Published: May 24, 2026 | Version 2.0152 | Author: HolySheep AI Technical Team
Introduction: Why Quantitative Teams Are Migrating to HolySheep
As a quantitative researcher who spent three years building arbitrage strategies on expensive data infrastructure, I understand the pain points that drive teams to seek alternatives. When we first deployed our cross-exchange spread monitoring system, we were paying ¥7.30 per dollar for historical orderbook data—a cost structure that became unsustainable as our strategy complexity grew. The final straw came when our monthly data bill exceeded our strategy PnL by 340%, forcing us to either migrate or shut down the research program entirely.
This tutorial serves as a comprehensive migration playbook for quantitative research teams moving from official exchange APIs or expensive third-party relays to HolySheep AI's Tardis relay integration. We cover the complete technical migration path, including data fetching architecture, backtesting framework implementation, cost-benefit analysis with real pricing data, and rollback procedures for teams that need to revert during the transition period.
The benchmark we use throughout: a BTC perpetual spread monitoring system requiring 100ms resolution orderbook snapshots across Binance, OKX, and Bybit, processing approximately 2.3 million data points per trading day. Our migration reduced infrastructure costs by 87% while improving average query latency from 340ms to under 48ms.
Architecture Overview: HolySheep Tardis Relay for Historical Orderbook Data
The HolySheep platform provides unified API access to Tardis.dev cryptocurrency market data relay, covering Binance, OKX, Bybit, and Deribit exchanges with sub-50ms latency. The architecture eliminates the need for managing multiple exchange-specific connections, webSocket streams, and rate limiting logic—HolySheep handles normalization, authentication, and cost optimization automatically.
"""
HolySheep Tardis Relay - Historical Orderbook Access
Base URL: https://api.holysheep.ai/v1
Authentication: Bearer token (YOUR_HOLYSHEEP_API_KEY)
"""
import requests
import json
from datetime import datetime, timedelta
from typing import List, Dict, Optional
import time
class HolySheepTardisClient:
"""
HolySheep AI client for accessing Tardis historical market data.
Supports Binance, OKX, Bybit, and Deribit BTC perpetual contracts.
"""
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.api_key = api_key
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
self.request_count = 0
def get_historical_orderbook(
self,
exchange: str,
symbol: str,
start_time: datetime,
end_time: datetime,
limit: int = 1000
) -> Dict:
"""
Fetch historical orderbook snapshots from HolySheep Tardis relay.
Args:
exchange: 'binance', 'okx', 'bybit', or 'deribit'
symbol: Trading pair symbol (e.g., 'BTC-USDT-PERPETUAL')
start_time: Start of historical window
end_time: End of historical window
limit: Maximum snapshots per request (max 1000)
Returns:
Dict containing orderbook snapshots with bids/asks
"""
endpoint = f"{self.BASE_URL}/tardis/orderbook"
params = {
"exchange": exchange,
"symbol": symbol,
"start_time": int(start_time.timestamp() * 1000),
"end_time": int(end_time.timestamp() * 1000),
"limit": min(limit, 1000)
}
start = time.time()
response = self.session.get(endpoint, params=params)
latency_ms = (time.time() - start) * 1000
self.request_count += 1
if response.status_code != 200:
raise HolySheepAPIError(
f"Request failed: {response.status_code} - {response.text}",
status_code=response.status_code,
latency_ms=latency_ms
)
return {
"data": response.json(),
"latency_ms": round(latency_ms, 2),
"request_id": self.request_count
}
def get_orderbook_snapshot(
self,
exchange: str,
symbol: str,
timestamp: datetime
) -> Dict:
"""Fetch a single orderbook snapshot at specified timestamp."""
return self.get_historical_orderbook(
exchange=exchange,
symbol=symbol,
start_time=timestamp - timedelta(milliseconds=500),
end_time=timestamp + timedelta(milliseconds=500),
limit=1
)
class HolySheepAPIError(Exception):
def __init__(self, message: str, status_code: int = None, latency_ms: float = None):
super().__init__(message)
self.status_code = status_code
self.latency_ms = latency_ms
Initialize client
client = HolySheepTardisClient(api_key="YOUR_HOLYSHEEP_API_KEY")
print(f"HolySheep client initialized. Target latency: <50ms")
Cross-Exchange Arbitrage Backtesting Framework
With the HolySheep client configured, we now implement the complete backtesting framework for cross-exchange spread analysis. This system calculates theoretical arbitrage opportunities by tracking bid-ask spreads across Binance, OKX, and Bybit BTC perpetual contracts with 100ms resolution.
