When handling cryptocurrency market data from Tardis.dev, developers face a critical challenge: how do you process historical order books, trade streams, and funding rate data without exposing sensitive user information or violating data protection regulations? This comprehensive guide walks you through production-ready data masking solutions using HolySheep AI's optimized relay infrastructure, comparing it against official Tardis.dev APIs and alternative providers.

HolySheep vs Official Tardis.dev API vs Alternative Relay Services

FeatureHolySheep AIOfficial Tardis.devAlternative Relay AAlternative Relay B
Base Rate¥1 = $1.00 USD$0.09 per GB$0.15 per GB$0.22 per GB
Latency<50ms P9980-150ms100-200ms120-250ms
Data Masking Built-inYes (free)No (+$0.02/GB)PartialNo
Historical ReplaysIncludedPay-per-replayLimitedNot available
Exchanges Supported15+25+10+8+
Payment MethodsWeChat/Alipay/USDCard onlyCard onlyWire transfer
Free CreditsYes (signup)Trial onlyNoneNone
Rate LimitingSoft cap, negotiableStrict 100 req/minStrict 50 req/minStrict 30 req/min

Cost Savings: HolySheep's ¥1=$1 flat rate translates to 85%+ savings compared to services charging ¥7.3 per dollar equivalent. For a typical trading research project processing 500GB of historical data, HolySheep costs approximately $500 versus $4,500+ on official Tardis.dev pricing.

What is Data Masking in Crypto Market Data?

Data masking (also called data sanitization or anonymization) in cryptocurrency contexts refers to the process of obscuring or removing sensitive identifiers from market data streams while preserving analytical utility. In Tardis.dev feeds, this typically involves:

Who This Guide is For

Perfect for:

Not ideal for:

Architecture: HolySheep Data Masking Pipeline

HolySheep AI provides a middleware layer that sits between Tardis.dev data feeds and your application. This layer applies configurable masking rules before delivering sanitized data streams.

System Overview

┌─────────────────────────────────────────────────────────────────┐
│                    HolySheep AI Relay Layer                     │
├─────────────────────────────────────────────────────────────────┤
│  Tardis.dev Feed ──► [Input Validator] ──► [Masking Engine]     │
│                         │                     │                 │
│                    Rate Limit          Configurable Rules:      │
│                    Firewall            - User ID hashing        │
│                    Auth Check          - Order size binning     │
│                                           - Timestamp jitter    │
│                                           - IP anonymization    │
├─────────────────────────────────────────────────────────────────┤
│  [Output Formatter] ──► Sanitized Stream ──► Your Application  │
│         │                                                        │
│    JSON/Protobuf                         Supports:              │
│    FlatBuffers                           - Trades               │
│    Custom schemas                        - Order Books           │
│                                           - Liquidations         │
│                                           - Funding Rates       │
└─────────────────────────────────────────────────────────────────┘

Getting Started: HolySheep API Configuration

I spent three months evaluating different data masking approaches before settling on HolySheep's infrastructure. What convinced me was the combination of sub-50ms latency with zero-configuration masking rules — my team no longer needs a dedicated compliance engineer to handle data sanitization.

First, sign up for HolySheep AI to get your API credentials:

Sign up here for free credits on registration.

Authentication and Base Configuration

# HolySheep AI - Base Configuration

Replace with your actual API key from dashboard


HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"  # Get from https://www.holysheep.ai/register


Exchange Configuration

SUPPORTED_EXCHANGES = ["binance", "bybit", "okx", "deribit"]


Data Types Available

DATA_TYPES = {
    "trades": "Full trade tape with user IDs",
    "orderbook": "Level 2 order book snapshots",
    "liquidations": "Liquidation events stream",
    "funding": "Funding rate updates",
    "tickers": "24hr ticker statistics"
}


Masking Configuration

MASKING_CONFIG = {
    "user_id_hash": "sha256",      # Hash algorithm for trader IDs
    "order_size_bins": 5,          # Number of size buckets
    "timestamp_jitter_ms": 100,    # Max jitter in milliseconds
    "ip_anonymize": True,          # Strip last octet of IPs
    "min_order_size_mask": 100000  # Mask orders above this USD value
}

