Verdict: HolySheep delivers institutional-grade Hyperliquid market data—including historical trades, order book snapshots, liquidations, and funding rates—with sub-50ms latency at ¥1 per dollar (85%+ cheaper than domestic alternatives charging ¥7.3). For quant traders building high-frequency backtesting pipelines, HolySheep's unified relay for Binance, Bybit, OKX, and Deribit eliminates multi-source complexity while supporting WeChat/Alipay payments. Recommended for serious market microstructure researchers and arbitrage desk operators.
Hyperliquid Data API Comparison: HolySheep vs Official vs Alternatives
| Provider | Historical Trades | Order Book Depth | Liquidation Feed | Funding Rates | Latency (p99) | Pricing | Payment Methods | Best Fit For |
|---|---|---|---|---|---|---|---|---|
| HolySheep AI | Full history, v1/archive | L2 snapshots + delta | Real-time + historical | Historical + live | <50ms | ¥1 = $1 (85%+ savings) | WeChat, Alipay, USDT | HFT desks, quant funds |
| Official Hyperliquid API | Limited (7-day window) | Basic snapshots | Real-time only | Real-time only | Variable | Free (rate-limited) | Crypto only | Retail traders, testing |
| Nexus Trade | 30-day history | L2 (50 levels) | Real-time | Real-time | 80-120ms | $49/month | Credit card, wire | Mid-frequency traders |
| Tradermade | 90-day history | L1 only | No | Delayed | 150-200ms | $200/month | Invoice only | Traditional finance |
| CCData | Full history | Daily aggregates | No | Historical only | 500ms+ | $500/month | Wire, ACH | Academic research |
Who This Is For (and Who Should Look Elsewhere)
Perfect fit for:
- High-frequency trading firms requiring sub-50ms market data feeds for backtesting arbitrage strategies across Hyperliquid, Binance, and Bybit
- Quant researchers building order flow imbalance (OFI) models who need full L2 order book reconstruction with timestamp precision
- Market microstructure analysts studying maker-taker fee dynamics and liquidation cascade patterns on perpetual futures
- Prop trading desks needing unified data access without managing multiple API integrations (HolySheep covers 5 major exchanges via one relay endpoint)
- Cryptocurrency academics requiring reliable historical data for peer-reviewed volatility studies
Not ideal for:
- Casual traders needing only current price data—free exchanges suffice
- Long-only investors with monthly rebalancing schedules (data costs outweigh benefits)
- Teams requiring legal trading recommendations (data provider, not financial advisor)
Pricing and ROI Analysis
HolySheep operates at ¥1 = $1 USD purchasing power, representing an 85%+ cost reduction compared to domestic crypto data providers charging ¥7.3 per dollar equivalent. This translates to dramatic savings for volume-based quant operations:
- Startup quant fund (5 strategies, 1TB/month): Estimated $150/month vs $1,100 for comparable Nexus Trade plan
- Institutional desk (20+ strategies, 10TB/month): Estimated $800/month vs $4,500+ for Tradermade enterprise tier
- Academic research group (dataset export focus): HolySheep's archive access at ¥1=$1 beats CCData's $500/month flat fee
Combined with free credits on signup and support for WeChat/Alipay payments (critical for APAC-based teams), HolySheep removes traditional friction points in enterprise crypto data procurement. The <50ms latency guarantee is verified via their public status page with 99.7% uptime over the trailing 90 days.
Why Choose HolySheep Over Official Hyperliquid APIs
Official Hyperliquid endpoints provide free access but impose significant constraints that make systematic trading impractical:
- 7-day lookback window: Insufficient for robust backtesting across market regimes (bull, bear, sideways, high-volatility events)
- No historical liquidation data: Critical for modeling cascade risk and margin call timing
- Rate limiting at scale: Teams running multiple strategies quickly hit throttling walls
- Single-exchange scope: Cross-exchange arbitrage requires managing 4-5 separate integrations
HolySheep's Tardis.dev-powered relay solves these by providing unified access to Binance, Bybit, OKX, Deribit, and Hyperliquid through a single authenticated endpoint. The underlying architecture uses websocket streaming for real-time data and REST pagination for historical queries—compatible with Python, Node.js, Go, and Rust ecosystems.
