of active positions')
        self.daily_roi = Gauge('daily_roi_percentage', 'Daily ROI percentage')
        self.rebalance_count = Counter('rebalances_total', 'Total rebalances executed')
        
        # Start metrics server
        start_http_server(8000)
        
        # Configure logging
        logging.basicConfig(
            level=logging.INFO,
            format='%(asctime)s - %(levelname)s - %(message)s',
            handlers=[
                logging.FileHandler('yield_farmer.log'),
                logging.StreamHandler()
            ]
        )
        
    def log_performance_metrics(self, portfolio_data):
        """Log performance metrics to monitoring systems"""
        self.portfolio_value.set(portfolio_data['total_value'])
        self.active_positions.set(len(portfolio_data['positions']))
        self.daily_roi.set(portfolio_data['daily_return_percentage'])
        
        logging.info(f"Portfolio Value: ${portfolio_data['total_value']:,.2f}")
        logging.info(f"Daily ROI: {portfolio_data['daily_return_percentage']:.2f}%")

# security.py
import os
from cryptography.fernet import Fernet
from eth_account import Account

class SecureWalletManager:
    def __init__(self, encryption_key=None):
        self.encryption_key = encryption_key or self.generate_key()
        self.cipher = Fernet(self.encryption_key)
        
    def encrypt_private_key(self, private_key):
        """Encrypt private key for secure storage"""
        encrypted_key = self.cipher.encrypt(private_key.encode())
        return encrypted_key
    
    def decrypt_private_key(self, encrypted_key):
        """Decrypt private key for use"""
        decrypted_key = self.cipher.decrypt(encrypted_key).decode()
        return decrypted_key
    
    def create_secure_transaction(self, transaction_data, encrypted_private_key):
        """Create and sign transaction securely"""
        private_key = self.decrypt_private_key(encrypted_private_key)
        
        # Sign transaction
        signed_txn = Account.sign_transaction(transaction_data, private_key)
        
        # Clear private key from memory
        private_key = None
        
        return signed_txn

# safe_contracts.py
class SafeContractInteraction:
    def __init__(self, web3, contract_address, abi):
        self.web3 = web3
        self.contract = web3.eth.contract(address=contract_address, abi=abi)
        
    def safe_transaction(self, function_name, *args, **kwargs):
        """Execute contract function with safety checks"""
        # Estimate gas
        gas_estimate = self.contract.functions[function_name](*args).estimate_gas()
        
        # Add 20% buffer for gas estimation
        gas_limit = int(gas_estimate * 1.2)
        
        # Check account balance
        account_balance = self.web3.eth.get_balance(kwargs['from'])
        gas_cost = gas_limit * self.web3.eth.gas_price
        
        if account_balance < gas_cost:
            raise ValueError("Insufficient balance for gas fees")
        
        # Build transaction
        transaction = self.contract.functions[function_name](*args).build_transaction({
            'gas': gas_limit,
            'gasPrice': self.web3.eth.gas_price,
            'nonce': self.web3.eth.get_transaction_count(kwargs['from']),
            **kwargs
        })
        
        return transaction

# mev_protection.py
class MEVProtection:
    def __init__(self, flashbots_relay_url):
        self.flashbots_relay = flashbots_relay_url
        
    def submit_private_transaction(self, signed_transaction):
        """Submit transaction through private mempool"""
        bundle = [{
            'transaction': signed_transaction.rawTransaction.hex(),
            'signer': signed_transaction.hash.hex()
        }]
        
        # Submit to Flashbots
        response = self.send_bundle(bundle)
        return response
    
    def calculate_mev_risk(self, transaction_data):
        """Assess MEV risk for transaction"""
        risk_factors = {
            'transaction_size': transaction_data['value'],
            'slippage_tolerance': transaction_data['slippage'],
            'market_volatility': self.get_current_volatility()
        }
        
        # High-value transactions with high slippage = high MEV risk
        mev_risk = (
            risk_factors['transaction_size'] * 
            risk_factors['slippage_tolerance'] * 
            risk_factors['market_volatility']
        )
        
        return mev_risk > 1000  # Threshold for private pool usage