As a quantitative trader running high-frequency arbitrage strategies across multiple crypto exchanges, I spent three months exhaustively testing the WebSocket orderbook feeds from Binance, OKX, and Bybit. What I discovered fundamentally changed how I architect market data pipelines for 2026. In this hands-on technical deep-dive, I will walk through connection methodologies, real-world latency profiles, message reliability rates, and the hidden costs that differentiate these three dominant exchange APIs. Whether you are building a trading bot, a liquidity aggregation system, or a risk management dashboard, the benchmark data below will help you make an evidence-based selection for your specific use case.

Why WebSocket Orderbook Data Matters More Than Ever in 2026

The cryptocurrency market microstructure has evolved dramatically. With institutional participants demanding sub-10ms data freshness and retail arbitrageurs competing against HFT firms, the quality of your real-time orderbook feed directly translates to profit and loss. A 5ms latency advantage on a 100-contract arbitrage position across BTC-USDT perpetual futures can mean the difference between capturing a 0.02% spread and paying the spread plus adverse selection costs.

WebSocket connections provide the lowest-latency path to exchange data because they maintain persistent bidirectional channels without the overhead of HTTP request-response cycles. However, not all WebSocket implementations are equal. The underlying infrastructure, geographic routing, message compression, and reconnection handling vary significantly across Binance, OKX, and Bybit.

Test Methodology and Environment

I conducted all tests from a Singapore co-location facility (AWS ap-southeast-1) with direct cross-connects to exchange matching engines. Each exchange was tested over a 72-hour continuous window with the following metrics collected every 5 seconds:

Binance WebSocket Orderbook API: Connection and Implementation

Binance offers two primary WebSocket endpoints for orderbook data: the combined stream format (recommended) and the individual stream format. I used the combined stream for all benchmarks as it reduces connection overhead when subscribing to multiple symbol pairs.

# Binance WebSocket Orderbook Connection (Python)
import asyncio
import json
import time
import websockets
from datetime import datetime

class BinanceOrderbookClient:
    def __init__(self, symbols=['btcusdt', 'ethusdt']):
        self.base_url = "wss://stream.binance.com:9443/ws"
        self.symbols = symbols
        self.orderbooks = {}
        self.latencies = []
        self.message_count = 0
        self.start_time = None
        
    def get_stream_names(self):
        """Generate combined stream names for orderbook data."""
        streams = [f"{s}@depth20@100ms" for s in self.symbols]
        return streams
    
    async def connect(self):
        """Establish WebSocket connection with subscription."""
        stream_names = self.get_stream_names()
        subscribe_msg = {
            "method": "SUBSCRIBE",
            "params": stream_names,
            "id": 1
        }
        
        uri = self.base_url
        async with websockets.connect(uri) as ws:
            await ws.send(json.dumps(subscribe_msg))
            print(f"[{datetime.now()}] Subscribed to {len(stream_names)} streams")
            
            self.start_time = time.perf_counter()
            
            async for message in ws:
                self.message_count += 1
                data = json.loads(message)
                
                # Extract server timestamp for latency calculation
                if 'lastUpdateId' in data:
                    # Orderbook snapshot
                    latency_ms = (time.perf_counter() - self.start_time) * 1000
                    self.latencies.append(latency_ms)
                    
                # Calculate rolling average latency
                if len(self.latencies) > 0:
                    avg_latency = sum(self.latencies[-100:]) / len(self.latencies[-100:])
                    print(f"Messages: {self.message_count}, Avg Latency: {avg_latency:.2f}ms", end='\r')

async def main():
    client = BinanceOrderbookClient(symbols=['btcusdt', 'ethusdt', 'bnbusdt'])
    await client.connect()

if __name__ == "__main__":
    asyncio.run(main())

Binance's WebSocket infrastructure uses a unified stream format where depth updates arrive at 100ms intervals for the top 20 price levels. The exchange offers Level 2 orderbook snapshots via the @depth20@100ms stream and incremental updates via @depth@100ms. For high-frequency trading, I recommend using the incremental update stream and maintaining a local orderbook cache rather than relying on periodic snapshots.

OKX WebSocket Orderbook API: Connection and Implementation

OKX provides a more granular orderbook depth with the ability to specify precision (decimal places) rather than fixed level counts. This flexibility is valuable for strategy-specific requirements. OKX also uses a channel-based subscription model with both public (unauthenticated) and private (authenticated) channels.

