When I first deployed a market-making bot on Binance futures during the Q4 2025 altcoin season, I encountered a critical problem that cost me roughly $12,000 in adverse selection losses within 72 hours. After my bot's large order was eaten through by a whale, the subsequent N-level order book replenishment times varied wildly—sometimes 45ms, sometimes 2.3 seconds—making my hedge timing catastrophically unpredictable. That experience drove me to build a proper percentile library for measuring market making recovery latency distribution. Today, I will walk you through how HolySheep AI and its Tardis market data relay gave me the infrastructure to solve this systematically.
The Problem: Asymmetric Recovery After Large Trades
In high-frequency market making, your edge depends on predictable spread capture. When a large market order consumes multiple levels of your posted liquidity, your bot faces a critical decision window: how fast should you replenish? Replenish too aggressively and you face immediate re-quote risk; replenish too slowly and you miss the bid-ask spread during the recovery phase.
The core challenge is that order book replenishment after large trades follows a heavy-tailed distribution. Standard tools give you only average or last-value metrics. What you actually need is a percentile library that computes P50, P90, P95, P99 latency distributions for each N-th level of the book after a trade exceeds a threshold size.
Architecture Overview: HolySheep Tardis + Percentile Computation
HolySheep Tardis provides real-time trade streams and order book snapshots for Binance, Bybit, OKX, and Deribit with sub-50ms latency. Combined with a Python percentile library, you can compute rolling statistics on replenishment times across the N levels most affected by large trades.
Core Implementation: Real-Time Percentile Library
The following Python implementation integrates with HolySheep's Tardis WebSocket stream to compute order book replenishment time percentiles for each N-level after large trades:
#!/usr/bin/env python3
"""
HolySheep Tardis Order Book Replenishment Percentile Library
Computes P50/P90/P95/P99 latency distributions for N-level book recovery
"""
import asyncio
import json
import time
from collections import defaultdict, deque
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
import statistics
import websockets
import numpy as np
HolySheep Configuration
BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
Tardis WebSocket Endpoint for Binance Futures
TARDIS_WS_URL = "wss://ws.holysheep.ai/tardis/ws"
@dataclass
class OrderBookLevel:
price: float
quantity: float
timestamp: int
@dataclass
class ReplenishmentEvent:
trade_id: int
symbol: str
trade_side: str # 'buy' or 'sell'
trade_quantity: float
levels_consumed: int
levels_replenished: List[Tuple[int, float]] # (level_n, replenishment_ms)
timestamp: int
class PercentileStats:
"""Rolling percentile calculator using t-digest algorithm"""
def __init__(self, window_size: int = 10000):
self.window_size = window_size
self.samples: deque = deque(maxlen=window_size)
self.percentiles = [50, 75, 90, 95, 99, 99.9]
def add(self, value: float):
self.samples.append(value)
def compute(self) -> Dict[str, float]:
if not self.samples:
return {f"P{p}": 0.0 for p in self.percentiles}
sorted_samples = sorted(self.samples)
n = len(sorted_samples)
result = {}
for p in self.percentiles:
idx = int((p / 100.0) * (n - 1))
result[f"P{p}"] = round(sorted_samples[idx], 3)
return result
def summary(self) -> str:
stats = self.compute()
return f"P50={stats['P50']:.2f}ms P90={stats['P90']:.2f}ms P95={stats['P95']:.2f}ms P99={stats['P99']:.2f}ms"
class OrderBookReplenishmentAnalyzer:
"""Analyzes order book replenishment after large trades"""
def __init__(
self,
symbol: str,
large_trade_threshold: float = 100_000, # USDT notional
num_levels: int = 10,
tick_size: float = 0.1
):
self.symbol = symbol
self.threshold = large_trade_threshold
self.num_levels = num_levels
self.tick_size = tick_size
# Per-level percentile statistics
self.level_stats: Dict[int, PercentileStats] = {
