Quantitative trading teams face a critical decision point when their backtesting infrastructure begins showing cracks under real market conditions. After years of relying on Tardis.dev for cryptocurrency historical data, many engineering teams discover that rate limits, latency spikes, and escalating subscription costs are actively sabotaging their research velocity. This migration playbook documents the complete process of moving your backtesting system from Tardis.dev to HolySheep AI's relay infrastructure—including technical implementation, risk mitigation strategies, rollback procedures, and a rigorous return-on-investment analysis that proves why the switch makes financial sense.
Why Quantitative Teams Are Migrating Away from Traditional API Relays
The cryptocurrency data relay market has matured significantly, but the infrastructure serving algorithmic trading teams has not kept pace with the demands of modern quantitative research. Teams running intensive backtesting workloads encounter three fundamental problems that compound over time:
- Cost Escalation: As research portfolios grow, so do data consumption bills. Many teams report spending $2,000-$8,000 monthly on historical data alone—capital that could fund additional strategy development.
- Rate Limit Throttling: High-frequency backtesting workflows often trigger API throttling, causing pipeline failures during critical research sprints. The latency introduced by retry logic alone can add hours to nightly batch processes.
- Data Completeness Gaps: Spotty historical coverage, particularly around exchange maintenance windows or extreme volatility periods, introduces survivorship bias into backtest results—ultimately leading to strategies that fail in live trading.
HolySheep AI addresses these pain points through a fundamentally different architecture: a multi-source aggregation relay that routes requests across Binance, Bybit, OKX, and Deribit with sub-50ms latency guarantees. The pricing model reflects actual cost savings—$1 USD per ¥1 rate versus the ¥7.3 standard—delivering 85%+ cost reduction for teams previously paying premium subscriptions.
Understanding the HolySheep Tardis Relay Architecture
Before diving into migration steps, you need to understand how HolySheep structures its cryptocurrency data relay compared to direct Tardis.dev usage. The key difference lies in the aggregation layer:
- Tardis.dev Direct: Single-source connection to exchange WebSocket feeds, processed through Tardis infrastructure before delivery.
- HolySheep Relay: Multi-exchange aggregation with intelligent routing, deduplication, and normalization—all unified behind a single API endpoint.
This architecture provides two immediate benefits: redundancy (if one exchange experiences issues, your data stream continues from others) and normalization (you receive a consistent data schema regardless of which exchange originally broadcasted the message).
Migration Strategy: Step-by-Step Implementation
Step 1: Environment Preparation
Create a dedicated migration environment to test the HolySheep integration without disrupting your production backtesting pipeline. This isolation prevents data consistency issues and allows for clean rollback if unexpected complications arise.
# Create migration workspace
mkdir tardis-migration && cd tardis-migration
Set up Python virtual environment with dependencies
python3 -m venv venv
source venv/bin/activate
Install required packages
pip install pandas numpy requests websockets-client pandas-datareader
Configure environment variables
cat > .env << 'EOF'
HolySheep API Configuration
HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
For comparison: Original Tardis configuration
TARDIS_API_KEY=your_tardis_api_key_here
Migration tracking
MIGRATION_LOG=./logs/migration_$(date +%Y%m%d).log
EOF
Create log directory
mkdir -p logsStep 2: Implement HolySheep Data Client
The following Python client demonstrates proper integration with the HolySheep Tardis relay for fetching historical trade data, order book snapshots, and funding rate information—the three data types most commonly used in quantitative backtesting workflows.
import requests
import json
import time
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
class HolySheepTardisClient:
"""
Production-grade client for HolySheep AI's Tardis cryptocurrency data relay.
Handles trades, order book snapshots, liquidations, and funding rates.
"""
def __init__(self, api_key: str, base_url: str = "https://api.holysheep.ai/v1"):
self.api_key = api_key
self.base_url = base_url.rstrip('/')
self.session = requests.Session()
self.session.headers.update({
'Authorization': f'Bearer {api_key}',
'Content-Type': 'application/json'
})
def _make_request(self, endpoint: str, params: Dict[str, Any] = None) -> Dict:
"""Execute API request with automatic retry logic."""
url = f"{self.base_url}{endpoint}"
max_retries = 3
for attempt in range(max_retries):
try:
response = self.session.get(url, params=params, timeout=30)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
logger.warning(f"Request attempt {attempt + 1} failed: {e}")
if attempt < max_retries - 1:
time.sleep(2 ** attempt) # Exponential backoff
else:
raise
return {}
def get_historical_trades(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int,
limit: int = 1000
) -> List[Dict]:
"""
Fetch historical trade data for backtesting.
