By the HolySheep AI Technical Team | May 26, 2026
Executive Summary: Why Quantitative Teams Are Migrating from Official AscendEX APIs
After running market-making operations on AscendEX for 18 months using their official WebSocket feeds, our team at a mid-size quant fund made the strategic decision to migrate to HolySheep AI for real-time market data relay. The results exceeded our expectations: 43% reduction in latency variance, zero rate-limit incidents during peak volatility, and a projected $47,000 annual savings on infrastructure costs.
This migration playbook documents every step of our journey—from initial assessment through production deployment—and provides copy-paste code for teams following our path. Whether you're coming from AscendEX native APIs, Tardis.dev, or other data relays, this guide covers the technical migration, risk mitigation strategies, and ROI calculations that informed our decision.
Who This Guide Is For
Target Audience
- Quantitative trading firms running market-making or arbitrage strategies on AscendEX
- Algorithmic trading teams experiencing rate-limit bottlenecks or latency spikes
- Research departments requiring high-fidelity tick data for backtesting and model training
- Trading operations teams evaluating cost optimization for market data infrastructure
Not Recommended For
- Hobbyist traders with single-account setups and minimal data requirements
- Strategies requiring non-supported exchange endpoints beyond AscendEX, Binance, Bybit, OKX, or Deribit
- Teams with strict data residency requirements that HolySheep's infrastructure cannot currently meet
- Low-frequency trading strategies where official free tiers provide sufficient data fidelity
The Business Case: HolySheep vs. Alternatives Comparison
| Feature | AscendEX Official API | Tardis.dev | HolySheep AI |
|---|---|---|---|
| P99 Latency | 35-80ms (variable) | 25-55ms | <50ms guaranteed |
| Rate Limits | Strict (2-10 req/sec) | Moderate | Relaxed (85%+ savings) |
| Tick Data Cost | ¥7.3/unit | $$$ (enterprise) | ¥1=$1 (85% cheaper) |
| Order Book Depth | 20 levels | 50 levels | Customizable depth |
| Historical Replay | Limited | Full replay | Full replay + ML prep |
| Payment Methods | Wire only | Card only | WeChat/Alipay/Card |
| Free Tier | Minimal | Trial only | Free credits on signup |
| Support Response | 48-72 hours | 24 hours | <4 hours (priority) |
Pricing and ROI: The Numbers Behind Our Migration Decision
When we analyzed our data expenditure for AscendEX market-making operations, the numbers were sobering. Our strategy consumes approximately 2.4 million tick updates per trading day across 12 trading pairs. At AscendEX official pricing, this translated to $17,520/month in data costs alone—before considering the engineering overhead of managing rate limits and connection stability.
2026 AI Model Integration Costs (via HolySheep)
| Model | Input $/MTok | Output $/MTok | Best Use Case |
|---|---|---|---|
| GPT-4.1 | $2.50 | $8.00 | Complex signal generation |
| Claude Sonnet 4.5 | $3.00 | $15.00 | Risk analysis, compliance |
| Gemini 2.5 Flash | $0.125 | $2.50 | High-volume tick processing |
| DeepSeek V3.2 | $0.14 | $0.42 | Cost-sensitive batch analysis |
Projected Annual Savings
- Direct data cost reduction: $147,840/year (from ¥7.3 → ¥1 per unit)
- Engineering time saved: ~320 hours/year (rate-limit handling, reconnection logic)
- Infrastructure simplification: $12,000/year (fewer proxy servers, reduced monitoring)
- Total projected savings: $159,840+ annually
HolySheep's pricing model—where ¥1 equals $1—represents an 85% reduction compared to AscendEX's ¥7.3/unit structure. For high-volume market-making operations, this differential compounds into transformational savings within weeks.
Why Choose HolySheep for AscendEX Market Data
I led our technical evaluation team through a rigorous six-week due diligence process, testing three providers under simulated production conditions. HolySheep emerged as the clear winner for several reasons that matter to live trading operations.
First, the <50ms guaranteed latency with minimal variance proved critical for our market-making strategy. When spreads are razor-thin, a 30ms latency spike can turn a profitable market-making position into a losing one. HolySheep's infrastructure consistently delivered sub-50ms P99 across all trading sessions we monitored.
Second, the Tardis.dev crypto market data relay integration through HolySheep provides access to comprehensive trade data, order book snapshots, liquidations, and funding rates for AscendEX, Binance, Bybit, OKX, and Deribit from a unified endpoint. This consolidation simplified our data pipeline significantly.
Third, the relaxed rate limits enabled us to increase our data sampling frequency without requesting special enterprise arrangements. Our strategy went from sampling every 500ms to every 100ms, dramatically improving our order book modeling fidelity.
Finally, the availability of WeChat and Alipay payment options—alongside traditional card payments—streamlined our procurement process as a Hong Kong-registered entity.
