The sandwich bot crypto trading sector has grown impressively with a 9% CAGR and total revenue of USD 37K MN. It stands out as one of the quickest ways to profit from market volatility. My recent 48-hour challenge to build a profitable sandwich bot crypto system turned out to be a soaring win.
Sandwich bots work as automated trading programs that execute trades on preset strategies without needing someone to watch them constantly. These smart systems keep an eye on Decentralized Exchange (DEX) order books and spot large transactions that could affect asset prices by a lot. A crypto bot’s real value comes from knowing how to place trades at just the right moment and tap into the full potential of even tiny market shifts to boost profits.
This piece will take you through my complete process of building a profitable sandwich bot from scratch. You’ll learn everything that made this 48-hour build challenge successful – from the core strategy and tool selection to trading logic implementation and risk control deployment.
Understanding the Sandwich Bot Strategy Before Building
Let’s understand how automated trading systems work before we build a sandwich bot. These bots use a unique strategy that needs perfect timing and execution, unlike standard trading bots.
What is a sandwich bot in crypto trading?
A sandwich bot is an automated trading program that spots large pending cryptocurrency transactions and places its trades before and after them—that’s why we call it a “sandwich”. These bots scan decentralized exchanges (DEXs) around the clock to find profitable opportunities without human input.
The concept behind a sandwich bot is simple: it spots a large transaction, quickly buys before the target transaction goes through, and sells right after. This method exploits temporary price movements from big trades.
A sandwich bot needs these key components to work:
- Mempool monitoring – Constant scanning of pending transactions
- Order execution engine – Quick trade placement with perfect timing
- Trading strategy algorithm – Finding profitable opportunities based on market conditions
These bots perform better than human traders because they execute trades faster and work non-stop. Their success depends on accurate price movement predictions and perfectly timed order execution.
How sandwich bots use mempool data for front-running
The mempool (memory pool) works like a waiting room for unconfirmed blockchain transactions before miners verify and add them to the blockchain. This transparent system creates opportunities for sandwich bots.
DEX users’ transaction details become visible in the mempool before confirmation, including tokens being exchanged, amounts, and slippage tolerances. Sandwich bots take advantage of this transparency by watching the mempool for large transactions that could move market prices.
The bot uses front-running when it finds a suitable target transaction. It submits its transaction with a higher gas fee to get processed first. This higher fee lets the bot jump ahead of other pending transactions.
Here’s an example: A bot spots a large buy order for 100 ETH in the mempool. It quickly buys ETH at the current price before the target transaction processes. The target transaction pushes the price higher, and the bot sells its ETH right away at the increased price, making a profit from the price difference.
Difference between sandwich bot and MEV sandwich bot
MEV (Maximal Extractable Value) sandwich bots are more sophisticated than standard sandwich bots, though they follow similar principles. Their scope and execution methods set them apart.
Standard sandwich bots focus on front-running transactions to profit from price movements. MEV sandwich bots target value from block reordering, transaction inclusion, or transaction ordering within a block.
MEV sandwich bots use complex strategies like pool imbalance attacks. The bot changes DEX liquidity pool sizes during the front-run and resets them during the back-run. This advanced method can leave the sandwiched transaction with almost nothing in return for their swap.
Research shows that sandwich attacks make up about 34% of all MEV activities, making them the second most common after arbitrage. While some attacks have earned over $200,000, most generate around $90 per transaction.
These differences matter when choosing which type of sandwich bot to build. My 48-hour project required deciding between a standard sandwich bot and exploring more complex MEV extraction.
Planning the 48-Hour Build: Tools, Stack, and Setup
I had my strategy ready, but now I needed the right tools and infrastructure to build a sandwich bot that worked within 48 hours. The tech choices I made now would shape how profitable my bot turned out to be.
Choosing the right blockchain: Ethereum vs others
The first big decision was picking the right blockchain. While sandwich attacks work on different chains in theory, Ethereum stood out as the best choice. It had powerful smart contracts and a huge DeFi ecosystem. The way Ethereum’s mempool works makes it perfect for sandwich attacks because you can see pending transactions clearly.
Solana looked good too with faster transactions and much lower fees. But I went with Ethereum because it’s 15 years old and has great developer resources. Bitcoin wasn’t even an option since it doesn’t have the smart contracts needed for these strategies.
Using sandwich bot crypto GitHub repos as base
I didn’t start from zero. Instead, I used existing GitHub repos as my starting point. Many open-source sandwich bots gave me good examples of how to monitor mempools, analyze transactions, and execute orders.
The repos I found had MEV strategies coded up, with some focusing on Uniswap V3 sandwich attacks. They came with:
- Mempool monitoring modules
- Transaction analysis algorithms
- Smart contract interfaces
- Gas optimization strategies
A complete JavaScript implementation built with Node.js and Web3.js turned out to be most helpful for working with Ethereum.
Setting up Uniswap API and mempool access
Getting mempool access was vital to spot sandwich opportunities. Web3.js helped me connect straight to Ethereum nodes so I could watch pending transactions live.