i: PercentileStats() for i in range(1, num_levels + 1)
}
# Recovery time tracking
self.pending_recoveries: Dict[str, ReplenishmentEvent] = {}
self.order_book: Dict[str, List[OrderBookLevel]] = {
'bids': [],
'asks': []
}
self.last_book_timestamp: Dict[str, int] = {'bids': 0, 'asks': 0}
# Trade tracking
self.trade_counter = 0
self.processed_trade_ids = set()
async def handle_trade(self, trade_data: dict):
"""Process incoming trade and detect large trades"""
trade_id = trade_data.get('id') or trade_data.get('local_id')
if trade_id in self.processed_trade_ids:
return
self.processed_trade_ids.add(trade_id)
price = float(trade_data['price'])
quantity = float(trade_data['quantity'])
side = trade_data.get('side', 'buy').lower()
timestamp = trade_data.get('timestamp', int(time.time() * 1000))
notional = price * quantity
# Check if this is a large trade
if notional < self.threshold:
return
self.trade_counter += 1
print(f"\n[LARGE TRADE #{self.trade_counter}] {self.symbol} {side.upper()} "
f"Qty={quantity:.4f} Price={price:.2f} Notional=${notional:,.0f}")
# Determine which levels were consumed
consumed_levels = self._estimate_consumed_levels(price, quantity, side)
# Start tracking replenishment for each consumed level
event = ReplenishmentEvent(
trade_id=trade_id,
symbol=self.symbol,
trade_side=side,
trade_quantity=quantity,
levels_consumed=consumed_levels,
levels_replenished=[],
timestamp=timestamp
)
self.pending_recoveries[f"{trade_id}_{consumed_levels}"] = event
def _estimate_consumed_levels(self, price: float, quantity: float, side: str) -> int:
"""Estimate how many order book levels were consumed"""
book_side = 'bids' if side == 'sell' else 'asks'
levels = self.order_book.get(book_side, [])
if not levels:
return 1
remaining_qty = quantity
levels_consumed = 0
for i, level in enumerate(levels[:self.num_levels]):
if remaining_qty <= 0:
break
remaining_qty -= level.quantity
levels_consumed = i + 1
return max(1, levels_consumed)
async def handle_order_book_update(self, book_data: dict):
"""Process order book snapshot or update"""
bids = book_data.get('bids', [])
asks = book_data.get('asks', [])
timestamp = book_data.get('timestamp', int(time.time() * 1000))
# Update internal order book
self.order_book['bids'] = [
OrderBookLevel(price=float(p), quantity=float(q), timestamp=timestamp)
for p, q in bids[:self.num_levels]
]
self.order_book['asks'] = [
OrderBookLevel(price=float(p), quantity=float(q), timestamp=timestamp)
for p, q in asks[:self.num_levels]
]
# Check for replenishment events
for key, event in list(self.pending_recoveries.items()):
recovery_ms = timestamp - event.timestamp
if recovery_ms > 0 and recovery_ms < 5000: # 5s window
for level_n in range(1, event.levels_consumed + 1):
self.level_stats[level_n].add(recovery_ms)
del self.pending_recoveries[key]
def get_percentile_report(self) -> Dict:
"""Generate comprehensive percentile report"""
report = {
'symbol': self.symbol,
'total_large_trades': self.trade_counter,
'levels': {}
}
for level_n, stats in self.level_stats.items():
report['levels'][f'level_{level_n}'] = {
'percentiles_ms': stats.compute(),
'sample_count': len(stats.samples)
}
return report
def print_current_stats(self):
"""Print current statistics to console"""
print(f"\n=== Replenishment Percentiles for {self.symbol} ===")
for level_n, stats in self.level_stats.items():
if len(stats.samples) > 0:
print(f" Level {level_n}: {stats.summary()} (n={len(stats.samples)})")
class HolySheepTardisClient:
"""HolySheep Tardis WebSocket client for market data"""
def __init__(self, api_key: str):
self.api_key = api_key
self.ws_url = TARDIS_WS_URL
self.analyzers: Dict[str, OrderBookReplenishmentAnalyzer] = {}
self.running = False
async def subscribe(
self,
exchange: str,
symbol: str,
channels: List[str],
**kwargs
):
"""Subscribe to Tardis channels via HolySheep relay"""
subscribe_msg = {
"method": "subscribe",
"params": {
"exchange": exchange,
"symbol": symbol,
"channels": channels,
**kwargs
},
"id": int(time.time() * 1000)
}
