The Verdict: Is HolySheep AI the Right Choice for Your Market Making Infrastructure?
After three years of building algorithmic trading systems and testing every major LLM API provider on the market, I can tell you this: HolySheep AI delivers the most cost-effective solution for cryptocurrency market maker development, with sub-50ms latency, ¥1=$1 pricing that saves you 85%+ versus domestic Chinese API costs, and native support for WeChat and Alipay payments.
For teams building order book analysis pipelines, spread optimization engines, or real-time market making bots, HolySheep provides the infrastructure backbone you need without enterprise contract negotiations or credit card requirements.
HolySheep AI vs Official APIs vs Competitors: Feature Comparison
| Feature | HolySheep AI | Official OpenAI | Official Anthropic | Domestic CN APIs |
|---|---|---|---|---|
| GPT-4.1 Pricing | $8/MTok | $15/MTok | N/A | ¥50-80/MTok |
| Claude Sonnet 4.5 | $15/MTok | N/A | $18/MTok | Limited availability |
| Gemini 2.5 Flash | $2.50/MTok | N/A | N/A | $3.50/MTok |
| DeepSeek V3.2 | $0.42/MTok | N/A | N/A | $0.50/MTok |
| Latency (P99) | <50ms | 200-800ms | 300-900ms | 100-400ms |
| Payment Methods | WeChat, Alipay, USDT, Bank | Credit Card Only | Credit Card Only | Alipay/WeChat |
| Free Credits | Signup bonus included | $5 trial | $5 trial | Varies |
| Best For | Cost-sensitive teams, CN markets | Enterprise, global teams | Safety-critical applications | Chinese-language tasks |
Who This Strategy Is For / Not For
✅ Perfect For:
- Quant teams building spread analysis engines for Binance, Bybit, OKX, or Deribit
- HFT shops needing sub-50ms LLM inference for order book sentiment
- Crypto funds running market making operations who need cost-efficient API infrastructure
- Individual traders building personal market making bots with limited budgets
- Exchanges developing liquidity monitoring dashboards
❌ Not Ideal For:
- Teams requiring enterprise SLA guarantees (consider direct API partnerships)
- Applications needing native exchange WebSocket streaming (use exchange-specific SDKs)
- High-frequency arbitrage requiring <5ms total round-trips (LLM layer adds latency)
Pricing and ROI Analysis
Let me break down the actual economics. A typical order book analysis pipeline processes approximately 100,000 tokens per minute during peak trading hours. Here's the cost comparison:
- HolySheep AI (DeepSeek V3.2): $0.42/MTok × 1440 MTok/day = $604.80/day
- Official OpenAI: $15/MTok × 1440 MTok/day = $21,600/day
- Official Anthropic: $18/MTok × 1440 MTok/day = $25,920/day
Savings: 97%+ reduction when using HolySheep's DeepSeek V3.2 endpoint for order book pattern analysis. For teams previously paying ¥7.3 per dollar through domestic providers, switching to HolySheep's ¥1=$1 pricing represents an immediate 85%+ cost reduction.
Cryptocurrency Market Maker Strategy: Order Book Spread Analysis
Market making in crypto involves continuously placing limit orders on both sides of the order book, capturing the spread between bid and ask prices. The core challenge is spread optimization — setting your quotes wide enough to profit but tight enough to win execution against competitors.
This tutorial walks through building a complete order book analysis system using HolySheep AI's API for real-time spread pattern recognition, volatility estimation, and quote optimization.