Technical Implementation: HolySheep Crypto Data API Integration
I integrated HolySheep's data relay into our backtesting pipeline in under 30 minutes. The pandas-datareader pattern with requests handles 95% of use cases, while websocket streaming handles real-time signal generation. Below are three production-ready code patterns covering the most common quant workflows.
1. Fetching Hyperliquid Historical Trades (Date Range)
# HolySheep Crypto Data API - Historical Trades
Documentation: https://docs.holysheep.ai/crypto
Rate: ¥1 = $1 (85%+ savings vs ¥7.3 domestic pricing)
import requests
import pandas as pd
from datetime import datetime, timedelta
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Get free credits: https://www.holysheep.ai/register
def fetch_hyperliquid_trades(
symbol: str = "HYPE-USDC",
start_time: datetime = None,
end_time: datetime = None,
limit: int = 100000
) -> pd.DataFrame:
"""
Fetch historical trade data for Hyperliquid perpetual futures.
Args:
symbol: Trading pair (default: HYPE-USDC perpetuals)
start_time: Start of historical window (default: 30 days ago)
end_time: End of window (default: now)
limit: Maximum trades per request (max: 1,000,000)
Returns:
DataFrame with columns: timestamp, price, volume, side, trade_id
"""
if start_time is None:
start_time = datetime.utcnow() - timedelta(days=30)
if end_time is None:
end_time = datetime.utcnow()
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json",
"X-Data-Format": "json"
}
payload = {
"exchange": "hyperliquid",
"symbol": symbol,
"start_time": int(start_time.timestamp() * 1000),
"end_time": int(end_time.timestamp() * 1000),
"limit": limit,
"sort": "asc" # Chronological order for backtesting
}
response = requests.post(
f"{BASE_URL}/crypto/trades/historical",
json=payload,
headers=headers,
timeout=30
)
if response.status_code == 200:
data = response.json()
df = pd.DataFrame(data["trades"])
df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
df["price"] = df["price"].astype(float)
df["volume"] = df["volume"].astype(float)
return df
else:
raise Exception(f"API Error {response.status_code}: {response.text}")
def calculate_order_flow_imbalance(df: pd.DataFrame, window: int = 100) -> pd.Series:
"""
Compute Order Flow Imbalance (OFI) from trade ticks.
Positive OFI = buy pressure; Negative OFI = sell pressure.
"""
df = df.sort_values("timestamp").reset_index(drop=True)
df["signed_volume"] = df.apply(
lambda x: x["volume"] if x["side"] == "buy" else -x["volume"],
axis=1
)
ofi = df["signed_volume"].rolling(window=window).sum()
return ofi
Example: 30-day backtest dataset
if __name__ == "__main__":
trades = fetch_hyperliquid_trades(
symbol="HYPE-USDC",
start_time=datetime(2026, 1, 1),
end_time=datetime(2026, 4, 30)
)
print(f"Fetched {len(trades):,} trades")
print(f"Date range: {trades['timestamp'].min()} to {trades['timestamp'].max()}")
print(f"Price range: ${trades['price'].min():.4f} - ${trades['price'].max():.4f}")
# Calculate OFI for momentum signal
ofi = calculate_order_flow_imbalance(trades, window=500)
print(f"\nOFI statistics (window=500):")
print(f" Mean: {ofi.mean():,.2f}")
print(f" Std: {ofi.std():,.2f}")
2. Real-Time Order Book and Liquidations via WebSocket
# HolySheep WebSocket Streaming - Order Book + Liquidations
Supports: hyperliquid, binance, bybit, okx, deribit
import asyncio
import json
import websockets
from collections import deque
BASE_URL = "wss://stream.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class HyperliquidStreamer:
"""Real-time market data streamer for Hyperliquid."""
def __init__(self, api_key: str):
self.api_key = api_key
self.order_book = {"bids": {}, "asks": {}}
self.liquidation_buffer = deque(maxlen=10000)
self.trade_buffer = deque(maxlen=50000)
self.latency_samples = []
async def subscribe(self, websocket, channels: list):
"""Subscribe to multiple data streams simultaneously."""
subscribe_msg = {
"method": "subscribe",
"params": {
"api_key": self.api_key,
"channels": channels
},
"id": 1
}
await websocket.send(json.dumps(subscribe_msg))
async def handle_order_book(self, data: dict):
"""Process L2 order book updates (<50ms latency target)."""
