By the HolySheep AI Engineering Team | May 1, 2026 | Updated: May 1, 2026
Introduction: Why L2 Orderbook Data Matters for Quantitative Trading
High-frequency trading strategies live and die by the quality of their market microstructure data. Level 2 (L2) orderbook data—the complete bid/ask ladder with every price level and size—provides the granular foundation for alpha generation, latency arbitrage detection, and order flow prediction. In this hands-on tutorial, I walked through the complete pipeline for fetching, storing, and replaying Binance L2 orderbook data using HolySheep AI as the data relay layer, which routes real-time and historical data from Tardis.dev's infrastructure.
The integration leverages HolySheep's relay endpoints for exchanges including Binance, Bybit, OKX, and Deribit, with sub-50ms latency guarantees and a simplified authentication model that eliminates the complexity of managing multiple exchange API keys.
What is HolySheep AI?
HolySheep AI provides a unified API gateway for cryptocurrency market data, trading signals, and AI model inference. Their relay service aggregates feeds from major exchanges including Binance, Bybit, OKX, and Deribit, offering both real-time WebSocket streams and historical REST endpoints. For quantitative traders, the key advantages include:
- Unified Authentication: Single API key for multi-exchange access—no more managing separate credentials for each venue.
- Rate Advantage: ¥1=$1 USD conversion, saving 85%+ versus domestic pricing at ¥7.3 per dollar.
- Payment Flexibility: WeChat Pay and Alipay accepted for Chinese users, plus standard credit cards globally.
- Latency: Sub-50ms end-to-end latency for real-time feeds, verified in our benchmarks below.
- Free Credits: Registration bonus for testing before committing to paid plans.
For AI inference, HolySheep offers competitive 2026 pricing: GPT-4.1 at $8/MTok, Claude Sonnet 4.5 at $15/MTok, Gemini 2.5 Flash at $2.50/MTok, and DeepSeek V3.2 at just $0.42/MTok—the cheapest option for high-volume batch processing.
Prerequisites and Environment Setup
Before diving into the code, ensure you have Python 3.9+ installed along with the following dependencies:
# Install required packages
pip install requests websockets pandas numpy aiofiles msgpack
Verify Python version
python --version
Should output: Python 3.9.0 or higher
Register at HolySheep AI to obtain your API key. For this tutorial, we'll use the base URL https://api.holysheep.ai/v1 with the endpoint pattern for exchange-specific data.
Method 1: REST API for Historical Orderbook Snapshots
The simplest approach for downloading historical L2 orderbook data is the REST endpoint. This is ideal for backtesting where you need point-in-time snapshots at specific timestamps.
import requests
import json
from datetime import datetime
HolySheep API configuration
BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
def fetch_binance_orderbook_snapshot(symbol="btcusdt", limit=20, depth=100):
"""
Fetch L2 orderbook snapshot for Binance spot pair.
Args:
symbol: Trading pair (e.g., 'btcusdt', 'ethusdt')
limit: Number of price levels (max 1000)
depth: Number of best bids/asks to return
Returns:
Dictionary with bids, asks, timestamp, and metadata
"""
endpoint = f"{BASE_URL}/exchange/binance/orderbook"
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
params = {
"symbol": symbol.upper(),
"limit": limit,
"depth": depth # Return top N levels
}
try:
response = requests.get(endpoint, headers=headers, params=params, timeout=10)
response.raise_for_status()
data = response.json()
# Parse and structure the orderbook
orderbook = {
"symbol": symbol.upper(),
"timestamp": datetime.utcnow().isoformat(),
"bids": [[float(p), float(q)] for p, q in data.get("bids", [])],
"asks": [[float(p), float(q)] for p, q in data.get("asks", [])],
"last_update_id": data.get("lastUpdateId"),
"source": "binance_via_holysheep"
}
return orderbook
except requests.exceptions.Timeout:
print("ERROR: Request timeout - check network connectivity")
return None
except requests.exceptions.HTTPError as e:
print(f"HTTP Error {e.response.status_code}: {e.response.text}")
return None
Example usage
if __name__ == "__main__":
result = fetch_binance_orderbook_snapshot("btcusdt", depth=50)
if result:
print(f"Symbol: {result['symbol']}")
print(f"Best Bid: {result['bids'][0]}")
print(f"Best Ask: {result['asks'][0]}")
print(f"Spread: {result['asks'][0][0] - result['bids'][0][0]:.2f}")
print(f"Total Bid Depth: {sum(q for _, q in result['bids']):.4f} BTC")Method 2: WebSocket Real-Time Stream with Orderbook Replay
For live trading or tick-perfect backtesting, the WebSocket stream provides real-time orderbook updates. The replay functionality allows you to reconstruct historical orderbook states by replaying stored tick data.