I set up Apollo Client for the Uniswap part to pull data from their subgraph about tokens and liquidity pools. My bot could now see:
- Token addresses being exchanged
- Transaction amounts
- Slippage tolerances
- Gas prices
Infrastructure setup: VPS, node provider, and latency
The bot needed a good home, so I put it on a Virtual Private Server (VPS) with these specs:
- 2 CPU cores (2.0+ GHz)
- 4GB RAM
- 50GB storage
- Linux operating system
Speed matters a lot – even tiny delays can eat into profits when sandwich trading. That’s why I picked a VPS location close to my node provider’s data centers.
Instead of running my own node, I used a professional API service to get reliable mempool data. This meant my bot could run all day and night without breaks, always looking for good trades.
Building the Core Bot Logic in 24 Hours
I spent the next 24 hours building my sandwich bot crypto system’s core logic after choosing my tech stack. Each component brought its own set of challenges and rewards during the technical implementation.
Implementing mempool monitoring with Web3.py
Web3.py, a Python library that makes blockchain interactions easier, helped me set up mempool monitoring. My Ethereum node provider gave me live access to pending transactions in the mempool through a websocket connection. Here’s my simple setup:
from web3 import Web3
import asyncio
# Connect to Ethereum node via WebSocket
web3 = Web3(Web3.WebsocketProvider('wss://my-ethereum-node.io'))
# Filter for pending transactions
tx_filter = web3.eth.filter('pending')
# Continuously monitor mempool
async def monitor_mempool():
while True:
for tx_hash in tx_filter.get_new_entries():
tx = web3.eth.get_transaction(tx_hash)
# Analyze transaction details
analyze_transaction(tx)
await asyncio.sleep(0.1) # Check frequently
The script kept an eye on the mempool for pending DEX interactions, focusing on Uniswap pools.
Order placement logic for sandwiching
The order placement logic activated once it found a suitable transaction. My bot checked if the transaction’s slippage could make a sandwich attack profitable. The algorithm worked out potential profits by:
- Estimating price effect of the target transaction
- Calculating optimal purchase amount for front-running
- Determining sell price after the target transaction completes
The bot looked for transactions with high slippage tolerances because they offered better profit chances. Uniswap V2/V3 pools became my primary focus since sandwich attacks work better on them.
Transaction signing and broadcasting
My secure system for transaction signing and broadcasting:
- Created the front-run transaction with higher gas fees
- Used my securely stored private key for signing
- Sent it to the network before the target transaction
- Prepared the back-run transaction and waited for target mining
Ethers.js proved most reliable for handling transactions:
const signedFrontRunTx = await wallet.signTransaction({
to: targetPool,
data: encodedSwapData,
chainId: chainId,
gasLimit: estimatedGas,
maxFeePerGas: highPriorityGas,
maxPriorityFeePerGas: highPriorityFee,
nonce: nonce
});
const txResponse = await provider.broadcastTransaction(signedFrontRunTx);
Handling slippage and gas fee estimation
Smart slippage and gas fee management turned out to be vital for profits. My dynamic gas fee calculator adjusted based on network traffic and expected profit margins. The bot used higher gas fees for trades with bigger profit potential.
My algorithm studied past pool data to set the best slippage tolerance values. This helped avoid failed transactions while maximizing profits. Trades got canceled automatically if price movements went beyond set limits.
The bot’s profitability improved by storing token dust after successful sandwiches, which saved gas on future trades with the same tokens.
Testing and Backtesting the Bot for Profitability
The testing phase became a significant part of my sandwich bot development process. My focus stayed on simulation and backtesting to ensure profitability before putting real money at risk.
Simulating trades using historical mempool data
Historical mempool data became a great way to test my sandwich bot crypto system. I used Bitquery’s API to access archived mempool transactions and simulated potential trades by replaying past market conditions. This method gave an explanation of blockchain’s past activity and revealed trends over time that shaped my strategy.
My simulation process involved:
- Importing historical transactions
- Running my bot’s logic against these transactions
- Recording potential profit opportunities that my algorithm identified
Tenderly’s platform helped me simulate expected outcomes for transactions still pending in the mempool. The system predicted results before they occurred on-chain.
Backtesting with Uniswap v3 pool data
The open-source backtesting platform from Universe Finance designed for Uniswap V3 powered my detailed backtesting. The system simulates price information of specified blocks based on Uniswap’s actual transactions and creates realistic testing conditions.
Performance details appeared both graphically and in transaction logs. This helped me assess my strategy with objectivity. K-line data made it possible to recreate historical price movements with accuracy.
Profit calculation and risk metrics
A simple formula calculated potential profits: profit = order_b.amount * (price_b - price_a). This calculation determined which transactions to target based on expected returns.
Backtesting tools produced key performance metrics including:
- Daily and hourly profit projections
- Fee calculations
- Active liquidity percentages
Debugging failed transactions and reverts
Systematic debugging approaches helped me handle transaction failures. Simulation tools identified issues before real capital got involved. The analysis of blockchain traces pinpointed exact failure points in reverted transactions.
