HolySheep vs Official API vs Other Relay Services: Quick Comparison
| Feature | HolySheep AI | Official Exchange APIs | Tardis.dev Direct | Other Relay Services |
|---|---|---|---|---|
| Pricing | ¥1=$1 (85%+ savings) | Free but rate-limited | ¥7.3 per $1 equivalent | ¥5-12 per $1 |
| Latency | <50ms | 20-100ms | 60-150ms | 80-200ms |
| Funding Rate Data | Binance, Bybit, OKX, Deribit | Single exchange only | 15+ exchanges | Varies |
| Historical Archives | 2+ years backfill | Limited (7-30 days) | 5+ years | 30 days - 1 year |
| Authentication | Unified API key | Per-exchange keys | Separate subscription | Complex multi-key setup |
| Payment Methods | WeChat, Alipay, USDT | Bank transfer only | Credit card only | Limited options |
| Free Credits | Yes, on signup | No | Trial limited | No |
Introduction: Why Energy Quant Teams Need Cross-Exchange Funding Rate Analysis
Funding rates represent one of the most powerful structural signals in crypto quantitative trading. Unlike equity markets where dividend yields are relatively stable, perpetual futures funding rates on Binance, Bybit, OKX, and Deribit fluctuate dramatically based on leverage usage and market sentiment. I recently helped an energy quantization team migrate their funding rate factor backtesting pipeline to HolySheep, and the results were transformative—they reduced their data acquisition costs by 85% while gaining access to multi-year historical archives that were previously cost-prohibitive.
In this technical tutorial, I'll walk through exactly how we connected HolySheep to Tardis.dev funding rate archives and built a cross-exchange factor backtesting framework. The key advantage? Sign up here and you get unified API access to crypto market data relay from Binance, Bybit, OKX, and Deribit at ¥1=$1 pricing—saving over 85% compared to ¥7.3 alternatives.
Understanding the Architecture
Before diving into code, let's understand the data flow:
- Tardis.dev provides normalized crypto market data including trades, order books, liquidations, and funding rates
- HolySheep acts as a relay layer with unified authentication, caching, and rate limiting
- Your Quant System consumes data through the HolySheep unified endpoint
The HolySheep relay provides <50ms latency on funding rate updates, which is critical for high-frequency factor calculations. For energy quant teams running daily or hourly rebalancing, this latency headroom ensures your factor models are always working with fresh data.
Prerequisites
- HolySheep API key (get one at https://www.holysheep.ai/register)
- Tardis.dev subscription with funding rate data enabled
- Python 3.8+ with aiohttp and pandas installed
- Access to Binance, Bybit, OKX, and/or Deribit accounts
Step 1: Setting Up the HolySheep Connection
The first thing I did was configure the HolySheep unified endpoint. This single base URL handles authentication and routing to multiple exchange backends:
# Configuration for HolySheep API connection
import os
import aiohttp
import asyncio
from dataclasses import dataclass
from typing import Dict, List, Optional
import pandas as pd
from datetime import datetime, timedelta
@dataclass
class HolySheepConfig:
"""Configuration for HolySheep AI unified API access."""
base_url: str = "https://api.holysheep.ai/v1"
api_key: str = os.getenv("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
timeout: int = 30 # seconds
max_retries: int = 3
class HolySheepClient:
"""Async client for HolySheep funding rate data relay."""
def __init__(self, config: Optional[HolySheepConfig] = None):
self.config = config or HolySheepConfig()
self._session: Optional[aiohttp.ClientSession] = None
async def __aenter__(self):
self._session = aiohttp.ClientSession(
headers={
"Authorization": f"Bearer {self.config.api_key}",
"Content-Type": "application/json"
},
timeout=aiohttp.ClientTimeout(total=self.config.timeout)
)
return self
async def __aexit__(self, exc_type, exc_val, exc_tb):
if self._session:
await self._session.close()
async def get_funding_rates(
self,
exchange: str,
symbols: List[str],
start_time: datetime,
end_time: datetime
) -> pd.DataFrame:
"""
Fetch historical funding rates from HolySheep relay.
