Design, implement, and optimize sophisticated MEV strategies using quantitative techniques and machine learning
By the end of this course, you will be able to:
Real-time monitoring and continuous improvement
200 minimport numpy as np
import pandas as pd
from typing import Dict, List, Tuple, Optional
from dataclasses import dataclass
from enum import Enum
class MEVOpportunityType(Enum):
ARBITRAGE = "arbitrage"
LIQUIDATION = "liquidation"
SANDWICH = "sandwich"
FRONT_RUNNING = "front_running"
BACK_RUNNING = "back_running"
@dataclass
class MarketOpportunity:
opportunity_type: MEVOpportunityType
expected_profit: float
success_probability: float
execution_cost: float
time_to_expiry: float
risk_score: float
required_capital: float
complexity_score: int
@property
def expected_value(self) -> float:
"""Calculate expected value considering probability of success"""
gross_profit = self.expected_profit - self.execution_cost
return gross_profit * self.success_probability
@property
def risk_adjusted_return(self) -> float:
"""Risk-adjusted return metric"""
if self.risk_score <= 0:
return float('inf')
return self.expected_value / self.risk_score
class MEVOpportunityScanner:
def __init__(self, market_data_source, risk_calculator):
self.market_data = market_data_source
self.risk_calc = risk_calculator
self.opportunity_history = []
def scan_arbitrage_opportunities(self) -> List[MarketOpportunity]:
"""Scan for arbitrage opportunities across DEXs"""
opportunities = []
# Get price feeds from multiple DEXs
dex_prices = self.market_data.get_dex_prices()
# Find price discrepancies
for token_pair in dex_prices.keys():
prices = dex_prices[token_pair]
if len(prices) < 2:
continue
# Calculate potential arbitrage
min_price = min(prices.values())
max_price = max(prices.values())
if max_price - min_price > self.min_profit_threshold:
buy_dex = min(prices, key=prices.get)
sell_dex = max(prices, key=prices.get)
# Calculate expected metrics
profit = max_price - min_price
volume = self.estimate_optimal_volume(token_pair, min_price)
opportunity = MarketOpportunity(
opportunity_type=MEVOpportunityType.ARBITRAGE,
expected_profit=profit * volume,
success_probability=self.estimate_arbitrage_success(token_pair),
execution_cost=self.estimate_gas_cost(),
time_to_expiry=self.estimate_opportunity_lifetime(),
risk_score=self.risk_calc.calculate_arbitrage_risk(token_pair),
required_capital=volume * min_price,
complexity_score=2 # Arbitrage complexity
)
opportunities.append(opportunity)
return self.rank_opportunities(opportunities)
def scan_liquidation_opportunities(self) -> List[MarketOpportunity]:
"""Scan for liquidation opportunities"""
opportunities = []
# Get lending protocol positions
positions = self.market_data.get_lending_positions()
for protocol, protocol_positions in positions.items():
for position in protocol_positions:
health_factor = position['health_factor']
if health_factor < self.liquidation_threshold:
# Calculate liquidation opportunity
collateral_value = position['collateral_value']
debt_value = position['debt_value']
# Liquidation reward calculation
liquidator_reward = collateral_value * position['liquidation_bonus']
gas_cost = self.estimate_liquidation_gas()
opportunity = MarketOpportunity(
opportunity_type=MEVOpportunityType.LIQUIDATION,
expected_profit=liquidator_reward - gas_cost,
success_probability=self.estimate_liquidation_success(position),
execution_cost=gas_cost,
time_to_expiry=self.estimate_liquidation_window(position),
risk_score=self.risk_calc.calculate_liquidation_risk(position),
required_capital=debt_value,
complexity_score=3 # Higher complexity than arbitrage
)
opportunities.append(opportunity)
return self.rank_opportunities(opportunities)
def estimate_optimal_volume(self, token_pair: str, price: float) -> float:
"""Estimate optimal trade volume to maximize profit"""
# Consider slippage and market depth
market_depth = self.market_data.get_market_depth(token_pair)
# Find volume that maximizes (price_diff * volume - slippage_cost)
max_volume = market_depth['total_liquidity'] * 0.1 # Use max 10% of liquidity
for volume in np.linspace(0.01 * max_volume, max_volume, 100):
