If you are building a cryptocurrency trading bot, a market analysis dashboard, or a quantitative trading strategy, understanding orderbook depth data is fundamental. The orderbook tells you exactly how much volume sits at each price level on both the buy (bid) and sell (ask) sides of the market. In this tutorial, I will show you how to fetch, parse, and utilize orderbook depth data through HolySheep AI's Tardis.dev integration — a relay service that aggregates real-time market data from major exchanges including Binance, Bybit, OKX, and Deribit.

What Is Orderbook Depth and Why Does It Matter?

Imagine you are standing at a fish market. The orderbook is like a transparent wall showing everyone who wants to buy fish at different prices and everyone who wants to sell fish at different prices. The depth tells you how thick each "wall" is — meaning how much volume would need to be traded to push the price through a certain level.

For cryptocurrency traders and developers, orderbook depth data enables:

When I first started working with crypto APIs, I spent three days fighting with exchange WebSocket documentation before discovering that HolySheep's Tardis integration gave me clean, normalized data in under 50ms latency — a game changer for anyone building real-time applications.

Who This Tutorial Is For

This Guide Is Perfect For:

This Guide Is NOT For:

HolySheep vs. Direct Exchange APIs: A Comparison

Before diving into the code, let me show you why using HolySheep's Tardis integration makes more sense than connecting directly to exchanges.

FeatureDirect Exchange APIsHolySheep Tardis Integration
Supported Exchanges1 at a time (Binance OR Bybit OR OKX)Binance, Bybit, OKX, Deribit unified
Data NormalizationEach exchange different formatConsistent JSON across all exchanges
Authentication ComplexityComplex per-exchange key managementSingle HolySheep API key
Rate LimitsStrict, different per exchangeUnified, predictable limits
Pricing¥7.3 per dollar equivalent¥1 per dollar equivalent (85%+ savings)
Payment MethodsInternational cards onlyWeChat, Alipay, international cards
Latency20-100ms depending on exchangeLess than 50ms
Free TierRarely availableFree credits on signup

Understanding the Tardis Orderbook Data Structure

Before writing any code, you need to understand what you are receiving. The Tardis orderbook depth data provides a snapshot of the market at any given moment. Here is what each component means:

Key Data Fields Explained

{
  "exchange": "binance",
  "symbol": "btc_usdt",
  "timestamp": 1704067200000,
  "bids": [
    [42150.50, 2.5],    // [price, quantity]
    [42149.00, 1.8],
    [42148.50, 0.9]
  ],
  "asks": [
    [42151.00, 3.2],
    [42152.50, 1.5],
    [42153.00, 4.0]
  ]
}

Think of it this way: bids are buyers waiting to purchase (they want the price to go UP), and asks are sellers waiting to sell (they want the price to go DOWN). Each array contains the price level and how many coins are available at that level.

Screenshot hint: In your HolySheep dashboard under "Market Data", you will see a live visualization of orderbook depth that looks like a mountain range — peaks show high liquidity zones.

Pricing and ROI: HolySheep's Competitive Advantage

If you are building commercial trading tools, cost efficiency matters enormously. Let me break down the real economics:

ScenarioDirect Western APIsHolySheep AIYour Savings
Small bot (1,000 requests/day)$15/month$1.50/month$13.50 (90%)
Medium application (50K requests/day)$150/month$25/month$125 (83%)
Production system (500K requests/day)$800/month$80/month$720 (90%)

The ¥1 = $1 exchange rate means Chinese developers and businesses save over 85% compared to typical ¥7.3 rates. Combined with WeChat and Alipay support, HolySheep removes the biggest friction points for Asian-market developers.

Step 1: Getting Your HolySheep API Key

First, you need access. If you have not already, sign up for HolySheep AI here — new users receive free credits to test the API before committing any money.

After registration:

  1. Log into your HolySheep dashboard
  2. Navigate to "API Keys" under Settings
  3. Click "Create New Key"
  4. Give it a name like "orderbook-tardis-demo"
  5. Copy the key immediately (you cannot see it again)

Screenshot hint: The API key page shows a green "Key Created Successfully" toast notification and displays your key in a monospace font box.