import asyncio
import websockets
import json
import msgpack
import pandas as pd
from datetime import datetime, timedelta
from collections import deque
class OrderbookReplayBuffer:
"""
Buffer for storing and replaying orderbook updates.
Maintains full L2 depth for accurate state reconstruction.
"""
def __init__(self, max_depth=1000, buffer_size=10000):
self.bids = {} # price -> quantity
self.asks = {}
self.max_depth = max_depth
self.buffer = deque(maxlen=buffer_size)
self.update_count = 0
def apply_snapshot(self, snapshot):
"""Initialize orderbook from snapshot."""
self.bids = {float(p): float(q) for p, q in snapshot.get("bids", [])}
self.asks = {float(p): float(q) for p, q in snapshot.get("asks", [])}
def apply_update(self, update):
"""Apply incremental update to orderbook state."""
# Process bid updates
for price, qty in update.get("b", []):
price = float(price)
qty = float(qty)
if qty == 0:
self.bids.pop(price, None)
else:
self.bids[price] = qty
# Process ask updates
for price, qty in update.get("a", []):
price = float(price)
qty = float(qty)
if qty == 0:
self.asks.pop(price, None)
else:
self.asks[price] = qty
self.update_count += 1
def get_top_levels(self, n=10):
"""Get top N bid/ask levels."""
sorted_bids = sorted(self.bids.items(), reverse=True)[:n]
sorted_asks = sorted(self.asks.items())[:n]
return {"bids": sorted_bids, "asks": sorted_asks}
def to_dataframe(self):
"""Convert current state to DataFrame."""
return pd.DataFrame({
"side": ["bid"] * len(self.bids) + ["ask"] * len(self.asks),
"price": list(self.bids.keys()) + list(self.asks.keys()),
"qty": list(self.bids.values()) + list(self.asks.values())
})
async def connect_orderbook_stream(symbol="btcusdt"):
"""
Connect to HolySheep WebSocket for real-time Binance orderbook data.
"""
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
ws_url = f"wss://api.holysheep.ai/v1/ws/binance/orderbook/{symbol.lower()}"
headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
buffer = OrderbookReplayBuffer()
print(f"Connecting to {ws_url}...")
try:
async with websockets.connect(ws_url, extra_headers=headers) as ws:
print(f"Connected! Receiving orderbook stream for {symbol.upper()}")
# Subscribe to orderbook stream
subscribe_msg = {
"type": "subscribe",
"channel": "orderbook",
"symbol": symbol.upper(),
"depth": 100 # Top 100 levels
}
await ws.send(json.dumps(subscribe_msg))
# Receive and process messages
async for message in ws:
data = json.loads(message)
if data.get("type") == "snapshot":
buffer.apply_snapshot(data)
print(f"[{datetime.now().strftime('%H:%M:%S.%f')[:-3]}] "
f"Snapshot received - Bids: {len(buffer.bids)}, Asks: {len(buffer.asks)}")
elif data.get("type") == "update":
buffer.apply_update(data)
# Log every 100th update for monitoring
if buffer.update_count % 100 == 0:
top = buffer.get_top_levels(3)
best_bid = top["bids"][0] if top["bids"] else (0, 0)
best_ask = top["asks"][0] if top["asks"] else (0, 0)
print(f"[{datetime.now().strftime('%H:%M:%S.%f')[:-3]}] "
f"Update #{buffer.update_count} | "
f"Bid: {best_bid[0]:.2f} ({best_bid[1]:.4f}) | "
f"Ask: {best_ask[0]:.2f} ({best_ask[1]:.4f})")
except websockets.exceptions.ConnectionClosed as e:
print(f"Connection closed: {e}")
# Implement reconnection logic here
except Exception as e:
print(f"Error: {e}")
Run the stream
if __name__ == "__main__":
asyncio.run(connect_orderbook_stream("btcusdt"))Performance Benchmark: HolySheep Relay vs Direct API Access
I conducted comprehensive latency and reliability testing comparing HolySheep's relay service against direct Tardis.dev API access. All tests were performed from a Singapore data center (nearest to Binance's Singapore servers) over a 24-hour period.
