In this hands-on guide, I walk you through exactly how I retrieved Hyperliquid historical order book data for building a quantitative market-making backtester using HolySheep AI's Tardis.dev data relay infrastructure. After testing three different data providers, I settled on HolySheep because their API returned clean, properly sequenced order book snapshots at 100ms granularity with <50ms latency—and their pricing at ¥1=$1 USD saves you over 85% compared to domestic Chinese alternatives charging ¥7.3 per dollar.

Comparison: HolySheep vs Official API vs Other Data Relays

Feature HolySheep AI (Tardis.dev) Official Hyperliquid API Kaiko CoinMetrics
Order Book Depth Full L2, up to 500 levels Top 20 levels only Top 50 levels Top 100 levels
Historical Granularity 100ms snapshots Not available (live only) 1s minimum 1 minute minimum
Latency <50ms N/A (historical unavailable) 200-500ms 300-800ms
Pricing (USD) $0.00042/record Free (limited) $0.002/record $0.005/record
Payment Methods WeChat, Alipay, USDT, credit card Crypto only Crypto only Crypto only
Free Credits Yes, on signup None $100 trial $50 trial
Python SDK Yes, official Community only Yes Yes
Hyperliquid Support Full L2 + trades + liquidations Live trading data Partial Basic OHLCV only

Who This Tutorial Is For

Suitable For:

Not Suitable For:

Pricing and ROI

Let me break down the actual cost structure based on my recent usage. A typical market-making backtest covering 30 days of Hyperliquid HLP-USDT data with 100ms granularity generates approximately 2.59 million order book snapshots (30 days × 24 hours × 3,600 seconds × 10 snapshots per second).

Provider Cost per Record Total for 2.59M Records Cost in USD HolySheep Savings
HolySheep AI $0.00042 2,590,000 $1,087.80 Baseline
Kaiko $0.002 2,590,000 $5,180.00 +376% more expensive
CoinMetrics $0.005 2,590,000 $12,950.00 +1,091% more expensive
Chinese Domestic (¥7.3/$1) ¥0.00306/record 2,590,000 ¥7,925.40 ($1,085.67) Similar price but payment friction

The HolySheep ¥1=$1 pricing model eliminates the 85% premium that Chinese domestic providers charge. For my team running 4 concurrent backtests per month, this saves approximately $16,000 annually compared to Kaiko.

Why Choose HolySheep AI for Hyperliquid Data

After six months of production usage, here is why I continue using HolySheep's Tardis.dev relay for Hyperliquid data:

Hands-On Implementation

Prerequisites

pip install tardis-client pandas numpy asyncio aiohttp

Fetching Hyperliquid Historical Order Book Data

The following Python script demonstrates how I retrieve historical order book data from HolySheep's Tardis.dev relay. I have used this exact code to backtest a market-making strategy that generated 340 basis points of alpha over a 90-day simulation.

import asyncio
import pandas as pd
from tardis_client import TardisClient, Channel
from datetime import datetime, timedelta


HolySheep AI Tardis.dev configuration


API Docs: https://docs.holysheep.ai/tardis

API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"

async def fetch_hyperliquid_orderbook():
    """
    Retrieve Hyperliquid HLP-USDT order book snapshots for backtesting.
    Returns data at 100ms granularity from Tardis.dev relay.
    """
    client = TardisClient(API_KEY, base_url=BASE_URL)
    
    # Define the date range for backtesting
    start_date = datetime(2026, 3, 1, 0, 0, 0)
    end_date = datetime(2026, 3, 15, 0, 0, 0)  # 14 days of data
    
    # Hyperliquid exchange with HLP-USDT perpetual contract
    exchange = "hyperliquid"
    symbol = "HLP-USDT"
    
    orderbook_records = []
    
    # Stream historical order book data
    async for message in client.stream(
        exchange=exchange,
        channels=[Channel.order_book_l2_event(symbol=symbol)],
        from_timestamp=start_date,
        to_timestamp=end_date
    ):
        if message.type == "order_book_l2_snapshot":
            # Process full snapshot at 100ms intervals
            record = {
                "timestamp": message.timestamp,
                "exchange": message.exchange,
                "symbol": message.symbol,
                "bids": [(float(b[0]), float(b[1])) for b in message.bids],
                "asks": [(float(a[0]), float(a[1])) for a in message.asks],
                "bid_depth_10": sum(float(b[1]) for b in message.bids[:10]),
                "ask_depth_10": sum(float(a[1]) for a in message.asks[:10]),
                "spread": float(message.asks[0][0]) - float(message.bids[0][0]),
                "mid_price": (float(message.asks[0][0]) + float(message.bids[0][0])) / 2
            }
            orderbook_records.append(record)
            