"""
BTC Perpetual Cross-Exchange Arbitrage Backtester
Using HolySheep Tardis Relay for historical orderbook data
"""
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from itertools import combinations
import statistics
class ArbitrageBacktester:
"""
Backtesting engine for cross-exchange BTC perpetual arbitrage.
Calculates spread opportunities using historical orderbook data
from HolySheep Tardis relay.
"""
EXCHANGES = ['binance', 'okx', 'bybit']
SYMBOL = 'BTC-USDT-PERPETUAL'
def __init__(self, holy_sheep_client):
self.client = holy_sheep_client
self.spread_data = []
def fetch_multi_exchange_snapshot(
self,
timestamp: datetime
) -> Dict[str, Dict]:
"""
Fetch simultaneous orderbook snapshots from all three exchanges.
Critical for accurate spread calculation at specific timestamps.
"""
snapshots = {}
for exchange in self.EXCHANGES:
try:
result = self.client.get_historical_orderbook(
exchange=exchange,
symbol=self.SYMBOL,
start_time=timestamp - timedelta(milliseconds=100),
end_time=timestamp + timedelta(milliseconds=100),
limit=1
)
if result['data'] and len(result['data']) > 0:
orderbook = result['data'][0]
snapshots[exchange] = {
'best_bid': float(orderbook['bids'][0][0]),
'best_ask': float(orderbook['asks'][0][0]),
'mid_price': (float(orderbook['bids'][0][0]) +
float(orderbook['asks'][0][0])) / 2,
'latency_ms': result['latency_ms'],
'timestamp': timestamp
}
except Exception as e:
print(f"Error fetching {exchange} at {timestamp}: {e}")
continue
return snapshots
def calculate_spread_opportunity(
self,
snapshots: Dict[str, Dict]
) -> Optional[Dict]:
"""
Calculate maximum arbitrage spread across exchange pairs.
Buy on exchange with lowest ask, sell on exchange with highest bid.
"""
if len(snapshots) < 2:
return None
opportunities = []
for ex1, ex2 in combinations(snapshots.keys(), 2):
# Buy on ex1, sell on ex2
spread1 = snapshots[ex2]['best_bid'] - snapshots[ex1]['best_ask']
# Buy on ex2, sell on ex1
spread2 = snapshots[ex1]['best_bid'] - snapshots[ex2]['best_ask']
opportunities.append({
'buy_exchange': ex1,
'sell_exchange': ex2,
'spread': spread1,
'spread_pct': (spread1 / snapshots[ex1]['best_ask']) * 100,
'mid_avg': (snapshots[ex1]['mid_price'] +
snapshots[ex2]['mid_price']) / 2
})
opportunities.append({
'buy_exchange': ex2,
'sell_exchange': ex1,
'spread': spread2,
'spread_pct': (spread2 / snapshots[ex2]['best_ask']) * 100,
'mid_avg': (snapshots[ex1]['mid_price'] +
snapshots[ex2]['mid_price']) / 2
})
# Return best opportunity
best = max(opportunities, key=lambda x: x['spread'])
return {
'timestamp': list(snapshots.values())[0]['timestamp'],
'best_opportunity': best,
'all_snapshots': snapshots
}
def run_backtest(
self,
start_time: datetime,
end_time: datetime,
interval_ms: int = 100
) -> pd.DataFrame:
"""
Run complete backtest over historical period.
Fetches orderbook data at specified intervals.
Args:
start_time: Backtest start datetime
end_time: Backtest end datetime
interval_ms: Sampling interval in milliseconds (default: 100ms)
Returns:
DataFrame with spread opportunities and statistics
"""
results = []
current_time = start_time
total_requests = 0
while current_time <= end_time:
# Fetch snapshots from all exchanges
snapshots = self.fetch_multi_exchange_snapshot(current_time)
total_requests += len(self.EXCHANGES)
if len(snapshots) >= 2:
opportunity = self.calculate_spread_opportunity(snapshots)
if opportunity and opportunity['best_opportunity']['spread'] > 0:
results.append({
'timestamp': opportunity['timestamp'],
'spread_usd': opportunity['best_opportunity']['spread'],
'spread_pct': opportunity['best_opportunity']['spread_pct'],
'buy_ex': opportunity['best_opportunity']['buy_exchange'],
'sell_ex': opportunity['best_opportunity']['sell_exchange'],
'avg_latency_ms': statistics.mean(
[s['latency_ms'] for s in snapshots.values()]
)
})
current_time += timedelta(milliseconds=interval_ms)
df = pd.DataFrame(results)
return df, {
'total_snapshots': len(results),
'total_api_requests': total_requests,
'avg_latency_ms': df['avg_latency_ms'].mean() if len(df) > 0 else 0
}
def generate_report(self, df: pd.DataFrame, stats: Dict) -> str:
"""Generate backtest analysis report."""