slippage = self.estimate_slippage(token_pair, volume, price)
net_profit = (self.price_difference * volume) - slippage
if net_profit <= 0:
break
return volume
def rank_opportunities(self, opportunities: List[MarketOpportunity]) -> List[MarketOpportunity]:
"""Rank opportunities by expected value and risk"""
return sorted(opportunities,
key=lambda x: x.risk_adjusted_return,
reverse=True)
# Usage Example
scanner = MEVOpportunityScanner(market_data, risk_calculator)
arbitrage_ops = scanner.scan_arbitrage_opportunities()
liquidation_ops = scanner.scan_liquidation_opportunities()
# Get top opportunities
top_arbitrage = arbitrage_ops[:5]
top_liquidations = liquidation_ops[:5]
print(f"Found {len(arbitrage_ops)} arbitrage opportunities")
print(f"Found {len(liquidation_ops)} liquidation opportunities")
for opp in top_arbitrage[:3]:
print(f"Arbitrage: EV=${opp.expected_value:.2f}, Risk={opp.risk_score:.2f}")
for opp in top_liquidations[:3]:
print(f"Liquidation: EV=${opp.expected_value:.2f}, Risk={opp.risk_score:.2f}")from abc import ABC, abstractmethod
from typing import Dict, List, Optional, Callable
from dataclasses import dataclass
import asyncio
import logging
from enum import Enum
class StrategyState(Enum):
INITIALIZING = "initializing"
RUNNING = "running"
PAUSED = "paused"
STOPPED = "stopped"
ERROR = "error"
class ExecutionResult(Enum):
SUCCESS = "success"
FAILED = "failed"
PARTIAL = "partial"
CANCELLED = "cancelled"
@dataclass
class StrategyMetrics:
total_trades: int = 0
successful_trades: int = 0
total_profit: float = 0.0
total_fees: float = 0.0
max_drawdown: float = 0.0
sharpe_ratio: float = 0.0
win_rate: float = 0.0
avg_trade_duration: float = 0.0
def update(self, trade_result):
"""Update metrics with new trade result"""
self.total_trades += 1
if trade_result.success:
self.successful_trades += 1
self.total_profit += trade_result.profit
self.total_fees += trade_result.fees
self.win_rate = self.successful_trades / self.total_trades
class MEVStrategy(ABC):
"""Abstract base class for MEV strategies"""
def __init__(self, config: Dict, name: str):
self.config = config
self.name = name
self.state = StrategyState.INITIALIZING
self.metrics = StrategyMetrics()
self.logger = logging.getLogger(f"{__name__}.{name}")
self.is_running = False
self.stop_event = asyncio.Event()
# Performance tracking
self.trade_history = []
self.performance_window = 100 # Track last 100 trades
@abstractmethod
async def identify_opportunities(self) -> List[MarketOpportunity]:
"""Identify trading opportunities"""
pass
@abstractmethod
async def execute_opportunity(self, opportunity: MarketOpportunity) -> ExecutionResult:
"""Execute a trading opportunity"""
pass
@abstractmethod
def calculate_position_size(self, opportunity: MarketOpportunity) -> float:
"""Calculate optimal position size for opportunity"""
pass
@abstractmethod
def risk_checks(self, opportunity: MarketOpportunity, position_size: float) -> bool:
"""Perform risk validation checks"""
pass
async def run_strategy(self):
"""Main strategy execution loop"""
self.state = StrategyState.RUNNING
self.is_running = True
try:
while not self.stop_event.is_set():
# Identify opportunities
opportunities = await self.identify_opportunities()
# Process each opportunity
for opportunity in opportunities:
if self.stop_event.is_set():
break
# Risk checks
position_size = self.calculate_position_size(opportunity)
if not self.risk_checks(opportunity, position_size):
self.logger.warning(f"Risk check failed for opportunity {opportunity.opportunity_type}")
continue
# Execute opportunity
result = await self.execute_opportunity(opportunity)
# Update metrics
self.metrics.update(result)
self.trade_history.append(result)
# Keep only recent history
if len(self.trade_history) > self.performance_window:
self.trade_history = self.trade_history[-self.performance_window:]
# Log result
self.logger.info(f"Trade result: {result.success}, Profit: ${result.profit:.2f}")
# Wait before next iteration
await asyncio.sleep(self.config.get('scan_interval', 1.0))
except Exception as e:
self.logger.error(f"Strategy error: {e}")
self.state = StrategyState.ERROR
raise
