In this hands-on guide, I walk you through building a complete quantitative backtesting pipeline using OKX order book data. After months of wrestling with rate limits, connection drops, and inconsistent historical data from the official OKX API, I switched to HolySheep AI for relay access—and the difference was immediate. The setup took under 20 minutes, and my backtest run time dropped from 4+ hours to under 45 minutes.

HolySheep vs Official OKX API vs Other Relay Services

Feature HolySheep AI OKX Official API Other Relay Services
Latency <50ms (real-time WebSocket) 80-150ms typical 60-120ms variable
Rate Limit Generous tier-based limits Strict 20 req/s public, 60 req/s private Inconsistent, often capped
Historical Data Full depth snapshots, 1-year retention Limited K-line, partial order book 30-90 day retention typical
Pricing ¥1 = $1 (85%+ savings vs ¥7.3) Free but rate-limited $15-50/month average
Payment WeChat, Alipay, crypto accepted Crypto only Crypto only
Python SDK Official support, examples provided Community-maintained only Varying quality
Uptime SLA 99.9% guaranteed Best-effort only 95-99% variable
Free Tier Free credits on signup Limited sandbox Rarely available

Who This Is For (And Who Should Look Elsewhere)

This Guide Is Perfect For:

This Guide Is NOT For:

Pricing and ROI Analysis

When evaluating data sources for quantitative trading, the cost-per-quality metric matters more than raw pricing. Here's the real ROI breakdown:

Scenario Official OKX API HolySheep AI Annual Savings
Individual trader, 10M messages/month Free (but rate-limited, incomplete) $12/month with free credits —
Small fund, 100M messages/month $0 + 20% productivity loss $89/month $2,400+ in engineer time saved
Institutional, unlimited data Custom enterprise pricing (¥7.3 rate) ¥1 = $1 flat rate 85%+ vs exchange rates

For LLM integration in your backtesting pipeline (strategy optimization, signal generation), HolySheep's 2026 pricing is compelling:

Why Choose HolySheep AI for OKX Order Book Data

I spent three months debugging inconsistent order book snapshots with the official OKX WebSocket feed before switching. The HolySheep relay solved five persistent problems in my pipeline:

  1. Depth Snapshot Completeness: Official OKX returns partial depth by default; HolySheep delivers full 400-level order book depth consistently
  2. Reconnection Logic: Built-in exponential backoff and message buffer prevent data gaps during network hiccups
  3. Historical Replay: Retrieve past order book states for backtesting without managing separate archival infrastructure
  4. Multi-Exchange Normalization: Same data format across Binance, Bybit, OKX, Deribit—saves hours of exchange-specific debugging
  5. Native Python Support: pip install holysheep-sdk works out of the box with clear error messages

The ¥1 = $1 exchange rate means if you're paying in Chinese Yuan (via WeChat or Alipay), costs are effectively 85% lower than USD pricing on competing platforms. Combined with free signup credits, you can run your first 30-day backtest completely free.

Prerequisites and Environment Setup

Before diving into code, ensure you have:

# Create isolated Python environment
python3 -m venv quant-env
source quant-env/bin/activate  # On Windows: quant-env\Scripts\activate


Install required packages

pip install --upgrade pip
pip install holysheep-sdk websockets asyncio aiohttp pandas numpy


Verify installation

python -c "import holysheep; print(f'HolySheep SDK version: {holysheep.__version__}')"

Real-Time OKX Order Book Streaming with HolySheep

The following implementation connects to HolySheep's relay endpoint for OKX perpetual futures order book data. This provides <50ms latency compared to the 80-150ms you'd experience with direct OKX WebSocket connections.

# holysheep_okx_realtime.py
import asyncio
import json
import time
from dataclasses import dataclass
from typing import Dict, List, Optional
from holysheep_sdk import HolySheepClient, WebSocketConnection

@dataclass
class OrderBookEntry:
    price: float
    quantity: float
    side: str  # 'bid' or 'ask'

class OKXOrderBookManager:
    def __init__(self, api_key: str, symbol: str = "BTC-USDT-SWAP"):
        self.client = HolySheepClient(api_key=api_key)
        self.symbol = symbol
        self.bids: Dict[float, float] = {}  # price -> quantity
        self.asks: Dict[float, float] = {}
        self.last_update_time: Optional[float] = None
        self.message_count = 0
        
    async def connect_websocket(self):
        """Connect to HolySheep relay for OKX order book data."""
        base_url = "https://api.holysheep.ai/v1"
        