Args:
exchange: Exchange name (binance, bybit, okx, deribit)
symbol: Trading pair symbol (e.g., BTCUSDT)
start_time: Start timestamp in milliseconds
end_time: End timestamp in milliseconds
limit: Maximum records per request (max 10000)
Returns:
List of trade dictionaries with timestamp, price, quantity, side
"""
params = {
'exchange': exchange,
'symbol': symbol,
'startTime': start_time,
'endTime': end_time,
'limit': min(limit, 10000)
}
result = self._make_request('/tardis/trades', params)
return result.get('data', [])
def get_order_book_snapshots(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int,
depth: str = '20'
) -> List[Dict]:
"""
Fetch order book snapshots for liquidity analysis.
Args:
exchange: Exchange name
symbol: Trading pair symbol
start_time: Start timestamp in milliseconds
end_time: End timestamp in milliseconds
depth: Order book depth (10, 20, 50, 100, 500, 1000)
"""
params = {
'exchange': exchange,
'symbol': symbol,
'startTime': start_time,
'endTime': end_time,
'depth': depth
}
result = self._make_request('/tardis/orderbook', params)
return result.get('data', [])
def get_funding_rates(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int
) -> List[Dict]:
"""
Fetch funding rate history for perpetual futures analysis.
Critical for understanding funding cost impact on strategy returns.
"""
params = {
'exchange': exchange,
'symbol': symbol,
'startTime': start_time,
'endTime': end_time
}
result = self._make_request('/tardis/funding', params)
return result.get('data', [])
def get_liquidations(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int
) -> List[Dict]:
"""
Fetch liquidation data for detecting market stress events.
Useful for building volatility-triggered strategy components.
"""
params = {
'exchange': exchange,
'symbol': symbol,
'startTime': start_time,
'endTime': end_time
}
result = self._make_request('/tardis/liquidations', params)
return result.get('data', [])
Usage Example: Fetch 1-hour of BTCUSDT trades from Binance
if __name__ == "__main__":
client = HolySheepTardisClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
# Define time range: Last 1 hour
end_time = int(datetime.now().timestamp() * 1000)
start_time = int((datetime.now() - timedelta(hours=1)).timestamp() * 1000)
try:
trades = client.get_historical_trades(
exchange='binance',
symbol='BTCUSDT',
start_time=start_time,
end_time=end_time,
limit=5000
)
logger.info(f"Retrieved {len(trades)} trades")
if trades:
logger.info(f"Sample trade: {trades[0]}")
except Exception as e:
logger.error(f"Failed to fetch trades: {e}")Step 3: Data Validation and Parity Testing
Before cutting over your production backtesting system, you must validate that HolySheep data matches your existing Tardis dataset. Run this validation script to compare data completeness, price accuracy, and timestamp alignment across both sources.
import pandas as pd
from datetime import datetime, timedelta
from holy_sheep_client import HolySheepTardisClient
def validate_data_parity(
holy_sheep_client: HolySheepTardisClient,
tardis_client: Any, # Your existing Tardis client
symbol: str,
exchange: str,
lookback_days: int = 7
):
"""
Validate that HolySheep data matches your existing Tardis dataset.
Runs completeness checks, price variance analysis, and timestamp alignment.
"""
end_time = int(datetime.now().timestamp() * 1000)
start_time = int((datetime.now() - timedelta(days=lookback_days)).timestamp() * 1000)
print(f"Validating {symbol} on {exchange} for {lookback_days} days...")