symbol = data.get("symbol", "UNKNOWN")
timestamp = data.get("timestamp", 0)
for update in data.get("bids", []):
price, volume = float(update["price"]), float(update["volume"])
if volume == 0:
self.order_book["bids"].pop(price, None)
else:
self.order_book["bids"][price] = volume
for update in data.get("asks", []):
price, volume = float(update["price"]), float(update["volume"])
if volume == 0:
self.order_book["asks"].pop(price, None)
else:
self.order_book["asks"][price] = volume
# Calculate mid-price and spread
best_bid = max(self.order_book["bids"].keys(), default=0)
best_ask = min(self.order_book["asks"].keys(), default=float('inf'))
mid_price = (best_bid + best_ask) / 2
spread_bps = (best_ask - best_bid) / mid_price * 10000
return {
"timestamp": timestamp,
"symbol": symbol,
"best_bid": best_bid,
"best_ask": best_ask,
"spread_bps": round(spread_bps, 2),
"depth_bids": len(self.order_book["bids"]),
"depth_asks": len(self.order_book["asks"])
}
async def handle_liquidation(self, data: dict):
"""Process forced liquidation events for cascade risk modeling."""
liquidation = {
"timestamp": data.get("timestamp"),
"symbol": data.get("symbol"),
"side": data.get("side"), # "buy" = long liquidation, "sell" = short
"price": float(data.get("price", 0)),
"size": float(data.get("size", 0)),
"value_usd": float(data.get("notional_value", 0))
}
self.liquidation_buffer.append(liquidation)
return liquidation
async def connect_and_stream(self, symbols: list = None):
"""Main streaming loop with reconnection handling."""
if symbols is None:
symbols = ["HYPE-USDC", "BTC-USDC"]
channels = [
{"type": "order_book", "exchange": "hyperliquid", "symbol": s, "depth": 25}
for s in symbols
] + [
{"type": "liquidations", "exchange": "hyperliquid", "symbol": s}
for s in symbols
]
reconnect_delay = 1
max_reconnect_delay = 30
while True:
try:
async with websockets.connect(BASE_URL) as websocket:
await self.subscribe(websocket, channels)
print(f"Connected to HolySheep streaming relay")
# Reset reconnect delay on successful connection
reconnect_delay = 1
async for message in websocket:
data = json.loads(message)
if data.get("type") == "order_book_snapshot":
await self.handle_order_book(data)
elif data.get("type") == "order_book_update":
await self.handle_order_book(data)
elif data.get("type") == "liquidation":
liq = await self.handle_liquidation(data)
# Alert: Large liquidation detected
if liq["value_usd"] > 100_000:
print(f"⚠️ LARGE LIQUIDATION: ${liq['value_usd']:,.0f} "
f"{liq['side']} {liq['symbol']} @ ${liq['price']}")
elif data.get("type") == "pong":
pass # Heartbeat response
except websockets.ConnectionClosed as e:
print(f"Connection closed: {e}. Reconnecting in {reconnect_delay}s...")
await asyncio.sleep(reconnect_delay)
reconnect_delay = min(reconnect_delay * 2, max_reconnect_delay)
except Exception as e:
print(f"Streaming error: {e}")
await asyncio.sleep(reconnect_delay)
async def run_backtest_simulation():
"""Simulate backtest using live liquidations feed."""
streamer = HyperliquidStreamer(API_KEY)
# Track liquidations for cascade analysis
cascade_events = []
async def monitor_liquidations():
while True:
if streamer.liquidation_buffer:
liq = streamer.liquidation_buffer[-1]
print(f" {liq['timestamp']} | {liq['symbol']} | "
f"{liq['side'].upper():5} | ${liq['value_usd']:>12,.0f}")
await asyncio.sleep(0.1)
# Run both streamer and monitor concurrently
await asyncio.gather(
streamer.connect_and_stream(["HYPE-USDC", "BTC-USDC", "ETH-USDC"]),
monitor_liquidations()
)
if __name__ == "__main__":
print("Starting HolySheep WebSocket streamer...")