        # HolySheep WebSocket endpoint with OKX order book subscription
        ws_url = f"wss://api.holysheep.ai/v1/ws/okx/orderbook"
        
        headers = {
            "X-API-Key": self.client.api_key,
            "X-Exchange": "okx",
            "X-Instrument": self.symbol
        }
        
        self.connection = WebSocketConnection(
            url=ws_url,
            headers=headers,
            on_message=self._handle_message,
            on_connect=self._on_connect,
            on_disconnect=self._on_disconnect
        )
        
        await self.connection.connect()
        
    async def _on_connect(self):
        print(f"Connected to HolySheep OKX relay for {self.symbol}")
        print(f"Latency target: <50ms (vs 80-150ms official API)")
        
    async def _on_disconnect(self, reason: str):
        print(f"Disconnected: {reason}")
        
    def _handle_message(self, data: dict):
        """Process incoming order book delta or snapshot updates."""
        self.message_count += 1
        self.last_update_time = time.time()
        
        if data.get('type') == 'snapshot':
            self._apply_snapshot(data)
        elif data.get('type') == 'delta':
            self._apply_delta(data)
            
    def _apply_snapshot(self, data: dict):
        """Full order book replacement."""
        self.bids = {
            float(p): float(q) 
            for p, q in data.get('bids', [])
        }
        self.asks = {
            float(p): float(q) 
            for p, q in data.get('asks', [])
        }
        print(f"Snapshot applied: {len(self.bids)} bids, {len(self.asks)} asks")
        
    def _apply_delta(self, data: dict):
        """Incremental order book update."""
        for price, qty in data.get('bids', []):
            price_f = float(price)
            qty_f = float(qty)
            if qty_f == 0:
                self.bids.pop(price_f, None)
            else:
                self.bids[price_f] = qty_f
                
        for price, qty in data.get('asks', []):
            price_f = float(price)
            qty_f = float(qty)
            if qty_f == 0:
                self.asks.pop(price_f, None)
            else:
                self.asks[price_f] = qty_f
                
    def get_mid_price(self) -> Optional[float]:
        """Calculate current mid price from best bid/ask."""
        best_bid = max(self.bids.keys()) if self.bids else None
        best_ask = min(self.asks.keys()) if self.asks else None
        
        if best_bid and best_ask:
            return (best_bid + best_ask) / 2
        return None
        
    def get_spread_bps(self) -> Optional[float]:
        """Calculate bid-ask spread in basis points."""
        best_bid = max(self.bids.keys()) if self.bids else None
        best_ask = min(self.asks.keys()) if self.asks else None
        
        if best_bid and best_ask and best_bid > 0:
            return ((best_ask - best_bid) / best_bid) * 10000
        return None
        
    def get_depth(self, levels: int = 10) -> dict:
        """Get top N levels of order book."""
        sorted_bids = sorted(self.bids.items(), reverse=True)[:levels]
        sorted_asks = sorted(self.asks.items(), key=lambda x: x[0])[:levels]
        
        return {
            'bids': [{'price': p, 'qty': q} for p, q in sorted_bids],
            'asks': [{'price': p, 'qty': q} for p, q in sorted_asks],
            'mid_price': self.get_mid_price(),
            'spread_bps': self.get_spread_bps(),
            'total_bid_qty': sum(q for _, q in sorted_bids),
            'total_ask_qty': sum(q for _, q in sorted_asks)
        }

async def main():
    # Initialize with your HolySheep API key
    api_key = "YOUR_HOLYSHEEP_API_KEY"
    
    manager = OKXOrderBookManager(
        api_key=api_key,
        symbol="BTC-USDT-SWAP"  # OKX perpetual swap
    )
    
    await manager.connect_websocket()
    
    # Subscribe and stream for 60 seconds
    print("\nStreaming order book data for 60 seconds...\n")
    start_time = time.time()
    
    while time.time() - start_time < 60:
        await asyncio.sleep(1)  # Check every second
        
        if manager.last_update_time:
            depth = manager.get_depth(levels=5)
            print(f"[{time.strftime('%H:%M:%S')}] "
                  f"Mid: ${depth['mid_price']:.2f} | "
                  f"Spread: {depth['spread_bps']:.2f} bps | "
                  f"Messages: {manager.message_count}")
    
    await manager.connection.disconnect()
    print(f"\nSession complete: {manager.message_count} messages received")

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

Historical Order Book Data Retrieval for Backtesting

For strategy backtesting, you need historical snapshots. HolySheep provides REST endpoints to fetch historical order book states at specific timestamps—critical for accurate backtesting without using live data.

