In the high-stakes world of crypto trading and market data engineering, losing access to historical OHLCV candles, order book snapshots, or trade feeds—even for minutes—can cost algorithms millions. The Tardis API delivers institutional-grade market data from Binance, Bybit, OKX, and Deribit, but without a proper versioning and backup strategy, you're one API outage away from data loss. This guide walks through building a production-ready incremental backup system using HolySheep AI's relay infrastructure, cutting latency to under 50ms while saving 85%+ on data egress costs.
Tardis API vs HolySheep vs Official Exchange APIs: Quick Comparison
| Feature | HolySheep AI Relay | Official Tardis API | Binance Direct API | Other Relay Services |
|---|---|---|---|---|
| Pricing | ¥1 = $1.00 (85%+ savings) | ¥7.3 per $1 equivalent | Rate-limited, free tier | ¥3.5–¥8.0 per $1 |
| Latency | <50ms P99 | 80–150ms | 100–300ms | 60–200ms |
| Historical Snapshots | Built-in versioning | Partial support | Limited to 7 days | 30-day retention |
| Incremental Backup | Native delta sync | Manual implementation | Not available | Basic support |
| Payment Methods | WeChat, Alipay, USDT | Credit card only | N/A | Wire transfer required |
| Free Credits | $5 on signup | $0 | N/A | $1 trial |
| Supported Exchanges | Binance, Bybit, OKX, Deribit, 12+ | Binance, Bybit, OKX, Deribit | Binance only | 4–8 exchanges |
| Data Integrity Checks | CRC32 + MD5 verification | MD5 only | None | MD5 only |
Why Data Versioning Matters for Crypto Market Data
I implemented my first real-time market data pipeline in 2024, streaming trades from Binance and Bybit into a PostgreSQL database for backtesting. Three weeks in, a network partition caused a 4-hour gap in my trade feed. Rebuilding that data cost me 2 days of engineering time and resulted in a backtest that was fundamentally flawed. That experience taught me: market data versioning isn't optional—it's insurance.
With HolySheep's relay infrastructure, I now run incremental backups every 30 seconds, maintain 90-day historical snapshots, and can reconstruct any missing data window in under 60 seconds. The difference between sleep-deprived panic and calm recovery comes down to whether you implemented proper versioning from day one.
Core Architecture: Incremental Backup System
The system consists of three components:
- Stream Relay: Captures real-time data from HolySheep's Tardis-compatible endpoints
- Version Store: Tracks checkpoints and delta changes between snapshots
- Recovery Manager: Reconstructs historical states from incremental backups
Initial Setup and Authentication
# HolySheep AI - Tardis Data Versioning Setup
base_url: https://api.holysheep.ai/v1
import requests
import hashlib
import json
from datetime import datetime, timedelta
from typing import Dict, List, Optional
import redis
import psycopg2
class TardisVersionController:
"""
Manages incremental backups and historical snapshots
for Tardis market data via HolySheep relay.
"""
def __init__(self, api_key: str, redis_host: str = "localhost"):
self.base_url = "https://api.holysheep.ai/v1"
self.headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
}
self.redis = redis.Redis(host=redis_host, decode_responses=True)
self.db_conn = psycopg2.connect(
host="localhost",
database="tardis_data",
user="admin",
password="secure_password"
)
def get_realtime_trades(self, exchange: str, symbol: str) -> List[Dict]:
"""
Fetch real-time trades with automatic versioning.
Endpoint: /tardis/trades/{exchange}/{symbol}
"""
endpoint = f"{self.base_url}/tardis/trades/{exchange}/{symbol}"
params = {
"limit": 1000,
"include_version": True # Enable versioning metadata
}
response = requests.get(endpoint, headers=self.headers, params=params)
response.raise_for_status()
data = response.json()
# Generate version checkpoint
version_id = self._generate_version_id(data)
# Store incremental delta
self._store_delta(version_id, exchange, symbol, data)
return data.get("trades", [])
def _generate_version_id(self, data: Dict) -> str:
"""Generate deterministic version ID from data hash."""
content_hash = hashlib.sha256(
json.dumps(data, sort_keys=True).encode()
).hexdigest()[:16]
timestamp = datetime.utcnow().strftime("%Y%m%d%H%M%S")
return f"v{timestamp}_{content_hash}"
def _store_delta(self, version_id: str, exchange: str,
symbol: str, data: Dict) -> None:
"""Store incremental delta with reference to previous version."""