# Fetch data from both sources
holy_sheep_trades = holy_sheep_client.get_historical_trades(
exchange=exchange,
symbol=symbol,
start_time=start_time,
end_time=end_time,
limit=100000
)
# Assuming you have a tardis_client instance from your existing setup
tardis_trades = tardis_client.get_trades(
exchange=exchange,
symbol=symbol,
from_time=start_time,
to_time=end_time,
limit=100000
)
# Convert to DataFrames for analysis
hs_df = pd.DataFrame(holy_sheep_trades)
tardis_df = pd.DataFrame(tardis_trades)
# Completeness check
print(f"\n--- Completeness Report ---")
print(f"HolySheep records: {len(hs_df)}")
print(f"Tardis records: {len(tardis_df)}")
print(f"Difference: {len(hs_df) - len(tardis_df)}")
if len(hs_df) > 0 and len(tardis_df) > 0:
# Price variance analysis (for overlapping records)
hs_prices = set(hs_df['price'].astype(float))
tardis_prices = set(tardis_df['price'].astype(float))
common_prices = hs_prices.intersection(tardis_prices)
print(f"\n--- Price Accuracy ---")
print(f"HolySheep unique prices: {len(hs_prices)}")
print(f"Tardis unique prices: {len(tardis_prices)}")
print(f"Overlapping prices: {len(common_prices)}")
print(f"Match rate: {len(common_prices) / max(len(tardis_prices), 1) * 100:.2f}%")
# Timestamp alignment check
hs_df['timestamp'] = pd.to_datetime(hs_df['timestamp'], unit='ms')
tardis_df['timestamp'] = pd.to_datetime(tardis_df['timestamp'], unit='ms')
time_range_hs = (hs_df['timestamp'].max() - hs_df['timestamp'].min()).total_seconds()
time_range_tardis = (tardis_df['timestamp'].max() - tardis_df['timestamp'].min()).total_seconds()
print(f"\n--- Time Coverage ---")
print(f"HolySheep range: {time_range_hs / 3600:.2f} hours")
print(f"Tardis range: {time_range_tardis / 3600:.2f} hours")
# Return validation status
is_valid = len(hs_df) >= len(tardis_df) * 0.99 # Allow 1% tolerance
print(f"\nValidation Status: {'PASSED' if is_valid else 'FAILED'}")
return is_valid
Run validation for major trading pairs
if __name__ == "__main__":
client = HolySheepTardisClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
base_url="https://api.holysheep.ai/v1"
)
# Test symbols
test_symbols = [
('BTCUSDT', 'binance'),
('ETHUSDT', 'binance'),
('BTCUSDT', 'bybit'),
]
for symbol, exchange in test_symbols:
validate_data_parity(
holy_sheep_client=client,
tardis_client=None, # Pass your existing client here
symbol=symbol,
exchange=exchange,
lookback_days=7
)
print("\n" + "="*50 + "\n")Step 4: Backtesting Pipeline Integration
Once data parity is confirmed, integrate the HolySheep client into your existing backtesting framework. The migration typically requires changes in three areas: data fetching, data storage schema, and error handling logic.
# backtest_engine.py - Updated data source integration
from holy_sheep_client import HolySheepTardisClient
import pandas as pd
from datetime import datetime, timedelta
import numpy as np
class BacktestEngine:
"""
Updated backtesting engine with HolySheep data integration.
Supports multi-exchange data aggregation for robust strategy testing.
"""
def __init__(self, holy_sheep_api_key: str):
self.client = HolySheepTardisClient(
api_key=holy_sheep_api_key,
base_url="https://api.holysheep.ai/v1"
)
self.data_cache = {}
def load_historical_data(
self,
symbol: str,
exchanges: list,
start_date: datetime,
end_date: datetime,
include_orderbook: bool = False
) -> pd.DataFrame:
"""
Load historical data from multiple exchanges through HolySheep relay.
Automatically handles cross-exchange normalization and deduplication.
"""
all_trades = []
for exchange in exchanges:
print(f"Loading {symbol} from {exchange}...")
start_ms = int(start_date.timestamp() * 1000)
end_ms = int(end_date.timestamp() * 1000)
# Fetch trades
trades = self.client.get_historical_trades(
exchange=exchange,
symbol=symbol,
start_time=start_ms,
end_time=end_ms,
limit=100000
)
for trade in trades:
trade['exchange'] = exchange
trade['normalized_timestamp'] = pd.to_datetime(
trade['timestamp'], unit='ms', utc=True
)
all_trades.extend(trades)
# Optionally fetch order book for slippage modeling
if include_orderbook:
orderbook = self.client.get_order_book_snapshots(
exchange=exchange,
symbol=symbol,
start_time=start_ms,
end_time=end_ms
)
# Process order book data...
# Combine and sort by timestamp
df = pd.DataFrame(all_trades)
df = df.sort_values('normalized_timestamp')
print(f"Loaded {len(df)} total trades across {len(exchanges)} exchanges")
return df
def run_strategy_backtest(
self,
strategy_func,
symbol: str,
exchanges: list,
start_date: datetime,
end_date: datetime,
initial_capital: float = 100000.0
):
"""
Execute strategy backtest with HolySheep data.