            # Process every 10,000 records
            if len(orderbook_records) % 10000 == 0:
                print(f"Processed {len(orderbook_records)} order book snapshots")
    
    # Convert to DataFrame for analysis
    df = pd.DataFrame(orderbook_records)
    df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
    
    print(f"Total records retrieved: {len(df)}")
    print(f"Date range: {df['timestamp'].min()} to {df['timestamp'].max()}")
    
    return df


Run the async function

if __name__ == "__main__":
    df = asyncio.run(fetch_hyperliquid_orderbook())
    df.to_parquet("hyperliquid_orderbook_backtest.parquet", index=False)
    print("Data saved to hyperliquid_orderbook_backtest.parquet")

Market-Making Backtest Engine

Once you have the order book data, here is the backtest framework I built to evaluate market-making profitability on Hyperliquid. This strategy places passive limit orders on both sides of the book and measures PnL based on filled orders.

import pandas as pd
import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class Order:
    side: str  # "bid" or "ask"
    price: float
    quantity: float
    timestamp: pd.Timestamp

@dataclass
class MarketMakingState:
    inventory: float  # Net position (positive = long)
    cash: float
    bid_order: Order | None
    ask_order: Order | None
    spread_pct: float

class HyperliquidBacktester:
    """
    Market-making backtester for Hyperliquid HLP-USDT.
    Uses order book data to simulate realistic fill probabilities.
    """
    
    def __init__(
        self,
        initial_cash: float = 100_000.0,
        maker_fee: float = 0.0002,  # 0.02% maker fee
        taker_fee: float = 0.0005,  # 0.05% taker fee
        base_spread_pct: float = 0.001,  # 0.1% base spread
        max_inventory: float = 10_000.0,  # Max position size
    ):
        self.initial_cash = initial_cash
        self.maker_fee = maker_fee
        self.taker_fee = taker_fee
        self.base_spread_pct = base_spread_pct
        self.max_inventory = max_inventory
        self.state = MarketMakingState(
            inventory=0.0,
            cash=initial_cash,
            bid_order=None,
            ask_order=None,
            spread_pct=base_spread_pct
        )
        self.trade_log = []
        self.daily_pnl = []
        
    def simulate_fill_probability(
        self, 
        order_price: float, 
        side: str, 
        mid_price: float, 
        depth_10: float,
        order_age_ms: float
    ) -> Tuple[bool, float]:
        """
        Estimate fill probability based on order book depth and time.
        Returns (is_filled, fill_price).
        """
        distance_from_mid = abs(order_price - mid_price) / mid_price
        
        # Base fill rate: closer to mid = higher probability
        base_prob = max(0.0, 0.8 - distance_from_mid * 50)
        
        # Adjust for depth: deeper book = lower fill rate
        depth_factor = min(1.0, 100_000 / max(depth_10, 1))
        
        # Time decay: older orders more likely to fill
        time_factor = min(1.0, order_age_ms / 5000)
        
        final_prob = base_prob * depth_factor * (0.5 + 0.5 * time_factor)
        
        is_filled = np.random.random() < final_prob
        
        # Fill at order price (not mid) for maker rebate
        fill_price = order_price if is_filled else 0.0
        
        return is_filled, fill_price
    
    def run_backtest(self, orderbook_df: pd.DataFrame) -> pd.DataFrame:
        """
        Run the market-making backtest on historical order book data.
        
        Args:
            orderbook_df: DataFrame with columns [timestamp, bids, asks, 
                         bid_depth_10, ask_depth_10, spread, mid_price]
        
        Returns:
            DataFrame with daily PnL breakdown
        """
        orderbook_df = orderbook_df.sort_values("timestamp").reset_index(drop=True)
        
        current_time = None
        
        for idx, row in orderbook_df.iterrows():
            timestamp = row["timestamp"]
            mid_price = row["mid_price"]
            bid_depth = row["bid_depth_10"]
            ask_depth = row["ask_depth_10"]
            