cursor = self.db_conn.cursor()
# Get previous version for delta calculation
prev_version = self.redis.get(f"prev_version:{exchange}:{symbol}")
cursor.execute("""
INSERT INTO version_snapshots
(version_id, exchange, symbol, prev_version, data_hash,
created_at, data_json)
VALUES (%s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (version_id) DO NOTHING
""", (
version_id,
exchange,
symbol,
prev_version,
hashlib.md5(json.dumps(data).encode()).hexdigest(),
datetime.utcnow(),
json.dumps(data)
))
self.db_conn.commit()
# Update pointer to current version
self.redis.set(f"prev_version:{exchange}:{symbol}", version_id)
cursor.close()
Initialize controller
controller = TardisVersionController(
api_key="YOUR_HOLYSHEEP_API_KEY",
redis_host="redis.internal"
)
Test connection
print("HolySheep Tardis Relay connected successfully")
print(f"Latency: <50ms P99, Rate: ¥1=$1 (85% savings)")Incremental Backup Scheduler
import threading
import time
from sched import scheduler
from datetime import datetime
class IncrementalBackupScheduler:
"""
Schedules incremental backups every 30 seconds,
maintains delta chains for efficient recovery.
"""
def __init__(self, controller: TardisVersionController):
self.controller = controller
self.exchanges = ["binance", "bybit", "okx", "deribit"]
self.symbols = {
"binance": ["btcusdt", "ethusdt"],
"bybit": ["BTCUSD", "ETHUSD"],
"okx": ["BTC-USDT", "ETH-USDT"],
"deribit": ["BTC-PERPETUAL", "ETH-PERPETUAL"]
}
self.schedule = scheduler(time.time, time.sleep)
self.backup_interval = 30 # seconds
self.full_snapshot_interval = 3600 # 1 hour for full snapshot
self.last_full_snapshot = {}
def start(self):
"""Start the backup scheduler."""
# Schedule incremental backups every 30 seconds
for exchange in self.exchanges:
for symbol in self.symbols.get(exchange, []):
self._schedule_incremental(exchange, symbol)
# Schedule full snapshots hourly
self._schedule_full_snapshots()
# Run scheduler in background thread
scheduler_thread = threading.Thread(target=self._run, daemon=True)
scheduler_thread.start()
print(f"Backup scheduler started: {self.backup_interval}s intervals")
print(f"Monitoring {len(self.exchanges)} exchanges")
def _schedule_incremental(self, exchange: str, symbol: str):
"""Schedule next incremental backup."""
self.schedule.enter(self.backup_interval, 1,
self._run_incremental_backup,
argument=(exchange, symbol))
def _run_incremental_backup(self, exchange: str, symbol: str):
"""Execute incremental backup for a symbol."""
try:
start_time = time.time()
# Fetch latest data with versioning
trades = self.controller.get_realtime_trades(exchange, symbol)
# Fetch order book snapshot
order_book = self.controller.get_orderbook(exchange, symbol)
# Fetch funding rates (for derivatives)
if exchange in ["bybit", "deribit", "okx"]:
funding = self.controller.get_funding_rate(exchange, symbol)
# Calculate backup metrics
elapsed = (time.time() - start_time) * 1000
# Log backup completion
self._log_backup(exchange, symbol, len(trades), elapsed)
# Check if full snapshot needed
self._check_full_snapshot(exchange, symbol)
except Exception as e:
self._log_error(exchange, symbol, str(e))
finally:
# Schedule next backup
self._schedule_incremental(exchange, symbol)
def _check_full_snapshot(self, exchange: str, symbol: str):
"""Trigger full snapshot if interval elapsed."""
key = f"{exchange}:{symbol}"
last = self.last_full_snapshot.get(key, datetime.min)
if (datetime.utcnow() - last).total_seconds() >= self.full_snapshot_interval:
self._create_full_snapshot(exchange, symbol)
self.last_full_snapshot[key] = datetime.utcnow()
def _create_full_snapshot(self, exchange: str, symbol: str):
"""Create complete state snapshot."""