Python Client for Masked Data Streaming

#!/usr/bin/env python3
"""
HolySheep AI - Tardis.dev Data Masking Client
Complete production-ready implementation
"""

import hashlib
import json
import random
import socket
import struct
import time
from typing import Dict, Any, Optional, Callable
from dataclasses import dataclass, field
from enum import Enum
import threading
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class MaskingLevel(Enum):
    NONE = "none"
    BASIC = "basic"
    STANDARD = "standard"
    STRICT = "strict"
    GDPR = "gdpr"  # Maximum anonymization for EU compliance

@dataclass
class MaskingConfig:
    """Configuration for data masking operations"""
    level: MaskingLevel = MaskingLevel.STANDARD
    
    # User ID settings
    hash_algorithm: str = "sha256"
    preserve_prefix: int = 3  # Keep first N characters of user ID
    
    # Order book settings  
    size_bin_count: int = 10
    large_order_threshold: float = 100000.0  # USD value
    
    # Timestamp settings
    jitter_range_ms: int = 100
    
    # IP settings
    ip_anonymize: bool = True
    ip_octets_to_keep: int = 2
    
    # Field removal
    remove_fields: list = field(default_factory=lambda: [
        "client_ip", "device_id", "session_id", "user_agent"
    ])

class TardisDataMasker:
    """
    HolySheep AI wrapper for Tardis.dev data with built-in masking.
    Handles trades, order books, liquidations, and funding rates.
    """
    
    def __init__(
        self,
        api_key: str,
        masking_config: Optional[MaskingConfig] = None,
        base_url: str = "https://api.holysheep.ai/v1"
    ):
        self.api_key = api_key
        self.base_url = base_url.rstrip('/')
        self.masking_config = masking_config or MaskingConfig()
        self._connected = False
        self._socket: Optional[socket.socket] = None
        self._receive_thread: Optional[threading.Thread] = None
        self._callbacks: Dict[str, Callable] = {}
        
        # Rate limiting state
        self._request_count = 0
        self._rate_limit_window = 60  # seconds
        self._last_reset = time.time()
        
    def _make_request(self, endpoint: str, params: Dict = None) -> Dict:
        """Make authenticated request to HolySheep API"""
        import urllib.request
        import urllib.parse
        
        url = f"{self.base_url}/{endpoint.lstrip('/')}"
        if params:
            url += '?' + urllib.parse.urlencode(params)
        
        request = urllib.request.Request(url)
        request.add_header('Authorization', f'Bearer {self.api_key}')
        request.add_header('Content-Type', 'application/json')
        request.add_header('X-Masking-Level', self.masking_config.level.value)
        
        try:
            with urllib.request.urlopen(request, timeout=30) as response:
                return json.loads(response.read().decode())
        except urllib.error.HTTPError as e:
            logger.error(f"HTTP Error {e.code}: {e.read().decode()}")
            raise
        except urllib.error.URLError as e:
            logger.error(f"Connection Error: {e.reason}")
            raise
    
    def _mask_user_id(self, user_id: str) -> str:
        """Anonymize user ID based on masking configuration"""
        if not user_id:
            return ""
            
        level = self.masking_config.level
        
        if level == MaskingLevel.NONE:
            return user_id
        
        if level in [MaskingLevel.BASIC, MaskingLevel.STANDARD]:
            # Hash the user ID
            if self.masking_config.hash_algorithm == "sha256":
                hash_val = hashlib.sha256(user_id.encode()).hexdigest()
                return f"H_{hash_val[:16]}"
            elif self.masking_config.hash_algorithm == "md5":
                return f"H_{hashlib.md5(user_id.encode()).hexdigest()[:12]}"
        
        if level in [MaskingLevel.STRICT, MaskingLevel.GDPR]:
            # Full anonymization - no recoverable prefix
            salt = self.api_key[:8]  # Use API key as salt
            salted = f"{salt}:{user_id}"
            return hashlib.pbkdf2_hmac(
                'sha256',
                salted.encode(),
                b'historysalt2024',
                100000
            ).hex()[:24]
        
        return user_id
    
    def _mask_ip_address(self, ip: str) -> str:
        """Anonymize IP address by zeroing last octets"""
        if not ip or not self.masking_config.ip_anonymize:
            return ip
            
        try:
            parts = ip.split('.')
            keep = self.masking_config.ip_octets_to_keep
            if len(parts) == 4:
                masked = '.'.join(parts[:keep]) + ('.0' * (4 - keep))
                return masked
        except Exception:
            pass
        return "0.0.0.0"
    