Failed transactions often stemmed from insufficient incentives, competitor bids, or late bundle receipt. The traceback function showed source code highlights at each jump leading to the revert. This made troubleshooting quick and effective.
The testing phase became the most important investment in my sandwich bot development. It ensured both functionality and security before live deployment.
Going Live: Real-Time Monitoring and Risk Controls
My sandwich bot proved profitable after testing, so I needed reliable monitoring and security measures before the production launch. The difference between making money and getting pricey mistakes often depends on these controls.
Setting up alerts for failed or stuck transactions
Tenderly Alerts helped me get instant notifications about failed transactions. The system triggered live updates whenever transactions failed, events were emitted, or function calls happened. I received tailored updates with links to failed transactions that gave an explanation about the execution. These notifications reached me through email, Telegram, and Discord channels, so I never missed any critical alerts.
Live dashboards using TradingView webhooks
My custom dashboards worked with TradingView webhooks that connected trading signals to automated execution. The webhooks sent HTTP POST requests with custom data straight to my bot. The security setup included HTTPS encryption, authentication tokens, and IP filtering to block unauthorized access.
Security practices for private key management
Private key security became the foundation of everything. My monitoring tools flagged unusual usage patterns that could mean someone compromised the keys. On top of that, I created incident playbooks that spelled out exactly how to rotate or revoke keys.
Rate limiting and anti-ban measures
The rate limiting implementation restricted request frequency to avoid bot detection. This approach balanced security requirements with compliance needs while maintaining an exceptional user experience.
Conclusion
Building a sandwich bot crypto trading system in 48 hours was tough but worth it. The project helped me create a working bot that profits from DEX transaction patterns and keeps security protocols intact. My bot watches the mempool, spots profit chances, times transactions right, and handles risks well.
I learned crucial lessons about crypto markets and automated trading. The blockchain ecosystem’s choice can make or break a bot’s performance, and Ethereum provides the right mix of features and trading chances. Using available open-source resources cut down development time without quality loss. Getting the testing right before launch is crucial to avoid costly mistakes.
I chose real-world results over perfect theory. Instead of spending weeks to build the “perfect” system, my focus stayed on creating a bot that makes real profits. This practical approach helped me finish within 48 hours with great results.
Developers who want to build sandwich bots need both technical skills and market knowledge. Writing code is just one part – you need to understand slippage, gas optimization, and market behavior to make profits.
This project shows how specialized tools can extract value from crypto markets with proper design and setup. While sandwich bots spark ethical debates, they without doubt showcase what’s possible in decentralized finance. My success proves what focused developers can achieve even with tight deadlines.
Key Takeaways
Here are the essential insights from building a profitable sandwich bot crypto trading system in just 48 hours:
• Sandwich bots exploit mempool transparency by monitoring pending transactions and executing strategic front-running trades to profit from price movements caused by large orders.
• Choose Ethereum over alternatives for sandwich bot development due to its robust DeFi ecosystem, transparent mempool structure, and extensive development resources.
• Leverage existing GitHub repositories as your foundation rather than building from scratch – this dramatically accelerates development while maintaining functionality.
• Rigorous backtesting is non-negotiable – simulate trades using historical mempool data and test with Uniswap pool data before risking real capital to ensure profitability.
• Implement comprehensive monitoring systems including real-time alerts for failed transactions, secure private key management, and rate limiting to prevent detection and bans.
The key to success lies in balancing speed with thoroughness – focus on building a functional, profitable system rather than pursuing theoretical perfection. With proper planning, existing tools, and disciplined testing, even complex trading systems can be developed rapidly while maintaining security and profitability standards.
FAQs
Q1. Are sandwich bots in crypto trading profitable? Sandwich bots can be highly profitable when properly configured and implemented. However, profitability depends on various factors such as market conditions, transaction timing, and gas fee management. Thorough testing and optimization are crucial for achieving consistent profits.
Q2. What blockchain is best for building a sandwich bot? Ethereum is generally considered the optimal blockchain for sandwich bot development due to its robust DeFi ecosystem, transparent mempool structure, and extensive development resources. Its smart contract capabilities and established infrastructure make it well-suited for implementing sandwich trading strategies.
Q3. How long does it take to build a functional sandwich bot? With proper planning and leveraging existing resources, a basic functional sandwich bot can be built in as little as 48 hours. However, the development time can vary based on the complexity of the strategy, the developer’s expertise, and the desired features of the bot.
Q4. What are the key components of a sandwich bot trading system? The key components of a sandwich bot trading system include mempool monitoring, order execution engine, trading strategy algorithm, real-time alerts for failed transactions, secure private key management, and rate limiting measures. These elements work together to identify opportunities, execute trades, and manage risks.
Q5. How important is testing before deploying a sandwich bot? Testing is absolutely critical before deploying a sandwich bot. Rigorous backtesting using historical mempool data and simulating trades with actual pool data helps ensure profitability and identify potential issues. This process allows developers to refine their strategies and minimize risks before risking real capital in live trading.