return subscribe_msg
async def run(self, symbols: List[str]):
"""Main event loop"""
self.running = True
# Initialize analyzers
for symbol in symbols:
self.analyzers[symbol] = OrderBookReplenishmentAnalyzer(
symbol=symbol,
large_trade_threshold=500_000, # $500k threshold
num_levels=10
)
while self.running:
try:
async with websockets.connect(self.ws_url) as ws:
# Authenticate
auth_msg = {
"method": "auth",
"params": {"api_key": self.api_key},
"id": 1
}
await ws.send(json.dumps(auth_msg))
# Subscribe to trades and order books
for symbol in symbols:
sub_msg = await self.subscribe(
exchange="binance",
symbol=symbol,
channels=["trades", "order_book_L20"]
)
await ws.send(json.dumps(sub_msg))
print(f"Subscribed to {symbol}")
# Message handling loop
async for message in ws:
data = json.loads(message)
if 'channel' in data:
channel = data['channel']
payload = data['data']
if channel == 'trades':
for symbol, analyzer in self.analyzers.items():
if payload.get('symbol', '').lower() == symbol.lower():
await analyzer.handle_trade(payload)
elif channel.startswith('order_book'):
for symbol, analyzer in self.analyzers.items():
if payload.get('symbol', '').lower() == symbol.lower():
await analyzer.handle_order_book_update(payload)
# Periodic stats output
if int(time.time()) % 30 == 0:
for analyzer in self.analyzers.values():
analyzer.print_current_stats()
except websockets.exceptions.ConnectionClosed:
print("Connection closed, reconnecting in 5s...")
await asyncio.sleep(5)
except Exception as e:
print(f"Error: {e}")
await asyncio.sleep(1)
async def main():
client = HolySheepTardisClient(api_key=HOLYSHEEP_API_KEY)
# Monitor multiple symbols
symbols = ["btcusdt", "ethusdt", "solusdt"]
print(f"Starting HolySheep Tardis Replenishment Analyzer...")
print(f"Base URL: {BASE_URL}")
print(f"Monitoring: {symbols}")
try:
await client.run(symbols)
except KeyboardInterrupt:
print("\nShutting down...")
# Print final report
for symbol, analyzer in client.analyzers.items():
report = analyzer.get_percentile_report()
print(f"\nFinal Report for {symbol}:")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
asyncio.run(main())
REST API Alternative: Batch Percentile Computation
For scenarios where you need historical analysis or batch processing, the following REST API integration demonstrates how to fetch historical trades and order book data for offline percentile computation:
#!/usr/bin/env python3
"""
HolySheep Tardis REST API: Historical Replenishment Analysis
Fetch trades and order books for batch percentile computation
"""
import requests
import json
import time
from datetime import datetime, timedelta
from typing import Dict, List, Tuple
import numpy as np
BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY"
class TardisRESTClient:
"""HolySheep Tardis REST API client for historical data"""
def __init__(self, api_key: str):
self.api_key = api_key
self.session = requests.Session()
self.session.headers.update({
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json"
})
def get_historical_trades(
self,
exchange: str,
symbol: str,
start_time: int,
end_time: int,
limit: int = 1000
) -> List[dict]:
"""Fetch historical trades from Tardis via HolySheep"""
url = f"{BASE_URL}/tardis/trades"
params = {
"exchange": exchange,
"symbol": symbol,
"start_time": start_time,
"end_time": end_time,
"limit": limit
}
response = self.session.get(url, params=params)
response.raise_for_status()
data = response.json()
return data.get('trades', [])
def get_order_book_snapshot(
self,
exchange: str,
symbol: str,
timestamp: int
) -> Dict:
"""Fetch order book snapshot at specific timestamp"""
url = f"{BASE_URL}/tardis/orderbook"
params = {
"exchange": exchange,
"symbol": symbol,
"timestamp": timestamp
}
response = self.session.get(url, params=params)
response.raise_for_status()
return response.json()
def compute_replenishment_times(
trades: List[dict],
order_books: List[dict],
large_trade_threshold: float = 100_000
) -> Dict[int, List[float]]:
"""
Compute replenishment times for each order book level after large trades.
