A professional-grade algorithmic trading framework for building, backtesting, and deploying strategies on Hyperliquid.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install nautilus-trader
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install nautilus-trader using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Nautilus Trader?
Nautilus Trader is a high-performance, event-driven algorithmic trading platform designed for Python developers who require precision and speed. This skill facilitates the entire lifecycle of a quantitative trading strategy, from initial development and historical backtesting using Parquet data catalogs to live execution on the Hyperliquid mainnet. By utilizing this integration within Openclaw Skills, developers gain access to a robust environment that handles complex trading operations, multi-timeframe indicators, and exchange-specific nuances.
The skill is particularly valuable for its inclusion of specialized patches that resolve common compatibility issues between the core framework and the Hyperliquid adapter. It ensures that price precision adheres to strict exchange rules, preventing order rejections and ensuring reliable fills. For developers looking to build sophisticated financial tools, these Openclaw Skills provide the necessary scaffolding for professional-grade automation.
Nautilus Trader Use Cases
- Developing custom algorithmic trading strategies using the NautilusTrader framework.
- Conducting high-fidelity backtests with historical data stored in Parquet format.
- Deploying live trading bots to the Hyperliquid mainnet with verified SDK patches.
- Implementing advanced technical indicators such as Heiken Ashi and multi-timeframe EMA crossovers.
- Managing high-frequency trading data and complex order management systems (OMS).
How Nautilus Trader Works
- The user initializes the environment by installing NautilusTrader and the official Hyperliquid Python SDK.
- Security credentials and vault addresses are configured via environment variables to maintain safety.
- A critical Hyperliquid SDK patch is applied to ensure price precision and serialization compatibility for live trading.
- The developer defines a strategy by extending the base Strategy and StrategyConfig classes, specifying instruments and bar types.
- Backtesting is performed by querying a ParquetDataCatalog to simulate historical market conditions.
- For live deployment, a TradingNode is instantiated to manage real-time data feeds and order execution on the exchange.
Nautilus Trader Setup
First, install the required dependencies for Openclaw Skills:
pip install nautilus_trader hyperliquid-python-sdk eth-account python-dotenv pandas numpy
Configure your environment variables in a .env file:
HYPERLIQUID_PK=your_private_key_without_0x_prefix
HYPERLIQUID_VAULT=0xYourVaultAddressHere
Ensure you import the hyperliquid_patch before any NautilusTrader imports in your execution scripts to enable live trading support.
Nautilus Trader Data Schema & Taxonomy
The skill utilizes a structured data management system centered around a Parquet catalog and environment-based configuration.
| Component | Type | Description |
|---|---|---|
| data_catalog/ | Directory | Contains Parquet files for bars, quotes, and instruments used in backtesting. |
| Bar Type | String | Formatted as {symbol}.{venue}-{step}-{aggregation}-{price_type}-{source}. |
| .env | File | Stores sensitive API keys and wallet addresses. |
| hyperliquid_patch.py | Script | Handles price formatting and SDK-based order submission. |
| TradingNode | Object | Manages the live lifecycle including DataEngine and ExecEngine configurations. |
Nautilus Trader Advanced Features
- Custom Heiken Ashi indicator implementation for smoothed price action analysis within Openclaw Skills.
- Automated 5-significant-figure price precision formatting to meet Hyperliquid mainnet requirements.
- Support for multi-timeframe (MTF) logic, enabling trend detection on high timeframes with entry timing on lower timeframes.
- Latency-optimized deployment guidelines for AWS ap-northeast-1 to minimize network jitter.
- Integrated leverage management tools for cross-margin trading on specific crypto assets.
SKILL.md
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