Triggers on Gas Price

The "Triggers on Gas Price" feature allows a market-making bot to execute trades based on fluctuations in gas prices, which are crucial for transaction costs on blockchain networks like Ethereum. By monitoring gas prices, the bot can optimize its trading strategy to ensure that transactions are executed efficiently and cost-effectively.

▎How It Works

1. Gas Price Monitoring: Continuously monitor the current gas prices on the blockchain network.

2. Define Triggers: Set specific gas price thresholds that will trigger actions such as placing orders or adjusting existing ones.

3. Execution Logic: When the gas price reaches a defined threshold, the bot can execute trades or modify orders accordingly.

▎Example Code

Here's an example implementation of the "Triggers on Gas Price" feature using Python and the web3 library to interact with Ethereum.

▎Sample Code

from web3 import Web3 import time

class GasPriceBasedMarketMakingBot: def init(self, provider_url, trading_pairs, gas_thresholds): self.web3 = Web3(Web3.HTTPProvider(provider_url)) self.trading_pairs = trading_pairs self.gas_thresholds = gas_thresholds # Dictionary of thresholds for each pair self.orders = {} # Store active orders

def get_current_gas_price(self):
    """Fetch the current gas price in Gwei."""
    gas_price_wei = self.web3.eth.gas_price
    return self.web3.fromWei(gas_price_wei, 'gwei')

def check_gas_triggers(self):
    current_gas_price = self.get_current_gas_price()
    print(f"Current Gas Price: {current_gas_price} Gwei")

    for pair in self.trading_pairs:
        if pair in self.gas_thresholds:
            buy_threshold = self.gas_thresholds[pair]['buy']
            sell_threshold = self.gas_thresholds[pair]['sell']

            # Check if the current gas price meets the buy trigger
            if current_gas_price <= buy_threshold:
                self.place_order(pair, 'buy')

            # Check if the current gas price meets the sell trigger
            elif current_gas_price >= sell_threshold:
                self.place_order(pair, 'sell')

def place_order(self, pair, side):
    try:
        # Simulate placing an order (replace with actual order logic)
        amount = 1  # Define the amount to trade
        print(f"Placed {side.capitalize()} Order for {pair} at Gas Price Trigger.")
        # Here you would add your logic to interact with the exchange API to place an order

        # Store the order (simulated)
        self.orders[pair] = {'side': side, 'amount': amount}

    except Exception as e:
        print(f"Error placing order for {pair}: {e}")

def run(self):
    while True:
        self.check_gas_triggers()
        time.sleep(10)  # Check every 10 seconds

if name == "main": provider_url = 'https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID' trading_pairs = ['ETH/USDT', 'DAI/USDT'] # List of trading pairs

# Define gas price thresholds for each trading pair
gas_thresholds = {
    'ETH/USDT': {'buy': 50, 'sell': 100},  # In Gwei
    'DAI/USDT': {'buy': 50, 'sell': 100}
}

bot = GasPriceBasedMarketMakingBot(provider_url, trading_pairs, gas_thresholds)
bot.run()

▎Explanation of the Code

1. Initialization:

   • The bot initializes with a provider URL for connecting to Ethereum and sets up trading pairs and gas price thresholds.

   • An empty dictionary orders is created to store active orders.

2. Fetching Current Gas Price:

   • The get_current_gas_price method retrieves the current gas price in Gwei using the web3 library.

3. Checking Gas Triggers:

   • The check_gas_triggers method checks if the current gas price meets any predefined thresholds.

   • If the gas price is below the buy threshold, it places a buy order.

   • If the gas price is above the sell threshold, it places a sell order.

4. Placing Orders:

   • The place_order method simulates placing an order (you would replace this with actual order placement logic using an exchange API).

5. Running the Bot:

   • The run method continuously checks gas price triggers in an infinite loop.

   • The bot sleeps briefly (10 seconds) between checks.

▎Considerations

• Dynamic Thresholds: Consider implementing logic to adjust thresholds dynamically based on market conditions or user input.

• Error Handling: Ensure robust error handling for API calls and order placements.

• Performance Monitoring: Implement logging to monitor trades and performance over time.