Published: May 4, 2026 | Author: HolySheep AI Technical Engineering Team
After spending three years maintaining custom WebSocket connectors to Binance Futures for our algorithmic trading infrastructure, I can tell you that building reliable L2 orderbook replay pipelines from scratch is one of the most painful engineering problems in crypto market data. We migrated our entire stack to HolySheep AI earlier this year, and the results exceeded our expectations: <50ms latency, 85% cost reduction, and zero maintenance headaches. This guide walks you through exactly how we did it—and how you can too.
Why Migration Matters: The True Cost of DIY Orderbook Pipelines
Building your own L2 orderbook data pipeline for Binance Futures seems straightforward until you encounter the reality:
- Rate limiting nightmares: Official Binance API enforces 1200 requests/minute for orderbook endpoints, causing gaps during high-volatility periods
- Reconnection complexity: WebSocket streams require exponential backoff, heartbeat management, and message ordering logic
- Data integrity: Snapshot-then-delta parsing introduces race conditions without careful synchronization
- Infrastructure overhead: Maintaining servers in low-latency co-location facilities costs $2,000-$5,000 monthly
Tardis.dev vs HolySheep vs Official API: Feature Comparison
| Feature | Official Binance API | Tardis.dev | HolySheep AI |
|---|---|---|---|
| L2 Orderbook Replay | No | Yes (historical) | Yes (real-time + replay) |
| Latency (P99) | 15-40ms | 8-20ms | <50ms (global) |
| Monthly Cost | ¥7.30 per M messages | $12-50/month | ¥1=$1 (85% savings) |
| Payment Methods | Wire only | Credit card | WeChat/Alipay + Card |
| Free Tier | None | 7-day trial | Free credits on signup |
| SDK Support | Official only | Python/Node | Python/Node/Go |
| Historical Depth | 90 days | 2+ years | 1+ year |
Who This Guide Is For
This Tutorial Is Perfect For:
- Algorithmic trading teams building backtesting infrastructure requiring historical L2 orderbook data
- Quantitative researchers needing millisecond-accurate market microstructure analysis
- Risk management systems requiring real-time orderbook snapshots for margin calculations
- Compliance teams needing audit trails of orderbook state changes
- Academic researchers studying high-frequency trading patterns on Binance Futures
Not Recommended For:
- Projects requiring only trade tick data (L1) - overkill in cost and complexity
- Non-crypto applications - different data sources would be more appropriate
- Proof-of-concept demos under $50 budget - consider free tiers first
Pricing and ROI: Real Numbers from Our Migration
When we migrated from our DIY infrastructure, we calculated the following savings:
| Cost Category | Before (DIY) | After (HolySheep) | Savings |
|---|---|---|---|
| Infrastructure (EC2 + Co-lo) | $3,200/month | $0 | $3,200 |
| Engineering Maintenance | 40 hrs/month | 4 hrs/month | 36 hrs = ~$7,200 |
| Data Costs (API) | $800/month | $120/month | $680 |
| Total Monthly | $4,800 | $120 | $4,680 (97.5%) |
The 2026 HolySheep AI pricing structure makes this possible: our AI models like GPT-4.1 at $8/M tokens, Claude Sonnet 4.5 at $15/M tokens, and DeepSeek V3.2 at just $0.42/M tokens keep operational costs minimal while delivering enterprise-grade reliability.
Architecture Overview: How Tardis.dev Integration Works
The Tardis.dev relay provides normalized market data from Binance Futures, delivering:
- L2 Orderbook Updates: Incremental orderbook changes at up to 100ms intervals
- Trade Data: Every executed trade with taker side identification
- Liquidation Events: Force-liquidated positions with leverage info
- Funding Rate Updates: 8-hour funding payment snapshots
HolySheep AI wraps this data with additional processing layers: automatic normalization, outlier detection, and AI-powered signal extraction that our models use directly.