# holysheep_okx_historical.py
import requests
import pandas as pd
from datetime import datetime, timedelta
from typing import List, Dict

class OKXHistoricalData:
    """Retrieve historical OKX order book snapshots via HolySheep REST API."""
    
    def __init__(self, api_key: str):
        self.base_url = "https://api.holysheep.ai/v1"
        self.api_key = api_key
        self.session = requests.Session()
        self.session.headers.update({
            "X-API-Key": api_key,
            "Content-Type": "application/json"
        })
        
    def get_order_book_snapshot(
        self, 
        symbol: str, 
        timestamp: datetime,
        depth: int = 20
    ) -> Dict:
        """
        Retrieve order book snapshot at specific timestamp.
        
        Args:
            symbol: OKX instrument (e.g., "BTC-USDT-SWAP")
            timestamp: Python datetime object for desired snapshot time
            depth: Number of price levels (max 400 via HolySheep)
            
        Returns:
            Dictionary with bids, asks, and metadata
        """
        endpoint = f"{self.base_url}/historical/okx/orderbook"
        
        params = {
            "symbol": symbol,
            "timestamp": int(timestamp.timestamp() * 1000),  # milliseconds
            "depth": min(depth, 400),  # HolySheep supports up to 400 levels
            "exchange": "okx"
        }
        
        response = self.session.get(endpoint, params=params)
        
        if response.status_code == 200:
            return response.json()
        elif response.status_code == 404:
            raise ValueError(f"No data available for timestamp {timestamp}")
        elif response.status_code == 429:
            raise RateLimitError("Rate limit exceeded, retry after backoff")
        else:
            raise Exception(f"API error {response.status_code}: {response.text}")
            
    def get_order_book_range(
        self,
        symbol: str,
        start_time: datetime,
        end_time: datetime,
        interval_minutes: int = 1,
        depth: int = 20
    ) -> pd.DataFrame:
        """
        Retrieve historical order books over a time range for backtesting.
        
        Args:
            symbol: OKX instrument
            start_time: Start of retrieval period
            end_time: End of retrieval period
            interval_minutes: Minutes between snapshots (1, 5, 15, 60 supported)
            depth: Levels per snapshot
            
        Returns:
            DataFrame with multi-index columns (bid/ask levels)
        """
        endpoint = f"{self.base_url}/historical/okx/orderbook/range"
        
        payload = {
            "symbol": symbol,
            "start_time": int(start_time.timestamp() * 1000),
            "end_time": int(end_time.timestamp() * 1000),
            "interval_minutes": interval_minutes,
            "depth": depth
        }
        
        response = self.session.post(endpoint, json=payload)
        
        if response.status_code != 200:
            raise Exception(f"Batch retrieval failed: {response.text}")
            
        data = response.json()
        return self._format_batch_response(data, depth)
        
    def _format_batch_response(self, data: dict, depth: int) -> pd.DataFrame:
        """Format raw API response into pandas DataFrame."""
        records = []
        
        for snapshot in data.get('snapshots', []):
            timestamp = pd.to_datetime(snapshot['timestamp'], unit='ms')
            
            record = {'timestamp': timestamp}
            
            # Flatten bid levels
            for i, (price, qty) in enumerate(snapshot.get('bids', [])[:depth]):
                record[f'bid_{i}_price'] = float(price)
                record[f'bid_{i}_qty'] = float(qty)
                
            # Flatten ask levels
            for i, (price, qty) in enumerate(snapshot.get('asks', [])[:depth]):
                record[f'ask_{i}_price'] = float(price)
                record[f'ask_{i}_qty'] = float(qty)
                
            records.append(record)
            
        df = pd.DataFrame(records)
        df.set_index('timestamp', inplace=True)
        return df
        
    def get_recent_ticker(self, symbol: str) -> Dict:
        """Get latest market ticker data."""
        endpoint = f"{self.base_url}/market/okx/ticker"
        
        params = {
            "symbol": symbol,
            "exchange": "okx"
        }
        
        response = self.session.get(endpoint, params=params)
        
        if response.status_code == 200:
            return response.json()
        raise Exception(f"Ticker fetch failed: {response.status_code}")


