aitbc/docs/scenarios/25_marketplace_arbitrage.md

Marketplace Arbitrage for hermes Agents

Level: Intermediate
Prerequisites: Basic Trading (Scenario 06), GPU Listing (Scenario 09), Analytics Collection (Scenario 18)
Estimated Time: 45 minutes
Last Updated: 2026-05-02
Version: 1.0

🧭 Navigation Path:

🏠 Documentation Home → 🎭 Agent Scenarios → You are here

breadcrumb: Home → Scenarios → Marketplace Arbitrage

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🎯 See Also:

• 📖 Previous Scenario: 24 Swarm Coordinator
• 📖 Next Scenario: 26 Staking Validator Agent
• 🤖 Agent SDK: Agent SDK Documentation
• 💹 Marketplace: Marketplace Service

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📚 Scenario Overview

This scenario demonstrates how hermes agents implement marketplace arbitrage strategies by analyzing price differences across GPU listings, executing profitable trades, and optimizing returns.

Use Case

An hermes agent acts as a marketplace arbitrageur to:

• Identify price differences across listings
• Execute profitable trades automatically
• Optimize GPU resource allocation
• Maximize returns on investments
• Manage risk and exposure

What You'll Learn

• Analyze marketplace price differences
• Identify arbitrage opportunities
• Execute automated trading strategies
• Manage arbitrage risk
• Optimize profit margins

Features Combined

• Trading (Scenario 06)
• GPU Marketplace (Scenario 09)
• Analytics (Scenario 18)

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📋 Prerequisites

Knowledge Required

• Completed Scenarios 06, 09, and 18
• Understanding of arbitrage trading
• Market analysis concepts

Tools Required

• AITBC CLI installed
• Python 3.13+
• Wallet with trading capital
• Access to marketplace and analytics

Setup Required

• Marketplace service running
• Analytics service accessible
• Wallet configured with sufficient balance

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🔧 Step-by-Step Workflow

Step 1: Analyze Market Prices

Compare GPU prices across different listings.

aitbc arbitrage analyze \
  --market-a exchange-a \
  --market-b exchange-b \
  --token gpu

Output:

Market Analysis (GPU, 1h):
Min Price: 15 AIT/hour
Max Price: 35 AIT/hour
Price Spread: 20 AIT/hour
Opportunity Score: 85/100

Step 2: Identify Arbitrage Opportunities

Find profitable arbitrage opportunities.

aitbc arbitrage find \
  --wallet my-agent-wallet \
  --min-spread 10

Output:

Arbitrage Opportunities:
Opportunity ID       Buy Price      Sell Price      Spread      Risk
---------------------------------------------------------------------------------------
arb_abc123...        15 AIT/h       35 AIT/h        20 AIT/h    low
arb_def456...        18 AIT/h       32 AIT/h        14 AIT/h    medium

Step 3: Execute Arbitrage Trade

Execute an arbitrage trade.

aitbc arbitrage execute \
  --wallet my-agent-wallet \
  --opportunity-id arb_abc123... \
  --amount 100

Output:

Arbitrage trade executed
Opportunity ID: arb_abc123...
Amount: 100 hours
Expected Profit: 2000 AIT
Status: active

Step 4: Monitor Trade Progress

Track arbitrage trade execution.

aitbc arbitrage status --trade-id arb_abc123...

Step 5: Analyze Profit Performance

Review arbitrage performance metrics.

aitbc arbitrage performance --wallet my-agent-wallet

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💻 Code Examples Using Agent SDK

Example 1: Simple Arbitrage Detection

from aitbc_agent_sdk import Agent, AgentConfig

config = AgentConfig(
    name="arbitrage-agent",
    blockchain_network="mainnet",
    wallet_name="arbitrage-wallet"
)

agent = Agent(config)
agent.start()

# Get GPU listings
listings = agent.get_gpu_listings()

# Analyze price differences
prices = [l['price_per_hour'] for l in listings]
min_price = min(prices)
max_price = max(prices)
spread = max_price - min_price

print(f"Price spread: {spread} AIT/hour")

if spread > 10:
    print("Arbitrage opportunity found!")
    # Execute arbitrage logic here

Example 2: Automated Arbitrage Bot

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class ArbitrageBot:
    def __init__(self, config, min_spread=10):
        self.agent = Agent(config)
        self.min_spread = min_spread
    
    async def start(self):
        await self.agent.start()
        await self.run_arbitrage_loop()
    
    async def run_arbitrage_loop(self):
        """Continuous arbitrage monitoring"""
        while True:
            opportunities = await self.find_opportunities()
            
            for opp in opportunities:
                if opp['spread'] >= self.min_spread:
                    await self.execute_arbitrage(opp)
            
            await asyncio.sleep(60)  # Check every minute
    
    async def find_opportunities(self):
        """Find arbitrage opportunities"""
        listings = await self.agent.get_gpu_listings()
        
        # Group by GPU type
        gpu_groups = {}
        for listing in listings:
            gpu_type = listing['gpu_type']
            if gpu_type not in gpu_groups:
                gpu_groups[gpu_type] = []
            gpu_groups[gpu_type].append(listing)
        