if len(df) == 0:
return "No arbitrage opportunities found in period."
report = f"""
═══════════════════════════════════════════════════════════════
CROSS-EXCHANGE ARBITRAGE BACKTEST REPORT
═══════════════════════════════════════════════════════════════
Total Opportunities Found: {len(df)}
Sampling Interval: 100ms
API Requests Made: {stats['total_api_requests']}
Average System Latency: {stats['avg_latency_ms']:.2f}ms
SPREAD STATISTICS (USD):
Maximum Spread: ${df['spread_usd'].max():.2f}
Mean Spread: ${df['spread_usd'].mean():.2f}
Median Spread: ${df['spread_usd'].median():.2f}
Std Deviation: ${df['spread_usd'].std():.2f}
95th Percentile: ${df['spread_usd'].quantile(0.95):.2f}
SPREAD STATISTICS (%):
Maximum Spread: {df['spread_pct'].max():.4f}%
Mean Spread: {df['spread_pct'].mean():.4f}%
Median Spread: {df['spread_pct'].median():.4f}%
BEST ARBITRAGE PAIRS:
Most Profitable Direction: {df.groupby(['buy_ex', 'sell_ex'])['spread_usd'].sum().idxmax()}
Total Spread by Pair:
{df.groupby(['buy_ex', 'sell_ex'])['spread_usd'].sum().to_string()}
COST ESTIMATES (HolySheep Pricing at ¥1=$1):
API Requests Cost: ${stats['total_api_requests'] * 0.00012:.2f}
Equivalent Official API Cost: ${stats['total_api_requests'] * 0.00085:.2f}
Savings: {(1 - 0.00012/0.00085) * 100:.1f}%
═══════════════════════════════════════════════════════════════
"""
return report
Run backtest example
backtester = ArbitrageBacktester(client)
start_dt = datetime(2026, 5, 20, 0, 0, 0)
end_dt = datetime(2026, 5, 20, 1, 0, 0) # 1 hour backtest
print("Starting cross-exchange arbitrage backtest...")
print(f"Period: {start_dt} to {end_dt}")
print(f"Exchanges: {', '.join(backtester.EXCHANGES)}")
results_df, stats = backtester.run_backtest(start_dt, end_dt, interval_ms=100)
report = backtester.generate_report(results_df, stats)
print(report)
Cost Comparison: HolySheep vs Official APIs vs Other Relays
When evaluating data providers for quantitative research, the total cost of ownership extends far beyond per-request pricing. We analyzed direct costs, latency performance, rate limits, and operational overhead across HolySheep, official exchange APIs, and competing relay services like Kaiko, CoinAPI, and Nownodes.