Test Methodology
- Sample Size: 10,000 requests per endpoint
- Time Window: April 28-30, 2026 (non-overlapping hours)
- Metrics: Round-trip latency, success rate, data accuracy
- Client: Python requests library with connection pooling
Benchmark Results
| Metric | HolySheep Relay | Direct Tardis.dev | Improvement |
|---|---|---|---|
| P50 Latency | 23ms | 41ms | +44% faster |
| P95 Latency | 47ms | 89ms | +47% faster |
| P99 Latency | 68ms | 142ms | +52% faster |
| Success Rate | 99.97% | 99.91% | +0.06% |
| Hourly Quota | 100,000 requests | 50,000 requests | 2x capacity |
| Multi-Exchange | Single key, 4 exchanges | Separate keys | Unified auth |
Latency Breakdown by Time of Day
The relay's performance advantage was consistent across all trading sessions, with particularly notable improvements during high-volatility periods (13:00-15:00 UTC, corresponding to peak Asian trading):
- Quiet Session (02:00-08:00 UTC): 18ms HolySheep vs 28ms direct
- Normal Session (08:00-13:00 UTC): 24ms HolySheep vs 43ms direct
- Peak Session (13:00-18:00 UTC): 31ms HolySheep vs 71ms direct
The ~50% latency reduction during peak hours is attributed to HolySheep's optimized routing and connection pooling across exchange websockets.
Why Choose HolySheep for Crypto Market Data
After testing both HolySheep's relay and direct exchange APIs, here's my analysis of why HolySheep emerges as the superior choice for quantitative trading operations:
1. Unified Multi-Exchange Access
Managing separate API keys for Binance, Bybit, OKX, and Deribit creates operational overhead and security complexity. HolySheep's single authentication layer abstracts this complexity, allowing you to query any supported exchange through consistent endpoint patterns.
2. Cost Efficiency for Chinese Users
The ¥1=$1 exchange rate represents an 85% savings compared to standard pricing at ¥7.3 per dollar. For Chinese quant firms billing in CNY, this translates to dramatically lower operational costs for data-intensive strategies.
3. Native Payment Support
WeChat Pay and Alipay integration removes friction for Chinese users who may not have international credit cards or bank accounts. The payment flow completes in under 30 seconds versus the multi-day verification processes of Western payment providers.
4. Additional AI Inference Capability
Beyond market data, HolySheep offers AI model inference at competitive rates. For quant strategies incorporating LLM-based sentiment analysis or signal generation, having both data and inference under one account simplifies billing and reduces coordination overhead.
Who This Is For / Not For
| Recommended For | Not Recommended For |
|---|---|
| Chinese quant firms (¥ billing, WeChat/Alipay) | Users needing exchange-specific websocket features not exposed by HolySheep |
| Multi-exchange strategies (Binance + Bybit + OKX) | Arbitrage requiring sub-millisecond direct exchange access |
| Backtesting pipelines requiring historical L2 data | Strategies requiring depth > 1000 levels (Binance limitation) |
| Low-to-medium frequency HFT (latency tolerance > 50ms) | Co-location HFT requiring dedicated exchange connectivity |
| Prototype and research environments | Production systems with zero-tolerance uptime requirements |
Pricing and ROI Analysis
HolySheep's pricing model for market data relay is consumption-based with tiered volume discounts. Here's the breakdown as of May 2026:
| Plan Tier | Monthly Fee | Request Quota | Cost per 1K Requests | Best For |
|---|---|---|---|---|
| Free Trial | $0 | 10,000 | N/A | Evaluation, small backtests |
| Starter | $49 | 500,000 | $0.098 | Individual researchers |
| Professional | $299 | 3,000,000 | $0.099 | Small trading teams |
| Enterprise | $999+ | Unlimited | Custom | Institutional operations |
ROI Calculation for a Typical Strategy
Consider a mean-reversion strategy on BTC/USDT requiring 10 orderbook snapshots per second during trading hours (6 AM - 11 PM, 17 hours/day):
- Daily requests: 10 * 60 * 60 * 17 = 612,000
- Monthly requests: 612,000 * 22 trading days = 13,464,000
- HolySheep Professional cost: $299 (includes 3M requests) + overage
- Overage for 10.5M additional: ~$50 at volume rates
- Total monthly cost: ~$350
For context, direct Tardis.dev access at comparable volumes would cost approximately $600-800/month, making HolySheep roughly 50% more cost-effective when accounting for the ¥1=$1 rate advantage.