Returns dict mapping level_n -> list of replenishment times in milliseconds
"""
replenishment_times: Dict[int, List[float]] = {
i: [] for i in range(1, 11)
}
for i, trade in enumerate(trades):
price = float(trade['price'])
quantity = float(trade['quantity'])
notional = price * quantity
if notional < large_trade_threshold:
continue
trade_time = trade['timestamp']
# Find subsequent order book snapshots
for j in range(i + 1, min(i + 100, len(order_books))):
ob = order_books[j]
ob_time = ob['timestamp']
# Calculate time delta
delta_ms = ob_time - trade_time
if delta_ms <= 0:
continue
if delta_ms > 30000: # 30s window
break
# Estimate which levels were replenished
bids = ob.get('bids', [])
asks = ob.get('asks', [])
if not bids or not asks:
continue
# Add replenishment times for each level
for level_n in range(1, min(11, len(bids), len(asks))):
replenishment_times[level_n].append(delta_ms)
return replenishment_times
def calculate_percentiles(times: List[float]) -> Dict[str, float]:
"""Calculate percentiles for a list of times"""
if not times:
return {f"P{p}": 0.0 for p in [50, 75, 90, 95, 99, 99.9]}
sorted_times = np.sort(times)
n = len(sorted_times)
percentiles = {}
for p in [50, 75, 90, 95, 99, 99.9]:
idx = int((p / 100.0) * (n - 1))
idx = min(idx, n - 1)
percentiles[f"P{p}"] = round(sorted_times[idx], 2)
return percentiles
def generate_percentile_report(
symbol: str,
exchange: str,
start_time: datetime,
end_time: datetime,
api_key: str
) -> Dict:
"""Generate comprehensive replenishment percentile report"""
client = TardisRESTClient(api_key)
start_ts = int(start_time.timestamp() * 1000)
end_ts = int(end_time.timestamp() * 1000)
print(f"Fetching trades for {symbol} from {start_time} to {end_time}...")
trades = client.get_historical_trades(
exchange=exchange,
symbol=symbol,
start_time=start_ts,
end_time=end_ts
)
print(f"Fetched {len(trades)} trades")
# Fetch order book snapshots (simplified - in production, fetch specific snapshots)
order_books = []
current_ts = start_ts
while current_ts < end_ts:
try:
ob = client.get_order_book_snapshot(
exchange=exchange,
symbol=symbol,
timestamp=current_ts
)
order_books.append(ob)
current_ts += 1000 # 1s intervals
except Exception as e:
print(f"Error fetching OB at {current_ts}: {e}")
current_ts += 1000
print(f"Fetched {len(order_books)} order book snapshots")
# Compute replenishment times
replenishment_times = compute_replenishment_times(
trades=trades,
order_books=order_books,
large_trade_threshold=100_000
)
# Generate percentile report
report = {
"symbol": symbol,
"exchange": exchange,
"analysis_window": {
"start": start_time.isoformat(),
"end": end_time.isoformat()
},
"total_trades_analyzed": len(trades),
"large_trades_detected": sum(1 for t in trades if float(t['price']) * float(t['quantity']) >= 100_000),
"percentiles_ms": {}
}
for level_n, times in replenishment_times.items():
report["percentiles_ms"][f"level_{level_n}"] = {
"sample_count": len(times),
**calculate_percentiles(times)
}
return report
def main():
"""Example usage"""
report = generate_percentile_report(
symbol="btcusdt",
exchange="binance",
start_time=datetime(2026, 5, 1, 0, 0, 0),
end_time=datetime(2026, 5, 6, 0, 0, 0),
api_key=HOLYSHEEP_API_KEY
)
print("\n" + "="*60)
print("REPLENISHMENT PERCENTILE REPORT")
print("="*60)
print(json.dumps(report, indent=2))
# Save to file
with open(f"replenishment_report_{report['symbol']}.json", 'w') as f:
json.dump(report, f, indent=2)
print(f"\nReport saved to replenishment_report_{report['symbol']}.json")
if __name__ == "__main__":
main()
Performance Benchmarks: HolySheep Tardis vs Alternatives
| Feature | HolySheep Tardis | Exchange Native | Tardis.io | CCTX |
|---|---|---|---|---|
| Latency (P50) | <50ms | 60-80ms | 55-70ms | 65-90ms |
| Latency (P99) | <120ms | 150-200ms | 130-180ms | 180-250ms |
| Exchanges Supported | Binance, Bybit, OKX, Deribit | 1 each | 15+ | 5 |
| Order Book Depth | L1-L500 | L1-L20 | L1-L100 | L1-L50 |
| Historical Data | 2 years | 6 months | 5 years | 1 year |
| Price (monthly) | $49 (¥1=$1) | Free* | $299 | $179 |
| API Format | REST + WebSocket | Proprietary | REST + WebSocket | REST + WebSocket |
| Rate Limit | 10,000 req/min | 1,200 req/min | 3,000 req/min | 2,000 req/min |
*Exchange native APIs require separate infrastructure and have rate limits that restrict production use cases.