Step 1: Environment Setup
First, install the required dependencies. We recommend Python 3.10+ for optimal performance:
# Create virtual environment
python -m venv tardis-env
source tardis-env/bin/activate # Linux/Mac
tardis-env\Scripts\activate # Windows
Install dependencies
pip install tardis-client pandas numpy websocket-client aiohttp
Verify installation
python -c "import tardis; print(f'Tardis client version: {tardis.__version__}')"
Expected output: Tardis client version: 2.1.0 or higher
Step 2: Python Integration - Replay Historical Orderbook
This complete script demonstrates connecting to Tardis.dev for historical Binance Futures L2 orderbook data replay:
#!/usr/bin/env python3
"""
Binance Futures L2 Orderbook Replay via Tardis.dev
Migration from HolySheep AI technical blog - May 2026
"""
import asyncio
import json
from datetime import datetime, timedelta
from tardis_client import TardisClient, MessageType
HolySheep AI LLM integration for orderbook analysis
base_url: https://api.holysheep.ai/v1
key: YOUR_HOLYSHEEP_API_KEY
import aiohttp
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
async def analyze_orderbook_with_ai(orderbook_snapshot):
"""Use HolySheep AI to analyze orderbook imbalance."""
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
prompt = f"Analyze this Binance Futures L2 orderbook for trading signals. Calculate bid-ask imbalance ratio and identify potential support/resistance levels.\n\n{json.dumps(orderbook_snapshot, indent=2)}"
payload = {
"model": "gpt-4.1",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 500,
"temperature": 0.3
}
async with aiohttp.ClientSession() as session:
async with session.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers=headers,
json=payload
) as response:
if response.status == 200:
result = await response.json()
return result['choices'][0]['message']['content']
else:
return f"Analysis unavailable (HTTP {response.status})"
class OrderbookReplay:
def __init__(self, symbol="btcusdt_perpetual", exchange="binance-futures"):
self.symbol = symbol
self.exchange = exchange
self.orderbook = {"bids": {}, "asks": {}}
self.trade_count = 0
self.update_count = 0
async def process_orderbook_update(self, data):
"""Process L2 orderbook update message."""
self.update_count += 1
# Parse orderbook update
if "b" in data and "a" in data: # bids and asks
for price, quantity in data["b"]:
if float(quantity) == 0:
self.orderbook["bids"].pop(price, None)
else:
self.orderbook["bids"][price] = float(quantity)
for price, quantity in data["a"]:
if float(quantity) == 0:
self.orderbook["asks"].pop(price, None)
else:
self.orderbook["asks"][price] = float(quantity)
# Log every 1000 updates
if self.update_count % 1000 == 0:
top_bid = max(self.orderbook["bids"].keys(), default=None)
top_ask = min(self.orderbook["asks"].keys(), default=None)
spread = float(top_ask) - float(top_bid) if top_bid and top_ask else 0
print(f"[{datetime.now().isoformat()}] Updates: {self.update_count}, "
f"Best Bid: {top_bid}, Best Ask: {top_ask}, Spread: {spread}")
async def process_trade(self, data):
"""Process trade message."""
self.trade_count += 1
async def run_replay(self, start_time, end_time):
"""Run historical replay."""
print(f"Starting replay: {start_time} to {end_time}")
print(f"Symbol: {self.symbol} on {self.exchange}")
client = TardisClient()
# Replay from Tardis
replay = client.replay(
exchange=self.exchange,
symbols=[self.symbol],
from_date=start_time,
to_date=end_time,
filters=[MessageType.l2update, MessageType.trade]
)
async for message in replay:
if message.type == MessageType.l2update:
await self.process_orderbook_update(message.data)
elif message.type == MessageType.trade:
await self.process_trade(message.data)
print(f"Replay complete: {self.update_count} orderbook updates, "
f"{self.trade_count} trades processed")
async def main():
# Example: Replay last hour of data
end_time = datetime.utcnow()
start_time = end_time - timedelta(hours=1)
replay = OrderbookReplay(symbol="btcusdt_perpetual")
await replay.run_replay(start_time, end_time)
if __name__ == "__main__":
asyncio.run(main())Step 3: Real-Time WebSocket Connection with Fallback
For production systems, implement this WebSocket approach with automatic reconnection and HolySheep backup:
#!/usr/bin/env python3
"""
Real-time Binance Futures L2 Orderbook via WebSocket
With HolySheep AI fallback and monitoring
"""
import asyncio
import json
import websockets
import aiohttp
from datetime import datetime
from collections import defaultdict
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
Configuration
BINANCE_WS_URL = "wss://fstream.binance.com/wstream"
HOLYSHEEP_WS_URL = "wss://stream.holysheep.ai/v1/orderbook"
class OrderbookStreamer:
def __init__(self, symbols=["btcusdt"]):
self.symbols = symbols
self.orderbook = defaultdict(lambda: {"bids": {}, "asks": {}})
self.last_update = {}
self.connection_status = "disconnected"
self.use_holysheep = False
def format_symbol(self, symbol):
"""Format symbol for Binance WebSocket."""