print("Markets: HYPE-USDC, BTC-USDC, ETH-USDC")
print("Data: Order Book (L2, 25 levels) + Liquidations")
asyncio.run(run_backtest_simulation())
3. Multi-Exchange Funding Rate Arbitrage Backtest
# HolySheep - Cross-Exchange Funding Rate Arbitrage Backtest
Strategy: Long low-funding exchange, Short high-funding exchange
import requests
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from itertools import combinations
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY"
def fetch_funding_rates(
exchanges: list = None,
symbol: str = "BTC-USDC",
start_time: datetime = None,
end_time: datetime = None
) -> pd.DataFrame:
"""Fetch historical funding rates across multiple exchanges."""
if exchanges is None:
exchanges = ["hyperliquid", "binance", "bybit", "okx"]
if start_time is None:
start_time = datetime.utcnow() - timedelta(days=90)
if end_time is None:
end_time = datetime.utcnow()
headers = {
"Authorization": f"Bearer {API_KEY}",
"Content-Type": "application/json"
}
all_rates = []
for exchange in exchanges:
payload = {
"exchange": exchange,
"symbol": symbol,
"start_time": int(start_time.timestamp() * 1000),
"end_time": int(end_time.timestamp() * 1000),
"interval": "1h" # Hourly funding rate snapshots
}
try:
response = requests.post(
f"{BASE_URL}/crypto/funding/historical",
json=payload,
headers=headers,
timeout=15
)
if response.status_code == 200:
data = response.json()
df = pd.DataFrame(data["funding_rates"])
df["timestamp"] = pd.to_datetime(df["timestamp"], unit="ms")
df["exchange"] = exchange
all_rates.append(df)
print(f"✓ {exchange}: {len(df)} funding rate records")
else:
print(f"✗ {exchange}: Error {response.status_code}")
except Exception as e:
print(f"✗ {exchange}: {str(e)}")
return pd.concat(all_rates, ignore_index=True)
def run_funding_arbitrage_backtest(
funding_df: pd.DataFrame,
min_spread_bps: float = 5.0,
maker_fee: float = 0.0002,
taker_fee: float = 0.0005,
funding_interval_hours: float = 8.0
) -> dict:
"""
Backtest cross-exchange funding rate arbitrage.
Returns:
Dictionary with performance metrics and trade log
"""
# Pivot to wide format (one column per exchange)
pivot_df = funding_df.pivot_table(
index="timestamp",
columns="exchange",
values="funding_rate",
aggfunc="first"
).sort_index()
# Calculate funding rate differential
exchange_cols = [c for c in pivot_df.columns if c in ["hyperliquid", "binance", "bybit", "okx"]]
results = {
"total_trades": 0,
"profitable_trades": 0,
"total_pnl": 0.0,
"max_drawdown": 0.0,
"trade_log": []
}
equity_curve = [1.0]
# Iterate through each possible pair
for ex1, ex2 in combinations(exchange_cols, 2):
if ex1 not in pivot_df.columns or ex2 not in pivot_df.columns:
continue
pair_df = pivot_df[[ex1, ex2]].dropna()
for idx, row in pair_df.iterrows():
rate_diff = (row[ex1] - row[ex2]) * 100 # Convert to percentage
# Entry signal: spread exceeds threshold (annualized)
if abs(rate_diff) >= min_spread_bps / 100 * 365 * 24 / funding_interval_hours:
# Determine long/short sides
if row[ex1] > row[ex2]:
long_ex, short_ex = ex1, ex2
else:
long_ex, short_ex = ex2, ex1
# Calculate PnL
long_funding = row[long_ex]
short_funding = row[short_ex]
net_funding = (long_funding - short_funding) / funding_interval_hours
# Gross PnL (100 bps = 1% per period)
gross_pnl = net_funding * 100 # As percentage
# Net PnL after fees
fees = maker_fee * 2 # Entry + exit (estimated)
net_pnl_pct = gross_pnl - fees
results["total_trades"] += 1
results["profitable_trades"] += 1 if net_pnl_pct > 0 else 0
results["total_pnl"] += net_pnl_pct
# Update equity curve
new_equity = equity_curve[-1] * (1 + net_pnl_pct / 100)
equity_curve.append(new_equity)
results["trade_log"].append({
"timestamp": idx,
"long_exchange": long_ex,
"short_exchange": short_ex,
"long_funding": long_funding,
"short_funding": short_funding,
"net_funding": net_funding,
"pnl_pct": net_pnl_pct,
"equity": new_equity
})
# Calculate metrics
if results["trade_log"]:
results["win_rate"] = results["profitable_trades"] / results["total_trades"]
results["avg_pnl"] = results["total_pnl"] / results["total_trades"]
equity_series = pd.Series(equity_curve)
rolling_max = equity_series.expanding().max()
drawdown = (equity_series - rolling_max) / rolling_max
results["max_drawdown"] = drawdown.min() * 100
# Sharpe ratio (assuming 8-hour funding intervals)
returns = pd.Series([t["pnl_pct"] for t in results["trade_log"]])
results["sharpe_ratio"] = returns.mean() / returns.std() * np.sqrt(365 * 3) if returns.std() > 0 else 0
return results
def print_backtest_report(results: dict, symbol: str):
"""Display formatted backtest report."""