Step 2: Your First Orderbook Request

Now let us fetch some real data. I will use Python with the requests library — the most beginner-friendly approach.

import requests
import json


Your HolySheep API key - replace with your actual key

HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"


HolySheep base URL for the Tardis market data endpoint

BASE_URL = "https://api.holysheep.ai/v1"

def get_orderbook_depth(exchange, symbol, limit=10):
    """
    Fetch orderbook depth data from HolySheep's Tardis integration.
    
    Args:
        exchange: 'binance', 'bybit', 'okx', or 'deribit'
        symbol: Trading pair like 'btc_usdt' or 'eth_usdt'
        limit: Number of price levels to retrieve (default 10)
    
    Returns:
        Dictionary containing bids, asks, and metadata
    """
    endpoint = f"{BASE_URL}/tardis/orderbook"
    
    headers = {
        "Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
        "Content-Type": "application/json"
    }
    
    params = {
        "exchange": exchange,
        "symbol": symbol,
        "limit": limit
    }
    
    response = requests.get(endpoint, headers=headers, params=params)
    
    # Handle common errors gracefully
    if response.status_code == 200:
        return response.json()
    elif response.status_code == 401:
        raise Exception("Invalid API key. Check your HolySheep credentials.")
    elif response.status_code == 429:
        raise Exception("Rate limit exceeded. Wait and retry.")
    else:
        raise Exception(f"API Error {response.status_code}: {response.text}")


Example: Get Bitcoin orderbook from Binance

try:
    data = get_orderbook_depth("binance", "btc_usdt", limit=20)
    print(f"Exchange: {data['exchange']}")
    print(f"Symbol: {data['symbol']}")
    print(f"Timestamp: {data['timestamp']}")
    print(f"\nTop 3 Bids (Buy Orders):")
    for price, quantity in data['bids'][:3]:
        print(f"  ${price:,.2f} — {quantity} BTC")
    print(f"\nTop 3 Asks (Sell Orders):")
    for price, quantity in data['asks'][:3]:
        print(f"  ${price:,.2f} — {quantity} BTC")
except Exception as e:
    print(f"Error: {e}")

When I ran this script for the first time, I was surprised by how clean the response was — no nested exchange-specific quirks, just the data I needed in a predictable format.

Step 3: Calculating Market Depth Metrics

Raw orderbook data is useful, but you usually want to derive meaningful metrics. Here is how to calculate important indicators:

import requests

HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
BASE_URL = "https://api.holysheep.ai/v1"

def analyze_orderbook_depth(exchange, symbol, limit=50):
    """
    Fetch orderbook and calculate depth metrics for trading decisions.
    """
    # Fetch the data
    endpoint = f"{BASE_URL}/tardis/orderbook"
    headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
    params = {"exchange": exchange, "symbol": symbol, "limit": limit}
    
    response = requests.get(endpoint, headers=headers, params=params)
    data = response.json()
    
    # Extract bids and asks
    bids = data['bids']  # [[price, quantity], ...]
    asks = data['asks']
    
    # Calculate total bid volume (buying pressure)
    total_bid_volume = sum(qty for _, qty in bids)
    
    # Calculate total ask volume (selling pressure)
    total_ask_volume = sum(qty for _, qty in asks)
    
    # Calculate volume-weighted average bid price
    bid_value = sum(price * qty for price, qty in bids)
    vwap_bid = bid_value / total_bid_volume if total_bid_volume > 0 else 0
    
    # Calculate volume-weighted average ask price
    ask_value = sum(price * qty for price, qty in asks)
    vwap_ask = ask_value / total_ask_volume if total_ask_volume > 0 else 0
    
    # Calculate spread (the gap between best bid and best ask)
    best_bid = bids[0][0] if bids else 0
    best_ask = asks[0][0] if asks else 0
    spread = best_ask - best_bid
    spread_percentage = (spread / best_bid * 100) if best_bid > 0 else 0
    
    # Calculate bid-ask ratio (values > 1 mean more buying pressure)
    bid_ask_ratio = total_bid_volume / total_ask_volume if total_ask_volume > 0 else 0
    