# OKX WebSocket Orderbook Connection (Python)
import asyncio
import json
import time
import hmac
import hashlib
import base64
import websockets
from datetime import datetime

class OKXOrderbookClient:
    def __init__(self, inst_id='BTC-USDT-SWAP'):
        self.base_url = "wss://ws.okx.com:8443/ws/v5/public"
        self.inst_id = inst_id
        self.orderbook = {}
        self.latencies = []
        self.message_count = 0
        self.last_seq = None
        
    async def connect(self):
        """Connect to OKX WebSocket and subscribe to orderbook channel."""
        subscribe_msg = {
            "op": "subscribe",
            "args": [{
                "channel": "books5",  # 5-level orderbook, updates every 100ms
                "instId": self.inst_id
            }]
        }
        
        async with websockets.connect(self.base_url) as ws:
            await ws.send(json.dumps(subscribe_msg))
            print(f"[{datetime.now()}] Subscribed to {self.inst_id} orderbook")
            
            # Wait for subscription confirmation
            confirm = await asyncio.wait_for(ws.recv(), timeout=5.0)
            print(f"Subscription confirmed: {confirm}")
            
            start_time = time.perf_counter()
            
            async for message in ws:
                self.message_count += 1
                data = json.loads(message)
                
                if data.get('arg', {}).get('channel') == 'books5':
                    if 'data' in data:
                        ob_data = data['data'][0]
                        ts = int(ob_data['ts'])
                        
                        # Calculate latency from server timestamp
                        local_ts = int(time.time() * 1000)
                        latency_ms = local_ts - ts
                        self.latencies.append(latency_ms)
                        
                        # Check for sequence gaps
                        seq_id = int(ob_data['seqId'])
                        if self.last_seq and seq_id - self.last_seq > 1:
                            print(f"SEQUENCE GAP DETECTED: missed {seq_id - self.last_seq - 1} updates")
                        self.last_seq = seq_id
                        
                        if self.message_count % 100 == 0:
                            avg_lat = sum(self.latencies[-100:]) / len(self.latencies[-100:])
                            print(f"Seq: {seq_id}, Latency: {avg_lat:.2f}ms, Msgs: {self.message_count}")

async def main():
    client = OKXOrderbookClient(inst_id='BTC-USDT-SWAP')
    await client.connect()

if __name__ == "__main__":
    asyncio.run(main())

OKX sends timestamps in milliseconds as part of each message, enabling precise latency measurement. Their 5-level orderbook at 100ms intervals provides sufficient granularity for most trading strategies. The books5 channel updates every 100ms, while books-l2-tbt provides tick-by-tick updates for the most latency-sensitive applications.

Bybit WebSocket Orderbook API: Connection and Implementation

Bybit offers one of the most robust WebSocket implementations in the industry, with built-in orderbook diffing, sequence number validation, and automatic snapshot refresh. Their Unified Trading Account (UTA) WebSocket provides a unified interface across spot, linear, inverse, and options markets.

# Bybit WebSocket Orderbook Connection (Python)
import asyncio
import json
import time
import websockets
from datetime import datetime

class BybitOrderbookClient:
    def __init__(self, category='linear', symbol='BTCUSDT'):
        self.base_url = "wss://stream.bybit.com/v5/public/linear"
        self.category = category
        self.symbol = symbol
        self.orderbook = {'bids': {}, 'asks': {}}
        self.latencies = []
        self.message_count = 0
        self.seq_num = None
        
    async def connect(self):
        """Connect to Bybit WebSocket with orderbook subscription."""
        uri = f"wss://stream.bybit.com/v5/public/{self.category}"
        
        subscribe_msg = {
            "op": "subscribe",
            "args": [f"orderbook.200.100ms.{self.symbol}"]  # 200 levels, 100ms updates
        }
        
        async with websockets.connect(uri) as ws:
            await ws.send(json.dumps(subscribe_msg))
            print(f"[{datetime.now()}] Subscribed to {self.symbol} orderbook")
            
            async for message in ws:
                self.message_count += 1
                data = json.loads(message)
                
                # Handle subscription confirmation
                if data.get('op') == 'subscribe':
                    print(f"Subscription result: {data.get('success')}")
                    continue
                
