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69. Cross-Exchange Arbitrage System

Overview

The Cross-Exchange Arbitrage System monitors price differences for the same asset across multiple exchanges in real-time, automatically detecting and executing profitable arbitrage opportunities. It supports direct arbitrage, triangular arbitrage, and intelligent decision-making based on fees, slippage, and transfer times.

Architecture & Module Breakdown

Module Description
Exchange Data Fetcher Real-time synchronization of exchange quotes
Arbitrage Detector Discovers cross-exchange price differences
Cost Estimator Estimates trading costs and net profit
Arbitrage Decision Engine Determines whether to execute arbitrage
Arbitrage Executor Executes cross-exchange buy/sell orders
API Query opportunities, manual trigger
Frontend Arbitrage monitoring dashboard and history

Microservice Directory

services/cross-exchange-arb-center/
├── src/
│   ├── main.py
│   ├── fetcher/exchange_data_fetcher.py
│   ├── detector/arbitrage_detector.py
│   ├── estimator/cost_estimator.py
│   ├── engine/arbitrage_decision_engine.py
│   ├── executor/arbitrage_executor.py
│   ├── api/arb_api.py
│   ├── config.py
│   └── requirements.txt
├── Dockerfile

Core Component Design

1. Exchange Data Fetcher 

class ExchangeDataFetcher:
    def fetch_market_data(self):
        return get_latest_quotes_from_all_exchanges()

2. Arbitrage Detector 

class ArbitrageDetector:
    def find_arbitrage_opportunities(self, market_data):
        opportunities = []
        for symbol in market_data.symbols:
            best_bid_binance = market_data.binance[symbol]["bid"]
            best_ask_okx = market_data.okx[symbol]["ask"]
            if best_bid_binance > best_ask_okx * 1.002:  # 0.2% profit margin
                opportunities.append({
                    "symbol": symbol,
                    "buy_exchange": "OKX",
                    "sell_exchange": "Binance",
                    "buy_price": best_ask_okx,
                    "sell_price": best_bid_binance
                })
        return opportunities

3. Cost Estimator 

class CostEstimator:
    def estimate_profit(self, opportunity, fees, slippage):
        expected_profit = opportunity["sell_price"] - opportunity["buy_price"] - fees - slippage
        return expected_profit

4. Arbitrage Decision Engine 

class ArbitrageDecisionEngine:
    def should_execute(self, opportunity):
        expected_profit = cost_estimator.estimate_profit(opportunity, total_fees, expected_slippage)
        return expected_profit > minimum_expected_profit

5. Arbitrage Executor 

class ArbitrageExecutor:
    async def execute_arbitrage(self, opportunity):
        await place_order(opportunity["buy_exchange"], opportunity["symbol"], "buy", opportunity["buy_price"])
        await place_order(opportunity["sell_exchange"], opportunity["symbol"], "sell", opportunity["sell_price"])

6. API Example 

from fastapi import APIRouter

router = APIRouter()

@router.get("/arbitrage/opportunities")
async def get_current_arbitrage_opportunities():
    return arbitrage_detector.find_arbitrage_opportunities(market_data)

Frontend Integration

CrossExchangeArbView.tsx - Current cross-exchange arbitrage opportunities table - Real-time arbitrage path visualization - Arbitrage history and profit statistics - Success rate and average profit curves

Implementation Roadmap

  • Phase 1: Core data fetcher, detector, estimator, and API
  • Phase 2: Decision engine, executor, and frontend dashboard
  • Phase 3: Distributed arbitrage bot network, priority queue management

System Integration

  • Monitors multiple exchanges simultaneously
  • Integrates with execution engines for cross-exchange orders
  • Provides real-time arbitrage opportunity detection and execution

Business & Technical Value

  • Profit Generation: Captures risk-free arbitrage opportunities
  • Automation: Fully automated detection and execution
  • Scalability: Supports multiple exchanges and asset pairs
  • Transparency: Full audit trail and profit tracking
  • Competitive Edge: Professional-grade arbitrage execution