    # Calculate cumulative depth at different levels
    cumulative_bids = []
    cumulative = 0
    for price, qty in bids[:10]:  # Top 10 levels
        cumulative += qty
        cumulative_bids.append((price, cumulative))
    
    return {
        "exchange": data['exchange'],
        "symbol": data['symbol'],
        "timestamp": data['timestamp'],
        "best_bid": best_bid,
        "best_ask": best_ask,
        "spread": round(spread, 2),
        "spread_percentage": round(spread_percentage, 4),
        "total_bid_volume": round(total_bid_volume, 4),
        "total_ask_volume": round(total_ask_volume, 4),
        "bid_ask_ratio": round(bid_ask_ratio, 4),
        "vwap_bid": round(vwap_bid, 2),
        "vwap_ask": round(vwap_ask, 2),
        "cumulative_depth": cumulative_bids
    }


Run analysis on Bitcoin

results = analyze_orderbook_depth("binance", "btc_usdt")

print("=" * 50)
print(f"ORDERBOOK ANALYSIS: {results['symbol'].upper()}")
print("=" * 50)
print(f"Best Bid:  ${results['best_bid']:,.2f}")
print(f"Best Ask:  ${results['best_ask']:,.2f}")
print(f"Spread:    ${results['spread']:,.2f} ({results['spread_percentage']}%)")
print(f"\nVolume Analysis:")
print(f"  Total Bid Volume:  {results['total_bid_volume']} BTC")
print(f"  Total Ask Volume:  {results['total_ask_volume']} BTC")
print(f"  Bid/Ask Ratio:     {results['bid_ask_ratio']:.4f}")
print(f"\nMarket Sentiment: ", end="")
if results['bid_ask_ratio'] > 1.2:
    print("BULLISH (more buying pressure)")
elif results['bid_ask_ratio'] < 0.8:
    print("BEARISH (more selling pressure)")
else:
    print("NEUTRAL (balanced)")

The bid-ask ratio is particularly powerful. When I built my first arbitrage bot, I used this ratio to detect when one exchange had significantly more buying pressure than another — often the first sign of an arbitrage opportunity.

Step 4: Building a Real-Time Depth Visualizer

For dashboards and trading interfaces, you need to visualize orderbook depth. Here is a simple web application structure:

<!-- Simple Orderbook Depth Visualizer -->
<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>Orderbook Depth Monitor</title>
    <style>
        body { font-family: 'Segoe UI', sans-serif; background: #1a1a2e; color: #eee; padding: 20px; }
        .container { max-width: 800px; margin: 0 auto; }
        .orderbook { display: flex; justify-content: space-between; margin-top: 20px; }
        .side { width: 45%; }
        .bid-row, .ask-row { display: flex; justify-content: space-between; padding: 5px 10px; margin: 2px 0; border-radius: 4px; }
        .bid-row { background: rgba(0, 128, 0, 0.3); }
        .ask-row { background: rgba(255, 0, 0, 0.3); }
        .depth-bar { height: 20px; background: linear-gradient(90deg, transparent, #4CAF50); margin-top: 2px; }
        .ask-bar { background: linear-gradient(90deg, #F44336, transparent); }
        h1 { color: #4CAF50; }
        .stats { background: #16213e; padding: 15px; border-radius: 8px; margin-bottom: 20px; }
        .stats span { margin-right: 20px; }
        .positive { color: #4CAF50; }
        .negative { color: #F44336; }
    </style>
</head>
<body>
    <div class="container">
        <h1>📊 Live Orderbook Depth Monitor</h1>
        
        <div class="stats">
            <span>Exchange: <strong id="exchange">-</strong></span>
            <span>Symbol: <strong id="symbol">-</strong></span>
            <span>Spread: <strong id="spread">-</strong></span>
            <span>Ratio: <strong id="ratio">-</strong></span>
        </div>
        
        <div class="orderbook">
            <div class="side" id="bids">
                <h3>Bids (Buy Orders)</h3>
                <div id="bid-rows"></div>
            </div>
            <div class="side" id="asks">
                <h3>Asks (Sell Orders)</h3>
                <div id="ask-rows"></div>
            </div>
        </div>
    </div>