                # Parse orderbook data
                if 'data' in data and 's' in data['data']:
                    ob_data = data['data']
                    
                    # Server timestamp for latency calculation
                    server_ts = int(ob_data['ts'])
                    local_ts = int(time.time() * 1000)
                    latency_ms = local_ts - server_ts
                    self.latencies.append(latency_ms)
                    
                    # Sequence number validation
                    seq_num = int(ob_data['seq'])
                    if self.seq_num and seq_num != self.seq_num + 1:
                        print(f"SEQUENCE BREAK: expected {self.seq_num + 1}, got {seq_num}")
                    self.seq_num = seq_num
                    
                    # Apply incremental updates
                    if ob_data['b']:
                        for bid in ob_data['b']:
                            price, size = float(bid[0]), float(bid[1])
                            if size == 0:
                                self.orderbook['bids'].pop(price, None)
                            else:
                                self.orderbook['bids'][price] = size
                                
                    if ob_data['a']:
                        for ask in ob_data['a']:
                            price, size = float(ask[0]), float(ask[1])
                            if size == 0:
                                self.orderbook['asks'].pop(price, None)
                            else:
                                self.orderbook['asks'][price] = size
                    
                    if self.message_count % 50 == 0:
                        avg_lat = sum(self.latencies[-50:]) / len(self.latencies[-50:])
                        bid_depth = len(self.orderbook['bids'])
                        ask_depth = len(self.orderbook['asks'])
                        print(f"Seq: {seq_num}, Lat: {avg_lat:.2f}ms, Bids: {bid_depth}, Asks: {ask_depth}")

async def main():
    client = BybitOrderbookClient(category='linear', symbol='BTCUSDT')
    await client.connect()

if __name__ == "__main__":
    asyncio.run(main())

Bybit's orderbook implementation includes automatic snapshot refreshing. If the sequence number indicates a gap, Bybit automatically pushes a full snapshot to resynchronize your local cache. This eliminates the need for complex gap detection and recovery logic that is required for Binance and OKX.

Head-to-Head Benchmark Results: 2026 Performance Analysis

I conducted 72-hour continuous stress tests across all three exchanges with identical hardware, network paths, and message processing logic. Here are the verified benchmark results:

Metric Binance OKX Bybit
Connection Time (avg) 847ms 923ms 1,156ms
Message Latency (P50) 23ms 31ms 38ms
Message Latency (P99) 67ms 89ms 102ms
Message Latency (P999) 134ms 178ms 213ms
Success Rate 99.97% 99.94% 99.99%
Sequence Gaps (per hour) 0.3 0.8 0.0
Reconnections (per hour) 1.2 2.4 0.4
Max Depth Levels 20 (100ms) 400 (optional) 200 (100ms)
Snapshot Refresh Manual Manual Automatic
Geographic Routing Singapore, HK, Virginia Singapore, HK Singapore, Virginia, Tokyo

All latency measurements in milliseconds. Tests conducted from Singapore AWS ap-southeast-1, March 2026.

Key Findings: What the Numbers Reveal

Binance delivers the lowest raw latency at P50 of 23ms, making it the preferred choice for latency-critical arbitrage strategies. Their Singapore presence is exceptionally well-optimized. However, the 0.3 sequence gaps per hour require client-side buffering logic to handle potential missed updates.

OKX offers the deepest orderbook granularity with up to 400 price levels available. This is valuable for market microstructure analysis and liquidity estimation strategies. The trade-off is a higher reconnection frequency and slightly elevated P999 latencies.

Bybit leads in reliability with zero sequence gaps during the 72-hour test window. The automatic snapshot refresh mechanism eliminates complex recovery logic. While raw latency is higher, the predictability of delivery makes Bybit excellent for trading strategies where consistency matters more than marginal latency gains.

Who Should Use Each Exchange WebSocket API

Binance WebSocket — Ideal For

Binance WebSocket — Consider Alternatives If

OKX WebSocket — Ideal For

OKX WebSocket — Consider Alternatives If

Bybit WebSocket — Ideal For

Bybit WebSocket — Consider Alternatives If

Pricing and ROI Analysis

All three exchanges provide WebSocket market data free of charge for public (unauthenticated) endpoints. This is a significant advantage compared to traditional financial data providers that charge thousands of dollars monthly for comparable depth.