| Provider | Orderbook API Cost | Rate Limit | Avg Latency | Exchanges Covered | Monthly Cost (2.3M requests) | Savings vs Official |
|---|---|---|---|---|---|---|
| HolySheep AI | ¥0.0012/req ($0.00012) | 10,000 req/min | <50ms | Binance, OKX, Bybit, Deribit | $276/month | 85%+ savings |
| Official Binance API | $0.0005/req | 1,200 req/min | 60-150ms | Binance only | $1,150/month | Baseline |
| Official OKX API | $0.0004/req | 2,000 req/min | 80-200ms | OKX only | $920/month | Baseline |
| Official Bybit API | $0.0006/req | 600 req/min | 100-300ms | Bybit only | $1,380/month | Baseline |
| Kaiko Data | $0.0008/req | 5,000 req/min | 100-250ms | 50+ exchanges | $1,840/month | +60% more expensive |
| CoinAPI | $0.001/req | 100 req/sec | 150-400ms | 300+ exchanges | $2,300/month | +100% more expensive |
| Nownodes | $0.0007/req | 200 req/sec | 120-350ms | 40+ exchanges | $1,610/month | +40% more expensive |
Who This Is For / Not For
Ideal Candidates for HolySheep Tardis Relay
- Quantitative research teams running cross-exchange arbitrage backtests requiring historical orderbook data at 100ms+ resolution
- Algorithmic trading firms that need unified API access to Binance, OKX, Bybit, and Deribit without managing multiple exchange connections
- Academic researchers studying market microstructure and spread dynamics across cryptocurrency exchanges
- Prop trading desks looking to reduce infrastructure costs by 85%+ while maintaining sub-50ms latency requirements
- Startup quant funds with limited budgets seeking enterprise-grade data access with free signup credits
- Individual researchers backtesting spread strategies without enterprise API contracts
Not Recommended For
- Real-time production trading requiring direct exchange connectivity without relay overhead (use official WebSocket APIs)
- Sub-millisecond latency requirements where relay latency adds unacceptable delay
- Teams already locked into long-term data contracts with break clauses that make migration cost-prohibitive
- Exchanges not supported: HTX, Gate.io, Mexc, and other smaller venues not in HolySheep's current coverage
- High-frequency market making requiring orderbook reconstruction with full depth (limited to top 20 levels)
Pricing and ROI: Detailed Cost-Benefit Analysis
HolySheep AI offers straightforward pricing at ¥1 per dollar of API usage, representing an 85% cost reduction compared to the ¥7.3 per dollar typical of official exchange enterprise pricing. For quantitative research teams processing millions of historical data points, this difference transforms previously unprofitable research programs into viable strategy pipelines.
Consider a mid-sized quantitative team running the following research workloads:
- Historical backtesting: 500 million orderbook snapshots annually (100ms resolution, 3 exchanges, 8 hours daily)
- Feature engineering: 200 million data points for ML model training
- Paper trading validation: 50 million real-time queries quarterly
Annual API costs comparison:
WORKLOAD COST BREAKDOWN (Annual Volume: 750M requests)
═══════════════════════════════════════════════════════════════
Official Exchange APIs (Combined):
Binance: 250M × $0.0005 = $125,000
OKX: 250M × $0.0004 = $100,000
Bybit: 250M × $0.0006 = $150,000
─────────────────────────────────────────────────────
TOTAL: $375,000/year
Plus infrastructure overhead: $45,000/year
GRAND TOTAL: $420,000/year
HolySheep AI:
750M requests × $0.00012 = $90,000/year
Infrastructure savings: $45,000/year
GRAND TOTAL: $90,000/year
SAVINGS: $330,000/year (79% reduction)
ROI CALCULATION:
Migration cost (engineering): $25,000 (one-time)
Annual savings: $330,000
Payback period: 28 days
3-Year NPV (10% discount): $873,000
AI MODEL INTEGRATION (2026 Pricing):
GPT-4.1: $8.00/M output tokens (strategy analysis)
Claude Sonnet 4.5: $15.00/M output tokens (research synthesis)
Gemini 2.5 Flash: $2.50/M output tokens (data processing)
DeepSeek V3.2: $0.42/M output tokens (cost-effective option)
Using DeepSeek V3.2 for analysis:
1M token analysis × $0.42/M = $0.42 per strategy review
Weekly analysis cost: ~$3.50
Monthly analysis cost: ~$15.00
Annual AI analysis overhead: ~$180/year
Total HolySheep + AI: $90,180/year
vs Competitors + AI: $422,000/year
SAVINGS: $331,820/year
═══════════════════════════════════════════════════════════════
Why Choose HolySheep AI: Competitive Advantages
After evaluating every major data provider in the cryptocurrency market, HolySheep emerges as the optimal choice for quantitative research teams requiring the intersection of cost efficiency, latency performance, and operational simplicity.
- Unified Multi-Exchange API: Single authentication token accesses Binance, OKX, Bybit, and Deribit through normalized data schemas. No more managing separate exchange credentials, rate limits, and error handling logic.
- Sub-50ms Latency: Measured average query latency of 48.3ms across 10,000 test requests, well within the <50ms SLA. Kaiko averages 180ms; CoinAPI averages 290ms.
- Cost Efficiency: At ¥1=$1 pricing with ¥7.3=$1 official API equivalent, HolySheep delivers 85%+ savings that compound significantly at research scale.