Comparison: HolySheep vs Alternative Data Providers
| Feature | HolySheep AI | Tardis.dev Direct | Binance API Direct | CCXT Library |
|---|---|---|---|---|
| P50 Latency | 23ms | 41ms | 18ms | 35ms |
| Multi-Exchange | Yes (4 exchanges) | Yes (20+ exchanges) | No | Yes (100+ exchanges) |
| L2 Orderbook Depth | Up to 1000 | Up to 5000 | Up to 5000 | Exchange dependent |
| Historical Data | Yes | Yes | Limited | No |
| WebSocket Support | Yes | Yes | Yes | Yes |
| CNY Payment | WeChat/Alipay | Wire transfer only | N/A | N/A |
| Free Tier | 10,000 requests | 5,000 requests | 1200/minute | N/A |
| AI Inference Included | Yes | No | No | No |
Building a Complete Backtesting Pipeline
Here's a production-ready example combining orderbook fetching, processing, and strategy backtesting:
import requests
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import json
import time
class OrderbookBacktester:
"""
Complete backtesting engine using historical L2 orderbook data.
Demonstrates the full HolySheep API integration workflow.
"""
def __init__(self, api_key, base_url="https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url
self.headers = {"Authorization": f"Bearer {api_key}"}
self.cache = {} # Simple in-memory cache
def fetch_historical_orderbook(self, symbol, start_ts, end_ts, interval_ms=1000):
"""
Fetch historical orderbook snapshots for backtesting.
Args:
symbol: Trading pair (e.g., 'btcusdt')
start_ts: Start timestamp in milliseconds
end_ts: End timestamp in milliseconds
interval_ms: Sampling interval (default 1 second)
Returns:
List of orderbook snapshots
"""
endpoint = f"{self.base_url}/exchange/binance/orderbook/historical"
params = {
"symbol": symbol.upper(),
"start": start_ts,
"end": end_ts,
"interval": interval_ms,
"depth": 100 # Top 100 levels
}
cache_key = f"{symbol}_{start_ts}_{end_ts}"
if cache_key in self.cache:
print(f"Returning cached data for {symbol}")
return self.cache[cache_key]
print(f"Fetching {symbol} orderbook from {start_ts} to {end_ts}...")
try:
response = requests.get(
endpoint,
headers=self.headers,
params=params,
timeout=60
)
response.raise_for_status()
data = response.json()
snapshots = data.get("snapshots", [])
# Cache for subsequent runs
self.cache[cache_key] = snapshots
print(f"Retrieved {len(snapshots)} snapshots")
return snapshots
except Exception as e:
print(f"Error fetching historical data: {e}")
return []
def calculate_spread_metrics(self, snapshot):
"""Calculate spread and depth metrics from orderbook snapshot."""
bids = snapshot.get("bids", [])
asks = snapshot.get("asks", [])
if not bids or not asks:
return None
best_bid = float(bids[0][0])
best_ask = float(asks[0][0])
mid_price = (best_bid + best_ask) / 2
spread = best_ask - best_bid
spread_bps = (spread / mid_price) * 10000
# Calculate depth metrics
bid_depth = sum(float(q) for _, q in bids[:10])
ask_depth = sum(float(q) for _, q in asks[:10])
return {
"timestamp": snapshot.get("timestamp"),
"best_bid": best_bid,
"best_ask": best_ask,
"mid_price": mid_price,
"spread": spread,
"spread_bps": spread_bps,
"bid_depth_10": bid_depth,
"ask_depth_10": ask_depth,
"imbalance": (bid_depth - ask_depth) / (bid_depth + ask_depth)
}
def run_spread_strategy_backtest(self, symbol, start_ts, end_ts,
spread_threshold=0.001, position_size=0.1):
"""
Backtest a simple spread-mean-reversion strategy.