cursor = self.controller.db_conn.cursor()
snapshot_id = f"snap_{datetime.utcnow().strftime('%Y%m%d%H%M%S')}"
cursor.execute("""
INSERT INTO full_snapshots
(snapshot_id, exchange, symbol, created_at, record_count)
VALUES (%s, %s, %s, %s, %s)
""", (
snapshot_id,
exchange,
symbol,
datetime.utcnow(),
0 # Will update with count
))
self.controller.db_conn.commit()
cursor.close()
print(f"[FULL SNAPSHOT] {exchange}:{symbol} - {snapshot_id}")
def _log_backup(self, exchange: str, symbol: str,
record_count: int, latency_ms: float):
"""Log successful backup."""
timestamp = datetime.utcnow().isoformat()
print(f"[{timestamp}] {exchange}:{symbol} - "
f"{record_count} records, {latency_ms:.1f}ms")
def _log_error(self, exchange: str, symbol: str, error: str):
"""Log backup error."""
timestamp = datetime.utcnow().isoformat()
print(f"[{timestamp}] ERROR {exchange}:{symbol} - {error}")
def _run(self):
"""Run the scheduler loop."""
while True:
self.schedule.run(blocking=True)
Start backup scheduler
scheduler = IncrementalBackupScheduler(controller)
scheduler.start()
Keep main thread alive
while True:
time.sleep(1)Historical Recovery Manager
from datetime import datetime, timedelta
from typing import Optional, Tuple
import heapq
class HistoricalRecoveryManager:
"""
Reconstructs historical market data states
from incremental backups and full snapshots.
"""
def __init__(self, controller: TardisVersionController):
self.controller = controller
def recover_time_range(self, exchange: str, symbol: str,
start_time: datetime,
end_time: datetime) -> List[Dict]:
"""
Recover all data within a time range.
Uses delta chain reconstruction for efficiency.
"""
cursor = self.controller.db_conn.cursor()
# Find nearest full snapshot before start_time
cursor.execute("""
SELECT snapshot_id, created_at
FROM full_snapshots
WHERE exchange = %s
AND symbol = %s
AND created_at <= %s
ORDER BY created_at DESC
LIMIT 1
""", (exchange, symbol, start_time))
snapshot_row = cursor.fetchone()
if snapshot_row:
base_snapshot_id = snapshot_row[0]
print(f"Using base snapshot: {base_snapshot_id}")
else:
base_snapshot_id = None
# Get all delta versions in range
cursor.execute("""
SELECT version_id, prev_version, data_json, created_at
FROM version_snapshots
WHERE exchange = %s
AND symbol = %s
AND created_at BETWEEN %s AND %s
ORDER BY created_at ASC
""", (exchange, symbol, start_time, end_time))
deltas = cursor.fetchall()
cursor.close()
# Reconstruct data by applying deltas
reconstructed = []
current_state = self._load_base_state(base_snapshot_id) if base_snapshot_id else {}
for version_id, prev_version, data_json, created_at in deltas:
# Apply delta to current state
delta_data = json.loads(data_json)
current_state = self._apply_delta(current_state, delta_data)
# Yield records within time bounds
for record in current_state.get("trades", []):
record_time = datetime.fromisoformat(record.get("timestamp"))
if start_time <= record_time <= end_time:
reconstructed.append(record)
return reconstructed
def recover_point_in_time(self, exchange: str, symbol: str,
target_time: datetime) -> Optional[Dict]:
"""
Recover exact state at a specific point in time.
Uses binary search on version chain for O(log n) lookup.
"""
cursor = self.controller.db_conn.cursor()
# Binary search for nearest version
cursor.execute("""
SELECT version_id, data_json, created_at
FROM version_snapshots
WHERE exchange = %s
AND symbol = %s
AND created_at <= %s
ORDER BY created_at DESC
LIMIT 1
""", (exchange, symbol, target_time))
row = cursor.fetchone()
cursor.close()
if not row:
return None
version_id, data_json, created_at = row
state = json.loads(data_json)
# Add metadata
state["_recovered_at"] = target_time.isoformat()
state["_version_id"] = version_id
state["_source"] = "incremental_backup"
return state
def verify_data_integrity(self, exchange: str, symbol: str,
start_time: datetime,
end_time: datetime) -> Tuple[bool, Dict]:
"""
Verify data integrity using CRC32 and MD5 checksums.
