Front-Running aka Sandwich Trading Bot (Cryptocurrency) in Python for Uniswap or DEX
A sandwich attack represents a sophisticated strategy used by traders to exploit pending transactions on blockchain networks, such as Ethereum. This strategy involves the simultaneous execution of two orders: one placed before and another immediately after a targeted pending transaction. By doing so, the attacker effectively sandwiches the original transaction, manipulating the asset’s price for their benefit.
The mechanics of a sandwich attack start with the attacker identifying a user-initiated transaction that is yet to be confirmed — for example, a swap from LINK to ETH. Anticipating the price movement of ETH, the attacker first purchases ETH at the current lower price. As the user’s transaction gets executed, it increases the demand for ETH, thereby inflating its price. The attacker then sells their previously acquired ETH at this new, higher price, realizing a profit from the price differential caused by their own initial purchase and the subsequent sale.
The impact of such an attack is significant: it artificially inflates the asset’s price, affecting the initial user’s transaction outcome by reducing the amount of ETH they receive. This sequence not only disrupts fair trading practices but also allows the attacker to profit from artificially created price volatility.
import web3
from uniswap_v2_sdk import UniswapV2Client
from metamask import MetaMask
def sandwich_trade(token_a, token_b):
# Get the current price of token A
price_a = client.get_price(token_a)
# Get the current price of token B
price_b = client.get_price(token_b)
# Calculate the spread between the two prices
spread = price_b - price_a
# If the spread is greater than or equal to 0.01%, then execute the trade
if spread >= 0.0001:
# Buy token A
amount_a = wallet.get_balance(token_a)
order_a = client.create_order(
token_a, token_b, "buy", amount_a, price_a
)
# Wait for the order to be filled
order_a.wait()
# Sell token B
amount_b = order_a.amount
order_b = client.create_order(
token_b, token_a, "sell", amount_b, price_b
)
# Wait for the order to be filled
order_b.wait()
# Calculate the profit
profit = order_b.amount * (price_b - price_a)
print("Profit:", profit)
else:
print("No trade found")
def list_possible_trades():
# Get a list of all the tokens on Uniswap
tokens = client.get_all_tokens()
# For each token, check if there is a profitable sandwich trade opportunity
for token in tokens:
# Identify possible trades
possible_trades = sandwich_trade(token, token)
# If there are possible trades, list them
if possible_trades:
print("Possible trades for token", token)
for trade in possible_trades:
print("-", trade)
def prompt_user_which_trades_to_execute():
# Get a list of all the possible trades
possible_trades = list_possible_trades()
# Print a list of all the possible trades
print("Possible trades:")
for trade in possible_trades:
print(f"{trade[0]} - {trade[1]} - {trade[2]}")
# Ask the user which trades to execute
response = input("Which trades would you like to execute? (Enter 'q' to quit)")
# If the user enters 'q', then quit the program
if response == "q":
return
# Otherwise, split the user's input into a list of tokens
tokens = response.split(",")
# For each token in the list, execute the trade
for token in tokens:
sandwich_trade(token, token)
def export_possible_trades_to_csv():
# Get a list of all the possible trades
possible_trades = list_possible_trades()
# Create a CSV file
with open("possible_trades.csv", "w") as f:
# Write the header row
f.write("token_a,token_b,spread\n")
# Write the data rows
for trade in possible_trades:
f.write(
f"{trade[0]},{trade[1]},{trade[2]}\n"
)
if __name__ == "__main__":
# Initialize the Web3 provider
web3 = Web3(Web3.HTTPProvider("https://mainnet.infura.io/v3/<YOUR_INFURA_PROJECT_ID>"))
# Initialize the Uniswap client
client = UniswapV2Client(web3)
# Initialize the MetaMask wallet
wallet = MetaMask()
# List possible trades
list_possible_trades()
# Prompt the user which trades to execute
prompt_user_which_trades_to_execute()
# Export possible trades to CSV file
export_possible_trades_to_csv()To combat or exploit such scenarios, functions like sandwich_trade are designed to automate the identification and execution of profitable sandwich trades. By analyzing the price spread between two tokens, this function determines whether a trade meets the minimum profit threshold — in this case, a spread of 0.01% or more. Successful identification leads to the execution of trades that buy low and sell high, securing profits in the process.
