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npx skills add https://github.com/dvcrn/openclaw-skills-marketplace --skill nautilus-traderيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
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استنادا إلى تصنيف SOC المهني
name nautilus-trader description NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyperliquid.
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
Prerequisites
Core Dependencies
pip install nautilus_trader
pip install hyperliquid-python-sdk eth-account python-dotenv
pip install pandas numpy
Verify Installation
import nautilus_trader
print (f"Nautilus Trader: {nautilus_trader.__version__} " )
Environment Variables
Create a .env file for Hyperliquid credentials:
HYPERLIQUID_PK=your_private_key_without_0x_prefix
HYPERLIQUID_VAULT=0xYourVaultAddressHere
Quick Start
1. Apply the Hyperliquid Patch (for live trading)
import hyperliquid_patch
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 ):
pass
def on_stop (self ):
self .close_all_positions(self .instrument_id)
Strategy Development
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.
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
Backtesting
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)
engine.add_venue(
venue=Venue("HYPERLIQUID" ),
oms_type=OmsType.NETTING,
account_type=AccountType.MARGIN,
base_currency=USD,
starting_balances=[Money(100_000 , USD)],
)
catalog = ParquetDataCatalog("./data_catalog" )
instruments = catalog.instruments()
for instrument in instruments:
engine.add_instrument(instrument)
bars = catalog.bars()
engine.add_data(bars)
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)
engine.run()
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 filesBarDataWrangler - Convert pandas DataFrames to Nautilus Bar objectswrite_data() - Persist data to catalogquery() - Retrieve data with time filters
Live Trading on Hyperliquid
Node Configuration import os
from dotenv import load_dotenv
load_dotenv()
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()
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)
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
Hyperliquid SDK Patch
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:
Price Valid? Sig Figs $139.05 Yes 5 $139.054 No 6 $1.2345 Yes 5 $1.23456 No 6 $12345 Yes 5 $123456 No 6
Usage
import hyperliquid_patch
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
Configuration
File Structure your_trading_project/
├── .env # Credentials (gitignored)
├── hyperliquid_patch.py # SDK patch for live trading
├── heiken_ashi.py # Heiken Ashi indicator
├── my_strategy.py # Strategy implementation
├── backtest.py # Backtest runner
├── live.py # Live trading runner
└── 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
Troubleshooting
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/:
File Description hyperliquid.mdComplete Hyperliquid integration guide hyperliquid_patch.pySDK patch source code strategies.mdStrategy patterns and examples backtesting.mdData catalog and backtest API data.mdData handling and wrangling getting_started.mdNautilusTrader fundamentals concepts.mdCore concepts and architecture api.mdFull API reference
Use view to read specific reference files when detailed information is needed.