print("\n" + "="*60)
print(f" FUNDING RATE ARBITRAGE BACKTEST REPORT")
print(f" Symbol: {symbol}")
print(f" Period: Last 90 days")
print("="*60)
if results["total_trades"] == 0:
print("No trades generated. Consider lowering min_spread_bps threshold.")
return
print(f"\n PERFORMANCE METRICS:")
print(f" {'Total Trades:':<25} {results['total_trades']:,}")
print(f" {'Profitable Trades:':<25} {results['profitable_trades']:,}")
print(f" {'Win Rate:':<25} {results['win_rate']:.1%}")
print(f" {'Average PnL per Trade:':<25} {results['avg_pnl']:.4f}%")
print(f" {'Total PnL:':<25} {results['total_pnl']:.2f}%")
print(f" {'Max Drawdown:':<25} {results['max_drawdown']:.2f}%")
print(f" {'Sharpe Ratio:':<25} {results['sharpe_ratio']:.2f}")
print(f"\n SAMPLE TRADES (first 5):")
print(f" {'Timestamp':<22} {'Long':<12} {'Short':<12} {'PnL %':<10}")
print(" " + "-"*56)
for trade in results["trade_log"][:5]:
print(f" {trade['timestamp'].strftime('%Y-%m-%d %H:%M'):<22} "
f"{trade['long_exchange']:<12} {trade['short_exchange']:<12} "
f"{trade['pnl_pct']:>+.4f}%")
print("="*60)
if __name__ == "__main__":
print("Fetching funding rates from HolySheep API...")
print("Exchanges: Hyperliquid, Binance, Bybit, OKX")
funding_df = fetch_funding_rates(symbol="BTC-USDC")
print(f"\nFetched {len(funding_df):,} funding rate records")
# Run backtest
results = run_funding_arbitrage_backtest(
funding_df,
min_spread_bps=3.0, # 3 bps minimum spread
maker_fee=0.0002,
taker_fee=0.0005
)
print_backtest_report(results, "BTC-USDC")
Common Errors and Fixes
Error 1: 401 Unauthorized - Invalid API Key
Symptom: API returns {"error": "Invalid API key", "code": 401} on all requests.
Causes:
- Using placeholder
"YOUR_HOLYSHEEP_API_KEY"without replacing with real key - Key has been invalidated or expired (e.g., team member left, key rotation policy)
- Key scope mismatch (v1 vs v2 endpoint permissions)
Fix:
# Verify API key is correctly set and active
import requests
BASE_URL = "https://api.holysheep.ai/v1"
API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Replace with actual key from dashboard
Test authentication
response = requests.get(
f"{BASE_URL}/auth/verify",
headers={"Authorization": f"Bearer {API_KEY}"}
)
if response.status_code == 200:
print("✓ API key is valid")
user_data = response.json()
print(f" Plan: {user_data.get('plan', 'N/A')}")
print(f" Credits remaining: ${user_data.get('credits_usd', 0):.2f}")
else:
print(f"✗ Authentication failed: {response.status_code}")
print(f" Response: {response.text}")
# If key is invalid, get new key:
# 1. Visit https://www.holysheep.ai/register
# 2. Create account or check existing dashboard
# 3. Generate new API key under Settings > API Keys
# 4. Ensure key has "crypto_data" scope enabled
Error 2: 429 Rate Limit Exceeded
Symptom: {"error": "Rate limit exceeded", "code": 429, "retry_after_ms": 5000}
Causes:
- Exceeding 1,000 requests/minute on historical endpoints
- Concurrent websocket connections exceeding plan limit
- Batch query without proper pagination delays
Fix:
# Implement exponential backoff with jitter for rate limit handling
import time
import random
import requests
def fetch_with_retry(url: str, headers: dict, max_retries: int = 5) -> requests.Response:
"""Fetch with exponential backoff for rate limit handling."""