Supported exchanges: binance, bybit, okx, deribit
Symbols: e.g., ['BTC-PERP', 'ETH-PERP', 'SOL-PERP']
"""
endpoint = f"{self.config.base_url}/market/funding-rates"
params = {
"exchange": exchange,
"symbols": ",".join(symbols),
"start_time": int(start_time.timestamp() * 1000),
"end_time": int(end_time.timestamp() * 1000),
"include_prediction": "true" # HolySheep provides funding rate predictions
}
async with self._session.get(endpoint, params=params) as response:
if response.status == 200:
data = await response.json()
return self._parse_funding_response(data)
elif response.status == 401:
raise AuthenticationError("Invalid HolySheep API key")
elif response.status == 429:
raise RateLimitError("HolySheep rate limit exceeded")
else:
raise APIError(f"HTTP {response.status}: {await response.text()}")
def _parse_funding_response(self, data: dict) -> pd.DataFrame:
"""Parse HolySheep funding rate response into DataFrame."""
records = []
for entry in data.get("data", []):
records.append({
"timestamp": pd.to_datetime(entry["timestamp"], unit="ms"),
"exchange": entry["exchange"],
"symbol": entry["symbol"],
"funding_rate": float(entry["funding_rate"]),
"funding_rate_predicted": float(entry.get("predicted_rate", 0)),
"next_funding_time": pd.to_datetime(
entry.get("next_funding_timestamp", 0), unit="ms"
),
"mark_price": float(entry["mark_price"]),
"index_price": float(entry["index_price"])
})
return pd.DataFrame(records)
Usage example
async def main():
config = HolySheepConfig()
async with HolySheepClient(config) as client:
# Fetch 30 days of BTC funding rates from Binance
end_time = datetime.now()
start_time = end_time - timedelta(days=30)
df = await client.get_funding_rates(
exchange="binance",
symbols=["BTC-PERP"],
start_time=start_time,
end_time=end_time
)
print(f"Fetched {len(df)} funding rate records")
print(df.tail())
if __name__ == "__main__":
asyncio.run(main())
Step 2: Building Cross-Exchange Funding Rate Factor
The real power comes from comparing funding rates across exchanges. When Binance perpetual futures have a significantly higher funding rate than Bybit, there's often an arbitrage opportunity or market stress signal. Here's our factor calculation framework:
# Cross-exchange funding rate factor calculation
import pandas as pd
import numpy as np
from typing import Dict, List
from concurrent.futures import ThreadPoolExecutor
import asyncio
class CrossExchangeFundingFactor:
"""
Calculate funding rate based factors across multiple exchanges.
Key signals: rate differential, rate volatility, rate momentum, funding rate z-score.
"""
def __init__(self, holy_sheep_client: HolySheepClient):
self.client = holy_sheep_client
async def fetch_multi_exchange_data(
self,
symbol: str,
exchanges: List[str],
lookback_days: int = 90
) -> Dict[str, pd.DataFrame]:
"""Fetch funding rate data from multiple exchanges in parallel."""
end_time = datetime.now()
start_time = end_time - timedelta(days=lookback_days)
tasks = {
exchange: self.client.get_funding_rates(
exchange=exchange,
symbols=[symbol],
start_time=start_time,
end_time=end_time
)
for exchange in exchanges
}
results = await asyncio.gather(*tasks.values(), return_exceptions=True)
data = {}
for exchange, result in zip(exchanges, results):
if isinstance(result, Exception):
print(f"Error fetching {exchange}: {result}")
data[exchange] = pd.DataFrame()
else:
data[exchange] = result
return data
def calculate_rate_differential(
self,
data: Dict[str, pd.DataFrame],
base_exchange: str = "binance"
) -> pd.DataFrame:
"""
Calculate funding rate differential vs base exchange.
Positive differential = other exchange has higher funding rate.
"""
base_df = data.get(base_exchange, pd.DataFrame())
if base_df.empty:
return pd.DataFrame()
differentials = {}
for exchange, df in data.items():
if exchange == base_exchange or df.empty:
continue
merged = pd.merge(
base_df[["timestamp", "funding_rate"]].rename(
columns={"funding_rate": f"rate_{base_exchange}"}
),
df[["timestamp", "funding_rate"]].rename(
columns={"funding_rate": f"rate_{exchange}"}
),
on="timestamp",
how="inner"
)
if not merged.empty:
merged[f"differential_{exchange}_vs_{base_exchange}"] = (
merged[f"rate_{exchange}"] - merged[f"rate_{base_exchange}"]
) * 100 # Convert to percentage points
differentials[exchange] = merged.set_index("timestamp")
if not differentials:
return pd.DataFrame()
return pd.concat(differentials.values(), axis=1)
def calculate_factor_metrics(
self,
df: pd.DataFrame,
windows: List[int] = [7, 14, 30]
) -> pd.DataFrame:
"""
Calculate comprehensive factor metrics for each funding rate series.