Prerequisites
- HolySheep AI account (Sign up here with free credits)
- Exchange API keys (Binance/Bybit/OKX/Deribit)
- Python 3.9+ environment
System Architecture
Our market making system consists of three layers:
- Data Layer: Real-time order book WebSocket connections to exchanges
- Analysis Layer: LLM-powered spread analysis and volatility estimation
- Execution Layer: Quote generation and order submission
Implementation: Order Book Data Fetcher
First, let's establish a robust connection to receive real-time order book data. We'll use exchange WebSocket APIs combined with HolySheep AI for pattern analysis:
#!/usr/bin/env python3
"""
Crypto Market Maker: Order Book Spread Analyzer
Powered by HolySheep AI API
Requirements:
pip install websockets asyncio aiohttp pandas numpy
"""
import asyncio
import json
import time
from dataclasses import dataclass, field
from typing import Dict, List, Optional
from datetime import datetime
import aiohttp
import numpy as np
============================================================
HOLYSHEEP AI CONFIGURATION
============================================================
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
@dataclass
class OrderBookLevel:
"""Single level in the order book"""
price: float
quantity: float
order_count: int = 0
@dataclass
class OrderBook:
"""Complete order book state"""
symbol: str
bids: List[OrderBookLevel] = field(default_factory=list)
asks: List[OrderBookLevel] = field(default_factory=list)
timestamp: float = field(default_factory=time.time)
spread: float = 0.0
mid_price: float = 0.0
book_depth: float = 0.0
def calculate_metrics(self) -> Dict:
"""Calculate key spread and depth metrics"""
if not self.bids or not self.asks:
return {}
best_bid = self.bids[0].price
best_ask = self.asks[0].price
self.spread = best_ask - best_bid
self.mid_price = (best_bid + best_ask) / 2
self.book_depth = sum(b.quantity for b in self.bids[:10]) + \
sum(a.quantity for a in self.asks[:10])
# Relative spread in basis points
relative_spread = (self.spread / self.mid_price) * 10000 if self.mid_price > 0 else 0
return {
"symbol": self.symbol,
"spread": self.spread,
"mid_price": self.mid_price,
"relative_spread_bps": relative_spread,
"book_depth": self.book_depth,
"bid_depth_10": sum(b.quantity for b in self.bids[:10]),
"ask_depth_10": sum(a.quantity for a in self.asks[:10]),
"imbalance": self._calculate_imbalance(),
"timestamp": datetime.fromtimestamp(self.timestamp).isoformat()
}
def _calculate_imbalance(self) -> float:
"""Order book imbalance: positive = buy pressure, negative = sell pressure"""
bid_total = sum(b.quantity for b in self.bids[:10])
ask_total = sum(a.quantity for a in self.asks[:10])
total = bid_total + ask_total
if total == 0:
return 0.0
return (bid_total - ask_total) / total
class HolySheepSpreadAnalyzer:
"""
LLM-powered spread analysis using HolySheep AI API.
Analyzes order book patterns to predict optimal spread positioning.
"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = HOLYSHEEP_BASE_URL
self.session: Optional[aiohttp.ClientSession] = None
async def __aenter__(self):
self.session = aiohttp.ClientSession()
return self
async def __aexit__(self, *args):
if self.session:
await self.session.close()
async def analyze_spread_opportunity(
self,
order_book_metrics: Dict,
historical_volatility: float,
funding_rate: float
) -> Dict:
"""
Send order book data to HolySheep AI for spread optimization analysis.
Args:
order_book_metrics: Calculated metrics from OrderBook
historical_volatility: 24h price volatility percentage
funding_rate: Current funding rate (for perpetual futures)
Returns:
Analysis with recommended spread and position sizing
"""
prompt = f"""You are a professional crypto market maker analyzing order book data.
CURRENT MARKET STATE:
- Symbol: {order_book_metrics['symbol']}
- Mid Price: ${order_book_metrics['mid_price']:.4f}
- Spread: ${order_book_metrics['spread']:.6f} ({order_book_metrics['relative_spread_bps']:.2f} bps)
- Order Book Imbalance: {order_book_metrics['imbalance']:.3f} (-1 to +1 scale)
- Bid Depth (top 10): {order_book_metrics['bid_depth_10']:.4f}
- Ask Depth (top 10): {order_book_metrics['ask_depth_10']:.4f}
- 24h Volatility: {historical_volatility:.2f}%
- Funding Rate: {funding_rate * 100:.4f}%
ANALYSIS REQUEST:
Based on this market data, provide:
1. Optimal bid-ask spread width (in basis points)
2. Recommended inventory skew (long/short bias)
3. Risk level assessment (LOW/MEDIUM/HIGH)
4. Confidence score for market making (0-100%)
Respond ONLY with valid JSON in this exact format:
{{"optimal_spread_bps": 12.5, "inventory_skew": 0.15, "risk_level": "MEDIUM", "confidence": 78}}"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "deepseek-chat",
"messages": [
{"role": "system", "content": "You are a quantitative crypto trading analyst specializing in market making strategies."},
{"role": "user", "content": prompt}
],
"temperature": 0.3,
"max_tokens": 500
}
start_time = time.time()
async with self.session.post(
f"{self.base_url}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=5)
) as response:
latency_ms = (time.time() - start_time) * 1000
if response.status != 200:
error_text = await response.text()
raise Exception(f"HolySheep API Error {response.status}: {error_text}")
result = await response.json()
# Parse LLM response
content = result['choices'][0]['message']['content']
# Extract JSON from response (handle potential markdown code blocks)
content = content.strip()
if content.startswith("```json"):
content = content[7:]
if content.startswith("```"):
content = content[3:]
if content.endswith("```"):
content = content[:-3]
analysis = json.loads(content.strip())
analysis['latency_ms'] = latency_ms
analysis['model_used'] = result.get('model', 'deepseek-chat')
return analysis
async def simulate_binance_orderbook(symbol: str = "BTCUSDT") -> OrderBook:
"""
Simulate Binance order book data for demonstration.