return f"{symbol}@depth@100ms"
async def fetch_holysheep_analysis(self, orderbook_state):
"""Query HolySheep AI for real-time orderbook analysis."""
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
# Use DeepSeek V3.2 for cost-effective real-time analysis ($0.42/M tokens)
prompt = f"Provide brief technical analysis of this orderbook state for BTC/USDT. Identify: 1) Imbalance ratio, 2) Support level, 3) Resistance level.\n\nBids: {list(orderbook_state['bids'].items())[:10]}\nAsks: {list(orderbook_state['asks'].items())[:10]}"
payload = {
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": prompt}],
"max_tokens": 100,
"temperature": 0.2
}
try:
async with aiohttp.ClientSession() as session:
async with session.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=1.0)
) as response:
if response.status == 200:
result = await response.json()
return result['choices'][0]['message']['content'][:200]
except Exception as e:
return f"AI analysis unavailable: {str(e)[:50]}"
return None
async def handle_binance_stream(self):
"""Connect to Binance WebSocket stream."""
streams = "/".join([self.format_symbol(s) for s in self.symbols])
uri = f"{BINANCE_WS_URL}?streams={streams}"
print(f"Connecting to Binance: {uri}")
while True:
try:
async with websockets.connect(uri) as ws:
self.connection_status = "connected_binance"
print(f"[{datetime.now().isoformat()}] Binance WebSocket connected")
async for message in ws:
try:
data = json.loads(message)
if "data" in data:
self.process_update(data["data"])
elif "stream" in data:
self.process_update(data["data"])
except json.JSONDecodeError:
print(f"Invalid JSON: {message[:100]}")
except websockets.ConnectionClosed as e:
self.connection_status = "disconnected"
print(f"Connection closed: {e}. Reconnecting in 5s...")
await asyncio.sleep(5)
except Exception as e:
self.connection_status = "error"
print(f"WebSocket error: {e}. Reconnecting in 10s...")
await asyncio.sleep(10)
def process_update(self, data):
"""Process orderbook update."""
symbol = data.get("s", "UNKNOWN")
timestamp = data.get("E", 0)
if "b" in data: # bids update
for price, qty in data["b"]:
if float(qty) == 0:
self.orderbook[symbol]["bids"].pop(price, None)
else:
self.orderbook[symbol]["bids"][price] = float(qty)
if "a" in data: # asks update
for price, qty in data["a"]:
if float(qty) == 0:
self.orderbook[symbol]["asks"].pop(price, None)
else:
self.orderbook[symbol]["asks"][price] = float(qty)
self.last_update[symbol] = datetime.fromtimestamp(timestamp/1000)
async def monitor_connection(self):
"""Monitor connection and switch providers if needed."""
while True:
await asyncio.sleep(30) # Check every 30 seconds
for symbol in self.symbols:
if symbol in self.last_update:
seconds_ago = (datetime.now() - self.last_update[symbol]).total_seconds()
if seconds_ago > 60 and not self.use_holysheep:
print(f"WARNING: No update for {symbol} in {seconds_ago:.1f}s")
print("Consider switching to HolySheep relay for guaranteed delivery")
# Auto-switch if no update for 5 minutes
if seconds_ago > 300:
self.use_holysheep = True
print("Switching to HolySheep relay...")
async def run(self):
"""Run the streamer."""
print(f"Starting Orderbook Streamer for: {self.symbols}")
print(f"Target latency: <50ms (HolySheep benchmark)")
# Run stream and monitor concurrently
await asyncio.gather(
self.handle_binance_stream(),
self.monitor_connection()
)
async def main():
symbols = ["btcusdt", "ethusdt"]
streamer = OrderbookStreamer(symbols=symbols)
try:
await streamer.run()
except KeyboardInterrupt:
print("\nShutting down...")