Additionally, the list_possible_trades function scans through tokens on exchanges like Uniswap to pinpoint potential sandwich trade opportunities. Once identified, these opportunities can be presented to the user via the prompt_user_which_trades_to_execute function, allowing them to select which trades to pursue. For record-keeping and analysis, the export_possible_trades_to_csv function facilitates the exportation of identified trades into a CSV file, organizing data into token pairs and their respective price spreads.
This comprehensive approach not only automates the search for arbitrage opportunities but also introduces a layer of strategy to cryptocurrency trading, enabling traders to navigate and exploit the dynamic and often unpredictable nature of blockchain-based transactions.
Other Items to Consider:
To enhance the functionality and efficiency of the sandwich attack trading bot as described, several improvements and additional features can be integrated. These enhancements aim to optimize performance, improve user experience, and ensure greater security and compliance with ethical trading practices. Here are some suggestions:
1. Real-time Transaction Monitoring and Analysis
- Implement a module for real-time monitoring of the mempool to detect pending transactions more efficiently. This would allow the bot to respond quicker to trading opportunities.
2. Slippage Control
- Integrate slippage control mechanisms to minimize the risk of significant price movements between the time a trade is executed and when it is settled. This could involve setting maximum slippage thresholds.
3. Gas Price Optimization
- Develop a dynamic gas price calculation feature that can adjust the transaction’s gas price based on network congestion. This optimizes transaction costs while ensuring timely execution.
4. Risk Management
- Incorporate risk management features to calculate and limit exposure on each trade. This could include setting maximum loss thresholds and adjusting trade sizes based on the volatility of the assets involved.
5. Machine Learning for Predictive Analysis
- Apply machine learning algorithms to predict the outcomes of trades based on historical data and current market conditions. This could improve the selection of profitable trades.
6. User Interface (UI)
- Create a user-friendly interface that allows users to interact with the bot more intuitively. This could include visual representations of potential trades, historical performance data, and real-time market analysis.
7. Compliance and Ethical Trading Module
- Ensure the trading bot operates within the legal and ethical boundaries of cryptocurrency trading. This module could monitor regulatory updates and adjust trading strategies accordingly to avoid practices considered manipulative or unfair.
8. Multi-Network Compatibility
- Expand the bot’s capabilities to operate across multiple blockchain networks beyond Ethereum, such as Binance Smart Chain or Polygon. This would increase the range of trading opportunities.
9. Automated Testing and Simulation Environment
- Implement an environment where strategies can be tested against historical data or in a simulation mode. This allows for safe testing of new strategies or adjustments without risking actual funds.
10. Detailed Logging and Reporting
- Enhance the logging system to capture detailed information about executed trades, performance metrics, and system errors. Additionally, develop reporting features that allow users to analyze the bot’s performance over time.
11. Smart Contract Interaction Enhancements
- For bots that interact with smart contracts directly, improvements could include more sophisticated contract interaction methods to take advantage of complex trading strategies and provide better execution efficiency.
12. Decentralized Finance (DeFi) Integrations
- Integrate with additional DeFi protocols for lending, borrowing, or yield farming to leverage arbitrage profits further or hedge against positions.
Implementing these features requires a deep understanding of blockchain technology, smart contract development, market dynamics, and regulatory considerations. Continuous development and adaptation to the rapidly evolving crypto landscape are crucial for maintaining the effectiveness and relevance of the trading bot.
Enjoy! Code Responsibly!
Rami Jaloudi, a veteran Systems Engineer with a career inception in 2008, has an extensive background that encompasses eDiscovery, Data Mining, Development, Technology, Security, Networking, and Blockchain. Jaloudi’s exceptional problem-solving skills and forward-thinking strategies have significantly contributed to refining data analysis processes in legal discoveries, bolstering system security, and enhancing network efficiency. His profound grasp of programming languages and frameworks has been instrumental in crafting flexible and expandable technological solutions, including those that leverage blockchain technology for increased security and transparency.
Beyond his professional milestones, Jaloudi harbors a deep-seated passion for democratizing technology, diligently working to demystify complex concepts via blogs and social media platforms. His endeavors aim not only to tackle immediate technological challenges but also to pave the way for future innovations, underpinned by his conviction in the transformative potential of technology to universally benefit mankind. Rami Jaloudi transcends the role of a mere technology expert to become a fervent mentor, motivating the next wave of innovators to delve into and shape the future of the digital and blockchain landscapes.
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