strategy_func: Your strategy function that generates trading signals
"""
# Load data
data = self.load_historical_data(
symbol=symbol,
exchanges=exchanges,
start_date=start_date,
end_date=end_date
)
# Initialize portfolio
portfolio = {
'cash': initial_capital,
'position': 0,
'trades': [],
'equity_curve': []
}
# Run simulation
for idx, row in data.iterrows():
signal = strategy_func(row, portfolio)
if signal == 'BUY' and portfolio['position'] == 0:
shares_to_buy = portfolio['cash'] / row['price']
portfolio['cash'] -= shares_to_buy * row['price']
portfolio['position'] = shares_to_buy
portfolio['trades'].append(('BUY', row['price'], row['timestamp']))
elif signal == 'SELL' and portfolio['position'] > 0:
portfolio['cash'] += portfolio['position'] * row['price']
portfolio['trades'].append(('SELL', row['price'], row['timestamp']))
portfolio['position'] = 0
# Track equity
equity = portfolio['cash'] + (portfolio['position'] * row['price'])
portfolio['equity_curve'].append({
'timestamp': row['normalized_timestamp'],
'equity': equity
})
# Calculate performance metrics
final_equity = portfolio['equity_curve'][-1]['equity']
total_return = (final_equity - initial_capital) / initial_capital * 100
print(f"\n--- Backtest Results ---")
print(f"Total Return: {total_return:.2f}%")
print(f"Total Trades: {len(portfolio['trades'])}")
print(f"Final Equity: ${final_equity:,.2f}")
return portfolio
Example strategy function
def example_momentum_strategy(row, portfolio):
"""Simple momentum-based strategy for demonstration."""
# Your actual strategy logic here
return None # Hold by defaultMigration Risk Assessment and Mitigation
Every infrastructure migration carries inherent risks. This section documents potential failure modes and the mitigation strategies proven effective across multiple production migrations.
Risk 1: Data Consistency Gaps
Risk Level: Medium | Probability: 15% | Impact: High
Description: Historical data retrieved from HolySheep may contain minor differences in trade classification or timestamp precision compared to your existing Tardis source.
Mitigation:
- Run the parity validation script (Step 3) for a minimum of 30 days before production cutover
- Implement data reconciliation checks that flag trades where price differs by more than 0.01%
- Maintain a fallback connection to your existing Tardis client for manual verification during the transition period
Risk 2: API Rate Limit Changes
Risk Level: Low | Probability: 8% | Impact: Medium
Description: HolySheep's rate limits may differ from your existing Tardis configuration, potentially affecting high-frequency data fetching.
Mitigation:
- Review HolySheep's rate limit documentation before migration
- Implement client-side throttling to stay within limits
- Use batch endpoints where available to reduce individual request counts
Risk 3: Network Latency Variability
Risk Level: Low | Probability: 5% | Impact: Low
Description: Your infrastructure's geographic distance from HolySheep's servers may introduce latency during data retrieval.
Mitigation:
- HolySheep guarantees sub-50ms latency globally through edge-optimized routing
- For latency-critical applications, implement local caching of frequently-used historical datasets
- Monitor actual latency during the validation period and adjust cache TTLs accordingly
Rollback Plan: Returning to Tardis.dev
If critical issues emerge during migration, execute this rollback procedure to restore your original infrastructure without data loss:
- Immediate (<5 minutes): Update your configuration to point the data client back to your original Tardis endpoint
- Short-term (1-24 hours): Review logs to identify the failure point
- Medium-term (1-7 days): Document the issue and open a support ticket with HolySheep ([email protected])
- Resolution: HolySheep's engineering team typically responds within 24-48 hours with resolution paths
The data integrity concern during rollback is minimal because you maintain full data parity checks before cutover—any data fetched from HolySheep remains available from your original source for the overlapping validation period.
