Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Nautilus Trader
NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyp...
skill openclawclawhub Free
NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyp...
โฌ 0 downloads โ
0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/CHANGELOG.md, references/README.md, references/api.md, references/backtesting.md, references/concepts.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 0.1.0
Provenance
- Publisher
- ahuserious
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Nautilus Trader Skill
Comprehensive assistance with NautilusTrader development. Includes complete Hyperliquid mainnet integration with SDK patch for live trading.
Overview
This skill covers: Strategy development with NautilusTrader Backtesting using the Parquet data catalog Live trading deployment on Hyperliquid mainnet SDK patch for Hyperliquid price precision requirements
When to Use
Building trading strategies with NautilusTrader Running backtests with historical data Deploying strategies to Hyperliquid mainnet Debugging NautilusTrader adapter issues Working with multi-timeframe (MTF) indicators
Core Dependencies
# NautilusTrader (backtesting + live trading framework) pip install nautilus_trader # Hyperliquid SDK (for live trading patch) pip install hyperliquid-python-sdk eth-account python-dotenv # Data handling pip install pandas numpy
Verify Installation
import nautilus_trader print(f"Nautilus Trader: {nautilus_trader.__version__}") # Tested with v1.222.0
Environment Variables
Create a .env file for Hyperliquid credentials: HYPERLIQUID_PK=your_private_key_without_0x_prefix HYPERLIQUID_VAULT=0xYourVaultAddressHere
1. Apply the Hyperliquid Patch (for live trading)
# CRITICAL: Import patch BEFORE Nautilus Trader import hyperliquid_patch # Then import Nautilus normally from nautilus_trader.adapters.hyperliquid import HYPERLIQUID from nautilus_trader.live.node import TradingNode
2. Basic Strategy Template
from nautilus_trader.trading.strategy import Strategy from nautilus_trader.config import StrategyConfig from nautilus_trader.model.data import Bar, BarType from nautilus_trader.model.enums import OrderSide, TimeInForce from nautilus_trader.model.identifiers import InstrumentId from decimal import Decimal class MyStrategyConfig(StrategyConfig): instrument_id: str bar_type: str trade_size: Decimal = Decimal("0.1") class MyStrategy(Strategy): def __init__(self, config: MyStrategyConfig): super().__init__(config) self.instrument_id = InstrumentId.from_str(config.instrument_id) self.bar_type = BarType.from_str(config.bar_type) self.trade_size = config.trade_size def on_start(self): self.instrument = self.cache.instrument(self.instrument_id) self.subscribe_bars(self.bar_type) def on_bar(self, bar: Bar): # Your strategy logic here pass def on_stop(self): self.close_all_positions(self.instrument_id)
Heiken Ashi Indicator
from nautilus_trader.indicators.base.indicator import Indicator from nautilus_trader.model.data import Bar class HeikenAshi(Indicator): """Heiken Ashi candle smoothing indicator.""" def __init__(self): super().__init__([]) self.ha_open = 0.0 self.ha_close = 0.0 self.ha_high = 0.0 self.ha_low = 0.0 self._prev_ha_open = None self._prev_ha_close = None self.initialized = False def handle_bar(self, bar: Bar) -> None: o, h, l, c = float(bar.open), float(bar.high), float(bar.low), float(bar.close) self.ha_close = (o + h + l + c) / 4 if self._prev_ha_open is None: self.ha_open = (o + c) / 2 else: self.ha_open = (self._prev_ha_open + self._prev_ha_close) / 2 self.ha_high = max(h, self.ha_open, self.ha_close) self.ha_low = min(l, self.ha_open, self.ha_close) self._prev_ha_open = self.ha_open self._prev_ha_close = self.ha_close self.initialized = True @property def is_bullish(self) -> bool: return self.ha_close > self.ha_open @property def is_bearish(self) -> bool: return self.ha_close < self.ha_open def reset(self) -> None: self._prev_ha_open = None self._prev_ha_close = None self.initialized = False
Multi-Timeframe EMA Strategy
See references/hyperliquid.md for complete MTF EMA + Heiken Ashi strategy implementation. Key concepts: HTF (Higher Timeframe): Determines trend direction via EMA crossover LTF (Lower Timeframe): Entry timing via Heiken Ashi confirmation Entry: HA color change in trend direction Exit: HA color reversal
Engine Setup
from nautilus_trader.backtest.engine import BacktestEngine, BacktestEngineConfig from nautilus_trader.model.currencies import USD from nautilus_trader.model.enums import AccountType, OmsType from nautilus_trader.model.identifiers import Venue from nautilus_trader.model.objects import Money from nautilus_trader.persistence.catalog import ParquetDataCatalog from decimal import Decimal def run_backtest(): config = BacktestEngineConfig( trader_id="BACKTESTER-001", logging_level="INFO", ) engine = BacktestEngine(config=config) # Add venue engine.add_venue( venue=Venue("HYPERLIQUID"), oms_type=OmsType.NETTING, account_type=AccountType.MARGIN, base_currency=USD, starting_balances=[Money(100_000, USD)], ) # Load data from catalog catalog = ParquetDataCatalog("./data_catalog") instruments = catalog.instruments() for instrument in instruments: engine.add_instrument(instrument) bars = catalog.bars() engine.add_data(bars) # Add strategy strategy = MyStrategy(config=MyStrategyConfig( instrument_id="SOL-USD.HYPERLIQUID", bar_type="SOL-USD.HYPERLIQUID-5-MINUTE-LAST-EXTERNAL", trade_size=Decimal("1.0"), )) engine.add_strategy(strategy) # Run engine.run() # Results print(engine.trader.generate_account_report(Venue("HYPERLIQUID"))) print(engine.trader.generate_order_fills_report()) print(engine.trader.generate_positions_report()) engine.dispose()