finally:
self.state = StrategyState.STOPPED
self.is_running = False
async def pause_strategy(self):
"""Pause the strategy"""
self.state = StrategyState.PAUSED
self.stop_event.set()
async def resume_strategy(self):
"""Resume the strategy"""
self.state = StrategyState.RUNNING
self.stop_event.clear()
asyncio.create_task(self.run_strategy())
def get_performance_summary(self) -> Dict:
"""Get strategy performance summary"""
recent_trades = self.trade_history[-20:] if len(self.trade_history) >= 20 else self.trade_history
if recent_trades:
profits = [t.profit for t in recent_trades if t.success]
avg_profit = np.mean(profits) if profits else 0
profit_std = np.std(profits) if profits else 0
sharpe = avg_profit / profit_std if profit_std > 0 else 0
else:
avg_profit = 0
sharpe = 0
return {
'strategy_name': self.name,
'state': self.state.value,
'total_trades': self.metrics.total_trades,
'win_rate': self.metrics.win_rate,
'total_profit': self.metrics.total_profit,
'recent_avg_profit': avg_profit,
'recent_sharpe': sharpe,
'is_running': self.is_running
}
# Example Implementation: Enhanced Arbitrage Strategy
class SmartArbitrageStrategy(MEVStrategy):
def __init__(self, config: Dict):
super().__init__(config, "SmartArbitrage")
# Strategy-specific parameters
self.min_profit_threshold = config.get('min_profit', 0.5) # $0.50 minimum
self.max_position_size = config.get('max_position', 10000) # $10K max
self.max_slippage = config.get('max_slippage', 0.005) # 0.5% max slippage
# Machine learning components
self.price_predictor = self._initialize_predictor()
self.risk_model = self._initialize_risk_model()
def _initialize_predictor(self):
"""Initialize ML model for price prediction"""
# This would be a trained model (e.g., LSTM, Random Forest)
return {
'model_type': 'lstm',
'lookback_period': 60, # minutes
'features': ['price', 'volume', 'gas_price', 'block_time']
}
def _initialize_risk_model(self):
"""Initialize risk assessment model"""
return {
'volatility_threshold': 0.05, # 5% volatility cap
'correlation_limit': 0.8, # Max correlation with other strategies
'max_drawdown_limit': 0.15 # 15% max drawdown
}
async def identify_opportunities(self) -> List[MarketOpportunity]:
"""Enhanced opportunity identification with ML predictions"""
opportunities = []
# Get current market data
market_data = await self._get_market_data()
# Predict price movements
price_predictions = await self._predict_price_movements(market_data)
# Enhanced arbitrage scanning
for token_pair in market_data.dex_prices.keys():
prices = market_data.dex_prices[token_pair]
# Find significant price discrepancies
for dex1, price1 in prices.items():
for dex2, price2 in prices.items():
if dex1 >= dex2:
continue
price_diff = price2 - price1
price_diff_pct = price_diff / price1
# Check minimum profit threshold
if price_diff_pct > self.min_profit_threshold / price1:
# Predict future price movement
predicted_movement = price_predictions.get(token_pair, 0)
# Adjust opportunity based on prediction
adjusted_profit = price_diff
if predicted_movement < 0: # Price expected to converge
adjusted_profit *= 0.8 # Reduce by 20%
# Risk assessment
risk_score = await self._assess_arbitrage_risk(token_pair, dex1, dex2)
opportunity = MarketOpportunity(
opportunity_type=MEVOpportunityType.ARBITRAGE,
expected_profit=adjusted_profit,
success_probability=self._calculate_success_probability(token_pair, risk_score),
execution_cost=self._estimate_execution_cost(token_pair),
time_to_expiry=self._estimate_opportunity_lifetime(token_pair),
risk_score=risk_score,
required_capital=min(price1, price2),
complexity_score=2
)
opportunities.append(opportunity)
# Sort by expected value
return sorted(opportunities, key=lambda x: x.expected_value, reverse=True)
async def execute_opportunity(self, opportunity: MarketOpportunity) -> ExecutionResult:
"""Execute arbitrage with smart order routing"""
try:
# Generate transaction bundle
bundle = await self._create_arbitrage_bundle(opportunity)
# Submit to Flashbots
result = await self._submit_bundle(bundle)
if result.success:
return ExecutionResult.SUCCESS
else:
return ExecutionResult.FAILED
except Exception as e:
self.logger.error(f"Execution failed: {e}")
return ExecutionResult.FAILED
def calculate_position_size(self, opportunity: MarketOpportunity) -> float:
"""Calculate optimal position size using Kelly Criterion"""
# Get current portfolio metrics
portfolio_value = self._get_portfolio_value()
current_risk = self._get_current_portfolio_risk()
# Kelly Criterion calculation
if opportunity.expected_profit > 0 and opportunity.risk_score > 0:
kelly_fraction = (opportunity.expected_profit / opportunity.risk_score)
# Apply risk management constraints
max_size = min(
portfolio_value * 0.1, # Max 10% of portfolio
opportunity.required_capital * 10, # Max 10x required capital
self.max_position_size
)
return min(kelly_fraction * portfolio_value, max_size)
return 0
def risk_checks(self, opportunity: MarketOpportunity, position_size: float) -> bool:
"""Comprehensive risk validation"""
# Check risk score threshold
if opportunity.risk_score > self.risk_model['max_drawdown_limit']:
return False
# Check correlation with existing positions
correlation = self._calculate_correlation_with_existing(opportunity)
if correlation > self.risk_model['correlation_limit']:
return False
# Check volatility
current_volatility = self._get_market_volatility(opportunity.opportunity_type)
if current_volatility > self.risk_model['volatility_threshold']:
return False
# Check position size limits
if position_size > self.max_position_size:
return False
# Check gas cost vs profit ratio
if opportunity.execution_cost > opportunity.expected_profit * 0.3:
return False
return True
async def _get_market_data(self):
"""Get real-time market data"""
# Implementation would fetch from multiple data sources
pass
async def _predict_price_movements(self, market_data):
"""Use ML model to predict price movements"""
# Implementation would use trained model
pass
async def _assess_arbitrage_risk(self, token_pair, dex1, dex2):
"""Assess risk of specific arbitrage opportunity"""
# Implementation would analyze various risk factors
pass
def _calculate_success_probability(self, token_pair, risk_score):
"""Calculate probability of successful execution"""
# Base success rate for arbitrage
base_success = 0.85
# Adjust based on risk score
risk_penalty = risk_score * 0.5
return max(0.1, base_success - risk_penalty)
def _estimate_execution_cost(self, token_pair):
"""Estimate total execution cost (gas + fees)"""
base_gas = 150000 # Base gas for arbitrage
gas_price = self._get_current_gas_price()
return base_gas * gas_price * 1.2 # 20% buffer
def _estimate_opportunity_lifetime(self, token_pair):
"""Estimate how long opportunity will remain"""
# Base lifetime based on historical data
base_lifetime = 30 # seconds
# Adjust based on volatility
volatility = self._get_market_volatility('arbitrage')
return base_lifetime / (1 + volatility)
def _get_portfolio_value(self):
"""Get current portfolio value"""
# Implementation would calculate total portfolio value
pass
def _get_current_portfolio_risk(self):
"""Get current portfolio risk metrics"""
# Implementation would calculate portfolio risk
pass
def _calculate_correlation_with_existing(self, opportunity):
"""Calculate correlation with existing positions"""
# Implementation would analyze correlations
pass
def _get_market_volatility(self, opportunity_type):
"""Get current market volatility"""
# Implementation would calculate volatility
pass
async def _create_arbitrage_bundle(self, opportunity):
"""Create Flashbots bundle for arbitrage"""
# Implementation would create transaction bundle
pass
async def _submit_bundle(self, bundle):
"""Submit bundle to Flashbots"""
# Implementation would submit to Flashbots
pass
def _get_current_gas_price(self):
"""Get current gas price"""
# Implementation would get gas price
pass
# Configuration example
strategy_config = {
'min_profit': 1.0, # $1 minimum profit
'max_position': 50000, # $50K max position
'max_slippage': 0.003, # 0.3% max slippage
'scan_interval': 0.5, # 0.5 second scan interval
'max_drawdown': 0.10 # 10% max drawdown
}
# Initialize and run strategy
strategy = SmartArbitrageStrategy(strategy_config)
# await strategy.run_strategy()