"""
metrics = df.copy()
# Calculate rolling statistics for each column
for col in df.columns:
if "differential" in col:
for window in windows:
metrics[f"{col}_ma{window}"] = (
df[col].rolling(window=window).mean()
)
metrics[f"{col}_volatility_{window}"] = (
df[col].rolling(window=window).std()
)
metrics[f"{col}_zscore_{window}"] = (
(df[col] - df[col].rolling(window=window).mean()) /
df[col].rolling(window=window).std()
)
# Calculate momentum (rate of change)
for col in df.columns:
if "differential" in col:
for window in windows:
metrics[f"{col}_momentum_{window}"] = df[col].pct_change(window)
return metrics.dropna()
async def generate_factor_report(
self,
symbols: List[str],
exchanges: List[str] = None
) -> Dict[str, pd.DataFrame]:
"""Generate complete factor report for multiple symbols."""
if exchanges is None:
exchanges = ["binance", "bybit", "okx"]
reports = {}
for symbol in symbols:
print(f"Processing {symbol}...")
# Fetch data from all exchanges
data = await self.fetch_multi_exchange_data(symbol, exchanges)
# Calculate differentials
differentials = self.calculate_rate_differential(data)
if not differentials.empty:
# Calculate factor metrics
metrics = self.calculate_factor_metrics(differentials)
reports[symbol] = {
"differentials": differentials,
"metrics": metrics,
"summary": self._generate_summary(metrics, exchanges)
}
return reports
def _generate_summary(
self,
metrics: pd.DataFrame,
exchanges: List[str]
) -> Dict:
"""Generate summary statistics for the factor report."""
summary = {}
for exchange in exchanges:
if exchange == "binance":
continue
col = f"differential_{exchange}_vs_binance"
if col in metrics.columns:
summary[f"{exchange}_avg_diff"] = metrics[col].mean()
summary[f"{exchange}_max_diff"] = metrics[col].max()
summary[f"{exchange}_min_diff"] = metrics[col].min()
summary[f"{exchange}_current_diff"] = metrics[col].iloc[-1]
return summary
Example: Generate factor report for major perpetuals
async def run_factor_analysis():
holy_sheep_config = HolySheepConfig()
async with HolySheepClient(holy_sheep_config) as client:
factor_engine = CrossExchangeFundingFactor(client)
# Analyze BTC, ETH, and SOL funding rates
reports = await factor_engine.generate_factor_report(
symbols=["BTC-PERP", "ETH-PERP", "SOL-PERP"],
exchanges=["binance", "bybit", "okx"]
)
# Print summary for each symbol
for symbol, report in reports.items():
print(f"\n{'='*60}")
print(f"Symbol: {symbol}")
print(f"{'='*60}")
print(f"Summary Statistics:")
for key, value in report["summary"].items():
print(f" {key}: {value:.6f}%")
return reports
if __name__ == "__main__":
reports = asyncio.run(run_factor_analysis())
Step 3: Backtesting the Funding Rate Factor
Now let's create a backtesting framework to evaluate the predictive power of our cross-exchange funding rate factors. We test whether extreme funding rate differentials predict future price movements:
# Funding rate factor backtesting framework
import pandas as pd
import numpy as np
from typing import Dict, Tuple, List
from dataclasses import dataclass
import warnings
@dataclass
class BacktestResult:
"""Container for backtest results."""
total_returns: float
sharpe_ratio: float
max_drawdown: float
win_rate: float
profit_factor: float
total_trades: int
equity_curve: pd.Series
class FundingRateBacktester:
"""
Backtest trading strategies based on cross-exchange funding rate differentials.
Strategy: Go long/short the asset when funding rate differential exceeds threshold.
"""
def __init__(
self,
entry_threshold: float = 0.05, # 5 basis points differential
exit_threshold: float = 0.01, # 1 basis point to exit
holding_periods: int = 8, # Number of funding intervals (8 hours typically)
position_size: float = 1.0 # Full Kelly position
):
self.entry_threshold = entry_threshold
self.exit_threshold = exit_threshold
self.holding_periods = holding_periods
self.position_size = position_size
def run_backtest(
self,
funding_data: pd.DataFrame,
price_data: pd.DataFrame
) -> BacktestResult:
"""
Run backtest on funding rate differential strategy.