    <script>
        const HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY";
        const API_BASE = "https://api.holysheep.ai/v1";
        
        async function fetchOrderbook() {
            try {
                const response = await fetch(
                    ${API_BASE}/tardis/orderbook?exchange=binance&symbol=btc_usdt&limit=15,
                    { headers: { "Authorization": Bearer ${HOLYSHEEP_API_KEY} } }
                );
                const data = await response.json();
                updateDisplay(data);
            } catch (error) {
                console.error("Fetch error:", error);
            }
        }
        
        function updateDisplay(data) {
            // Update stats
            document.getElementById("exchange").textContent = data.exchange.toUpperCase();
            document.getElementById("symbol").textContent = data.symbol.replace("_", "/").toUpperCase();
            
            const spread = data.asks[0][0] - data.bids[0][0];
            document.getElementById("spread").textContent = $${spread.toFixed(2)};
            
            // Calculate ratio
            const bidVol = data.bids.reduce((sum, b) => sum + b[1], 0);
            const askVol = data.asks.reduce((sum, a) => sum + a[1], 0);
            const ratio = (bidVol / askVol).toFixed(3);
            document.getElementById("ratio").textContent = ratio;
            document.getElementById("ratio").className = ratio > 1 ? "positive" : "negative";
            
            // Render bid rows
            const bidContainer = document.getElementById("bid-rows");
            bidContainer.innerHTML = data.bids.map(([price, qty]) =>
                `<div class="bid-row">
                    <span>$${price.toLocaleString()}</span>
                    <span>${qty.toFixed(4)} BTC</span>
                </div>`
            ).join("");
            
            // Render ask rows
            const askContainer = document.getElementById("ask-rows");
            askContainer.innerHTML = data.asks.map(([price, qty]) =>
                `<div class="ask-row">
                    <span>${qty.toFixed(4)} BTC</span>
                    <span>$${price.toLocaleString()}</span>
                </div>`
            ).join("");
        }
        
        // Refresh every 5 seconds
        fetchOrderbook();
        setInterval(fetchOrderbook, 5000);
    </script>
</body>
</html>

Practical Applications for Orderbook Depth Data

Now that you can fetch and parse orderbook data, here are real-world use cases where this becomes valuable:

1. Slippage Calculator

Before executing a large trade, estimate how much the price will move:

def estimate_slippage(exchange, symbol, side, quantity):
    """
    Estimate price slippage for a given trade size.
    
    Args:
        exchange: Exchange name
        symbol: Trading pair
        side: 'buy' or 'sell'
        quantity: Amount of base currency to trade
    
    Returns:
        Estimated average fill price and slippage percentage
    """
    endpoint = f"{BASE_URL}/tardis/orderbook"
    headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
    params = {"exchange": exchange, "symbol": symbol, "limit": 100}
    
    response = requests.get(endpoint, headers=headers, params=params)
    data = response.json()
    
    if side == 'buy':
        levels = data['asks']  # Buying consumes asks
    else:
        levels = data['bids']  # Selling consumes bids
    
    remaining_qty = quantity
    total_cost = 0
    
    for price, available in levels:
        if remaining_qty <= 0:
            break
        fill_qty = min(remaining_qty, available)
        total_cost += fill_qty * price
        remaining_qty -= fill_qty
    
    if remaining_qty > 0:
        return {"error": "Insufficient liquidity at this size"}
    
    avg_price = total_cost / quantity
    best_price = levels[0][0]
    slippage = abs(avg_price - best_price) / best_price * 100
    
    return {
        "estimated_avg_price": round(avg_price, 2),
        "best_price": best_price,
        "slippage_percentage": round(slippage, 4),
        "fillable": True
    }


Example: Estimate slippage for buying 5 BTC

result = estimate_slippage("binance", "btc_usdt", "buy", 5)
print(f"Buying 5 BTC:")
print(f"  Estimated Average Price: ${result['estimated_avg_price']:,.2f}")
print(f"  Slippage: {result['slippage_percentage']}%")

2. Multi-Exchange Arbitrage Scanner

Compare orderbooks across exchanges to find price differences:

def find_arbitrage_opportunities(symbol, min_spread_pct=0.1):
    """
    Scan multiple exchanges for arbitrage opportunities.
    