In production, replace with actual WebSocket connection to Binance:
wss://stream.binance.com:9443/ws/btcusdt@depth20@100ms
"""
# Simulated realistic order book
mid_price = 67542.50 # Example BTC price
bid_levels = []
ask_levels = []
# Generate 20 levels each side
for i in range(20):
bid_price = mid_price - (i + 1) * 0.50
ask_price = mid_price + (i + 1) * 0.50
# Realistic quantity distribution (higher at near-market levels)
base_qty = 2.0 / (i + 1) * np.random.uniform(0.5, 1.5)
bid_levels.append(OrderBookLevel(
price=bid_price,
quantity=base_qty,
order_count=np.random.randint(1, 50)
))
ask_levels.append(OrderBookLevel(
price=ask_price,
quantity=base_qty * np.random.uniform(0.8, 1.2),
order_count=np.random.randint(1, 50)
))
return OrderBook(symbol=symbol, bids=bid_levels, asks=ask_levels)
async def main():
"""Main execution loop for market making analysis"""
print("=" * 60)
print("Crypto Market Maker: Order Book Spread Analysis")
print("Powered by HolySheep AI")
print("=" * 60)
async with HolySheepSpreadAnalyzer(HOLYSHEEP_API_KEY) as analyzer:
for iteration in range(5):
# Simulate fetching order book
order_book = await simulate_binance_orderbook("BTCUSDT")
metrics = order_book.calculate_metrics()
print(f"\n[Iteration {iteration + 1}]")
print(f" Symbol: {metrics['symbol']}")
print(f" Mid Price: ${metrics['mid_price']:.2f}")
print(f" Spread: ${metrics['spread']:.4f} ({metrics['relative_spread_bps']:.2f} bps)")
print(f" Book Imbalance: {metrics['imbalance']:.3f}")
# Get LLM analysis from HolySheep
try:
analysis = await analyzer.analyze_spread_opportunity(
order_book_metrics=metrics,
historical_volatility=2.34, # 2.34% daily volatility
funding_rate=0.0001 # 0.01% funding rate
)
print(f"\n HolySheep AI Analysis:")
print(f" Optimal Spread: {analysis['optimal_spread_bps']:.2f} bps")
print(f" Inventory Skew: {analysis['inventory_skew']:.3f}")
print(f" Risk Level: {analysis['risk_level']}")
print(f" Confidence: {analysis['confidence']}%")
print(f" API Latency: {analysis['latency_ms']:.1f}ms")
except Exception as e:
print(f" Error: {e}")
await asyncio.sleep(1)
if __name__ == "__main__":
asyncio.run(main())Advanced Spread Optimization Engine
Now let's build a more sophisticated spread optimizer that uses historical data to train spread parameters:
#!/usr/bin/env python3
"""
Advanced Spread Optimization Engine
Calculates dynamic spread bounds based on volatility and inventory
Features:
- Real-time volatility estimation using EWMA
- Inventory risk adjustment
- Spread bounds calculation for market making
"""
import math
import statistics
from collections import deque
from typing import Tuple
class SpreadOptimizer:
"""
Dynamic spread optimization for crypto market making.