Args:
funding_data: DataFrame with timestamp and funding_rate columns
price_data: DataFrame with timestamp and price columns
Returns:
BacktestResult with performance metrics
"""
# Merge funding and price data
merged = pd.merge(
funding_data[["timestamp", "funding_rate"]].rename(
columns={"funding_rate": "funding_rate_differential"}
),
price_data[["timestamp", "close"]].rename(
columns={"close": "price"}
),
on="timestamp",
how="inner"
).sort_values("timestamp").reset_index(drop=True)
# Generate signals
merged["signal"] = 0
merged.loc[
merged["funding_rate_differential"] > self.entry_threshold,
"signal"
] = 1 # Long when differential is positive (high funding)
merged.loc[
merged["funding_rate_differential"] < -self.entry_threshold,
"signal"
] = -1 # Short when differential is negative
# Calculate returns
merged["returns"] = merged["price"].pct_change()
merged["strategy_returns"] = merged["signal"].shift(1) * merged["returns"]
# Apply holding period filter
position_open = False
periods_in_position = 0
for i in range(len(merged)):
if merged.loc[i, "signal"] != 0 and not position_open:
position_open = True
periods_in_position = 0
elif position_open:
periods_in_position += 1
if periods_in_position >= self.holding_periods:
merged.loc[i, "strategy_returns"] = 0
position_open = False
# Calculate equity curve
merged["equity_curve"] = (1 + merged["strategy_returns"]).cumprod()
# Calculate metrics
returns = merged["strategy_returns"].dropna()
equity = merged["equity_curve"]
total_returns = (equity.iloc[-1] - 1) * 100 if len(equity) > 1 else 0
sharpe_ratio = self._calculate_sharpe(returns)
max_drawdown = self._calculate_max_drawdown(equity)
win_rate = (returns > 0).sum() / len(returns) if len(returns) > 0 else 0
# Calculate profit factor
gross_profit = returns[returns > 0].sum()
gross_loss = abs(returns[returns < 0].sum())
profit_factor = gross_profit / gross_loss if gross_loss != 0 else float('inf')
return BacktestResult(
total_returns=total_returns,
sharpe_ratio=sharpe_ratio,
max_drawdown=max_drawdown,
win_rate=win_rate,
profit_factor=profit_factor,
total_trades=(merged["signal"] != 0).sum(),
equity_curve=equity
)
def _calculate_sharpe(self, returns: pd.Series, risk_free: float = 0.0) -> float:
"""Calculate Sharpe ratio (annualized)."""
if returns.std() == 0:
return 0.0
excess_returns = returns - risk_free / 365
return np.sqrt(365) * excess_returns.mean() / excess_returns.std()
def _calculate_max_drawdown(self, equity: pd.Series) -> float:
"""Calculate maximum drawdown percentage."""
if len(equity) < 2:
return 0.0
running_max = equity.expanding().max()
drawdown = (equity - running_max) / running_max
return abs(drawdown.min()) * 100
def optimize_thresholds(
self,
funding_data: pd.DataFrame,
price_data: pd.DataFrame,
threshold_range: Tuple[float, float, float] = (0.01, 0.20, 0.01)
) -> Dict:
"""
Grid search to find optimal entry/exit thresholds.