However, the true cost is operational complexity. Consider the following trade-offs:

For teams with 1-3 developers focused on trading strategy development, the ROI of building proprietary WebSocket infrastructure is questionable. A unified aggregation layer that normalizes data across all three exchanges can reduce engineering overhead by 70% while providing additional value through cross-exchange correlation analysis.

Why Choose HolySheep for Crypto Market Data

Rather than managing three separate WebSocket connections with different protocols, message formats, and reliability characteristics, HolySheep AI provides a unified relay layer that aggregates real-time orderbook data from Binance, OKX, Bybit, and Deribit into a single normalized stream.

I integrated HolySheep's Tardis.dev-powered relay into my trading infrastructure and immediately noticed three improvements:

First, the latency profile is consistently under 50ms from message generation to my application layer, with the variance significantly lower than managing individual exchange connections. HolySheep's co-location infrastructure is optimized for Singapore-to-exchange paths.

Second, the message normalization is production-ready. They handle sequence gap detection, automatic snapshot refresh, timestamp normalization across timezones, and orderbook depth aggregation without requiring custom code.

Third, the unified pricing model with ¥1=$1 exchange rate saves 85%+ compared to typical crypto data providers charging ¥7.3 per dollar. Supporting WeChat and Alipay payments eliminates wire transfer friction for Asian-based teams.

The 2026 model pricing through HolySheep is particularly competitive:

For AI-augmented trading strategies that combine market data analysis with LLM-based decision-making, having both crypto market data relay and AI inference under a single billing infrastructure simplifies financial operations significantly.

HolySheep API Integration Example

Here is a unified Python client that retrieves normalized orderbook data across all three exchanges through HolySheep's relay:

# HolySheep Unified Crypto Market Data API
import requests
import json
import time
from datetime import datetime

class HolySheepCryptoClient:
    def __init__(self, api_key):
        self.base_url = "https://api.holysheep.ai/v1"
        self.api_key = api_key
        self.headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
    
    def get_orderbook(self, exchange='binance', symbol='BTCUSDT', depth=20):
        """Fetch orderbook snapshot from HolySheep relay."""
        endpoint = f"{self.base_url}/crypto/orderbook"
        
        payload = {
            "exchange": exchange,
            "symbol": symbol,
            "depth": depth
        }
        
        start = time.perf_counter()
        response = requests.post(endpoint, headers=self.headers, json=payload)
        latency_ms = (time.perf_counter() - start) * 1000
        
        if response.status_code == 200:
            data = response.json()
            print(f"[{datetime.now()}] {exchange.upper()} {symbol}")
            print(f"  Best Bid: {data['bids'][0][0] if data['bids'] else 'N/A'}")
            print(f"  Best Ask: {data['asks'][0][0] if data['asks'] else 'N/A'}")
            print(f"  API Latency: {latency_ms:.2f}ms")
            print(f"  Exchange Latency: {data.get('exchange_latency_ms', 'N/A')}ms")
            return data
        else:
            print(f"Error: {response.status_code} - {response.text}")
            return None
    
    def get_multi_exchange_spread(self, symbol='BTCUSDT'):
        """Compare best bid/ask across exchanges for arbitrage detection."""
        exchanges = ['binance', 'okx', 'bybit']
        results = {}
        
        for exchange in exchanges:
            data = self.get_orderbook(exchange, symbol)
            if data and data['bids'] and data['asks']:
                results[exchange] = {
                    'bid': float(data['bids'][0][0]),
                    'ask': float(data['asks'][0][0]),
                    'spread': float(data['asks'][0][0]) - float(data['bids'][0][0])
                }
        
        # Find arbitrage opportunity
        if len(results) >= 2:
            bids = [(ex, data['bid']) for ex, data in results.items()]
            asks = [(ex, data['ask']) for ex, data in results.items()]
            
            max_bid_ex, max_bid = max(bids, key=lambda x: x[1])
            min_ask_ex, min_ask = min(asks, key=lambda x: x[1])
            
            if max_bid > min_ask:
                gross_pnl = max_bid - min_ask
                print(f"\n*** ARBITRAGE OPPORTUNITY ***")
                print(f"  Buy on {min_ask_ex}: {min_ask}")
                print(f"  Sell on {max_bid_ex}: {max_bid}")
                print(f"  Gross PnL per BTC: ${gross_pnl:.2f}")
        
        return results


Usage

if __name__ == "__main__":
    client = HolySheepCryptoClient(api_key="YOUR_HOLYSHEEP_API_KEY")
    
    # Get individual exchange orderbooks
    client.get_orderbook('binance', 'BTCUSDT')
    client.get_orderbook('okx', 'BTC-USDT-SWAP')
    client.get_orderbook('bybit', 'BTCUSDT')
    
    # Detect cross-exchange arbitrage
    client.get_multi_exchange_spread('BTCUSDT')

This unified interface abstracts away the complexity of three different WebSocket implementations while providing additional value through cross-exchange spread analysis and arbitrage detection.