    Args:
        symbol: Trading pair (e.g., 'btc_usdt')
        min_spread_pct: Minimum spread percentage to flag
    
    Returns:
        List of arbitrage opportunities with profit estimates
    """
    exchanges = ['binance', 'bybit', 'okx']
    orderbooks = {}
    
    headers = {"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
    
    # Fetch from all exchanges simultaneously
    for exchange in exchanges:
        try:
            response = requests.get(
                f"{BASE_URL}/tardis/orderbook",
                headers=headers,
                params={"exchange": exchange, "symbol": symbol, "limit": 5}
            )
            if response.status_code == 200:
                orderbooks[exchange] = response.json()
        except Exception as e:
            print(f"Failed to fetch {exchange}: {e}")
    
    opportunities = []
    
    # Compare each pair of exchanges
    exchange_list = list(orderbooks.keys())
    for i, ex1 in enumerate(exchange_list):
        for ex2 in exchange_list[i+1:]:
            ob1 = orderbooks[ex1]
            ob2 = orderbooks[ex2]
            
            # Best bid on exchange 1 vs best ask on exchange 2
            spread1 = ob1['bids'][0][0] - ob2['asks'][0][0]
            spread1_pct = (spread1 / ob2['asks'][0][0]) * 100
            
            # Best bid on exchange 2 vs best ask on exchange 1
            spread2 = ob2['bids'][0][0] - ob1['asks'][0][0]
            spread2_pct = (spread2 / ob1['asks'][0][0]) * 100
            
            if spread1_pct >= min_spread_pct:
                opportunities.append({
                    "buy_exchange": ex2,
                    "sell_exchange": ex1,
                    "spread_usd": round(spread1, 2),
                    "spread_pct": round(spread1_pct, 4),
                    "strategy": f"Buy on {ex2}, Sell on {ex1}"
                })
            
            if spread2_pct >= min_spread_pct:
                opportunities.append({
                    "buy_exchange": ex1,
                    "sell_exchange": ex2,
                    "spread_usd": round(spread2, 2),
                    "spread_pct": round(spread2_pct, 4),
                    "strategy": f"Buy on {ex1}, Sell on {ex2}"
                })
    
    return opportunities


Scan for Bitcoin arbitrage

opps = find_arbitrage_opportunities("btc_usdt", min_spread_pct=0.05)
if opps:
    print("⚡ ARBITRAGE OPPORTUNITIES FOUND:")
    for opp in opps:
        print(f"  {opp['strategy']}")
        print(f"    Spread: ${opp['spread_usd']} ({opp['spread_pct']}%)")
else:
    print("No significant arbitrage opportunities at this time.")

Common Errors and Fixes

Here are the most frequent issues beginners encounter when working with the HolySheep Tardis API, along with their solutions:

Error 1: "401 Unauthorized - Invalid API Key"

# ❌ WRONG: Key with extra spaces or wrong format
headers = {
    "Authorization": "Bearer YOUR_HOLYSHEEP_API_KEY "  # Note the trailing space!
}


✅ CORRECT: Clean key without spaces

headers = {
    "Authorization": f"Bearer {HOLYSHEEP_API_KEY.strip()}"
}


Alternative: Use environment variables (recommended for production)

import os
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY")
if not HOLYSHEEP_API_KEY:
    raise ValueError("HOLYSHEEP_API_KEY environment variable not set")

Error 2: "429 Rate Limit Exceeded"

# ❌ WRONG: Flooding the API without rate limit handling
while True:
    data = fetch_orderbook()  # Will hit rate limits quickly


✅ CORRECT: Implement exponential backoff retry logic

import time
import random

def fetch_with_retry(endpoint, headers, params, max_retries=3):
    for attempt in range(max_retries):
        response = requests.get(endpoint, headers=headers, params=params)
        
        if response.status_code == 200:
            return response.json()
        elif response.status_code == 429:
            wait_time = (2 ** attempt) + random.uniform(0, 1)
            print(f"Rate limited. Waiting {wait_time:.2f} seconds...")
            time.sleep(wait_time)
        else:
            raise Exception(f"API Error: {response.status_code}")
    
    raise Exception("Max retries exceeded")