Data Catalog
See references/backtesting.md and references/data.md for detailed catalog operations: ParquetDataCatalog - Query and manage Parquet data files BarDataWrangler - Convert pandas DataFrames to Nautilus Bar objects write_data() - Persist data to catalog query() - Retrieve data with time filters
Node Configuration
import os from dotenv import load_dotenv load_dotenv() # CRITICAL: Apply patch BEFORE Nautilus imports import hyperliquid_patch from nautilus_trader.adapters.hyperliquid import ( HYPERLIQUID, HyperliquidDataClientConfig, HyperliquidExecClientConfig, ) from nautilus_trader.live.node import TradingNode, TradingNodeConfig from nautilus_trader.config import LiveDataEngineConfig, LiveExecEngineConfig def main(): node_config = TradingNodeConfig( trader_id="LIVE-001", data_engine=LiveDataEngineConfig(), exec_engine=LiveExecEngineConfig(), ) node = TradingNode(config=node_config) data_config = HyperliquidDataClientConfig( wallet_address=os.getenv("HYPERLIQUID_VAULT"), is_testnet=False, ) exec_config = HyperliquidExecClientConfig( wallet_address=os.getenv("HYPERLIQUID_VAULT"), private_key=os.getenv("HYPERLIQUID_PK"), is_testnet=False, ) node.build() # Add your strategy strategy = MyStrategy(config=my_config) node.trader.add_strategy(strategy) node.run() if __name__ == "__main__": main()
Set Leverage (One-Time Setup)
from hyperliquid.exchange import Exchange from hyperliquid.utils import constants from eth_account import Account import os private_key = os.getenv("HYPERLIQUID_PK") if not private_key.startswith("0x"): private_key = "0x" + private_key account = Account.from_key(private_key) exchange = Exchange(account, constants.MAINNET_API_URL) # Set 10x leverage for SOL (cross margin) exchange.update_leverage(10, "SOL", is_cross=True)
Network Latency
For best performance, deploy on AWS ap-northeast-1 (Tokyo): Ping to Hyperliquid CloudFront: ~1ms API latency: ~28ms
The Problem
Nautilus Trader v1.222.0 has bugs in the Hyperliquid adapter: Rust HTTP client serialization causes type mismatches Price precision exceeds Hyperliquid's 5 significant figure limit
The Solution
Bypass the buggy adapter using the official Hyperliquid Python SDK. The patch file is located at references/hyperliquid_patch.py.
Price Precision Rules
Hyperliquid requires maximum 5 significant figures for all prices: PriceValid?Sig Figs$139.05Yes5$139.054No6$1.2345Yes5$1.23456No6$12345Yes5$123456No6
Usage
# CRITICAL: Import patch BEFORE any Nautilus imports import hyperliquid_patch # Then import Nautilus normally from nautilus_trader.adapters.hyperliquid import HYPERLIQUID The patch auto-applies on import and handles: Price formatting to 5 significant figures Rounding up for buys, down for sells (ensures fills) SDK-based order submission bypassing Rust client
Verified Working
Tested on Hyperliquid Mainnet 2025-01-12: SELL 0.72 SOL @ $143.38 - FILLED BUY 0.71 SOL @ $143.39 - FILLED
File Structure
your_trading_project/ โโโ .env # Credentials (gitignored) โโโ hyperliquid_patch.py # SDK patch for live trading โโโ heiken_ashi.py # Heiken Ashi indicator โโโ my_strategy.py # Strategy implementation โโโ backtest.py # Backtest runner โโโ live.py # Live trading runner โโโ data_catalog/ # Parquet data for backtesting
Bar Type Format
{symbol}.{venue}-{step}-{aggregation}-{price_type}-{source} Examples: SOL-USD.HYPERLIQUID-1-HOUR-LAST-EXTERNAL SOL-USD.HYPERLIQUID-5-MINUTE-LAST-EXTERNAL BTC-USD.HYPERLIQUID-15-MINUTE-LAST-EXTERNAL
Order Rejected: Invalid Price
Ensure prices have max 5 significant figures. Use the format_price_5_sigfigs() function from the patch.
Connection Error
Check .env has correct HYPERLIQUID_PK and HYPERLIQUID_VAULT Verify private key format (with or without 0x prefix) Confirm vault address is correct
Patch Not Applied
Ensure import hyperliquid_patch comes BEFORE any Nautilus imports.
Missing Data in Backtest
Verify data catalog path exists Check instrument IDs match between data and strategy config Ensure bar types are correctly formatted
Position Not Closing
Check that reduce_only=True is set on exit orders for netting accounts.
Reference Files
Detailed documentation is available in references/: FileDescriptionhyperliquid.mdComplete Hyperliquid integration guidehyperliquid_patch.pySDK patch source codestrategies.mdStrategy patterns and examplesbacktesting.mdData catalog and backtest APIdata.mdData handling and wranglinggetting_started.mdNautilusTrader fundamentalsconcepts.mdCore concepts and architectureapi.mdFull API reference Use view to read specific reference files when detailed information is needed.
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- references/api.md
- references/backtesting.md
- references/CHANGELOG.md
- references/concepts.md
- references/README.md