Nautilus Trader

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.

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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

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Prerequisites

Core Dependencies

bash

# 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

python

import nautilus_trader
print(f"Nautilus Trader: {nautilus_trader.__version__}")
# Tested with v1.222.0

Environment Variables

Create a .env file for Hyperliquid credentials:

bash

HYPERLIQUID_PK=your_private_key_without_0x_prefix
HYPERLIQUID_VAULT=0xYourVaultAddressHere

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Quick Start

1. Apply the Hyperliquid Patch (for live trading)

python

# 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

python

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)

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Strategy Development

Heiken Ashi Indicator

python

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

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Backtesting

Engine Setup

python

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

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Live Trading on Hyperliquid

Node Configuration

python

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)

python

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

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Hyperliquid SDK Patch

The Problem

Nautilus Trader v1.222.0 has bugs in the Hyperliquid adapter:

1. Rust HTTP client serialization causes type mismatches
2. 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

python

# 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:

text

SELL 0.72 SOL @ $143.38 - FILLED
BUY 0.71 SOL @ $143.39 - FILLED

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Configuration

File Structure

text

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

text

{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

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Troubleshooting

Order Rejected: Invalid Price

Ensure prices have max 5 significant figures. Use the format_price_5_sigfigs() function from the patch.

Connection Error

1. Check .env has correct HYPERLIQUID_PK and HYPERLIQUID_VAULT
2. Verify private key format (with or without 0x prefix)
3. Confirm vault address is correct

Patch Not Applied

Ensure import hyperliquid_patch comes BEFORE any Nautilus imports.

Missing Data in Backtest

1. Verify data catalog path exists
2. Check instrument IDs match between data and strategy config
3. Ensure bar types are correctly formatted

Position Not Closing

Check that reduce_only=True is set on exit orders for netting accounts.

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Reference Files

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.