Error 3: "Symbol Not Found or Invalid Format"

# ❌ WRONG: Using wrong symbol format
get_orderbook_depth("binance", "BTC/USDT")  # Wrong: uses "/" instead of "_"
get_orderbook_depth("binance", "BTCUSDT")   # Wrong: missing separator


✅ CORRECT: Use underscore format for HolySheep Tardis API

get_orderbook_depth("binance", "btc_usdt")   # Correct
get_orderbook_depth("bybit", "eth_usdt")     # Correct
get_orderbook_depth("okx", "sol_usdt")       # Correct


If you receive data with "/" format, normalize it:

def normalize_symbol(symbol):
    """Convert any symbol format to HolySheep format."""
    return symbol.replace("/", "_").replace("-", "_").lower()


Test normalization

test_symbols = ["BTC/USDT", "ETH-USDT", "Btc_Usdt"]
for sym in test_symbols:
    print(f"{sym} -> {normalize_symbol(sym)}")

Error 4: Handling Empty or Stale Data

# ❌ WRONG: Assuming data is always fresh and complete
data = response.json()
best_bid = data['bids'][0][0]  # Crashes if bids is empty!


✅ CORRECT: Always validate data structure before accessing

def safe_get_best_bid(data, default=0):
    """Safely extract best bid with fallback."""
    if not data or 'bids' not in data or not data['bids']:
        return default
    return data['bids'][0][0]

def validate_orderbook_data(data):
    """Check if orderbook data is complete and fresh."""
    required_fields = ['exchange', 'symbol', 'timestamp', 'bids', 'asks']
    
    for field in required_fields:
        if field not in data:
            return False, f"Missing field: {field}"
    
    if not data['bids'] or not data['asks']:
        return False, "Empty orderbook sides"
    
    # Check data age (warn if older than 10 seconds)
    import time
    age_seconds = (time.time() * 1000 - data['timestamp']) / 1000
    if age_seconds > 10:
        return False, f"Stale data: {age_seconds:.1f} seconds old"
    
    return True, "Valid"

Why Choose HolySheep for Your Market Data Needs

After building multiple trading applications and testing various data providers, here is why I recommend HolySheep's Tardis integration:

AdvantageDetails
Unified Data AccessOne API key connects to Binance, Bybit, OKX, and Deribit — no managing multiple exchange credentials
85%+ Cost Savings¥1 = $1 exchange rate vs. typical ¥7.3 rates means dramatically lower operating costs
Local Payment OptionsWeChat Pay and Alipay support removes international payment barriers for Asian developers
Sub-50ms LatencyReal-time data relay optimized for algorithmic trading and time-sensitive applications
Clean Normalized DataNo more parsing exchange-specific quirks — one format works everywhere
Free Testing CreditsStart building immediately without upfront payment commitment
AI Integration ReadySame platform supports GPT-4.1, Claude Sonnet 4.5, Gemini 2.5 Flash, and DeepSeek V3.2 for building intelligent trading systems

Building Your First Orderbook Trading Bot

Here is a simple mean-reversion strategy using orderbook depth to illustrate the power of this data:

"""
Simple Orderbook Imbalance Strategy

Logic: When there are significantly more bids than asks,
the price tends to rise. When more asks than bids, price tends to fall.
We trade this imbalance.
"""

def calculate_imbalance_score(data):
    """Score from -1 (extreme sell pressure) to +1 (extreme buy pressure)."""
    total_bid_vol = sum(qty for _, qty in data['bids'][:10])
    total_ask_vol = sum(qty for _, qty in data['asks'][:10])
    
    if total_bid_vol + total_ask_vol == 0:
        return 0
    
    # Score ranges from -1 to +1
    score = (total_bid_vol - total_ask_vol) / (total_bid_vol + total_ask_vol)
    return score

def should_trade(imbalance_score, threshold=0.3):
    """
    Decide whether to trade based on imbalance.
    
    Args:
        imbalance_score: -1 to +1 from calculate_imbalance_score()
        threshold: Minimum absolute score to trigger a trade (0.3 = 30% imbalance)
    
    Returns:
        'buy', 'sell', or 'hold'
    """
    if imbalance

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