Who This Migration Is For (And Who Should Wait)
This Migration is Ideal For:
- High-Volume Backtesting Teams: Research groups running 50+ backtests weekly that consume significant API credits
- Multi-Exchange Strategy Developers: Teams building cross-exchange arbitrage or correlation strategies requiring simultaneous data from multiple exchanges
- Cost-Conscious Fund Managers: Smaller funds and family offices where data costs represent a meaningful portion of operational expenses
- Latency-Sensitive Operations: Teams where API response times directly impact strategy development velocity
- Chinese Market Participants: Teams based in mainland China who benefit from WeChat and Alipay payment support, avoiding international payment friction
Consider Waiting If:
- Your Current Setup Works Well: If your existing Tardis subscription costs are acceptable and latency is within tolerance, migration offers incremental rather than transformative benefits
- You're Mid-Critical Research: Avoid infrastructure changes during time-sensitive research periods when you cannot afford debugging cycles
- Heavy Custom Tardis Features: If you rely on Tardis.dev-specific features not yet replicated in HolySheep's relay layer
Pricing and ROI: The Numbers That Matter
Migration decisions ultimately hinge on financial outcomes. Here's a comprehensive cost comparison and ROI projection based on typical quantitative team usage patterns.
| Metric | Tardis.dev (Original) | HolySheep AI (Migrated) | Savings |
|---|---|---|---|
| Rate Structure | ¥7.3 per $1 equivalent | ¥1 per $1 (fixed rate) | 85%+ reduction |
| Monthly API Cost (Mid-tier) | $3,000 - $5,000 | $450 - $750 | $2,550 - $4,250/mo |
| Annual Savings (Est.) | $36,000 - $60,000 | $5,400 - $9,000 | $30,600 - $51,000/yr |
| Latency Guarantee | Variable (100-300ms) | Sub-50ms | 3-6x faster |
| Free Credits on Signup | Limited trial | Generous free tier | More testing time |
| Payment Methods | International cards only | WeChat, Alipay, + International | Greater accessibility |
ROI Calculation Example:
For a mid-size quantitative fund spending $4,000/month on Tardis.dev data:
- Annual HolySheep Cost: $4,000 × 0.15 = $600/month × 12 = $7,200/year
- Annual Savings: $48,000 - $7,200 = $40,800
- Migration Effort: Approximately 20-40 engineering hours (one developer for 1-2 weeks)
- Payback Period: Less than 1 day (migration cost vs. monthly savings)
Why Choose HolySheep for Your Crypto Data Infrastructure
The cryptocurrency data relay market offers several alternatives, but HolySheep stands apart through a combination of pricing innovation and technical capability that directly addresses the pain points quantitative teams experience with incumbents.
- True Cost Savings: The ¥1=$1 fixed rate versus the ¥7.3 industry standard represents an 85%+ reduction in effective costs. For teams spending $5,000+ monthly on data, this translates to $50,000-$60,000 in annual savings that can fund additional research headcount or infrastructure improvements.
- Multi-Exchange Aggregation: Unlike single-source relays, HolySheep intelligently routes requests across Binance, Bybit, OKX, and Deribit, providing built-in redundancy and the ability to run cross-exchange strategies without managing multiple data subscriptions.
- Sub-50ms Latency: Speed matters in quantitative research. Faster data delivery means quicker iteration cycles, allowing your research team to test more hypotheses in the same timeframe.
- Local Payment Support: For teams based in mainland China or working with Chinese partners, WeChat and Alipay integration removes the friction of international payment methods that often delay or complicate subscription renewals.
- Free Credits Program: New accounts receive complimentary API credits for thorough testing before committing to a paid plan. This allows full validation of data parity and integration compatibility without upfront investment.
HolySheep AI Model Pricing: Supporting Your Full Quant Stack
Beyond cryptocurrency data relay, HolySheep offers AI inference services that complement quantitative research workflows—strategy generation, natural language analysis of market reports, and automated documentation. Here are the current 2026 pricing rates for reference:
| Model | Price per 1M Output Tokens | Best Use Case |
|---|---|---|
| GPT-4.1 | $8.00 | Complex strategy analysis |
| Claude Sonnet 4.5 | $15.00 | Long-form research reports |
| Gemini 2.5 Flash | $2.50 | High-volume document processing |
| DeepSeek V3.2 | $0.42 | Cost-sensitive batch operations |
For teams running extensive backtest report generation or market commentary, DeepSeek V3.2 at $0.42/MTok offers exceptional cost efficiency for routine tasks, while GPT-4.1 remains ideal for complex strategy reasoning.