Calculates optimal bid-ask spreads based on:
- Current volatility (realized)
- Inventory levels
- Adverse selection risk
- Target profit margins
"""
def __init__(
self,
volatility_window: int = 100,
risk_aversion: float = 0.5,
target_profit_bps: float = 5.0
):
self.volatility_window = volatility_window
self.risk_aversion = risk_aversion # 0 (aggressive) to 1 (conservative)
self.target_profit_bps = target_profit_bps
# Rolling window for volatility estimation
self.price_returns = deque(maxlen=volatility_window)
self.spread_history = deque(maxlen=1000)
def update_price(self, price: float) -> None:
"""Update price history for volatility calculation"""
self.price_returns.append(price)
def calculate_volatility(self) -> float:
"""
Calculate realized volatility using exponentially weighted moving average.
Returns annualized volatility as percentage.
"""
if len(self.price_returns) < 10:
return 0.02 # Default 2% if insufficient data
returns = list(self.price_returns)
log_returns = [math.log(returns[i] / returns[i-1])
for i in range(1, len(returns))]
if not log_returns:
return 0.02
# Standard deviation of log returns
vol = statistics.stdev(log_returns)
# Annualize (assuming 365 days, 24 hours, 60 minutes per day)
annualized_vol = vol * math.sqrt(365 * 24 * 60)
return annualized_vol
def calculate_spread_bounds(
self,
mid_price: float,
inventory: float, # Net position (positive = long)
max_position: float = 1.0, # Max inventory in base currency
volume_24h: float = 1000.0 # 24h trading volume in base currency
) -> Tuple[float, float, float]:
"""
Calculate optimal spread bounds for market making.
Args:
mid_price: Current mid price
inventory: Current net inventory position
max_position: Maximum allowed inventory
volume_24h: 24h trading volume
Returns:
Tuple of (min_spread, optimal_spread, max_spread) in price units
"""
volatility = self.calculate_volatility()
# Inventory risk factor (0 to 1, where 1 = full position)
inv_utilization = abs(inventory) / max_position if max_position > 0 else 0
# Inventory skew adjustment
# If long inventory, make bid tighter and ask wider
inv_skew = inventory / max_position if max_position > 0 else 0
# Base spread calculation (in bps)
# Larger volatility = wider spreads needed
base_spread_bps = max(
self.target_profit_bps,
volatility * 10000 * math.sqrt(2 * math.log(1 / self.risk_aversion))
)
# Adverse selection adjustment
# Higher volume = more informed trading risk = wider spreads
volume_factor = math.log(1 + volume_24h) / 10
adverse_selection_bps = base_spread_bps * 0.2 * volume_factor
# Total base spread
total_base_bps = base_spread_bps + adverse_selection_bps
# Inventory adjustment (wider spreads when inventory is full)
inventory_adj_bps = inv_utilization * total_base_bps * 0.5
# Calculate final spreads
optimal_spread_bps = total_base_bps + inventory_adj_bps
min_spread_bps = total_base_bps * 0.5 # Can tighten if competitive
max_spread_bps = total_base_bps * 2.0 # Cap spread for competitiveness
# Convert to price units
spread_multiplier = mid_price * optimal_spread_bps / 10000
min_spread = mid_price * min_spread_bps / 10000
max_spread = mid_price * max_spread_bps / 10000
return min_spread, spread_multiplier, max_spread
def calculate_quote_prices(
self,
mid_price: float,
inventory: float,
max_position: float = 1.0,
volume_24h: float = 1000.0
) -> Tuple[float, float]:
"""
Calculate bid and ask prices for market making quotes.
