Building a Hyperliquid perpetual futures bot from scratch

Building a Hyperliquid perpetual futures bot from scratch has been a challenging yet rewarding experience for me. As a developer with a passion for trading and automation, I've always been fascinated by the concept of creating a bot that can trade perpetual futures contracts with high leverage. In this post, I'll share my experience and insights on building such a bot, including the technical difficulties I encountered and how I overcame them.

Designing the Bot Architecture

The first step in building the bot was to design its architecture. I decided to use a microservices approach, with separate modules for trading, risk management, and strategy execution. This allowed me to develop and test each module independently, making it easier to debug and maintain the code. I chose Python as the programming language, mainly due to its extensive libraries and frameworks for trading and automation.

Implementing Trading Logic

The trading logic was implemented using a combination of technical indicators and machine learning algorithms. I used the popular CCXT library to interact with the Hyperliquid DEX, which provided a unified API for trading and market data. The trading logic was implemented in a separate module, which made it easy to switch between different strategies and parameters. Here's an example of how I implemented a simple momentum-based strategy:

import ccxt
import pandas as pd

# Initialize the exchange and fetch market data
exchange = ccxt.hyperliquid({
    'apiKey': 'YOUR_API_KEY',
    'apiSecret': 'YOUR_API_SECRET',
})
markets = exchange.fetch_markets()

# Define the trading strategy
def momentum_strategy(symbol, timeframe):
    # Fetch historical data
    bars = exchange.fetch_ohlcv(symbol, timeframe=timeframe)
    df = pd.DataFrame(bars, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])

    # Calculate momentum indicators
    df['momentum'] = df['close'].rolling(window=20).mean()
    df['signal'] = df['momentum'].rolling(window=10).mean()

    # Generate trading signals
    df['signal'] = df['momentum'] > df['signal']

    return df

# Execute the trading strategy
symbol = 'BTC/USDT'
timeframe = '1m'
df = momentum_strategy(symbol, timeframe)
if df['signal'].iloc[-1]:
    # Send a buy order
    exchange.place_order(symbol, 'limit', 'buy', 100, 5000)
else:
    # Send a sell order
    exchange.place_order(symbol, 'limit', 'sell', 100, 5000)

Risk Management and Position Sizing

Risk management and position sizing were crucial components of the bot. I implemented a simple risk management system that adjusted the position size based on the account equity and the desired risk level. The bot also included a trailing stop-loss feature, which helped to limit losses and lock in profits.

Putting it all Together

After implementing the trading logic, risk management, and position sizing, I was able to put the bot together and test it in a live trading environment. The results were promising, with the bot generating consistent profits over a prolonged period. I actually packaged this into a tool called Hyperliquid Perpetual Futures Trading Bot if you want the full working version, which you can find at https://lukegraggster.gumroad.com/l/hyperliquid-perp-bot?utm_source=devto&utm_medium=blog&utm_campaign=traffic_bot. The bot supports long and short positions, up to 50x leverage, and includes three built-in strategies, making it a versatile tool for traders of all levels.