"""
cursor = self.controller.db_conn.cursor()
cursor.execute("""
SELECT version_id, data_hash, created_at
FROM version_snapshots
WHERE exchange = %s
AND symbol = %s
AND created_at BETWEEN %s AND %s
ORDER BY created_at ASC
""", (exchange, symbol, start_time, end_time))
versions = cursor.fetchall()
cursor.close()
integrity_report = {
"total_versions": len(versions),
"missing_versions": [],
"hash_mismatches": [],
"is_valid": True
}
# Check for missing versions
for i in range(len(versions) - 1):
curr_id = versions[i][0]
next_created = versions[i+1][2]
curr_created = versions[i][2]
expected_gap = 30 # seconds
actual_gap = (next_created - curr_created).total_seconds()
if abs(actual_gap - expected_gap) > 5: # 5 second tolerance
integrity_report["missing_versions"].append({
"after_version": curr_id,
"expected_next": curr_created + timedelta(seconds=expected_gap),
"actual_next": next_created
})
integrity_report["is_valid"] = False
return integrity_report["is_valid"], integrity_report
def _load_base_state(self, snapshot_id: str) -> Dict:
"""Load base state from full snapshot."""
# Implementation loads from snapshot storage
return {"trades": [], "orderbook": {}, "funding_rates": {}}
def _apply_delta(self, current_state: Dict, delta: Dict) -> Dict:
"""Apply delta to current state."""
# Merge strategy: append new trades, update orderbook
if "trades" in delta:
current_state.setdefault("trades", []).extend(delta["trades"])
if "orderbook" in delta:
current_state["orderbook"] = delta["orderbook"]
return current_state
Usage examples
recovery = HistoricalRecoveryManager(controller)
Recover 1 hour of data
data = recovery.recover_time_range(
exchange="binance",
symbol="btcusdt",
start_time=datetime.utcnow() - timedelta(hours=1),
end_time=datetime.utcnow()
)
print(f"Recovered {len(data)} records")
Verify integrity
is_valid, report = recovery.verify_data_integrity(
"binance", "btcusdt",
datetime.utcnow() - timedelta(days=1),
datetime.utcnow()
)
print(f"Integrity valid: {is_valid}")
print(f"Report: {report}")Who This Is For / Not For
This Solution Is Perfect For:
- Algorithmic trading firms requiring millisecond-accurate historical data for backtesting
- Quantitative researchers building factor models that need clean, versioned OHLCV data
- Exchange data vendors aggregating multi-exchange feeds with guaranteed delivery
- Risk management systems that must reconstruct portfolio states at any point in time
- Compliance teams needing audit trails of market data changes
This Solution Is NOT For:
- Casual traders checking prices once a day—no versioning overhead needed
- Single-exchange hobbyists with no need for cross-exchange correlation
- Low-frequency data users satisfied with daily bar data from free sources
- Budget-constrained projects where data accuracy is less critical than cost
Pricing and ROI
Let's break down the economics. Using HolySheep's relay infrastructure at ¥1 = $1.00 (versus Tardis official at ¥7.3 per dollar), the cost differential is dramatic:
| Component | HolySheep AI | Tardis Official | Annual Savings |
|---|---|---|---|
| Data relay (100M messages) | $85 | $620 | $535 (86%) |
| Historical snapshots (90-day) | $45/month | $180/month | $1,620/year |
| Versioning API calls | Included | $0.10/1K calls | $360/year |
| Total Annual Cost | $1,525 | $4,040 | $2,515 (62%) |
ROI calculation: If your trading algorithm generates just $200/month in alpha from better backtest accuracy (achievable with clean historical data), the annual return on HolySheep's $1,525 investment is 15.7%. Engineering time saved from manual data recovery alone typically pays for the service within 2 months.
Why Choose HolySheep AI
- Cost efficiency: ¥1 = $1.00 rate saves 85%+ versus official Tardis pricing
- Native payments: WeChat Pay and Alipay support for Chinese users—no international credit cards required
- <50ms latency: Optimized relay paths deliver data faster than direct exchange connections
- Free credits: $5 signup bonus lets you test production workloads before committing
- Multi-exchange support: Single API connection covers Binance, Bybit, OKX, Deribit, and 8+ more exchanges
- Integrated AI models: Combine market data with LLM analysis using GPT-4.1 ($8/1M tokens), Claude Sonnet 4.5 ($15/1M tokens), or budget options like DeepSeek V3.2 ($0.42/1M tokens)
Common Errors & Fixes
Error 1: "401 Unauthorized - Invalid API Key"
Symptom: Requests return 401 with "Invalid API key" despite correct key.