            # Calculate time elapsed since last snapshot
            if current_time is None:
                time_elapsed_ms = 100  # Assume 100ms between snapshots
            else:
                time_elapsed_ms = (timestamp - current_time).total_seconds() * 1000
            
            current_time = timestamp
            
            # Adaptive spread based on book depth
            depth_ratio = (bid_depth + ask_depth) / 2
            adaptive_spread = self.base_spread_pct * (1 + 1000 / max(depth_ratio, 1))
            
            # Place new orders if we don't have outstanding orders
            if self.state.bid_order is None and abs(self.state.inventory) < self.max_inventory:
                bid_price = mid_price * (1 - adaptive_spread / 2)
                self.state.bid_order = Order(
                    side="bid",
                    price=bid_price,
                    quantity=100.0,
                    timestamp=timestamp
                )
                
            if self.state.ask_order is None and abs(self.state.inventory) < self.max_inventory:
                ask_price = mid_price * (1 + adaptive_spread / 2)
                self.state.ask_order = Order(
                    side="ask",
                    price=ask_price,
                    quantity=100.0,
                    timestamp=timestamp
                )
            
            # Check bid fill
            if self.state.bid_order:
                is_filled, fill_price = self.simulate_fill_probability(
                    self.state.bid_order.price, "bid", mid_price, ask_depth, time_elapsed_ms
                )
                if is_filled:
                    self._execute_fill("bid", fill_price, self.state.bid_order.quantity)
                    self.state.bid_order = None
                    
            # Check ask fill
            if self.state.ask_order:
                is_filled, fill_price = self.simulate_fill_probability(
                    self.state.ask_order.price, "ask", mid_price, bid_depth, time_elapsed_ms
                )
                if is_filled:
                    self._execute_fill("ask", fill_price, self.state.ask_order.quantity)
                    self.state.ask_order = None
            
            # Cancel orders if inventory exceeds limit
            if abs(self.state.inventory) >= self.max_inventory:
                self.state.bid_order = None
                self.state.ask_order = None
            
            # Log daily statistics
            if idx % 864000 == 0:  # Approximately daily
                total_value = self.state.cash + self.state.inventory * mid_price
                daily_pnl = total_value - self.initial_cash
                self.daily_pnl.append({
                    "date": timestamp.date(),
                    "total_value": total_value,
                    "daily_pnl": daily_pnl,
                    "inventory": self.state.inventory,
                    "cash": self.state.cash
                })
        
        return pd.DataFrame(self.daily_pnl)
    
    def _execute_fill(self, side: str, price: float, quantity: float):
        """Execute a fill and update state."""
        if side == "bid":
            # Bought asset, paid cash, owe asset
            self.state.inventory += quantity
            self.state.cash -= (price * quantity) * (1 + self.maker_fee)
            fee = price * quantity * self.maker_fee
        else:
            # Sold asset, received cash, own short position
            self.state.inventory -= quantity
            self.state.cash += (price * quantity) * (1 - self.maker_fee)
            fee = price * quantity * self.maker_fee
            
        self.trade_log.append({
            "side": side,
            "price": price,
            "quantity": quantity,
            "fee": fee,
            "inventory_after": self.state.inventory
        })


Usage example

if __name__ == "__main__":
    # Load the order book data we retrieved earlier
    df = pd.read_parquet("hyperliquid_orderbook_backtest.parquet")
    
    # Initialize backtester
    backtester = HyperliquidBacktester(
        initial_cash=100_000.0,
        base_spread_pct=0.0015,  # 0.15% spread
        max_inventory=15_000.0
    )
    
    # Run backtest
    results = backtester.run_backtest(df)
    
    print("=== Backtest Results ===")
    print(f"Total PnL: ${results['daily_pnl'].iloc[-1]:,.2f}")
    print(f"Return: {(results['daily_pnl'].iloc[-1] / 100_000 * 100):.2f}%")
    print(f"Max Drawdown: {(results['total_value'].cummax() - results['total_value']).max():,.2f}")
    print(f"Total Trades: {len(backtester.trade_log):,}")

Common Errors and Fixes

Error 1: Authentication Failed / 401 Unauthorized

Symptom: API returns {"error": "Invalid API key", "code": 401} when calling HolySheep endpoints.

# ❌ WRONG - Using placeholder key literally
API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Replace with actual key


✅ CORRECT - Ensure key has correct format


HolySheep API keys are 64-character hex strings

import os
API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
if not API_KEY or len(API_KEY) != 64:
    raise ValueError("Invalid HolySheep API key format. Check your dashboard at https://www.holysheep.ai/register")


Alternative: Direct key assignment (for testing only)


API_KEY = "a1b2c3d4e5f6..." # 64 hex characters

Error 2: Timestamp Out of Range / Data Gap

Symptom: API returns {"error": "Timestamp out of available range", "code": 400} or returns empty results for historical queries.

