from praisonaiagents import Agent, Task, PraisonAIAgents
import time
from typing import Dict, List
import asyncio
def analyze_market_conditions():
"""Simulates market analysis"""
conditions = {
"liquidity_pools": {
"pool_A": {
"token_0": {"balance": 1000000, "price": 1.0},
"token_1": {"balance": 500000, "price": 2.0},
"total_value_locked": 2000000
},
"pool_B": {
"token_0": {"balance": 800000, "price": 1.0},
"token_1": {"balance": 400000, "price": 2.0},
"total_value_locked": 1600000
}
},
"market_volatility": 0.15 + (time.time() % 10) / 100,
"trading_volume_24h": 5000000 + (time.time() % 1000000),
"gas_prices": 50 + (time.time() % 30)
}
return conditions
def detect_arbitrage():
"""Simulates arbitrage opportunity detection"""
opportunities = [
{
"path": ["pool_A", "pool_B"],
"profit_potential": 0.02 + (time.time() % 5) / 100,
"required_capital": 10000,
"execution_cost": 50,
"risk_level": "low"
},
{
"path": ["pool_B", "pool_C"],
"profit_potential": 0.03 + (time.time() % 5) / 100,
"required_capital": 20000,
"execution_cost": 75,
"risk_level": "medium"
}
]
return opportunities[int(time.time()) % 2]
def optimize_liquidity(market_conditions: Dict):
"""Optimizes liquidity distribution"""
optimizations = {
"pool_A": {
"target_ratio": 2.0,
"suggested_actions": [
{
"action": "rebalance",
"token": "token_1",
"amount": 10000,
"direction": "add"
}
],
"expected_improvement": 0.05
},
"pool_B": {
"target_ratio": 1.8,
"suggested_actions": [
{
"action": "rebalance",
"token": "token_0",
"amount": 5000,
"direction": "remove"
}
],
"expected_improvement": 0.03
}
}
return optimizations
def assess_risks(arbitrage: Dict, optimizations: Dict):
"""Assesses potential risks"""
risk_assessment = {
"impermanent_loss_risk": 0.1 + (time.time() % 5) / 100,
"smart_contract_risk": "low",
"market_manipulation_risk": "medium" if arbitrage["profit_potential"] > 0.025 else "low",
"gas_price_risk": "high" if arbitrage["execution_cost"] > 60 else "medium",
"mitigation_strategies": [
"implement_slippage_protection",
"set_maximum_gas_price",
"use_multiple_liquidity_sources"
]
}
return risk_assessment
def execute_trades(arbitrage: Dict, risks: Dict):
"""Simulates trade execution"""
execution_results = {
"status": "success" if time.time() % 2 == 0 else "failed",
"executed_volume": arbitrage["required_capital"] * 0.95,
"actual_profit": arbitrage["profit_potential"] * 0.9,
"gas_used": arbitrage["execution_cost"] * 1.1,
"timestamp": time.time()
}
return execution_results
# Create specialized agents
market_analyzer = Agent(
name="Market Analyzer",
role="Market Analysis",
goal="Analyze market conditions",
instructions="Monitor and analyze DeFi market conditions",
tools=[analyze_market_conditions]
)
arbitrage_detector = Agent(
name="Arbitrage Detector",
role="Arbitrage Detection",
goal="Detect arbitrage opportunities",
instructions="Identify profitable trading opportunities",
tools=[detect_arbitrage]
)
liquidity_optimizer = Agent(
name="Liquidity Optimizer",
role="Liquidity Optimization",
goal="Optimize liquidity distribution",
instructions="Optimize pool liquidity ratios",
tools=[optimize_liquidity]
)
risk_assessor = Agent(
name="Risk Assessor",
role="Risk Assessment",
goal="Assess trading risks",
instructions="Evaluate potential risks and mitigation strategies",
tools=[assess_risks]
)
trade_executor = Agent(
name="Trade Executor",
role="Trade Execution",
goal="Execute optimized trades",
instructions="Execute trades based on analysis",
tools=[execute_trades]
)
# Create workflow tasks
market_task = Task(
name="analyze_market",
description="Analyze market conditions",
expected_output="Market analysis",
agent=market_analyzer,
is_start=True,
next_tasks=["detect_arbitrage", "optimize_liquidity"]
)
arbitrage_task = Task(
name="detect_arbitrage",
description="Detect arbitrage opportunities",
expected_output="Arbitrage opportunities",
agent=arbitrage_detector,
next_tasks=["assess_risks"]
)
liquidity_task = Task(
name="optimize_liquidity",
description="Optimize liquidity",
expected_output="Liquidity optimization",
agent=liquidity_optimizer,
context=[market_task],
next_tasks=["assess_risks"]
)
risk_task = Task(
name="assess_risks",
description="Assess trading risks",
expected_output="Risk assessment",
agent=risk_assessor,
context=[arbitrage_task, liquidity_task],
next_tasks=["execute_trades"]
)
execution_task = Task(
name="execute_trades",
description="Execute trades",
expected_output="Trade execution results",
agent=trade_executor,
task_type="decision",
condition={
"success": ["analyze_market"], # Continue monitoring if successful
"failed": ["optimize_liquidity"] # Reoptimize if failed
}
)
# Create workflow
workflow = PraisonAIAgents(
agents=[market_analyzer, arbitrage_detector, liquidity_optimizer,
risk_assessor, trade_executor],
tasks=[market_task, arbitrage_task, liquidity_task,
risk_task, execution_task],
process="workflow",
verbose=True
)
async def main():
print("\nStarting DeFi Market Making Workflow...")
print("=" * 50)
# Run workflow
results = await workflow.astart()
# Print results
print("\nMarket Making Results:")
print("=" * 50)
for task_id, result in results["task_results"].items():
if result:
print(f"\nTask: {task_id}")
print(f"Result: {result.raw}")
print("-" * 50)
if __name__ == "__main__":
asyncio.run(main())