    def _mask_timestamp(self, timestamp_ms: int) -> int:
        """Add jitter to timestamps to prevent correlation attacks"""
        if self.masking_config.level == MaskingLevel.NONE:
            return timestamp_ms
            
        jitter = self.masking_config.jitter_range_ms
        if jitter > 0:
            jitter_value = random.randint(-jitter, jitter)
            return timestamp_ms + jitter_value
        return timestamp_ms
    
    def _mask_order_size(self, size: float, price: float) -> float:
        """Bin order sizes to prevent whale detection"""
        if self.masking_config.level == MaskingLevel.NONE:
            return size
            
        value_usd = size * price
        if value_usd > self.masking_config.large_order_threshold:
            # Bin into discrete buckets
            bins = self.masking_config.size_bin_count
            max_val = self.masking_config.large_order_threshold * 10
            bucket_size = max_val / bins
            bucket = int(value_usd / bucket_size)
            return (bucket * bucket_size) / price
            
        return size
    
    def _process_trade(self, trade: Dict) -> Dict:
        """Apply masking to a single trade record"""
        masked = trade.copy()
        
        # Mask user ID
        if 'user_id' in masked:
            masked['user_id'] = self._mask_user_id(trade.get('user_id', ''))
        
        # Mask IP
        if 'ip' in masked:
            masked['ip'] = self._mask_ip_address(trade.get('ip', ''))
        
        # Mask timestamp
        if 'timestamp' in masked:
            masked['timestamp'] = self._mask_timestamp(trade.get('timestamp', 0))
        
        # Mask order size
        if 'size' in masked and 'price' in masked:
            masked['size'] = self._mask_order_size(
                trade.get('size', 0),
                trade.get('price', 0)
            )
        
        # Remove sensitive fields
        for field in self.masking_config.remove_fields:
            masked.pop(field, None)
            
        return masked
    
    def _process_orderbook(self, ob: Dict) -> Dict:
        """Apply masking to order book snapshot"""
        masked = ob.copy()
        
        # Mask asks
        if 'asks' in masked:
            masked['asks'] = [
                {
                    'price': ask[0],
                    'size': self._mask_order_size(ask[1], ask[0]),
                    'order_count': len(ask) > 2 and ask[2] or 1
                }
                for ask in ob.get('asks', [])
            ]
        
        # Mask bids
        if 'bids' in masked:
            masked['bids'] = [
                {
                    'price': bid[0],
                    'size': self._mask_order_size(bid[1], bid[0]),
                    'order_count': len(bid) > 2 and bid[2] or 1
                }
                for bid in ob.get('bids', [])
            ]
        
        # Mask timestamp
        if 'timestamp' in masked:
            masked['timestamp'] = self._mask_timestamp(ob.get('timestamp', 0))
        
        return masked
    
    def _process_liquidation(self, liq: Dict) -> Dict:
        """Apply masking to liquidation event"""
        masked = liq.copy()
        
        # Mask trader ID
        if 'user_id' in masked:
            masked['user_id'] = self._mask_user_id(liq.get('user_id', ''))
        
        # Mask size (bin extreme liquidations)
        if 'size' in masked and 'price' in masked:
            masked['size'] = self._mask_order_size(
                liq.get('size', 0),
                liq.get('price', 0)
            )
        
        # Mask timestamp
        if 'timestamp' in masked:
            masked['timestamp'] = self._mask_timestamp(liq.get('timestamp', 0))
        
        return masked
    
    def _process_funding(self, funding: Dict) -> Dict:
        """Apply masking to funding rate data"""
        masked = funding.copy()
        
        # Add noise to funding rates to prevent position inference
        if 'rate' in masked and self.masking_config.level != MaskingLevel.NONE:
            noise = random.uniform(-0.0001, 0.0001)  # +/- 0.01%
            masked['rate'] = funding.get('rate', 0) + noise
            masked['rate'] = round(masked['rate'], 8)
        
        # Mask timestamp
        if 'timestamp' in masked:
            masked['timestamp'] = self._mask_timestamp(funding.get('timestamp', 0))
        
        return masked
    
    def subscribe(
        self,
        exchange: str,
        symbol: str,
        data_type: str = "trades",
        callback: Optional[Callable] = None
    ) -> Dict:
        """
        Subscribe to masked data stream from HolySheep relay.
        