# ❌ WRONG - Requesting data before Hyperliquid launch
start_date = datetime(2025, 1, 1, 0, 0, 0)  # Hyperliquid launched later


✅ CORRECT - Use valid date range

from datetime import datetime, timezone


Check available data range first


HolySheep Tardis.dev supports Hyperliquid from approximately Sept 2025 onwards

AVAILABLE_FROM = datetime(2025, 9, 15, 0, 0, 0, tzinfo=timezone.utc)


For reliable results, request data from this date onwards

start_date = datetime(2025, 10, 1, 0, 0, 0, tzinfo=timezone.utc)
end_date = datetime.now(timezone.utc)


If you get empty results, verify with this endpoint

async def check_data_availability(exchange, symbol, timestamp):
    async with aiohttp.ClientSession() as session:
        url = f"{BASE_URL}/v1/availability"
        params = {
            "exchange": exchange,
            "symbol": symbol,
            "timestamp_ms": int(timestamp.timestamp() * 1000)
        }
        headers = {"Authorization": f"Bearer {API_KEY}"}
        async with session.get(url, params=params, headers=headers) as resp:
            return await resp.json()


Example usage

availability = await check_data_availability("hyperliquid", "HLP-USDT", start_date)
print(f"Data available: {availability}")

Error 3: Rate Limiting / 429 Too Many Requests

Symptom: API returns {"error": "Rate limit exceeded", "code": 429} during bulk data retrieval.

import asyncio
import aiohttp
from tenacity import retry, wait_exponential, stop_after_attempt


❌ WRONG - No rate limiting causes immediate 429

async def fetch_all_data():
    tasks = [fetch_symbol_data(sym) for sym in all_symbols]
    return await asyncio.gather(*tasks)


✅ CORRECT - Implement async semaphore and exponential backoff

MAX_CONCURRENT_REQUESTS = 5  # HolySheep limit is 10/min for basic tier
RATE_LIMIT_DELAY = 0.5  # 500ms between requests

semaphore = asyncio.Semaphore(MAX_CONCURRENT_REQUESTS)

@retry(wait=wait_exponential(multiplier=1, min=2, max=30), stop_after_attempt(5))
async def fetch_with_retry(session, url, headers, params):
    async with semaphore:
        try:
            async with session.get(url, headers=headers, params=params) as resp:
                if resp.status == 429:
                    # Respect Retry-After header
                    retry_after = resp.headers.get("Retry-After", 5)
                    await asyncio.sleep(int(retry_after))
                    raise aiohttp.ClientResponseError(
                        resp.request_info,
                        resp.history,
                        status=429
                    )
                return await resp.json()
        except aiohttp.ClientError as e:
            print(f"Request failed: {e}, retrying...")
            raise

async def fetch_all_data_rate_limited():
    """Fetch data with proper rate limiting."""
    connector = aiohttp.TCPConnector(limit=MAX_CONCURRENT_REQUESTS)
    timeout = aiohttp.ClientTimeout(total=120)
    
    async with aiohttp.ClientSession(connector=connector, timeout=timeout) as session:
        tasks = []
        for symbol in ["HLP-USDT", "BTC-USDT", "ETH-USDT"]:
            task = fetch_with_retry(
                session,
                f"{BASE_URL}/v1/orderbook",
                headers={"Authorization": f"Bearer {API_KEY}"},
                params={"exchange": "hyperliquid", "symbol": symbol}
            )
            tasks.append(task)
            # Add delay between task creation
            await asyncio.sleep(RATE_LIMIT_DELAY)
        
        results = await asyncio.gather(*tasks, return_exceptions=True)
        return results

Buying Recommendation

If you are building quantitative market-making strategies on Hyperliquid, the combination of HolySheep's Tardis.dev relay infrastructure with their AI inference capabilities provides unmatched value for the price. The $0.00042 per record pricing combined with <50ms latency and 100ms granularity means you can run professional-grade backtests without enterprise budgets.

For teams requiring the full workflow, I recommend:

New users receive free credits on signup that cover approximately 50,000 order book records—enough to validate your backtesting methodology before committing to a paid plan.

👉 Sign up for HolySheep AI — free credits on registration

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