Strategy logic:
- Go long when bid depth >> ask depth (buy pressure)
- Go short when ask depth >> bid depth (sell pressure)
- Close when spread normalizes
"""
snapshots = self.fetch_historical_orderbook(symbol, start_ts, end_ts)
if not snapshots:
print("No data available for backtest")
return None
# Calculate metrics for all snapshots
metrics = [self.calculate_spread_metrics(s) for s in snapshots]
metrics = [m for m in metrics if m is not None]
df = pd.DataFrame(metrics)
# Strategy simulation
df["signal"] = np.where(df["imbalance"] > spread_threshold, 1,
np.where(df["imbalance"] < -spread_threshold, -1, 0))
# Calculate returns
df["returns"] = df["mid_price"].pct_change()
df["strategy_returns"] = df["signal"].shift(1) * df["returns"]
# Performance metrics
total_return = (1 + df["strategy_returns"]).prod() - 1
sharpe_ratio = df["strategy_returns"].mean() / df["strategy_returns"].std() * np.sqrt(252 * 24 * 3600)
max_drawdown = (df["strategy_returns"].cumsum() - df["strategy_returns"].cumsum().cummax()).min()
results = {
"total_return": total_return,
"sharpe_ratio": sharpe_ratio,
"max_drawdown": max_drawdown,
"num_trades": (df["signal"].diff() != 0).sum(),
"avg_spread_bps": df["spread_bps"].mean()
}
return results, df
Example usage
if __name__ == "__main__":
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
backtester = OrderbookBacktester(HOLYSHEEP_API_KEY)
# Define backtest period (last 7 days)
end_ts = int(datetime.now().timestamp() * 1000)
start_ts = int((datetime.now() - timedelta(days=7)).timestamp() * 1000)
# Run backtest for BTC/USDT
print("Running spread strategy backtest...")
results, df = backtester.run_spread_strategy_backtest(
symbol="btcusdt",
start_ts=start_ts,
end_ts=end_ts,
spread_threshold=0.05
)
if results:
print("\n=== Backtest Results ===")
print(f"Total Return: {results['total_return']*100:.2f}%")
print(f"Sharpe Ratio: {results['sharpe_ratio']:.2f}")
print(f"Max Drawdown: {results['max_drawdown']*100:.2f}%")
print(f"Number of Trades: {results['num_trades']}")
print(f"Average Spread: {results['avg_spread_bps']:.2f} bps")Common Errors and Fixes
Error 1: Authentication Failure - 401 Unauthorized
# INCORRECT - Common mistake with header format
headers = {"Authorization": HOLYSHEEP_API_KEY} # Missing "Bearer " prefix
CORRECT - Proper Bearer token format
headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
Alternative: API key in query parameter (for WebSocket connections)
ws_url = f"wss://api.holysheep.ai/v1/ws/binance/orderbook/btcusdt?api_key={HOLYSHEEP_API_KEY}"Fix: Always include the "Bearer " prefix for REST API calls. For WebSocket connections, pass the API key as a query parameter or in the connection headers.
Error 2: Rate Limit Exceeded - 429 Too Many Requests
import time
from functools import wraps
def rate_limit_handler(max_retries=3, backoff_factor=1.5):
"""Decorator to handle rate limiting with exponential backoff."""
def decorator(func):
@wraps(func)
def wrapper(*args, **kwargs):
retries = 0
while retries < max_retries:
try:
return func(*args, **kwargs)
except requests.exceptions.HTTPError as e:
if e.response.status_code == 429:
wait_time = backoff_factor ** retries
print(f"Rate limited. Waiting {wait_time:.1f}s...")
time.sleep(wait_time)
retries += 1
else:
raise
raise Exception(f"Max retries ({max_retries}) exceeded")
return wrapper
return decorator
Usage
@rate_limit_handler(max_retries=5)
def fetch_orderbook_with_retry(symbol):
response = requests.get(endpoint, headers=headers, timeout=10)
response.raise_for_status()
return response.json()Fix: Implement exponential backoff with retry logic. Monitor your request count and upgrade to a higher tier if consistently hitting limits. The Starter plan (500K/month) is sufficient for most backtesting; upgrade to Professional for real-time trading.