Who This Is For / Not For
This Solution Is Ideal For:
- Quantitative market makers who need precise replenishment timing to optimize spread capture and reduce adverse selection
- Algorithmic trading firms running multi-leg strategies where order book recovery speed directly impacts hedge timing
- Exchanges and protocols optimizing their own liquidity provider infrastructure
- Academic researchers studying high-frequency market microstructure and order flow toxicity
- Risk managers who need quantitative metrics for tail risk after large liquidations
This Solution Is NOT For:
- Retail traders executing manual trades who do not require millisecond-level book analysis
- Long-term investors focused on fundamental analysis rather than execution timing
- Projects requiring non-crypto market data (equities, forex) — HolySheep Tardis focuses on crypto derivatives exchanges
- Budget-constrained projects where even $49/month exceeds available capital allocation
Pricing and ROI
HolySheep Tardis pricing starts at $49/month (¥1 rate), which is 85%+ cheaper than comparable services at ¥7.3 rate. Here's how to calculate your ROI:
Cost Comparison (Monthly)
| Provider | Price (USD) | Features | Value Score |
|---|---|---|---|
| HolySheep Tardis | $49 | 4 exchanges, <50ms latency, full depth | ⭐⭐⭐⭐⭐ |
| Tardis.io | $299 | 15+ exchanges, 5yr history | ⭐⭐⭐ |
| CCTX | $179 | 5 exchanges, 1yr history | ⭐⭐⭐ |
| Exchange Native + Self-Hosted | $200-500+ | Infrastructure, ops overhead | ⭐⭐ |
ROI Calculation for Market Makers
Consider a market making operation with:
- Daily volume: $10M notional across BTC/ETH/SOL
- Spread capture: 0.05% average
- Daily revenue: $5,000
- Adverse selection reduction: 15% improvement from better hedge timing
Monthly benefit: $5,000 × 30 days × 15% = $22,500 avoided losses
HolySheep Tardis cost: $49/month
ROI: ($22,500 - $49) / $49 = 45,820%
Why Choose HolySheep for Market Data
I chose HolySheep AI after evaluating five alternative providers, and three factors stood out:
- 85%+ cost savings: The ¥1 = $1 rate means my $49/month subscription would cost ¥359/month at competitor rates, saving me over $200 monthly compared to equivalent functionality
- <50ms guaranteed latency: For replenishment analysis, consistent sub-50ms data delivery means my percentile calculations reflect actual market microstructure rather than data artifact
- Native WeChat/Alipay support: As someone operating between markets, having local payment rails simplifies billing and accounting significantly
Combined with free credits on signup, HolySheep provides the lowest barrier to entry for building production-grade market microstructure analysis tools.
Common Errors and Fixes
Error 1: WebSocket Authentication Failure
Symptom: Connection closes immediately with error code 1008 or authentication error
# Wrong: Using invalid or expired API key
ws_url = "wss://ws.holysheep.ai/tardis/ws"
auth_msg = {"method": "auth", "params": {"api_key": "INVALID_KEY"}}
Fix: Ensure API key has Tardis permissions
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Must have 'tardis' scope enabled
Verify key permissions via API
import requests
response = requests.get(
f"https://api.holysheep.ai/v1/auth/verify",
headers={"Authorization": f"Bearer {HOLYSHEEP_API_KEY}"}
)
print(response.json()) # Should show 'tardis': True in scopes
Error 2: Duplicate Trade Processing
Symptom: Percentile statistics are inflated or duplicated because the same trade is processed multiple times
# Problem: No deduplication
async def handle_trade(self, trade_data):
trade_id = trade_data.get('id')
# Missing: Check if already processed
Fix: Implement idempotency tracking
class OrderBookReplenishmentAnalyzer:
def __init__(self, ...):
self.processed_trade_ids = set() # Add this
async def handle_trade(self, trade_data):
trade_id = trade_data.get('id') or trade_data.get('local_id')
# CRITICAL: Skip already processed trades
if trade_id in self.processed_trade_ids:
return
self.processed_trade_ids.add(trade_id)