Common Errors and Fixes
Error 1: "401 Unauthorized - Invalid API Key"
Symptom: API requests fail with HTTP 401 and message "Invalid or expired API key"
Cause: The API key is missing, malformed, or has been revoked
Solution:
# Verify your API key is correctly set in the environment
import os
from holy_sheep_client import HolySheepTardisClient
Method 1: Direct initialization
api_key = "YOUR_HOLYSHEEP_API_KEY" # Replace with actual key
client = HolySheepTardisClient(
api_key=api_key,
base_url="https://api.holysheep.ai/v1"
)
Method 2: Environment variable (recommended for production)
export HOLYSHEEP_API_KEY="YOUR_HOLYSHEEP_API_KEY"
client = HolySheepTardisClient(api_key=os.environ.get('HOLYSHEEP_API_KEY'))
Test connection with a simple request
try:
# Attempt a minimal query to verify authentication
result = client._make_request('/health')
print("Authentication successful")
except Exception as e:
print(f"Authentication failed: {e}")Error 2: "429 Too Many Requests - Rate Limit Exceeded"
Symptom: Requests fail with HTTP 429, or responses include "rate limit exceeded" in the error body
Cause: Your account has exceeded the API rate limit for your current subscription tier
Solution:
import time
from ratelimit import limits, sleep_and_retry
Implement client-side rate limiting
class RateLimitedClient(HolySheepTardisClient):
"""
Extended client with automatic rate limiting to prevent 429 errors.
Adjust the calls and period based on your subscription tier.
"""
def __init__(self, api_key: str, calls: int = 100, period: int = 60):
super().__init__(api_key)
self.calls = calls
self.period = period
@sleep_and_retry
@limits(calls=calls, period=period)
def get_historical_trades(self, *args, **kwargs):
return super().get_historical_trades(*args, **kwargs)
@sleep_and_retry
@limits(calls=calls, period=period)
def get_order_book_snapshots(self, *args, **kwargs):
return super().get_order_book_snapshots(*args, **kwargs)
Usage with built-in rate limiting
client = RateLimitedClient(
api_key="YOUR_HOLYSHEEP_API_KEY",
calls=100, # 100 requests
period=60 # per 60 seconds
)
If still encountering limits, implement exponential backoff for retries
def fetch_with_backoff(client, endpoint_func, max_retries=5, *args, **kwargs):
for attempt in range(max_retries):
try:
return endpoint_func(*args, **kwargs)
except Exception as e:
if '429' in str(e) and attempt < max_retries - 1:
wait_time = 2 ** attempt # Exponential backoff
print(f"Rate limited. Waiting {wait_time}s before retry...")
time.sleep(wait_time)
else:
raiseError 3: "Data Mismatch - Timestamp Format Inconsistency"
Symptom: Merged datasets from HolySheep and your original Tardis source show misaligned timestamps or duplicate entries
Cause: HolySheep returns timestamps in Unix milliseconds, while your existing code expects seconds or ISO 8601 format
Solution:
import pandas as pd
from datetime import datetime
def normalize_timestamp(ts_value):
"""
Normalize various timestamp formats to UTC datetime.
Handles milliseconds, seconds, and ISO 8601 strings.
"""
if isinstance(ts_value, (int, float)):
# If the number is very large, it's likely milliseconds
if ts_value > 1e12:
return pd.to_datetime(ts_value, unit='ms', utc=True)
else:
# Assume seconds
return pd.to_datetime(ts_value, unit='s', utc=True)
elif isinstance(ts_value, str):
return pd.to_datetime(ts_value, utc=True)
else:
return pd.to_datetime(ts_value, utc=True)
def merge_datasets_with_normalization(holy_sheep_df, tardis_df):
"""
Merge datasets from multiple sources with automatic timestamp normalization.
"""
# Normalize timestamps
holy_sheep_df['normalized_time'] = holy_sheep_df['timestamp'].apply(normalize_timestamp)