Example usage for backtesting

def run_backtest_example():
    """Demonstrate historical data retrieval for strategy backtesting."""
    api_key = "YOUR_HOLYSHEEP_API_KEY"
    client = OKXHistoricalData(api_key)
    
    # Fetch 24 hours of 5-minute order book snapshots for BTC/USDT perp
    end_time = datetime.now()
    start_time = end_time - timedelta(days=1)
    
    print("Fetching historical order book data from HolySheep...")
    print(f"Period: {start_time} to {end_time}")
    
    df = client.get_order_book_range(
        symbol="BTC-USDT-SWAP",
        start_time=start_time,
        end_time=end_time,
        interval_minutes=5,
        depth=20
    )
    
    print(f"\nRetrieved {len(df)} snapshots")
    print(f"Columns: {len(df.columns)}")
    print("\nSample data (first 5 rows):")
    print(df.head())
    
    # Calculate mid price for each snapshot
    df['mid_price'] = (df['bid_0_price'] + df['ask_0_price']) / 2
    
    # Calculate spread
    df['spread_bps'] = (
        (df['ask_0_price'] - df['bid_0_price']) / df['mid_price'] * 10000
    )
    
    # Calculate order book imbalance
    df['bid_imbalance'] = (
        df['bid_0_qty'] / (df['bid_0_qty'] + df['ask_0_qty'])
    )
    
    print("\nCalculated features summary:")
    print(df[['mid_price', 'spread_bps', 'bid_imbalance']].describe())
    
    # Save to parquet for fast backtesting
    output_file = f"okx_orderbook_{start_time.date()}_{end_time.date()}.parquet"
    df.to_parquet(output_file, index=True)
    print(f"\nSaved to {output_file}")
    
    return df

if __name__ == "__main__":
    run_backtest_example()

Simple Mean Reversion Backtest Implementation

With historical order book data loaded, here's a simplified mean reversion strategy backtest using the order book imbalance signal:

# okx_backtest_engine.py
import pandas as pd
import numpy as np
from dataclasses import dataclass
from typing import List, Tuple

@dataclass
class Trade:
    timestamp: pd.Timestamp
    side: str  # 'long' or 'short'
    entry_price: float
    quantity: float
    exit_price: float = None
    pnl: float = None
    
    def close(self, exit_price: float, timestamp: pd.Timestamp):
        self.exit_price = exit_price
        self.timestamp = timestamp
        multiplier = 1 if self.side == 'long' else -1
        self.pnl = (exit_price - self.entry_price) * self.quantity * multiplier

class MeanReversionBacktester:
    """
    Backtest mean reversion strategy using order book imbalance.
    
    Strategy logic:
    - BUY when bid_imbalance < 0.3 (selling pressure exhausted)
    - SELL when bid_imbalance > 0.7 (buying pressure exhausted)
    - Exit on mid-price mean reversion or time-based stop
    """
    
    def __init__(
        self,
        data: pd.DataFrame,
        entry_threshold: float = 0.3,
        exit_threshold: float = 0.5,
        stop_loss_bps: float = 50.0,
        take_profit_bps: float = 100.0,
        max_hold_minutes: int = 60
    ):
        self.data = data.copy()
        self.entry_threshold = entry_threshold
        self.exit_threshold = exit_threshold
        self.stop_loss_bps = stop_loss_bps
        self.take_profit_bps = take_profit_bps
        self.max_hold_minutes = max_hold_minutes
        self.trades: List[Trade] = []
        self.current_position = None
        
    def run(self) -> dict:
        """Execute backtest and return performance metrics."""
        for idx in range(len(self.data) - 1):
            row = self.data.iloc[idx]
            next_row = self.data.iloc[idx + 1]
            
            if self.current_position is None:
                # Check entry signals
                if row['bid_imbalance'] < self.entry_threshold:
                    # BUY signal
                    self.current_position = Trade(
                        timestamp=row.name,
                        side='long',
                        entry_price=row['mid_price'],
                        quantity=1.0
                    )
                    print(f"[{row.name}] LONG ENTRY @ ${row['mid_price']:.2f} "
                          f"(imbalance: {row['bid_imbalance']:.3f})")
                          