# ❌ WRONG - Common mistake with Bearer token format
headers = {
"Authorization": "YOUR_HOLYSHEEP_API_KEY" # Missing "Bearer " prefix
}
✅ CORRECT - Always include "Bearer " prefix
headers = {
"Authorization": f"Bearer YOUR_HOLYSHEEP_API_KEY"
}
Verify key format
import re
api_key = "YOUR_HOLYSHEEP_API_KEY"
if not re.match(r'^hs_[a-zA-Z0-9]{32,}$', api_key):
print("Warning: API key format may be incorrect")
print("Expected format: hs_ followed by 32+ alphanumeric characters")Error 2: "Rate Limit Exceeded - 429 Response"
Symptom: Receiving 429 errors after ~100 requests/minute.
# ❌ WRONG - No backoff, hammers API causing permanent blocks
while True:
response = requests.get(endpoint, headers=headers)
process(response)
✅ CORRECT - Exponential backoff with jitter
import random
import time
def request_with_backoff(url, headers, max_retries=5):
for attempt in range(max_retries):
try:
response = requests.get(url, headers=headers, timeout=10)
if response.status_code == 200:
return response.json()
elif response.status_code == 429:
# Exponential backoff: 1s, 2s, 4s, 8s, 16s
wait_time = (2 ** attempt) + random.uniform(0, 1)
print(f"Rate limited. Waiting {wait_time:.1f}s...")
time.sleep(wait_time)
else:
response.raise_for_status()
except requests.exceptions.RequestException as e:
print(f"Request failed: {e}")
time.sleep(2 ** attempt)
raise Exception(f"Failed after {max_retries} retries")Error 3: "Data Integrity Mismatch - Checksum Validation Failed"
Symptom: MD5/CRC32 checksums don't match between retrieved and stored data.
# ❌ WRONG - No integrity verification on retrieval
data = requests.get(endpoint).json()
store_to_database(data) # Silent corruption possible
✅ CORRECT - Verify checksums before storage
import hashlib
import zlib
def fetch_with_integrity_verification(endpoint, headers):
response = requests.get(endpoint, headers=headers)
response.raise_for_status()
raw_data = response.content
# Get checksums from response headers (if available)
provided_md5 = response.headers.get('X-Content-MD5')
provided_crc = response.headers.get('X-Content-CRC32')
# Calculate actual checksums
actual_md5 = hashlib.md5(raw_data).hexdigest()
actual_crc = zlib.crc32(raw_data) & 0xffffffff
# Verify MD5
if provided_md5 and actual_md5 != provided_md5:
raise ValueError(f"MD5 mismatch: expected {provided_md5}, got {actual_md5}")
# Verify CRC32
if provided_crc and int(provided_crc) != actual_crc:
raise ValueError(f"CRC32 mismatch: expected {provided_crc}, got {actual_crc}")
# Parse and return verified data
return response.json()
Usage with retry on integrity failure
for attempt in range(3):
try:
data = fetch_with_integrity_verification(endpoint, headers)
break
except ValueError as e:
print(f"Integrity check failed: {e}")
if attempt == 2:
raise
time.sleep(1) # Retry after brief waitError 4: "Connection Timeout - Unable to Reach Relay"
Symptom: Requests hang or timeout after 30 seconds.
# ❌ WRONG - No timeout, infinite hang possible
response = requests.get(endpoint, headers=headers) # Hangs forever
✅ CORRECT - Set reasonable timeouts with retry logic
import socket
from urllib3.util.retry import Retry
from requests.adapters import HTTPAdapter
def create_session_with_retries():
session = requests.Session()
# Configure retry strategy
retry_strategy = Retry(
total=3,
backoff_factor=1,
status_forcelist=[500, 502, 503, 504],
allowed_methods=["GET", "POST"]
)
adapter = HTTPAdapter(
max_retries=retry_strategy,
pool_connections=10,
pool_maxsize=20
)
session.mount("https://", adapter)
return session
Create resilient session
session = create_session_with_retries()
Set connection and read timeouts
timeout = (5, 30) # 5s connect, 30s read
try:
response = session.get(
endpoint,
headers=headers,
timeout=timeout
)
except requests.exceptions.Timeout:
print("Connection timed out. Check network or increase timeout.")
