NautilusTrader algorithmic trading platform for strategy development and live trading. Use when building trading strategies, backtesting, or deploying to Hyperliquid.
Comprehensive assistance with NautilusTrader development. Includes complete Hyperliquid mainnet integration with SDK patch for live trading.
This skill covers:
# 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
import nautilus_trader
print(f"Nautilus Trader: {nautilus_trader.__version__}")
# Tested with v1.222.0
Create a .env file for Hyperliquid credentials:
HYPERLIQUID_PK=your_private_key_without_0x_prefix
HYPERLIQUID_VAULT=0xYourVaultAddressHere
# 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
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)
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
See references/hyperliquid.md for complete MTF EMA + Heiken Ashi strategy implementation.
Key concepts:
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()
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 filtersimport 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()
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)
For best performance, deploy on AWS ap-northeast-1 (Tokyo):
Nautilus Trader v1.222.0 has bugs in the Hyperliquid adapter:
Bypass the buggy adapter using the official Hyperliquid Python SDK. The patch file is located at references/hyperliquid_patch.py.
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 |
# 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:
Tested on Hyperliquid Mainnet 2025-01-12:
SELL 0.72 SOL @ $143.38 - FILLED
BUY 0.71 SOL @ $143.39 - FILLED
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
{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
Ensure prices have max 5 significant figures. Use the format_price_5_sigfigs() function from the patch.
.env has correct HYPERLIQUID_PK and HYPERLIQUID_VAULT0x prefix)Ensure import hyperliquid_patch comes BEFORE any Nautilus imports.
Check that reduce_only=True is set on exit orders for netting accounts.
Detailed documentation is available in references/:
| File | Description |
|---|---|
hyperliquid.md | Complete Hyperliquid integration guide |
hyperliquid_patch.py | SDK patch source code |
strategies.md | Strategy patterns and examples |
backtesting.md | Data catalog and backtest API |
data.md | Data handling and wrangling |
getting_started.md | NautilusTrader fundamentals |
concepts.md | Core concepts and architecture |
api.md | Full API reference |
Use view to read specific reference files when detailed information is needed.