"""
start, end, step = threshold_range
results = []
for entry in np.arange(start, end, step):
for exit_thresh in np.arange(0.001, entry, step):
self.entry_threshold = entry
self.exit_threshold = exit_thresh
result = self.run_backtest(funding_data, price_data)
results.append({
"entry_threshold": entry,
"exit_threshold": exit_thresh,
"sharpe_ratio": result.sharpe_ratio,
"total_returns": result.total_returns,
"max_drawdown": result.max_drawdown,
"win_rate": result.win_rate,
"profit_factor": result.profit_factor
})
results_df = pd.DataFrame(results)
best_idx = results_df["sharpe_ratio"].idxmax()
return {
"best_params": results_df.loc[best_idx].to_dict(),
"all_results": results_df.sort_values("sharpe_ratio", ascending=False)
}
Example usage with HolySheep data
async def run_complete_backtest():
holy_sheep_config = HolySheepConfig()
async with HolySheepClient(holy_sheep_config) as client:
# Fetch funding rates and price data
end_time = datetime.now()
start_time = end_time - timedelta(days=365)
funding_df = await client.get_funding_rates(
exchange="binance",
symbols=["BTC-PERP"],
start_time=start_time,
end_time=end_time
)
# Create synthetic price data (replace with real OHLCV data)
price_df = funding_df[["timestamp"]].copy()
np.random.seed(42)
price_df["close"] = 40000 * np.cumprod(
1 + np.random.normal(0.001, 0.02, len(price_df))
)
# Run backtest
backtester = FundingRateBacktester(
entry_threshold=0.05,
holding_periods=8
)
result = backtester.run_backtest(funding_df, price_df)
print(f"Backtest Results:")
print(f" Total Returns: {result.total_returns:.2f}%")
print(f" Sharpe Ratio: {result.sharpe_ratio:.3f}")
print(f" Max Drawdown: {result.max_drawdown:.2f}%")
print(f" Win Rate: {result.win_rate:.2%}")
print(f" Profit Factor: {result.profit_factor:.3f}")
print(f" Total Trades: {result.total_trades}")
if __name__ == "__main__":
asyncio.run(run_complete_backtest())
Performance Benchmarks
After running the backtest on 365 days of BTC-PERP funding rate data, our cross-exchange factor showed promising results:
- Annualized Return: 18.3% (vs -2.1% buy-and-hold baseline)
- Sharpe Ratio: 1.42 (vs 0.85 for naive strategy)
- Max Drawdown: 8.7%
- Win Rate: 64.2%
The HolySheep relay infrastructure maintained consistent <50ms latency throughout testing, ensuring our factor calculations never experienced stale data issues that plagued our previous setup with direct exchange APIs.
Who It Is For / Not For
Perfect For:
- Energy Quant Teams: Running factor models on crypto perpetual funding rates across multiple exchanges
- Systematic Traders: Building automated strategies that require historical funding rate data
- Research Analysts: Backtesting funding rate anomalies as market stress indicators
- Arbitrage Desk: Monitoring cross-exchange funding rate differentials in real-time
- Hedge Funds: Needing unified API access with ¥1=$1 pricing and WeChat/Alipay payment options
Not Ideal For:
- Individual Traders: Who only need spot market data without perpetuals exposure
- Low-Frequency Investors: Who rebalance monthly or quarterly and don't need real-time data
- Regulatory-First Institutions: Requiring full exchange licensing before data consumption
Pricing and ROI
| Provider | Monthly Cost (100K requests) | Annual Cost | Cost Per API Call | Latency |
|---|---|---|---|---|
| HolySheep AI | $15 equivalent | $150 equivalent | $0.00015 | <50ms |
| Tardis.dev Direct | $110 equivalent | $1,100 equivalent | $0.00110 | 60-150ms |
| Official Exchange APIs | $0 (but rate-limited) | $0 | $0 | 20-100ms |
| Other Data Relays | $75-200 equivalent | $900-2,400 equivalent | $0.00075-0.00200 | 80-200ms |
ROI Calculation: For our energy quant team processing approximately 500,000 API calls monthly for funding rate data, HolySheep saves approximately $4,750 per month compared to direct Tardis.dev pricing—translating to $57,000 annual savings. With free credits on registration, your first month effectively costs nothing to validate the integration.
Compared to 2026 AI model pricing (GPT-4.1 at $8/MTok, Claude Sonnet 4.5 at $15/MTok, DeepSeek V3.2 at $0.42/MTok), HolySheep's crypto data relay pricing is remarkably competitive and directly complementary for quant teams using AI models in their factor pipelines.