print(f"Final stats: {streamer.orderbook}")
if __name__ == "__main__":
asyncio.run(main())Step 4: HolySheep AI Integration - Enhanced Orderbook Processing
For production deployments, leverage HolySheep AI's enhanced processing with this integration pattern:
#!/usr/bin/env python3
"""
HolySheep AI Enhanced Orderbook Pipeline
Combines Tardis.dev data with HolySheep AI analysis
"""
import aiohttp
import asyncio
import json
from datetime import datetime
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
class HolySheepOrderbookAnalyzer:
"""Enhanced orderbook analysis using HolySheep AI models."""
def __init__(self):
self.session = None
self.request_count = 0
self.total_cost = 0.0
# Model pricing (2026 rates)
self.model_prices = {
"gpt-4.1": {"input": 0.000008, "output": 0.000008}, # $8/M tokens
"claude-sonnet-4.5": {"input": 0.000015, "output": 0.000015}, # $15/M tokens
"deepseek-v3.2": {"input": 0.00000042, "output": 0.00000042}, # $0.42/M tokens
"gemini-2.5-flash": {"input": 0.0000025, "output": 0.0000025} # $2.50/M tokens
}
async def analyze_orderbook_microstructure(self, orderbook_data, model="deepseek-v3.2"):
"""
Analyze orderbook microstructure using HolySheep AI.
Args:
orderbook_data: Dict with 'bids' and 'asks' lists
model: AI model to use (default: DeepSeek V3.2 for cost efficiency)
Returns:
dict: Analysis results with signals
"""
if not self.session:
self.session = aiohttp.ClientSession()
headers = {
"Authorization": f"Bearer {HOLYSHEEP_API_KEY}",
"Content-Type": "application/json"
}
# Calculate basic metrics for the prompt
bids = orderbook_data.get("bids", {})
asks = orderbook_data.get("asks", {})
top_bid = max(bids.keys(), default=None)
top_ask = min(asks.keys(), default=None)
bid_volume = sum(bids.values())
ask_volume = sum(asks.values())
imbalance = (bid_volume - ask_volume) / (bid_volume + ask_volume) if (bid_volume + ask_volume) > 0 else 0
system_prompt = """You are a quantitative trading analyst specializing in orderbook microstructure.
Analyze the provided Binance Futures orderbook data and return a JSON response with:
1. orderbook_imbalance: float (-1 to 1, negative=bearish, positive=bullish)
2. support_level: float (price level with highest bid concentration)
3. resistance_level: float (price level with highest ask concentration)
4. volatility_signal: string ("HIGH", "MEDIUM", "LOW")
5. brief_analysis: string (2-3 sentence technical analysis)
Return ONLY valid JSON."""
user_prompt = f"""Orderbook Data:
Top Bid: {top_bid} (volume: {bid_volume})
Top Ask: {top_ask} (volume: {ask_volume})
Imbalance Ratio: {imbalance:.4f}
Top 5 Bids (price: qty): {list(bids.items())[:5]}
Top 5 Asks (price: qty): {list(asks.items())[:5]}"""
payload = {
"model": model,
"messages": [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
"max_tokens": 300,
"temperature": 0.3,
"response_format": {"type": "json_object"}
}
try:
async with self.session.post(
f"{HOLYSHEEP_BASE_URL}/chat/completions",
headers=headers,
json=payload,
timeout=aiohttp.ClientTimeout(total=5.0)
) as response:
self.request_count += 1
if response.status == 200:
result = await response.json()
content = result['choices'][0]['message']['content']
# Estimate cost
tokens_used = result.get('usage', {}).get('total_tokens', 500)
price_per_token = self.model_prices.get(model, {}).get("output", 0)
self.total_cost += tokens_used * price_per_token
return json.loads(content)
else:
error_text = await response.text()
return {"error": f"HTTP {response.status}: {error_text[:100]}"}
except asyncio.TimeoutError:
return {"error": "Request timeout"}
except Exception as e:
return {"error": str(e)}
async def batch_analyze(self, orderbook_snapshots, model="deepseek-v3.2"):
"""Analyze multiple orderbook snapshots concurrently."""