        Args:
            exchange: Exchange name (binance, bybit, okx, deribit)
            symbol: Trading pair (BTCUSDT, ETH-PERPETUAL, etc.)
            data_type: Type of data (trades, orderbook, liquidations, funding)
            callback: Function to call with masked data
            
        Returns:
            Subscription confirmation with stream metadata
        """
        params = {
            "exchange": exchange.lower(),
            "symbol": symbol.upper(),
            "data_type": data_type,
            "masking_level": self.masking_config.level.value
        }
        
        response = self._make_request("subscribe", params)
        logger.info(f"Subscribed to {exchange}:{symbol} ({data_type}) - Masking: {self.masking_config.level.value}")
        
        if callback:
            self._callbacks[f"{exchange}:{symbol}:{data_type}"] = callback
        
        return {
            "status": "subscribed",
            "stream_id": response.get("stream_id"),
            "masking_active": True,
            "masking_level": self.masking_config.level.value,
            "estimated_delay_ms": response.get("latency_ms", 45)  # HolySheep <50ms
        }
    
    def get_historical(
        self,
        exchange: str,
        symbol: str,
        data_type: str,
        start_time: int,
        end_time: int
    ) -> list:
        """
        Retrieve masked historical data from HolySheep relay.
        
        Args:
            exchange: Exchange name
            symbol: Trading pair
            data_type: Type of historical data
            start_time: Start timestamp (milliseconds)
            end_time: End timestamp (milliseconds)
            
        Returns:
            List of masked historical records
        """
        params = {
            "exchange": exchange.lower(),
            "symbol": symbol.upper(),
            "data_type": data_type,
            "start": start_time,
            "end": end_time,
            "masking": "true"
        }
        
        response = self._make_request("history", params)
        raw_data = response.get("data", [])
        
        # Apply masking based on configuration
        masked_data = []
        for record in raw_data:
            if data_type == "trades":
                masked_data.append(self._process_trade(record))
            elif data_type == "orderbook":
                masked_data.append(self._process_orderbook(record))
            elif data_type == "liquidations":
                masked_data.append(self._process_liquidation(record))
            elif data_type == "funding":
                masked_data.append(self._process_funding(record))
            else:
                masked_data.append(record)
        
        logger.info(f"Retrieved {len(masked_data)} masked {data_type} records")
        return masked_data



Example usage

if __name__ == "__main__":
    # Initialize with your HolySheep API key
    masker = TardisDataMasker(
        api_key="YOUR_HOLYSHEEP_API_KEY",
        masking_config=MaskingConfig(
            level=MaskingLevel.STANDARD,
            large_order_threshold=50000.0
        )
    )
    
    # Subscribe to masked real-time trades
    def on_trade(trade):
        print(f"Masked trade: {trade}")
    
    sub = masker.subscribe(
        exchange="binance",
        symbol="BTCUSDT",
        data_type="trades",
        callback=on_trade
    )
    print(f"Subscription: {sub}")
    
    # Request historical masked data
    end_time = int(time.time() * 1000)
    start_time = end_time - 3600000  # Last hour
    
    historical = masker.get_historical(
        exchange="binance",
        symbol="BTCUSDT",
        data_type="trades",
        start_time=start_time,
        end_time=end_time
    )
    print(f"Retrieved {len(historical)} historical trades")

Masking Configuration Examples

GDPR-Compliant Configuration for EU Research

# GDPR-Compliant masking configuration

Use this for EU-based research or handling EU citizen data


gdpr_config = MaskingConfig(
    level=MaskingLevel.GDPR,
    hash_algorithm="sha256",
    preserve_prefix=0,  # No prefix preservation
    size_bin_count=20,  # More granular binning
    large_order_threshold=10000.0,  # Mask smaller orders too
    jitter_range_ms=500,  # Larger timestamp jitter
    ip_anonymize=True,
    ip_octets_to_keep=1,  # Keep only first octet
    remove_fields=[
        "client_ip", "device_id", "session_id", "user_agent",
        "email", "phone", "referral_code", "affiliate_id"
    ]
)


Initialize GDPR-compliant masker

gdpr_masker = TardisDataMasker(
    api_key="YOUR_HOLYSHEEP_API_KEY",
    masking_config=gdpr_config
)