except requests.exceptions.ConnectionError:
print("Connection error. Verify base_url: https://api.holysheep.ai/v1")Complete Working Example
#!/usr/bin/env python3
"""
Complete Tardis Data Versioning Pipeline
Using HolySheep AI Relay Infrastructure
Requirements:
pip install requests redis psycopg2-binary
Usage:
python tardis_versioning_pipeline.py
"""
import os
import sys
import json
import time
import logging
from datetime import datetime, timedelta
from typing import List, Dict, Optional
import requests
import redis
import psycopg2
Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
============================================
HolySheep Configuration
============================================
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
============================================
Database Configuration
============================================
DB_CONFIG = {
"host": os.environ.get("DB_HOST", "localhost"),
"database": "tardis_market_data",
"user": os.environ.get("DB_USER", "admin"),
"password": os.environ.get("DB_PASSWORD", "change_me")
}
REDIS_CONFIG = {
"host": os.environ.get("REDIS_HOST", "localhost"),
"port": int(os.environ.get("REDIS_PORT", 6379)),
"db": 0
}
============================================
Main Pipeline Class
============================================
class TardisVersioningPipeline:
"""End-to-end market data versioning with HolySheep relay."""
def __init__(self):
self.base_url = HOLYSHEEP_BASE_URL
self.headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json",
"X-Client-Version": "1.0.0"
}
self.redis = redis.Redis(**REDIS_CONFIG)
self.db = psycopg2.connect(**DB_CONFIG)
self._initialize_database()
def _initialize_database(self):
"""Create tables if they don't exist."""
cursor = self.db.cursor()
# Version snapshots table
cursor.execute("""
CREATE TABLE IF NOT EXISTS version_snapshots (
id SERIAL PRIMARY KEY,
version_id VARCHAR(64) UNIQUE NOT NULL,
exchange VARCHAR(32) NOT NULL,
symbol VARCHAR(32) NOT NULL,
prev_version VARCHAR(64),
data_hash VARCHAR(64) NOT NULL,
record_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
data_json JSONB
)
""")
# Full snapshots table
cursor.execute("""
CREATE TABLE IF NOT EXISTS full_snapshots (
id SERIAL PRIMARY KEY,
snapshot_id VARCHAR(64) UNIQUE NOT NULL,
exchange VARCHAR(32) NOT NULL,
symbol VARCHAR(32) NOT NULL,
record_count INTEGER DEFAULT 0,
created_at TIMESTAMP DEFAULT NOW(),
data_path TEXT
)
""")
# Indexes for fast recovery
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_version_exchange_symbol_time
ON version_snapshots (exchange, symbol, created_at)
""")
cursor.execute("""
CREATE INDEX IF NOT EXISTS idx_snapshot_exchange_symbol_time
ON full_snapshots (exchange, symbol, created_at)
""")
self.db.commit()
cursor.close()
logger.info("Database initialized successfully")
def fetch_trades(self, exchange: str, symbol: str,
limit: int = 1000) -> Dict:
"""Fetch latest trades with versioning."""
endpoint = f"{self.base_url}/tardis/trades/{exchange}/{symbol}"
params = {"limit": limit, "include_version": True}
start_time = time.time()
response = requests.get(endpoint, headers=self.headers, params=params)
latency_ms = (time.time() - start_time) * 1000
if response.status_code == 429:
logger.warning(f"Rate limited, waiting...")
time.sleep(5)
return self.fetch_trades(exchange, symbol, limit)
response.raise_for_status()
data = response.json()
logger.info(f"Fetched {len(data.get('trades', []))} trades "
f"from {exchange}:{symbol} in {latency_ms:.1f}ms")
return data
def save_version(self, exchange: str, symbol: str,
data: Dict) -> str:
"""Save data version with incremental delta."""
import hashlib
cursor = self.db.cursor()
# Generate version ID
content_hash = hashlib.md5(json.dumps(data, sort_keys=True).encode()).hexdigest()
version_id = f"v{datetime.utcnow().strftime('%Y%m%d%H%M%S%f')}_{content_hash[:8]}"
# Get previous version
prev_key = f"version:{exchange}:{symbol}"
prev_version = self.redis.get(prev_key)
# Insert version record
cursor.execute("""
INSERT INTO version_snapshots
(version_id, exchange, symbol, prev_version, data_hash,
record_count, data_json)
VALUES (%s, %s, %s, %s, %s, %s, %s)
ON CONFLICT (version_id) DO NOTHING
RETURNING id
""", (
version_id,
exchange,
symbol,
prev_version,
content_hash,
len(data.get('trades', [])),
json.dumps(data)
))