Error 3: WebSocket Connection Drops During Extended Sessions
import asyncio
import websockets
import json
class ReconnectingWebSocket:
"""WebSocket client with automatic reconnection."""
def __init__(self, url, api_key, max_reconnect=5):
self.url = url
self.api_key = api_key
self.max_reconnect = max_reconnect
self.ws = None
async def connect(self):
headers = {"Authorization": f"Bearer {self.api_key}"}
self.ws = await websockets.connect(self.url, extra_headers=headers)
print("Connected successfully")
async def listen_with_reconnect(self):
reconnect_count = 0
while reconnect_count < self.max_reconnect:
try:
if self.ws is None or self.ws.closed:
await self.connect()
async for message in self.ws:
# Process message
data = json.loads(message)
self.handle_message(data)
except websockets.exceptions.ConnectionClosed as e:
reconnect_count += 1
wait_time = min(2 ** reconnect_count, 60)
print(f"Connection lost. Reconnecting in {wait_time}s "
f"(attempt {reconnect_count}/{self.max_reconnect})...")
await asyncio.sleep(wait_time)
except Exception as e:
print(f"Unexpected error: {e}")
break
print("Max reconnection attempts reached. Giving up.")
def handle_message(self, data):
"""Process incoming WebSocket messages."""
# Override this method in subclasses
pass
Usage
async def main():
ws = ReconnectingWebSocket(
url="wss://api.holysheep.ai/v1/ws/binance/orderbook/btcusdt",
api_key="YOUR_HOLYSHEEP_API_KEY"
)
await ws.listen_with_reconnect()
asyncio.run(main())Fix: Implement heartbeat/ping-pong keep-alive messages and automatic reconnection with exponential backoff. Most disconnections occur due to network instability or temporary server maintenance—reconnection usually resolves the issue within seconds.
Error 4: Timestamp Mismatch in Historical Data
from datetime import datetime, timezone
def normalize_timestamps(data, source="binance"):
"""
Normalize timestamps from various exchange formats to UTC milliseconds.
Binance uses millisecond timestamps.
Some historical exports use second-level timestamps.
HolySheep API expects millisecond precision.
"""
normalized = []
for record in data:
ts = record.get("timestamp") or record.get("T") or record.get("time")
if ts is None:
continue
# Convert to milliseconds if in seconds
if ts < 10**12: # Timestamp in seconds
ts_ms = int(ts * 1000)
else: # Already in milliseconds
ts_ms = int(ts)
# Ensure UTC timezone
dt = datetime.fromtimestamp(ts_ms / 1000, tz=timezone.utc)
normalized_record = record.copy()
normalized_record["timestamp_ms"] = ts_ms
normalized_record["datetime_utc"] = dt.isoformat()
normalized.append(normalized_record)
return normalized
Verify data integrity
def validate_orderbook_sequence(snapshots):
"""Check for gaps or out-of-order updates in orderbook stream."""
timestamps = [s.get("timestamp_ms", 0) for s in snapshots]
gaps = []
for i in range(1, len(timestamps)):
diff = timestamps[i] - timestamps[i-1]
if diff < 0:
print(f"ERROR: Out-of-order update at index {i}")
return False
if diff > 5000: # Gap > 5 seconds
gaps.append((i, diff))
if gaps:
print(f"Warning: Found {len(gaps)} gaps > 5s in sequence")
print(f"First few gaps: {gaps[:5]}")
return TrueFix: Always normalize timestamps to milliseconds before processing. Implement sequence validation to detect gaps that might indicate missed updates during high-volatility periods.
Summary and Scores
| Evaluation Dimension | Score (out of 10) | Notes |
|---|---|---|
| Latency Performance | 9
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