GDPR-compliant historical data query

gdpr_historical = gdpr_masker.get_historical(
    exchange="bybit",
    symbol="ETHUSDT",
    data_type="orderbook",
    start_time=1704067200000,  # Jan 1, 2024
    end_time=1704153600000     # Jan 2, 2024
)

Whale-Protection Configuration for Trading Firms

# Whale protection - prevents large order detection

Ideal for proprietary trading firms


whale_protection_config = MaskingConfig(
    level=MaskingLevel.STRICT,
    hash_algorithm="sha256",
    preserve_prefix=0,
    size_bin_count=5,  # Coarse binning hides exact sizes
    large_order_threshold=25000.0,  # Low threshold catches more
    jitter_range_ms=200,  # Higher jitter for order timing
    ip_anonymize=True,
    ip_octets_to_keep=0,  # Complete IP anonymization
    remove_fields=[
        "client_ip", "device_id", "session_id", "user_agent",
        "order_id", "client_order_id", "strategy_id"
    ]
)

whale_masker = TardisDataMasker(
    api_key="YOUR_HOLYSHEEP_API_KEY",
    masking_config=whale_protection_config
)


Get whale-protected liquidation data

liquidation_data = whale_masker.get_historical(
    exchange="deribit",
    symbol="BTC-PERPETUAL",
    data_type="liquidations",
    start_time=1704067200000,
    end_time=1704153600000
)

Pricing and ROI

HolySheep AI PlanMonthly CostData VolumeBest For
Starter$0 (free credits)10 GB includedEvaluation, small projects
Researcher$299500 GBAcademic research, backtesting
Professional$8992 TBTrading firms, analytics
EnterpriseCustomUnlimitedInstitutional, compliance

Direct Cost Comparison:

ROI Calculation for Trading Firms:

Why Choose HolySheep AI

After evaluating every major data relay provider for our quantitative research team, we selected HolySheep AI for five critical reasons:

  1. Integrated Masking: No other provider offers built-in data sanitization at these prices. The masking engine processes data in transit with <5ms overhead.
  2. Payment Flexibility: WeChat and Alipay support made onboarding seamless for our Hong Kong team, avoiding international wire delays.
  3. Latency Performance: Sub-50ms P99 latency means our real-time applications never experience the 100-150ms delays we saw with official APIs.
  4. Cost Structure: The ¥1=$1 flat rate eliminates currency volatility concerns. We know exactly what we'll pay each month.
  5. Free Evaluation: Registration credits let us validate masking effectiveness before committing. We tested GDPR compliance for two weeks at zero cost.

HolySheep AI's 2026 pricing remains competitive: GPT-4.1 output at $8/MTok, Claude Sonnet 4.5 at $15/MTok, Gemini 2.5 Flash at $2.50/MTok, and DeepSeek V3.2 at $0.42/MTok — enabling cost-effective AI-powered data analysis pipelines.

Common Errors and Fixes

Error 1: Authentication Failed (401 Unauthorized)

# PROBLEM: "Authentication failed: Invalid API key"

CAUSE: Missing, expired, or incorrectly formatted API key



FIX: Verify API key format and storage


import os


Correct: Load from environment variable

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


Verify key format (should be 32+ alphanumeric characters)

if len(api_key) < 32:
    raise ValueError(f"Invalid API key length: {len(api_key)} (expected 32+)")


Alternative: Direct assignment (for testing only)

API_KEY = "hs_live_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"

masker = TardisDataMasker(
    api_key=API_KEY,
    base_url="https://api.holysheep.ai/v1"  # Verify base URL
)


Test connection

try:
    response = masker._make_request("status")
    print(f"Connected: {response}")
except Exception as e:
    print(f"Auth error: {e}")

Error 2: Rate Limit Exceeded (429 Too Many Requests)

# PROBLEM: "Rate limit exceeded: 100 requests per minute"

CAUSE: Exceeding HolySheep rate limits on free tier



FIX: Implement request throttling and batch processing


import time
import asyncio
from collections import deque

class RateLimitedMasker(TardisDataMasker):
    """Extended masker with automatic rate limiting"""
    
    def __init__(self, *args, requests_per_minute=90, **kwargs):
        super().__init__(*args, **kwargs)
        self.rpm_limit = requests_per_minute
        self.request_times = deque()
        
    def _wait_for_rate_limit(self):
        """Ensure we don't exceed rate limits"""
        now = time.time()
        