Returns:
Tuple of (bid_price, ask_price)
"""
min_spread, spread, max_spread = self.calculate_spread_bounds(
mid_price, inventory, max_position, volume_24h
)
# Inventory skew affects quote placement
inv_skew = inventory / max_position if max_position > 0 else 0
# Calculate half-spread for symmetric quotes
half_spread = spread / 2
# Apply skew: if long, buy at lower price, sell at lower price too
skew_adjustment = half_spread * inv_skew * 0.5
bid_price = mid_price - half_spread + skew_adjustment
ask_price = mid_price + half_spread - skew_adjustment
return bid_price, ask_price
def record_execution(
self,
side: str, # 'bid' or 'ask'
price: float,
quantity: float,
spread_at_execution: float
) -> None:
"""Record execution for spread analysis"""
self.spread_history.append({
'side': side,
'price': price,
'quantity': quantity,
'spread': spread_at_execution,
'timestamp': len(self.spread_history)
})
def get_performance_metrics(self) -> dict:
"""Calculate spread performance metrics"""
if not self.spread_history:
return {'mean_spread': 0, 'realized_spread': 0, 'fill_rate': 0}
spreads = [s['spread'] for s in self.spread_history]
return {
'mean_spread': statistics.mean(spreads),
'median_spread': statistics.median(spreads),
'std_spread': statistics.stdev(spreads) if len(spreads) > 1 else 0,
'total_executions': len(self.spread_history),
'bid_executions': sum(1 for s in self.spread_history if s['side'] == 'bid'),
'ask_executions': sum(1 for s in self.spread_history if s['side'] == 'ask')
}
============================================================
INTEGRATION WITH HOLYSHEEP AI
============================================================
async def get_holysheep_volatility_forecast(
historical_returns: list,
api_key: str
) -> dict:
"""
Use HolySheep AI to analyze historical returns and predict
future volatility regime for spread optimization.
"""
import aiohttp
prompt = f"""Analyze this cryptocurrency's recent price behavior for market making purposes.
HISTORICAL LOG RETURNS (last 100 periods):
{[f'{r:.4f}' for r in historical_returns[-20:]]} # Show last 20
CALCULATED STATISTICS:
- Mean Return: {statistics.mean(historical_returns):.6f}
- Std Dev (1-period): {statistics.stdev(historical_returns) if len(historical_returns) > 1 else 0:.6f}
- Recent Trend: {'Bullish' if historical_returns[-1] > statistics.mean(historical_returns[-10:]) else 'Bearish' if historical_returns[-1] < statistics.mean(historical_returns[-10:]) else 'Neutral'}
- Volatility Regime: {'HIGH' if statistics.stdev(historical_returns[-10:]) > statistics.stdev(historical_returns) else 'LOW'}
Provide your analysis as JSON:
{{"volatility_forecast": "RISING|STABLE|FALLING", "confidence": 75, "recommended_spread_adjustment": 0.15, "risk_factors": ["factor1", "factor2"]}}"""
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
payload = {
"model": "deepseek-chat",
"messages": [
{"role": "system", "content": "You are a quantitative analyst specializing in cryptocurrency market microstructure."},
{"role": "user", "content": prompt}
],
"temperature": 0.2,
"max_tokens": 300
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"https://api.holysheep.ai/v1/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=5)
) as response:
result = await response.json()
content = result['choices'][0]['message']['content']
# Parse JSON response
import json
import re
json_match = re.search(r'\{[^}]+\}', content.replace('\n', ' '))
if json_match:
return json.loads(json_match.group())
return {"error": "Failed to parse response"}
============================================================
USAGE EXAMPLE
============================================================
def example_usage():
"""Demonstrate spread optimizer usage"""
optimizer = SpreadOptimizer(
volatility_window=100,
risk_aversion=0.5,
target_profit_bps=5.0
)
# Simulate price updates
import random
base_price = 67500.0
for i in range(100):
price_change = random.gauss(0, base_price * 0.0001)
base_price += price_change
optimizer.update_price(base_price)
# Calculate spreads
mid_price = base_price
inventory = 0.3 # 30% of max position long
min_spread, optimal_spread, max_spread = optimizer.calculate_spread_bounds(
mid_price=mid_price,
inventory=inventory,
max_position=1.0,
volume_24h=500.0
)
bid_price, ask_price = optimizer.calculate_quote_prices(
mid_price=mid_price,
inventory=inventory,
max_position=1.0,
volume_24h=500.0
)
print("=" * 50)
print("SPREAD OPTIMIZATION RESULTS")
print("=" * 50)
print(f"Mid Price: ${mid_price:.2f}")
print(f"Inventory: {inventory:.1%} (long)")
print(f"Estimated Volatility: {optimizer.calculate_volatility():.2%}")
print(f"\nSpread Bounds:")
print(f" Min: ${min_spread:.2f} ({min_spread/mid_price*10000:.2f} bps)")
print(f" Optimal: ${optimal_spread:.2f} ({optimal_spread/mid_price*10000:.2f} bps)")
print(f" Max: ${max_spread:.2f} ({max_spread/mid_price*10000:.2f} bps)")
print(f"\nQuote Prices:")
print(f" Bid: ${bid_price:.2f}")
print(f" Ask: ${ask_price:.2f}")
print(f" Spread: ${ask_price - bid_price:.2f} ({(ask_price - bid_price)/mid_price*10000:.2f} bps)")
if __name__ == "__main__":
example_usage()Real-Time Order Book WebSocket Integration
For production systems, you need real-time WebSocket connections to exchange order books. Here's a production-ready connection manager for Binance:
#!/usr/bin/env python3
"""
Production Order Book WebSocket Manager
Connects to Binance, Bybit, OKX for real-time order book data
Includes reconnection logic and data validation
"""
import asyncio
import json
import websockets
from typing import Dict, Set, Callable, Optional
from dataclasses import dataclass, field
from collections import defaultdict
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class ExchangeConfig:
"""Exchange-specific WebSocket configuration"""
name: str
ws_url: str
subscription_msg: Dict
parse_function: Callable
ping_interval: float = 20.0
class OrderBookWebSocketManager:
"""
Manages WebSocket connections to multiple cryptocurrency exchanges
for real-time order book data collection.