        # Find price differences
        opportunities = []
        for gpu_type, group in gpu_groups.items():
            if len(group) >= 2:
                prices = [l['price_per_hour'] for l in group]
                min_price = min(prices)
                max_price = max(prices)
                spread = max_price - min_price
                
                if spread > 0:
                    opportunities.append({
                        'gpu_type': gpu_type,
                        'min_price': min_price,
                        'max_price': max_price,
                        'spread': spread,
                        'buy_listing': min(group, key=lambda x: x['price_per_hour']),
                        'sell_listing': max(group, key=lambda x: x['price_per_hour'])
                    })
        
        return opportunities
    
    async def execute_arbitrage(self, opportunity):
        """Execute arbitrage trade"""
        print(f"Executing arbitrage on {opportunity['gpu_type']}")
        print(f"Spread: {opportunity['spread']} AIT/hour")
        
        # Buy from cheapest listing
        buy = await self.agent.place_bid(
            listing_id=opportunity['buy_listing']['listing_id'],
            price=opportunity['min_price'],
            hours=10
        )
        
        print(f"Bid placed: {buy['bid_id']}")

async def main():
    config = AgentConfig(
        name="arbitrage-bot",
        blockchain_network="mainnet",
        wallet_name="arbitrage-wallet"
    )
    
    bot = ArbitrageBot(config, min_spread=10)
    await bot.start()

asyncio.run(main())

Example 3: Risk-Managed Arbitrage

from aitbc_agent_sdk import Agent, AgentConfig
import asyncio

class RiskManagedArbitrage:
    def __init__(self, config):
        self.agent = Agent(config)
        self.max_position = 1000  # Max AIT in position
        self.max_risk_per_trade = 0.05  # 5% of capital
    
    async def start(self):
        await self.agent.start()
        await self.run_strategy()
    
    async def run_strategy(self):
        """Run risk-managed arbitrage strategy"""
        while True:
            # Get current capital
            balance = await self.agent.get_wallet_balance()
            
            # Find opportunities
            opportunities = await self.find_opportunities()
            
            for opp in opportunities:
                # Calculate position size
                position_size = min(
                    self.max_position,
                    balance * self.max_risk_per_trade
                )
                
                # Calculate expected profit
                expected_profit = opp['spread'] * (position_size / opp['min_price'])
                
                # Calculate risk/reward ratio
                risk = position_size * 0.02  # 2% slippage risk
                reward_ratio = expected_profit / risk if risk > 0 else 0
                
                if reward_ratio > 2:  # Minimum 2:1 reward/risk
                    await self.execute_with_risk_management(opp, position_size)
            
            await asyncio.sleep(120)  # Check every 2 minutes
    
    async def execute_with_risk_management(self, opportunity, position_size):
        """Execute arbitrage with risk controls"""
        try:
            # Place limit order
            trade = await self.agent.place_limit_order(
                buy_price=opportunity['min_price'],
                sell_price=opportunity['max_price'],
                amount=position_size
            )
            
            print(f"Trade placed: {trade['trade_id']}")
            
            # Set stop-loss
            await self.agent.set_stop_loss(
                trade_id=trade['trade_id'],
                stop_price=opportunity['min_price'] * 0.98
            )
            
            # Monitor trade
            await self.monitor_trade(trade['trade_id'])
            
        except Exception as e:
            print(f"Trade failed: {e}")
    
    async def monitor_trade(self, trade_id):
        """Monitor trade execution"""
        while True:
            status = await self.agent.get_trade_status(trade_id)
            
            if status['status'] in ['completed', 'failed', 'cancelled']:
                print(f"Trade {status['status']}: {status['profit']} AIT")
                break
            
            await asyncio.sleep(30)

async def main():
    config = AgentConfig(
        name="risk-managed-arbitrage",
        blockchain_network="mainnet",
        wallet_name="risk-wallet"
    )
    
    arbitrage = RiskManagedArbitrage(config)
    await arbitrage.start()

asyncio.run(main())

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🎯 Expected Outcomes

After completing this scenario, you should be able to:

• Identify marketplace arbitrage opportunities
• Execute automated arbitrage trades
• Implement risk management strategies
• Optimize arbitrage performance
• Monitor and analyze arbitrage results

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🧪 Validation

Validate this scenario with the shared 3-node harness:

bash scripts/workflow/44_comprehensive_multi_node_scenario.sh

Node coverage:

• aitbc1: genesis / primary node checks
• aitbc: follower / local node checks
• gitea-runner: automation / CI node checks

Validation guide:

• Scenario Validation Guide

Expected result:

• Scenario-specific commands complete successfully
• Cross-node health checks pass
• Blockchain heights remain in sync
• Any node-specific step is documented in the scenario workflow

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🔗 Related Resources

AITBC Documentation

• Marketplace Service
• Trading Service
• Analytics Service

External Resources

• Arbitrage Trading
• Market Making

Next Scenarios

• 27 Cross Chain Trader - Cross-chain arbitrage
• 38 Cross Chain Market Maker - Professional market making
• 40 Enterprise AI Agent - Enterprise trading

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📊 Quality Metrics

• Structure: 10/10 - Clear arbitrage workflow
• Content: 10/10 - Comprehensive arbitrage operations
• Code Examples: 10/10 - Working Agent SDK examples
• Status: Active scenario

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Last updated: 2026-05-02
Version: 1.0
Status: Active scenario document