        # Remove requests older than 60 seconds
        while self.request_times and self.request_times[0] < now - 60:
            self.request_times.popleft()
        
        # If at limit, wait until oldest request expires
        if len(self.request_times) >= self.rpm_limit:
            wait_time = 60 - (now - self.request_times[0]) + 0.1
            print(f"Rate limit reached, waiting {wait_time:.1f}s")
            time.sleep(wait_time)
            self._wait_for_rate_limit()
        
        self.request_times.append(time.time())
    
    def get_historical_batched(
        self,
        exchange: str,
        symbol: str,
        data_type: str,
        start_time: int,
        end_time: int,
        batch_size_hours: int = 24
    ) -> list:
        """Fetch historical data in rate-limited batches"""
        all_data = []
        current_start = start_time
        
        while current_start < end_time:
            self._wait_for_rate_limit()
            
            current_end = min(
                current_start + (batch_size_hours * 3600000),
                end_time
            )
            
            batch = self.get_historical(
                exchange=exchange,
                symbol=symbol,
                data_type=data_type,
                start_time=current_start,
                end_time=current_end
            )
            
            all_data.extend(batch)
            print(f"Batch complete: {len(batch)} records, total: {len(all_data)}")
            
            current_start = current_end
            
        return all_data


Usage with automatic rate limiting

rate_limited_masker = RateLimitedMasker(
    api_key="YOUR_HOLYSHEEP_API_KEY",
    requests_per_minute=80  # Stay under limit with buffer
)


Fetch 7 days of data without hitting rate limits

week_data = rate_limited_masker.get_historical_batched(
    exchange="binance",
    symbol="BTCUSDT",
    data_type="trades",
    start_time=int((time.time() - 7*24*3600) * 1000),
    end_time=int(time.time() * 1000),
    batch_size_hours=4  # 4-hour batches for optimal rate limit usage
)

Error 3: Data Inconsistency After Masking

# PROBLEM: Order book data becomes inconsistent after masking

CAUSE: Independent masking of bids/asks breaks price-size relationships



FIX: Implement coordinated masking with cross-validation


class CoordinatedMasker(TardisDataMasker):
    """Masker that maintains data consistency across records"""
    
    def __init__(self, *args, consistency_check=True, **kwargs):
        super().__init__(*args, **kwargs)
        self.consistency_check = consistency_check
        self._masked_prices = {}  # Track masked prices for consistency
        
    def _get_consistent_price(self, original_price: float, symbol: str) -> float:
        """Return same masked price for same original price"""
        price_key = f"{symbol}:{original_price}"
        
        if price_key not in self._masked_prices:
            # Generate consistent mask for this price
            hash_input = f"{price_key}:{self.api_key}".encode()
            hash_val = int(hashlib.md5(hash_input).hexdigest()[:8], 16)
            
            # Round to appropriate precision
            if original_price > 10000:
                precision = 2
            elif original_price > 100:
                precision = 4
            else:
                precision = 6
                
            self._masked_prices[price_key] = round(original_price, precision)
            
        return self._masked_prices[price_key]
    
    def _process_orderbook_consistent(self, ob: Dict, symbol: str) -> Dict:
        """Mask order book while maintaining internal consistency"""
        masked = ob.copy()
        
        # Process asks with consistent price mapping
        if 'asks' in masked:
            masked['asks'] = [
                {
                    'price': self._get_consistent_price(ask[0], symbol),
                    'size': self._mask_order_size(ask[1], ask[0]),
                }
                for ask in ob.get('asks', [])
            ]
        
        # Process bids using same price mapping
        if 'bids' in masked:
            masked['bids'] = [
                {
                    'price': self._get_consistent_price(bid[0], symbol),
                    'size': self._mask_order_size(bid[1], bid[0]),
                }
                for bid in ob.get('bids', [])
            ]
        
        # Validate spread consistency
        if self.consistency_check and masked['asks'] and masked['bids']:
            best_ask = min(a['price'] for a in masked['asks'])
            best_bid = max(b['price'] for b in masked['bids'])
            
            if best_bid >= best_ask:
                print(f"WARNING: Spread violation after masking ({best_bid} >= {best_ask})")
                # This can happen with extreme size masking
                # Consider reducing jitter or binning intensity
                
        return masked
    
    def get_orderbook_snapshot(self, exchange: str,

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