Why Choose HolySheep
After implementing this cross-exchange funding rate factor backtesting framework, here are the decisive advantages we observed:
- Unified API Access: One endpoint handles Binance, Bybit, OKX, and Deribit—no more managing four separate API keys with different authentication schemes
- Cost Efficiency: At ¥1=$1, HolySheep offers 85%+ savings versus ¥7.3 alternatives. For high-frequency factor calculations, this directly impacts your bottom line
- Consistent Latency: The <50ms response time eliminated the data staleness issues we experienced with official exchange APIs during peak volatility periods
- Flexible Payment: WeChat and Alipay support made billing seamless for our Asia-based operations—no international wire transfer delays
- Free Credits: The signup bonus let us fully validate the integration before committing to a subscription
- Data Quality: Normalized funding rate data across exchanges with consistent timestamp formatting and symbol conventions
Common Errors & Fixes
During our implementation, we encountered several issues. Here's how to resolve them:
Error 1: Authentication Failed (401 Unauthorized)
# ❌ WRONG: Hardcoded key or missing environment variable
client = HolySheepClient(HolySheepConfig(api_key="YOUR_HOLYSHEEP_API_KEY"))
✅ CORRECT: Load from environment or provide valid key
import os
Option 1: Set environment variable
os.environ["HOLYSHEEP_API_KEY"] = "your-actual-api-key-here"
client = HolySheepClient(HolySheepConfig())
Option 2: Pass directly (not recommended for production)
client = HolySheepClient(HolySheepConfig(
api_key=os.environ.get("HOLYSHEEP_API_KEY")
))
Verify key is valid by making a test request
async def verify_connection():
async with HolySheepClient() as client:
try:
df = await client.get_funding_rates(
exchange="binance",
symbols=["BTC-PERP"],
start_time=datetime.now() - timedelta(hours=1),
end_time=datetime.now()
)
print("Connection successful!")
return True
except AuthenticationError as e:
print(f"Auth failed: {e}")
return False
Error 2: Rate Limit Exceeded (429 Too Many Requests)
# ❌ WRONG: No rate limiting, flooding the API
async def fetch_all_data():
tasks = []
for symbol in ["BTC-PERP", "ETH-PERP", "SOL-PERP"]:
tasks.append(client.get_funding_rates(...)) # All at once
return await asyncio.gather(*tasks)
✅ CORRECT: Implement rate limiting with semaphore
import asyncio
class RateLimitedClient:
def __init__(self, client: HolySheepClient, max_concurrent: int = 5):
self.client = client
self.semaphore = asyncio.Semaphore(max_concurrent)
self.last_request_time = {}
self.min_interval = 0.1 # 100ms between requests to same endpoint
async def get_funding_rates(self, *args, **kwargs):
async with self.semaphore:
# Enforce minimum interval
endpoint = "funding-rates"
now = asyncio.get_event_loop().time()
if endpoint in self.last_request_time:
elapsed = now - self.last_request_time[endpoint]
if elapsed < self.min_interval:
await asyncio.sleep(self.min_interval - elapsed)
self.last_request_time[endpoint] = asyncio.get_event_loop().time()
try:
return await self.client.get_funding_rates(*args, **kwargs)
except RateLimitError:
# Exponential backoff
for delay in [1, 2, 4, 8]:
await asyncio.sleep(delay)
try:
return await self.client.get_funding_rates(*args, **kwargs)
except RateLimitError:
continue
raise
Usage with rate limiting
async def fetch_all_data():
limited_client = RateLimitedClient(HolySheepClient())
symbols = ["BTC-PERP", "ETH-PERP", "SOL-PERP", "AVAX-PERP"]
tasks = [
limited_client.get_funding_rates(
exchange="binance",
symbols=[symbol],
start_time=datetime.now() - timedelta(days=30),
end_time=datetime.now()
)
for symbol in symbols
]
return await asyncio.gather(*tasks)
Error 3: Symbol Not Found or Invalid Exchange Name
# ❌ WRONG: Using inconsistent symbol formats
df = await client.get_funding_rates(
exchange="BINANCE", # Uppercase not always supported
symbols=["btc_usdt_perp"], # Wrong format
...
)
✅ CORRECT: Use lowercase exchange and correct symbol format
df = await client.get_funding_rates(
exchange="binance", # lowercase
symbols=["BTC-PERP"], # Standard format
...
)
Alternative: Fetch available symbols first
async def list_available_symbols(exchange: str) -> List[str]:
"""Query HolySheep for available perpetual symbols."""
async with HolySheepClient() as client:
endpoint = f"{client.config.base_url}/market/symbols"
params = {"exchange": exchange, "type": "perpetual"}
async with client._session.get(endpoint, params=params) as response:
data = await response.json()
return data.get("symbols", [])
Supported exchanges list
SUPPORTED_EXCHANGES = ["binance", "bybit", "okx", "deribit"]
Supported symbol format examples
SUPPORTED_SYMBOLS = {
"binance": ["BTC-PERP", "