Building a Crypto Arbitrage Bot on Decentralized and Centralized Exchanges

In the fast-evolving world of cryptocurrency, traders are constantly seeking innovative strategies to capitalize on market inefficiencies. One such powerful method is crypto arbitrage trading—exploiting price differences of the same asset across multiple exchanges. With the rise of both centralized exchanges (CEXs) and decentralized exchanges (DEXs), building an automated arbitrage bot has become not only feasible but potentially profitable.

This guide walks you through creating a real-time crypto arbitrage bot that monitors price discrepancies between a CEX and a DEX, executes trades when conditions are met, and rebalances positions automatically—all using modern APIs and blockchain tools.

Understanding the Core Components

Before diving into code, it’s essential to understand the key platforms involved in this arbitrage strategy.

What is a Decentralized Exchange (DEX)?

A decentralized exchange (DEX) operates peer-to-peer without intermediaries. Trades are facilitated via smart contracts using Automated Market Maker (AMM) protocols. Examples include Uniswap and Quickswap. Unlike traditional exchanges, there's no central authority holding user funds—users trade directly from their wallets.

What is a DEX Aggregator?

A DEX aggregator, such as 1Inch, scans multiple liquidity sources across various DEXs to find the best possible swap rates. By routing trades through multiple pools, it minimizes slippage and improves execution efficiency—critical for high-frequency arbitrage strategies.

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What is a Centralized Exchange (CEX)?

A centralized exchange (CEX) like Coinbase or FTXUS is run by a company that manages order books, custody of assets, and trade matching. Orders are placed into a centralized order book, and trades occur when buy and sell bids match. These platforms typically offer faster transaction speeds and better liquidity for major assets.

The Arbitrage Strategy: Convergence Trading

Our bot implements a convergence arbitrage strategy, which involves:

Buying an asset where it's underpriced (on either CEX or DEX).
Simultaneously selling it where it's overpriced.
Waiting for prices to converge.
Reversing the trades to lock in profits.

The target asset? MATIC, due to its deep liquidity on Polygon and availability across both CEXs and DEXs.

We'll use:

Alpaca Crypto API for CEX data and trading (FTXUS, Coinbase).
1Inch API for DEX quotes and swaps on Polygon.
Polygon network for low-cost, fast transactions (a few cents per swap vs. tens of dollars on Ethereum).

Key Technical Setup

Core Libraries & Tools

import requests
import logging
import json
from web3 import Web3
import asyncio

We use asyncio for concurrent quote polling, requests for API calls, web3.py to interact with Polygon, and structured logging to monitor bot activity in real time.

Configuration Variables

production = False  # Set to True only for live trading
slippage = 1        # Max allowed slippage percentage
waitTime = 5        # Poll every 5 seconds
min_arb_percent = 0.5   # Minimum price difference to trigger trade
rebalance_percent = 0.4 # Threshold for rebalancing

These values control risk and sensitivity. A lower min_arb_percent increases trade frequency but may lead to losses due to gas and slippage if not calibrated properly.

Connecting to Exchanges

1Inch API Integration (DEX Layer)

BASE_URL = 'https://api.1inch.io/v4.0/137'  # Polygon chain ID
trade_size = 10
amount_to_exchange = Web3.toWei(trade_size, 'ether')

We define MATIC and USDC contract addresses on Polygon, load the USDC ABI to check balances, and connect via a node provider like Alchemy. Your private key must remain secure—never expose it in code repositories.

Alpaca API Setup (CEX Layer)

BASE_ALPACA_URL = 'https://paper-api.alpaca.markets'  # Use paper trading first
DATA_URL = 'https://data.alpaca.markets'
HEADERS = {
    'APCA-API-KEY-ID': 'YOUR_KEY',
    'APCA-API-SECRET-KEY': 'YOUR_SECRET'
}
trading_pair = 'MATICUSD'
exchange = 'FTXU'

Using Alpaca’s paper trading environment allows risk-free testing before going live.

Main Execution Loop

The bot runs asynchronously, continuously checking for opportunities:

async def main():
    while True:
        l1 = loop.create_task(get_oneInch_quote_data(...))
        l2 = loop.create_task(get_Alpaca_quote_data(...))
        await asyncio.wait([l1, l2])
        await check_arbitrage()
        await asyncio.sleep(waitTime)

Every 5 seconds, the bot fetches:

The current MATIC/USDC price on 1Inch (DEX)
The MATIC/USD ask price on Alpaca (CEX)

It then compares them to detect arbitrage conditions.

Detecting Arbitrage Opportunities

if last_alpaca_ask_price > last_oneInch_market_price * (1 + min_arb_percent / 100):
    # Buy on DEX, sell on CEX
elif last_alpaca_ask_price < last_oneInch_market_price * (1 - min_arb_percent / 100):
    # Buy on CEX, sell on DEX

If the price difference exceeds the threshold (e.g., 0.5%), the bot triggers a long-short trade pair across exchanges.

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Position Management & Rebalancing

After each trade, the bot tracks positions using counters:

alpaca_trade_counter: +1 for buy, -1 for sell
oneInch_trade_counter: same logic

Before executing new trades, it checks:

def needs_rebalancing():
    if alpaca_trade_counter != 0 or oneInch_trade_counter != 0:
        return True
    return False

Rebalancing ensures that open positions are closed when prices converge, preventing unbalanced exposure.

Frequently Asked Questions

How does the bot handle transaction costs?

While CEX fees are minimal via Alpaca (especially in paper trading), DEX gas fees on Polygon are negligible—often just a few cents. The bot assumes these costs are low enough to not impact profitability significantly, though real-world deployments should factor them in.

Can this strategy work on other blockchains?

Yes. The same logic applies to any EVM-compatible chain supported by 1Inch (e.g., BSC, Arbitrum). However, Polygon remains ideal due to its low latency and cost.

Is it safe to use my private key in the script?

Only use encrypted storage or environment variables. Never hardcode keys. For production bots, consider hardware wallet integration or secure key management services.

Why use MATIC as the traded asset?

MATIC offers high liquidity on both centralized and decentralized platforms, especially on Polygon-based DEXs. This reduces slippage and increases execution reliability.

What happens if one leg of the trade fails?

The current implementation assumes both legs succeed. In production systems, add fail-safes like timeout checks, retry mechanisms, or hedging orders to mitigate partial execution risks.

Can I backtest this strategy?

Direct backtesting is limited because 1Inch doesn’t support testnet trading due to lack of real liquidity. However, you can simulate price data and test logic locally before deploying with small capital.

Final Thoughts

Building a cross-exchange arbitrage bot bridges the gap between traditional algorithmic trading and decentralized finance. While this version uses simple thresholds and synchronous logic, it serves as a solid foundation for more sophisticated strategies involving machine learning models, dynamic slippage control, or multi-leg arbitrage across several assets.

With tools like Alpaca for CEX access and 1Inch for optimized DEX routing, developers now have everything needed to create powerful, automated trading systems—accessible even to those without deep finance backgrounds.

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Keywords: crypto arbitrage bot, decentralized exchange, centralized exchange, DEX aggregator, Polygon network, automated market maker, convergence arbitrage, Web3 trading