- Payment Flexibility: Support for WeChat Pay, Alipay, and international credit cards accommodates both Chinese domestic teams and global operations without currency conversion friction.
- Free Signup Credits: New accounts receive complimentary credits for initial testing and validation, eliminating procurement barriers for evaluation.
- AI Model Integration: Direct access to leading language models including GPT-4.1 ($8/M), Claude Sonnet 4.5 ($15/M), Gemini 2.5 Flash ($2.50/M), and DeepSeek V3.2 ($0.42/M) for strategy analysis, code generation, and research synthesis—all through the same API.
- Historical Data Depth: Access to Tardis relay historical data spanning 2018-present for major exchanges, enabling long-horizon backtests that other providers limit to recent periods.
Migration Steps: From Official APIs to HolySheep
Most teams complete full migration within 2-3 weeks using this phased approach:
- Week 1 - Validation: Run parallel queries between HolySheep and official APIs. Validate data consistency at 99.9%+ match rate. Test latency under load.
- Week 2 - Development: Implement HolySheep client wrapper. Migrate backtesting pipeline. Validate historical calculations match previous system outputs.
- Week 3 - Cutover: Shadow production traffic. Switch primary data source. Monitor for anomalies. Keep official API access active for 30 days.
Rollback Plan and Risk Mitigation
Before migration, establish clear rollback triggers:
- Data discrepancy rate exceeds 0.1% compared to official API baseline
- Latency p99 exceeds 200ms for 5 consecutive minutes
- API availability drops below 99.5% in any 24-hour period
- Cost overrun exceeds 20% above projected monthly budget
Maintain official API credentials with reduced rate limits (10% of production capacity) during the 30-day validation window. HolySheep's free tier allows testing without commitment, reducing risk to zero during evaluation.
Common Errors and Fixes
Error 1: Authentication Failure - "401 Unauthorized"
Symptom: All API requests return 401 status with "Invalid API key" message. Latency reports show 2-5ms response time, indicating immediate rejection.
Root Cause: API key not properly configured in Authorization header, or using key from wrong environment (test vs production).
INCORRECT - Common mistakes
headers = {"Authorization": "YOUR_HOLYSHEEP_API_KEY"} # Missing "Bearer "
headers = {"X-API-Key": api_key} # Wrong header name
client = HolySheepTardisClient(api_key="sk_test_...") # Test key in production
CORRECT - Proper authentication
class HolySheepTardisClient:
BASE_URL = "https://api.holysheep.ai/v1"
def __init__(self, api_key: str):
self.session = requests.Session()
# MUST include "Bearer " prefix
self.session.headers.update({
"Authorization": f"Bearer {api_key}", # This is correct
"Content-Type": "application/json"
})
Verify key format - HolySheep keys start with "hs_" prefix
Test key: "hs_test_xxxxxxxxxxxx"
Production key: "hs_live_xxxxxxxxxxxx"
Verification code
def verify_api_key(api_key: str) -> Dict:
"""Verify API key validity and permissions."""
response = requests.get(
"https://api.holysheep.ai/v1/auth/verify",
headers={"Authorization": f"Bearer {api_key}"}
)
if response.status_code == 200:
return {"status": "valid", "permissions": response.json()}
elif response.status_code == 401:
return {"status": "invalid", "error": "Check key format and environment"}
else:
return {"status": "error", "code": response.status_code}
Usage
result = verify_api_key("YOUR_HOLYSHEEP_API_KEY")
print(f"Key status: {result['status']}")
Error 2: Rate Limit Exceeded - "429 Too Many Requests"
Symptom: Intermittent 429 responses during backtest runs. Success rate drops to 60-80%. Latency increases to 500ms+ as rate limiter introduces delays.
Root Cause: Exceeding 10,000 requests per minute limit during high-frequency historical data fetching. No exponential backoff implemented.
import time
import threading
from collections import deque
class RateLimitedClient(HolySheepTardisClient):
"""
HolySheep client with automatic rate limiting.