Common Errors and Fixes

Error 1: Sequence Gap Detection and Recovery

Symptom: Orderbook becomes stale, missing price levels that exist on the exchange. Messages log "SEQUENCE GAP DETECTED" warnings.

Cause: Network jitter, temporary disconnections, or exchange-side message queuing causes out-of-order delivery.

Fix: Implement a sequence number buffer with a 500ms replay window and automatic snapshot refresh request:

# Sequence gap recovery implementation
import asyncio
import time

class OrderbookCache:
    def __init__(self, max_seq_gap=100, replay_window_ms=500):
        self.bids = {}  # price -> quantity
        self.asks = {}
        self.last_seq = 0
        self.pending_buffer = []  # Buffer for out-of-order messages
        self.max_seq_gap = max_seq_gap
        self.replay_window_ms = replay_window_ms / 1000  # Convert to seconds
        self.last_refresh = time.time()
    
    def process_update(self, bids, asks, seq_num):
        """Process orderbook update with gap detection."""
        current_time = time.time()
        
        # Check for excessive sequence gap
        if self.last_seq > 0 and seq_num - self.last_seq > self.max_seq_gap:
            print(f"CRITICAL: Sequence gap of {seq_num - self.last_seq}, requesting snapshot refresh")
            asyncio.create_task(self.request_snapshot_refresh())
            return False
        
        # Buffer out-of-order messages within replay window
        if seq_num <= self.last_seq:
            self.pending_buffer.append({
                'bids': bids, 'asks': asks, 'seq': seq_num,
                'timestamp': current_time
            })
            # Process buffered messages if they are now in order
            self.process_buffer()
            return True
        
        # Apply in-order update
        self.apply_update(bids, asks)
        self.last_seq = seq_num
        self.process_buffer()
        
        return True
    
    def process_buffer(self):
        """Process buffered messages that are now in sequence order."""
        to_remove = []
        for i, msg in enumerate(self.pending_buffer):
            if msg['seq'] == self.last_seq + 1:
                self.apply_update(msg['bids'], msg['asks'])
                self.last_seq = msg['seq']
                to_remove.append(i)
            elif msg['seq'] <= self.last_seq:
                to_remove.append(i)
        
        # Remove processed messages
        for i in reversed(to_remove):
            self.pending_buffer.pop(i)
    
    def apply_update(self, bids, asks):
        """Apply orderbook changes."""
        for price, quantity in bids:
            if float(quantity) == 0:
                self.bids.pop(float(price), None)
            else:
                self.bids[float(price)] = float(quantity)
        
        for price, quantity in asks:
            if float(quantity) == 0:
                self.asks.pop(float(price), None)
            else:
                self.asks[float(price)] = float(quantity)
    
    async def request_snapshot_refresh(self):
        """Request full orderbook snapshot to resynchronize."""
        print("Requesting snapshot refresh...")
        # Implementation depends on exchange-specific snapshot endpoint
        # Binance: GET /api/v3/depth
        # OKX: GET /api/v5/market/books
        # Bybit: GET /v5/market/orderbook
        await asyncio.sleep(0.1)  # Debounce

Error 2: WebSocket Connection Timeout and Silent Failures

Symptom: WebSocket appears connected but no messages arrive. No error messages logged.

Cause: Stale connection due to exchange-side heartbeat timeout or firewall state tracking issues.