for attempt in range(max_retries):
response = requests.get(url, headers=headers)
if response.status_code == 200:
return response
elif response.status_code == 429:
# Parse retry delay from response
retry_after_ms = response.headers.get("Retry-After-Ms", 5000)
wait_seconds = max(int(retry_after_ms) / 1000, 1)
# Add jitter (±20%) to prevent thundering herd
jitter = random.uniform(-0.2, 0.2)
wait_seconds *= (1 + jitter)
print(f"Rate limited. Waiting {wait_seconds:.1f}s (attempt {attempt + 1}/{max_retries})")
time.sleep(wait_seconds)
else:
raise Exception(f"Unexpected error: {response.status_code} - {response.text}")
raise Exception(f"Max retries ({max_retries}) exceeded for {url}")
def paginate_historical_data(base_url: str, api_key: str, symbol: str,
start_time: int, end_time: int,
page_size: int = 100000) -> list:
"""Paginate through large historical queries to avoid rate limits."""
all_trades = []
current_start = start_time
headers = {"Authorization": f"Bearer {api_key}"}
while current_start < end_time:
payload = {
"exchange": "hyperliquid",
"symbol": symbol,
"start_time": current_start,
"end_time": end_time,
"limit": page_size
}
response = fetch_with_retry(
f"{base_url}/crypto/trades/historical",
headers=headers,
method="POST",
json=payload
)
data = response.json()
page_trades = data.get("trades", [])
all_trades.extend(page_trades)
if len(page_trades) < page_size:
break # No more data
# Move window forward (exclusive end)
last_timestamp = max(int(t["timestamp"]) for t in page_trades)
current_start = last_timestamp + 1
print(f" Fetched {len(all_trades):,} trades total...")
return all_trades
Error 3: WebSocket Disconnection and Data Gap Detection
Symptom: WebSocket connects but disconnects within seconds, or reconnect causes gaps in order book data.
Causes:
- Missing heartbeat (pong) responses causing server-side timeout
- Network proxy/firewall interrupting long-lived connections
- Subscription format mismatch (case sensitivity on channel names)
Fix:
import asyncio
import json
import websockets
from datetime import datetime
class RobustWebSocketClient:
"""WebSocket client with automatic reconnection and heartbeat."""
def __init__(self, api_key: str):
self.api_key = api_key
self.ws = None
self.last_heartbeat = datetime.utcnow()
self.heartbeat_interval = 15 # seconds
self.max_reconnect_delay = 60
self.data_gaps = []
async def heartbeat(self, ws):
"""Send periodic heartbeat to keep connection alive."""
while True:
await asyncio.sleep(self.heartbeat_interval)
try:
pong_msg = {"method": "ping", "id": int(datetime.utcnow().timestamp())}
await ws.send(json.dumps(pong_msg))
self.last_heartbeat = datetime.utcnow()
except Exception as e:
print(f"Heartbeat failed: {e}")
break
async def validate_subscription(self, ws, channels: list) -> bool:
"""Verify subscriptions are actually active."""
sub_msg = {
"method": "subscribe",
"params": {"api_key": self.api_key, "channels": channels},
"id": 1
}
await ws.send(json.dumps(sub_msg))
# Wait for subscription confirmation
try:
response = await asyncio.wait_for(ws.recv(), timeout=5.0)
data = json.loads(response)
if data.get("status") == "subscribed":
print(f"✓ Subscribed to {len(channels)} channels")
return True
else:
print(f"✗ Subscription failed: {data}")
return False
except asyncio.TimeoutError:
print("✗ No subscription confirmation received")
return False
async def connect(self):
"""Establish WebSocket connection with retry logic."""
delay = 1
while True:
try:
ws_url = "wss://stream.holysheep.ai/v1"
self.ws = await webs