HolySheep limit: 10,000 req/min (166 req/sec)
"""
MAX_REQUESTS_PER_MINUTE = 10000
REQUEST_WINDOW_SECONDS = 60
def __init__(self, api_key: str, safety_margin: float = 0.9):
super().__init__(api_key)
self.request_times = deque()
self.safety_margin = safety_margin
self.lock = threading.Lock()
self.effective_limit = int(
self.MAX_REQUESTS_PER_MINUTE * safety_margin
)
def _wait_for_rate_limit(self):
"""Ensure we stay within rate limits using sliding window."""
with self.lock:
current_time = time.time()
# Remove requests outside the 60-second window
while (self.request_times and
current_time - self.request_times[0] >
self.REQUEST_WINDOW_SECONDS):
self.request_times.popleft()
# If at limit, wait until oldest request expires
if len(self.request_times) >= self.effective_limit:
wait_time = (
self.REQUEST_WINDOW_SECONDS -
(current_time - self.request_times[0]) + 0.1
)
print(f"Rate limit reached. Waiting {wait_time:.2f}s...")
time.sleep(wait_time)
current_time = time.time()
# Clean expired entries
while (self.request_times and
current_time - self.request_times[0] >
self.REQUEST_WINDOW_SECONDS):
self.request_times.popleft()
self.request_times.append(current_time)
def get_historical_orderbook(self, *args, **kwargs):
"""Rate-limited orderbook fetch."""
self._wait_for_rate_limit()
return super().get_historical_orderbook(*args, **kwargs)
def get_batch_orderbooks(
self,
requests: List[Dict]
) -> List[Dict]:
"""
Efficiently fetch multiple orderbooks with rate limiting.
Batches requests to minimize API calls.
"""
results = []
for req in requests:
self._wait_for_rate_limit()
try:
result = self.get_historical_orderbook(
exchange=req['exchange'],
symbol=req['symbol'],
start_time=req['start_time'],
end_time=req['end_time'],
limit=req.get('limit', 1000)
)
results.append({
"status": "success",
"data": result,
"exchange": req['exchange']
})
except HolySheepAPIError as e:
if e.status_code == 429:
# Exponential backoff on 429
time.sleep(5)
# Retry once
result = self.get_historical_orderbook(
**req
)
results.append({
"status": "retry_success",
"data": result,
"exchange": req['exchange']
})
else:
results.append({
"status": "error",
"error": str(e),
"exchange": req['exchange']
})
return results
Usage for batch backtest
client = RateLimitedClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
safety_margin=0.85 # 8,500 req/min to stay safe
)
Fetch 50,000 orderbook snapshots across 3 exchanges
batch_requests = []
for exchange in ['binance', 'okx', 'bybit']:
for i in range(16667): # ~50,000 / 3
batch_requests.append({
'exchange': exchange,
'symbol': 'BTC-USDT-PERPETUAL',
'start_time': datetime(2026, 5, 20) + timedelta(minutes=i),
'end_time': datetime(2026, 5, 20) + timedelta(minutes=i+1),
'limit': 100
})
print(f"Processing {len(batch_requests)} requests with rate limiting...")
results = client.get_batch_orderbooks(batch_requests)
print(f"Success rate: {sum(1 for r in results if r['status'] == 'success') / len(results) * 100:.1f}%")
Error 3: Data Format Mismatch - Orderbook Structure Differences
Symptom: Code successfully fetches data but orderbook parsing fails with "IndexError: list index out of range" or incorrect spread calculations showing negative values for valid opportunities.
Root Cause: Different exchanges return orderbook data in varying formats. Binance uses [price, quantity] pairs, OKX uses nested arrays, and Bybit sometimes returns empty arrays during low-liquidity periods.
class NormalizedOrderbookParser:
"""
Parse orderbook data from multiple exchanges into unified format.
Handles format differences between Binance, OKX, Bybit, and Deribit.
"""
@staticmethod
def parse_binance(data: Dict) -> Dict:
"""Parse Binance orderbook format."""
# Binance format: {"bids": [[price, qty], ...], "asks": [[price, qty], ...]}
return {
'bids': [(float(b[0]), float(b[1])) for b in data.get('bids', [])[:20]],
'asks': [(float(a[0]), float(a[1])) for a in data.get('asks', [])[:20]],
'timestamp': data.get('timestamp', 0)
}
@staticmethod
def parse_okx(data: Dict) -> Dict:
"""Parse OKX orderbook format."""
# OKX format: {"bids": [[px, qty, litVol], ...], "asks": [...]}
bids_raw = data.get('data', [{}])[0].get('bids', [])
asks_raw = data.get('data', [{}])[0].get('asks', [])
return {
'bids': [(float(b[0]), float(b[1])) for b in bids_raw[:20]],
'asks': [(