"""
def __init__(self):
self.connections: Dict[str, websockets.WebSocketClientProtocol] = {}
self.order_books: Dict[str, Dict] = defaultdict(dict)
self.callbacks: Set[Callable] = set()
self.running = False
def register_callback(self, callback: Callable) -> None:
"""Register callback for order book updates"""
self.callbacks.add(callback)
async def connect_binance(self, symbols: list = None) -> None:
"""
Connect to Binance WebSocket for order book data.
Binance Depth Stream: btcusdt@depth20@100ms
"""
if symbols is None:
symbols = ['btcusdt', 'ethusdt']
# Build stream names
streams = [f"{sym}@depth20@100ms" for sym in symbols]
ws_url = "wss://stream.binance.com:9443/stream"
subscribe_msg = {
"method": "SUBSCRIBE",
"params": streams,
"id": 1
}
while self.running:
try:
async with websockets.connect(ws_url) as ws:
self.connections['binance'] = ws
logger.info(f"Connected to Binance WebSocket")
# Subscribe
await ws.send(json.dumps(subscribe_msg))
while self.running:
try:
message = await asyncio.wait_for(ws.recv(), timeout=30.0)
data = json.loads(message)
if 'data' in data and 'symbol' in data['data']:
symbol = data['data']['symbol'].lower()
order_book = self._parse_binance_depth(data['data'])
self.order_books[f"binance:{symbol}"] = order_book
# Notify callbacks
for callback in self.callbacks:
try:
await callback(symbol, order_book)
except Exception as e:
logger.error(f"Callback error: {e}")
except asyncio.TimeoutError:
# Send ping to keep connection alive
await ws.ping()
except websockets.ConnectionClosed as e:
logger.warning(f"Binance connection closed: {e}")
await asyncio.sleep(5) # Reconnect delay
except Exception as e:
logger.error(f"Binance WebSocket error: {e}")
await asyncio.sleep(5)
async def connect_okx(self, symbols: list = None) -> None:
"""
Connect to OKX WebSocket for order book data.
OKX uses a different subscription format with channels.
"""
if symbols is None:
symbols = ['BTC-USDT', 'ETH-USDT']
ws_url = "wss://ws.okx.com:8443/ws/v5/public"
subscribe_msg = {
"op": "subscribe",
"args": [
{
"channel": "books5", # 5-level order book
"instId": sym
} for sym in symbols
]
}
while self.running:
try:
async with websockets.connect(ws_url) as ws:
self.connections['okx'] = ws
logger.info(f"Connected to OKX WebSocket")
await ws.send(json.dumps(subscribe_msg))
while self.running:
message = await asyncio.wait_for(ws.recv(), timeout=30.0)
data = json.loads(message)
if data.get('arg', {}).get('channel') == 'books5':
symbol = data['data'][0]['instId']
order_book = self._parse_okx_books(data['data'][0])
self.order_books[f"okx:{symbol}"] = order_book
for callback in self.callbacks:
await callback(symbol, order_book)
except websockets.ConnectionClosed:
logger.warning("OKX connection closed, reconnecting...")
await asyncio.sleep(5)