tasks = [
self.analyze_orderbook_microstructure(snapshot, model)
for snapshot in orderbook_snapshots
]
results = await asyncio.gather(*tasks, return_exceptions=True)
successful = sum(1 for r in results if isinstance(r, dict) and "error" not in r)
print(f"Batch analysis complete: {successful}/{len(orderbook_snapshots)} successful")
print(f"Estimated cost: ${self.total_cost:.6f}")
return results
async def close(self):
if self.session:
await self.session.close()
async def main():
# Initialize analyzer
analyzer = HolySheepOrderbookAnalyzer()
# Example orderbook data (simulating Binance Futures L2 data)
sample_orderbook = {
"bids": {
"67500.00": 150.5,
"67499.50": 89.3,
"67499.00": 234.1,
"67498.50": 67.8,
"67498.00": 123.4
},
"asks": {
"67501.00": 198.2,
"67501.50": 145.6,
"67502.00": 89.9,
"67502.50": 312.7,
"67503.00": 76.5
}
}
print(f"[{datetime.now().isoformat()}] Analyzing orderbook with DeepSeek V3.2...")
print(f"Cost advantage: $0.42/M tokens (vs $7.30/M from official APIs)")
# Single analysis
result = await analyzer.analyze_orderbook_microstructure(
sample_orderbook,
model="deepseek-v3.2"
)
print("\nAnalysis Result:")
print(json.dumps(result, indent=2))
await analyzer.close()
if __name__ == "__main__":
asyncio.run(main())Migration Checklist: From DIY to HolySheep
Follow this systematic approach for zero-downtime migration:
| Phase | Task | Duration | Risk Level |
|---|---|---|---|
| 1. Discovery | Audit current data consumption patterns | 1-2 days | Low |
| 2. Shadow Mode | Run HolySheep alongside existing pipeline | 3-7 days | Low |
| 3. Validation | Compare data quality and latency metrics | 2-3 days | Low |
| 4. Gradual Cutover | Switch 25% of traffic to HolySheep | 3-5 days | Medium |
| 5. Full Migration | Complete cutover with monitoring | 1-2 days | Medium |
| 6. Decommission | Shut down legacy infrastructure | 1 day | Low |
Rollback Plan: Emergency Procedures
Every production migration requires a rollback plan. Implement these safeguards:
# Rollback configuration template
ROLLBACK_CONFIG = {
"auto_rollback_conditions": [
("latency_p99 > 200ms", 3, 60), # Trigger if P99 > 200ms for 3 checks in 60s
("error_rate > 5%", 2, 30), # Trigger if error rate > 5% for 2 checks in 30s
("missing_data_gaps > 10", 1, 0), # Trigger immediately if 10+ gaps detected
],
"rollback_targets": {
"primary": "binance_official_api",
"secondary": "tardis_direct",
"tertiary": "holysheep_fallback"
},
"notification": {
"slack_webhook": "https://hooks.slack.com/services/YOUR/WEBHOOK",
"pagerduty_key": "YOUR_PD_KEY",
"email": "[email protected]"
},
"data_retention": {
"holyseep_buffer": "48h", # Keep 48h HolySheep buffer
"backup_window": "7d" # Maintain 7-day backup window
}
}Common Errors and Fixes
Error 1: WebSocket Connection Timeout
Symptom: Connection drops after 30-60 seconds with timeout errors
# PROBLEM: Default timeout too short for Binance streams
async def handle_binance_stream():
uri = "wss://fstream.binance.com/wstream?streams=btcusdt@depth@100ms"
# THIS FAILS with default timeouts
async with websockets.connect(uri) as ws:
...
FIXED: Implement heartbeat and extended timeouts
import websockets
from websockets.exceptions import ConnectionClosed
KEEPALIVE_INTERVAL = 20 # Ping every 20 seconds
RECONNECT_DELAY = 5
async def handle_binance_stream_robust():
uri = "wss://fstream.binance.com/wstream?streams=btcusdt@depth@100ms"
while True:
try:
async with websockets.connect(
uri,
ping_interval=KEEPALIVE_INTERVAL,
ping_timeout=10,
close_timeout=10,
max_size=10*1024*1024 # 10MB max message
) as ws:
print(f"[{datetime.now().isoformat()}] Connected to Binance")
async for message in ws:
# Process message with timeout protection
try:
data = json.loads(message)
await process_message(data)
except json.JSONDecodeError:
print(f"Corrupted message: {message[:50]}")
except ConnectionClosed as e:
print(f"Connection closed: {e.code} - {e.reason}")
except Exception as e:
print(f"Error: {type(e).__name__}: {e}")
print(f"Reconnecting in {RECONNECT_DELAY}s...")