                elif row['bid_imbalance'] > (1 - self.entry_threshold):
                    # SELL signal
                    self.current_position = Trade(
                        timestamp=row.name,
                        side='short',
                        entry_price=row['mid_price'],
                        quantity=1.0
                    )
                    print(f"[{row.name}] SHORT ENTRY @ ${row['mid_price']:.2f} "
                          f"(imbalance: {row['bid_imbalance']:.3f})")
            else:
                # Check exit conditions
                entry_price = self.current_position.entry_price
                current_price = row['mid_price']
                
                if self.current_position.side == 'long':
                    pnl_bps = (current_price - entry_price) / entry_price * 10000
                else:
                    pnl_bps = (entry_price - current_price) / entry_price * 10000
                
                # Time-based exit
                hold_minutes = (row.name - self.current_position.timestamp).total_seconds() / 60
                
                # Check exit conditions
                should_exit = False
                reason = ""
                
                if row['bid_imbalance'] >= self.exit_threshold:
                    should_exit = True
                    reason = "imbalance mean reversion"
                elif pnl_bps >= self.take_profit_bps:
                    should_exit = True
                    reason = "take profit"
                elif pnl_bps <= -self.stop_loss_bps:
                    should_exit = True
                    reason = "stop loss"
                elif hold_minutes >= self.max_hold_minutes:
                    should_exit = True
                    reason = "time exit"
                    
                if should_exit:
                    self.current_position.close(current_price, row.name)
                    self.trades.append(self.current_position)
                    print(f"[{row.name}] EXIT @ ${current_price:.2f} "
                          f"({reason}) PnL: {pnl_bps:.1f} bps")
                    self.current_position = None
                    
        # Close any open position at end
        if self.current_position:
            final_price = self.data.iloc[-1]['mid_price']
            self.current_position.close(final_price, self.data.index[-1])
            self.trades.append(self.current_position)
            self.current_position = None
            
        return self.calculate_metrics()
        
    def calculate_metrics(self) -> dict:
        """Calculate performance metrics from closed trades."""
        if not self.trades:
            return {"error": "No trades executed"}
            
        closed_trades = [t for t in self.trades if t.pnl is not None]
        
        pnls = [t.pnl for t in closed_trades]
        entry_prices = [t.entry_price for t in closed_trades]
        
        # Calculate returns in bps
        returns_bps = []
        for pnl, entry in zip(pnls, entry_prices):
            returns_bps.append((pnl / entry) * 10000)
            
        winning_trades = [r for r in returns_bps if r > 0]
        losing_trades = [r for r in returns_bps if r <= 0]
        
        return {
            "total_trades": len(closed_trades),
            "winning_trades": len(winning_trades),
            "losing_trades": len(losing_trades),
            "win_rate": len(winning_trades) / len(closed_trades) if closed_trades else 0,
            "avg_return_bps": np.mean(returns_bps),
            "total_return_bps": sum(returns_bps),
            "max_drawdown_bps": min(returns_bps) if returns_bps else 0,
            "best_trade_bps": max(returns_bps) if returns_bps else 0,
            "worst_trade_bps": min(returns_bps) if returns_bps else 0,
            "sharpe_ratio": (
                np.mean(returns_bps) / np.std(returns_bps) 
                if len(returns_bps) > 1 and np.std(returns_bps) > 0 
                else 0
            )
        }

def run_strategy_backtest():
    """Load data and run mean reversion backtest."""
    # Load data from previous historical data fetch
    df = pd.read_parquet("okx_orderbook_*.parquet")
    
    # Ensure required columns exist
    required_cols = ['bid_imbalance', 'mid_price']
    if not all(col in df.columns for col in required_cols):
        # Recalculate if loaded from different source
        df['mid_price'] = (df['bid_0_price'] + df['ask_0_price']) / 2
        df['bid_imbalance'] = df['bid_0_qty'] / (df['bid_0_qty'] + df['ask_0_qty'])
    
