nautilus-trader_skill

This skill provides comprehensive NautilusTrader guidance for strategy development, backtesting, and Hyperliquid live deployment to accelerate profitable

Python

2.6k

GitHub Stars

2

Bundled Files

2 months ago

Catalog Refreshed

3 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill openclaw/skills --skill nautilus-trader

_meta.json286 B
SKILL.md11.5 KB

Overview

This skill provides practical guidance for developing, backtesting, and deploying trading strategies with NautilusTrader, including a Hyperliquid mainnet integration and SDK patch for price precision. It covers strategy templates, indicators (Heiken Ashi), multi-timeframe EMA patterns, and a complete backtest-to-live workflow. The Hyperliquid patch ensures price formatting and uses the official SDK to avoid known adapter bugs.

How this skill works

It inspects code patterns and configuration needed to run NautilusTrader workflows: local backtests using a Parquet data catalog and live trading via Hyperliquid. The skill explains applying the SDK patch (must be imported before Nautilus) to enforce 5 significant-figure price rules and to route orders through the Hyperliquid Python SDK. It also outlines engine setup, node configuration, and common troubleshooting steps for connectivity, pricing, and data issues.

When to use it

Building or iterating NautilusTrader strategies
Running historical backtests with a Parquet data catalog
Deploying strategies to Hyperliquid mainnet for live execution
Debugging Hyperliquid adapter or order rejection issues
Implementing multi-timeframe EMA + Heiken Ashi strategies

Best practices

Import the hyperliquid_patch module before any nautilus_trader imports to ensure patching applies
Verify .env contains HYPERLIQUID_PK and HYPERLIQUID_VAULT and confirm private key format
Use the ParquetDataCatalog and BarDataWrangler to prepare consistent bar data for backtests
Enforce Hyperliquid 5 significant-figure price formatting and use patch helpers to round buys up and sells down
Deploy low-latency live nodes near Hyperliquid infrastructure (recommend AWS ap-northeast-1 for Tokyo proximity)

Example use cases

Backtest an MTF EMA strategy against a Parquet catalog and generate account, fills, and positions reports
Patch Nautilus to submit orders via the Hyperliquid SDK to avoid Rust client serialization issues
Run a live TradingNode on Hyperliquid with configured leverage and vault credentials
Implement Heiken Ashi indicator and composite entry/exit rules across higher and lower timeframes

FAQ

It replaces the buggy Hyperliquid adapter by routing orders through the official Python SDK, enforces 5 significant-figure price formatting, and adjusts rounding so buys round up and sells round down.

How do I ensure the patch is applied?

Import hyperliquid_patch before any nautilus_trader import; the patch auto-applies on import and formats prices and order submissions.

Why am I getting invalid price rejections?

Hyperliquid requires a maximum of five significant figures. Use the patch's format_price_5_sigfigs helper to format and round prices correctly.