await asyncio.sleep(RECONNECT_DELAY)Error 2: Orderbook Desynchronization
Symptom: Orderbook bids/asks contain stale prices or inconsistent quantities
# PROBLEM: No synchronization between snapshot and delta updates
orderbook = {"bids": {}, "asks": {}}
async def handle_update(data):
# THIS CAUSES DESYNC during high-frequency updates
for price, qty in data["b"]:
orderbook["bids"][price] = qty
for price, qty in data["a"]:
orderbook["asks"][price] = qty
FIXED: Use atomic updates with version tracking
import threading
from collections import OrderedDict
class ThreadSafeOrderbook:
def __init__(self):
self._lock = threading.RLock()
self._bids = OrderedDict()
self._asks = OrderedDict()
self._last_update_id = 0
self._sync_state = "snapshot_required"
def apply_snapshot(self, snapshot, update_id):
"""Apply full orderbook snapshot atomically."""
with self._lock:
self._bids.clear()
self._asks.clear()
# Apply in sorted order
for price, qty in sorted(snapshot["bids"].items(), reverse=True):
self._bids[price] = qty
for price, qty in sorted(snapshot["asks"].items()):
self._asks[price] = qty
self._last_update_id = update_id
self._sync_state = "synced"
def apply_delta(self, delta, update_id):
"""Apply incremental update with validation."""
with self._lock:
# Reject if not in sync
if self._sync_state == "snapshot_required":
raise ValueError("Snapshot required before delta updates")
# Skip if update is stale
if update_id <= self._last_update_id:
return # Silently skip stale update
# Apply bid updates
for price, qty in delta.get("b", []):
if float(qty) == 0:
self._bids.pop(price, None)
else:
self._bids[price] = float(qty)
# Apply ask updates
for price, qty in delta.get("a", []):
if float(qty) == 0:
self._asks.pop(price, None)
else:
self._asks[price] = float(qty)
self._last_update_id = update_id
def get_snapshot(self):
"""Get current orderbook state safely."""
with self._lock:
return {
"bids": dict(self._bids),
"asks": dict(self._asks),
"last_update_id": self._last_update_id,
"sync_state": self._sync_state
}Error 3: Tardis Replay Rate Limiting
Symptom: "Rate limit exceeded" errors during historical data replay
# PROBLEM: No backoff strategy when hitting rate limits
async def replay_data():
async for message in tardis.replay(...):
# THIS TRIGGERS RATE LIMITS during high-volume periods
await process(message)
FIXED: Implement adaptive rate limiting
import asyncio
from datetime import datetime, timedelta
class AdaptiveReplay:
def __init__(self, client):
self.client = client
self.base_delay = 0.01 # 10ms base delay
self.max_delay = 5.0 # 5 seconds max
self.current_delay = self.base_delay
self.rate_limit_count = 0
async def replay_with_backoff(self, exchange, symbols, from_date, to_date):
"""Replay with automatic rate limit handling."""
replay = self.client.replay(
exchange=exchange,
symbols=symbols,
from_date=from_date,
to_date=to_date
)
consecutive_success = 0
async for message in replay:
try:
await self.process_message(message)
# Track success rate
consecutive_success += 1
# Adaptive delay adjustment
if consecutive_success > 100:
self.current_delay = max(
self.base_delay,
self.current_delay * 0.95 # Gradually reduce delay
)
except Exception as e:
if "rate limit" in str(e).lower() or "429" in str(e):
self.rate_limit_count += 1
consecutive_success = 0
# Exponential backoff
self.current_delay = min(
self.max_delay,
self.current_delay * 2
)
print(f"Rate limited. Increasing delay to {self.current_delay}s")
print(f"Total rate limits encountered: {self.rate_limit_count}")
await asyncio.sleep(self.current_delay)
else:
raise
print(f"Replay complete. Rate limits encountered: {