    # Initialize backtester with parameters
    backtester = MeanReversionBacktester(
        data=df,
        entry_threshold=0.25,  # More aggressive entry
        exit_threshold=0.5,
        stop_loss_bps=75.0,
        take_profit_bps=150.0,
        max_hold_minutes=45
    )
    
    print("Running mean reversion backtest on OKX BTC/USDT perpetual...")
    print(f"Data period: {df.index[0]} to {df.index[-1]}")
    print(f"Total snapshots: {len(df)}\n")
    
    results = backtester.run()
    
    print("\n" + "="*50)
    print("BACKTEST RESULTS")
    print("="*50)
    print(f"Total Trades: {results['total_trades']}")
    print(f"Win Rate: {results['win_rate']*100:.1f}%")
    print(f"Average Return: {results['avg_return_bps']:.2f} bps")
    print(f"Total Return: {results['total_return_bps']:.2f} bps")
    print(f"Best Trade: {results['best_trade_bps']:.2f} bps")
    print(f"Worst Trade: {results['worst_trade_bps']:.2f} bps")
    print(f"Max Drawdown: {results['max_drawdown_bps']:.2f} bps")
    print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")
    
    return results

if __name__ == "__main__":
    run_strategy_backtest()

Common Errors and Fixes

Error 1: "401 Unauthorized - Invalid API Key"

Symptom: Receiving 401 errors when connecting to HolySheep endpoints despite having a valid account.

# INCORRECT - Missing header or wrong key format
headers = {
    "Authorization": f"Bearer {api_key}"  # Wrong format for HolySheep
}


CORRECT - HolySheep uses X-API-Key header

headers = {
    "X-API-Key": api_key  # Direct key in header
}


Verify key format matches your dashboard


HolySheep keys are alphanumeric, 32+ characters

print(f"Key length: {len(api_key)}")  # Should be 32+

Error 2: "Rate limit exceeded (429)" During Batch Historical Retrieval

Symptom: 429 errors when fetching large historical datasets, especially during backtesting initialization.

# INCORRECT - Sending rapid sequential requests
for timestamp in timestamps:
    data = client.get_order_book_snapshot(symbol, timestamp)  # Too fast!


CORRECT - Implement exponential backoff

import time
from requests.adapters import HTTPAdapter
from requests.packages.urllib3.util.retry import Retry

def create_resilient_session():
    session = requests.Session()
    
    retry_strategy = Retry(
        total=5,
        backoff_factor=2,  # 2s, 4s, 8s, 16s, 32s backoff
        status_forcelist=[429, 500, 502, 503, 504],
        allowed_methods=["GET", "POST"]
    )
    
    adapter = HTTPAdapter(max_retries=retry_strategy)
    session.mount("https://", adapter)
    session.mount("http://", adapter)
    
    return session


Use resilient session for batch operations

session = create_resilient_session()
for timestamp in timestamps:
    try:
        data = get_snapshot_with_session(session, timestamp)
    except Exception as e:
        print(f"Retrying after error: {e}")
        time.sleep(60)  # Manual additional backoff

Error 3: WebSocket Disconnection with "Connection Reset by Peer"

Symptom: WebSocket drops connection periodically with "Connection reset by peer" errors, causing data gaps in live trading.

# INCORRECT - No reconnection logic
async def run_stream():
    await ws.connect()
    async for msg in ws:
        process(msg)  # Crashes on disconnect!


CORRECT - Implement robust reconnection

class ResilientWebSocket:
    def __init__(self, url, headers, max_retries=10):
        self.url = url
        self.headers = headers
        self.max_retries = max_retries
        self.ws = None
        
    async def connect(self):
        for attempt in range(self.max_retries):
            try:
                self.ws = await websockets.connect(
                    self.url,
                    extra_headers=self.headers,
                    ping_interval=20,  # Keepalive
                    ping_timeout=10
                )
                print(f"Connected on attempt {attempt + 1}")
                return True
            except Exception as e:
                wait_time = min(2 ** attempt * 0.5, 30)  # Cap at 30s
                print(f"Connection failed: {e}")
                print(f"Retrying in {wait_time}s (attempt {attempt + 1}/{self.max_retries})")
                await asyncio.sleep(wait_time)
        raise ConnectionError("Max retries exceeded")
        
    async def receive_loop(self, callback):
        while True:
            try:
                async for message in self.ws:
                    callback(message)
            except websockets.exceptions.ConnectionClosed:
                print("Connection closed, reconnecting...")
                await asyncio.sleep(1)
                await self.connect()
            except Exception as e:
                print(f"Unexpected error: {e}")
                await asyncio.sleep(5)
                await self.connect()

Error 4: Order Book Data Mismatch During Backtesting

Symptom:

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