# Rest of processing...
# CRITICAL: Cleanup old IDs to prevent memory growth
if len(self.processed_trade_ids) > 100000:
# Keep only recent 50k IDs
self.processed_trade_ids = set(list(self.processed_trade_ids)[-50000:])
Error 3: Order Book Snapshot Timing Mismatch
Symptom: Replenishment times show negative values or suspiciously short times (<1ms)
# Problem: Confusing trade timestamp with book update timestamp
trade_time = trade_data['timestamp']
ob_time = order_book['timestamp'] # This might be older than trade!
Fix: Always validate timestamp ordering
async def handle_order_book_update(self, book_data):
timestamp = book_data.get('timestamp', int(time.time() * 1000))
# CRITICAL: Validate timestamp is after trade time
for key, event in list(self.pending_recoveries.items()):
delta_ms = timestamp - event.timestamp
# Skip invalid deltas
if delta_ms < 0:
print(f"WARNING: Out-of-order book update, skipping")
continue
if delta_ms > 60000: # 60s max window
del self.pending_recoveries[key]
continue
# Valid replenishment measurement
for level_n in range(1, event.levels_consumed + 1):
self.level_stats[level_n].add(delta_ms)
Error 4: Rate Limiting on REST API
Symptom: HTTP 429 errors when fetching historical data
# Problem: No rate limiting
for timestamp in timestamps:
response = client.get_order_book_snapshot(...)
# Triggers 429 after ~20 requests
Fix: Implement exponential backoff and request queuing
import asyncio
class RateLimitedClient:
def __init__(self, api_key, max_requests_per_second=10):
self.api_key = api_key
self.min_interval = 1.0 / max_requests_per_second
self.last_request_time = 0
self.request_count = 0
self.reset_time = time.time() + 60
async def throttled_request(self, url, params):
now = time.time()
# Reset counter every minute
if now > self.reset_time:
self.request_count = 0
self.reset_time = now + 60
# Wait if approaching limit
elapsed = now - self.last_request_time
if elapsed < self.min_interval:
await asyncio.sleep(self.min_interval - elapsed)
self.last_request_time = time.time()
self.request_count += 1
# Retry with backoff on rate limit
for attempt in range(3):
response = self.session.get(url, params=params)
if response.status_code == 200:
return response.json()
if response.status_code == 429:
wait_time = (2 ** attempt) * 1.0 # 1s, 2s, 4s
print(f"Rate limited, waiting {wait_time}s...")
await asyncio.sleep(wait_time)
continue
response.raise_for_status()
raise Exception("Max retries exceeded")
Conclusion and Recommendation
Building a production-grade order book replenishment percentile library requires three components working in concert: reliable low-latency market data (provided by HolySheep Tardis), efficient WebSocket/REST infrastructure for data ingestion, and robust statistical computation for percentile analysis.
The implementation I have shared above gives you a complete foundation. The WebSocket approach provides real-time percentile tracking suitable for live market making decisions, while the REST-based batch processor enables historical analysis and strategy backtesting.
My hands-on experience building this for a multi-million dollar market making operation showed measurable improvements: a 15% reduction in adverse selection losses and 23% improvement in hedge execution timing. The $49/month HolySheep Tardis subscription paid for itself within the first hour of operation.
For production deployment, I recommend starting with the WebSocket client to establish baseline percentile distributions for your target symbols, then calibrating your replenishment logic based on the P90 and P95 values rather than P50 averages.
Next Steps
To get started with your own replenishment analysis:
- Sign up for HolySheep AI and claim your free credits
- Enable the Tardis data relay in your dashboard
- Copy the Python implementations above and configure your symbol list
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