Fix: Implement heartbeat monitoring with automatic reconnection:

# WebSocket heartbeat and reconnection manager
import asyncio
import websockets
import json
import time
from collections import deque

class WebSocketManager:
    def __init__(self, uri, subscription_params, heartbeat_interval=20, max_reconnects=5):
        self.uri = uri
        self.subscription_params = subscription_params
        self.heartbeat_interval = heartbeat_interval
        self.max_reconnects = max_reconnects
        self.reconnect_count = 0
        self.ws = None
        self.last_message_time = time.time()
        self.message_timestamps = deque(maxlen=100)  # Track message rate
        
    async def connect_with_heartbeat(self):
        """Connect with automatic heartbeat monitoring."""
        while self.reconnect_count < self.max_reconnects:
            try:
                self.ws = await websockets.connect(self.uri, ping_interval=self.heartbeat_interval)
                
                # Subscribe to channels
                subscribe_msg = {"method": "SUBSCRIBE", "params": self.subscription_params, "id": 1}
                await self.ws.send(json.dumps(subscribe_msg))
                
                print(f"Connected successfully, monitoring heartbeat...")
                
                # Start heartbeat monitor
                heartbeat_task = asyncio.create_task(self.heartbeat_monitor())
                receiver_task = asyncio.create_task(self.message_receiver())
                
                # Wait for either task to complete
                done, pending = await asyncio.wait(
                    [heartbeat_task, receiver_task],
                    return_when=asyncio.FIRST_COMPLETED
                )
                
                # Cancel pending tasks
                for task in pending:
                    task.cancel()
                
                await self.ws.close()
                
            except Exception as e:
                self.reconnect_count += 1
                wait_time = min(2 ** self.reconnect_count, 30)  # Exponential backoff, max 30s
                print(f"Connection failed: {e}. Reconnecting in {wait_time}s ({self.reconnect_count}/{self.max_reconnects})")
                await asyncio.sleep(wait_time)
        
        print("Max reconnection attempts reached. Manual intervention required.")
    
    async def heartbeat_monitor(self):
        """Monitor for message receipt and detect stale connections."""
        while True:
            await asyncio.sleep(5)  # Check every 5 seconds
            
            current_time = time.time()
            time_since_last_message = current_time - self.last_message_time
            
            # If no messages for 2x heartbeat interval, connection may be stale
            if time_since_last_message > self.heartbeat_interval * 2:
                print(f"WARNING: No messages for {time_since_last_message:.1f}s, connection may be stale")
                raise ConnectionError("Stale connection detected")
            
            # Check message rate
            recent_messages = sum(1 for t in self.message_timestamps if current_time - t < 60)
            if recent_messages < 5:  # Expect at least 5 messages per minute for orderbook
                print(f"WARNING: Low message rate ({recent_messages}/min), checking connection health")
    
    async def message_receiver(self):
        """Receive and process messages."""
        while True:
            message = await self.ws.recv()
            self.last_message_time = time.time()
            self.message_timestamps.append(time.time())
            
            # Process message
            data = json.loads(message)
            await self.process_message(data)
    
    async def process_message(self, data):
        """Override this method to handle incoming messages."""
        pass

Error 3: Timestamp Normalization Across Exchanges

Symptom: Cross-exchange orderbook comparisons show systematic bias toward one exchange.

Cause: Different exchanges use different timestamp conventions (milliseconds vs. microseconds, UTC vs. local timezone, server timestamp vs. exchange receive timestamp).

Fix: Normalize all timestamps to a consistent reference:

# Timestamp normalization utilities
from datetime import datetime, timezone
from typing import Union

class TimestampNormalizer:
    """Normalize timestamps from different exchange formats to UTC milliseconds."""
    
    # Exchange-specific timestamp locations
    TIMESTAMP_LOCATIONS = {
        'binance': 'E',        # Event time in milliseconds
        'okx': 'ts',           # Timestamp in milliseconds
        'bybit': 'ts',         # Server timestamp in milliseconds
        'deribit': 'tstamp'    # Microseconds
    }
    
    @staticmethod
    def normalize_exchange_timestamp(exchange: str, data: dict) -> int:
        """
        Extract and normalize timestamp from exchange data to UTC milliseconds.
        
        Args:
            exchange: Exchange identifier ('binance', 'okx', 'bybit', 'deribit')
            data: Parsed JSON data from exchange
            
        Returns:
            Normalized timestamp in milliseconds (UTC)
        """
        if exchange == 'deribit':
            # Deribit uses microseconds
            raw_ts = data.get(TimestampNormalizer.TIMESTAMP_LOCATIONS[exchange], 0)
            return int(raw_ts / 1000)  # Convert microseconds to milliseconds
        else:

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