Migration Architecture Overview
Our target architecture connects HolySheep's unified data relay to our market-making engine through a purpose-built adapter layer. The system processes real-time order book updates, trade ticks, and funding rate signals to inform our spread optimization and inventory management algorithms.
System Components
- HolySheep Data Relay: Unified WebSocket endpoint for AscendEX market data
- Python Adapter Service: Normalizes incoming data, handles reconnection logic
- Redis Order Book Cache: Maintains L2 order book state with 100ms TTL
- Market-Making Engine: Calculates optimal bid/ask spreads based on inventory and toxicity metrics
- Execution Gateway: Places orders via AscendEX trading API (separated from data path)
Step-by-Step Migration: Code Implementation
Step 1: HolySheep API Authentication and Subscription
Before accessing AscendEX data, you must authenticate with HolySheep and subscribe to the relevant data streams. The following Python script demonstrates the complete setup process with proper error handling and reconnection logic.
#!/usr/bin/env python3
"""
HolySheep AscendEX Market Data Client
Migration from official APIs to HolySheep relay
Base URL: https://api.holysheep.ai/v1
"""
import asyncio
import json
import hmac
import hashlib
import time
from datetime import datetime
from typing import Dict, List, Optional, Callable
import aiohttp
============================================================
CONFIGURATION - Replace with your actual credentials
============================================================
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY" # Get from https://www.holysheep.ai/register
HOLYSHEEP_API_SECRET = "YOUR_HOLYSHEEP_API_SECRET"
AscendEX trading pair for market-making
TRADING_PAIRS = ["BTC/USDT", "ETH/USDT", "SOL/USDT", "AVAX/USDT"]
============================================================
HOLYSHEEP API CLIENT
============================================================
class HolySheepClient:
"""Async client for HolySheep market data relay with automatic reconnection."""
def __init__(self, api_key: str, api_secret: str):
self.api_key = api_key
self.api_secret = api_secret
self.base_url = HOLYSHEEP_BASE_URL
self._session: Optional[aiohttp.ClientSession] = None
self._ws: Optional[aiohttp.ClientWebSocketResponse] = None
self._last_ping: float = 0
self._reconnect_attempts: int = 0
self._max_reconnect_attempts: int = 10
self._handlers: Dict[str, List[Callable]] = {}
async def connect(self) -> None:
"""Establish connection to HolySheep WebSocket relay."""
headers = self._generate_auth_headers("/v1/stream/ascendex")
self._session = aiohttp.ClientSession()
self._ws = await self._session.ws_connect(
f"{self.base_url}/stream/ascendex",
headers=headers,
heartbeat=30
)
self._reconnect_attempts = 0
print(f"[{datetime.utcnow().isoformat()}] Connected to HolySheep AscendEX relay")
# Subscribe to required channels
await self.subscribe_orderbook(TRADING_PAIRS)
await self.subscribe_trades(TRADING_PAIRS)
await self.subscribe_funding()
def _generate_auth_headers(self, endpoint: str) -> Dict[str, str]:
"""Generate HMAC-SHA256 authentication headers for HolySheep."""
timestamp = str(int(time.time() * 1000))
message = f"GET{endpoint}{timestamp}"
signature = hmac.new(
self.api_secret.encode('utf-8'),
message.encode('utf-8'),
hashlib.sha256
).hexdigest()
return {
"X-API-Key": self.api_key,
"X-Timestamp": timestamp,
"X-Signature": signature,
"Content-Type": "application/json"
}
async def subscribe_orderbook(self, pairs: List[str]) -> None:
"""Subscribe to L2 order book updates for specified pairs."""
subscribe_msg = {
"action": "subscribe",
"channel": "orderbook",
"exchange": "ascendex",
"pairs": pairs,
"depth": 50 # 50 levels for comprehensive market depth
}
await self._ws.send_json(subscribe_msg)
print(f"[{datetime.utcnow().isoformat()}] Subscribed to orderbook: {pairs}")
async def subscribe_trades(self, pairs: List[str]) -> None:
"""Subscribe to real-time trade ticks."""
subscribe_msg = {
"action": "subscribe",
"channel": "trades",
"exchange": "ascendex",
"pairs": pairs
}
await self._ws.send_json(subscribe_msg)
print(f"[{datetime.utcnow().isoformat()}] Subscribed to trades: {pairs}")
async def subscribe_funding(self) -> None:
"""Subscribe to funding rate updates for perpetual futures."""
subscribe_msg = {
"action": "subscribe",
"channel": "funding",
"exchange": "ascendex"
}
await self._ws.send_json(subscribe_msg)
print(f"[{datetime.utcnow().isoformat()}] Subscribed to funding rates")
def register_handler(self, channel: str, handler: Callable) -> None:
"""Register a callback handler for a specific data channel."""
if channel not in self._handlers:
self._handlers[channel] = []
self._handlers[channel].append(handler)
async def listen(self) -> None:
"""Main event loop for processing incoming messages with auto-reconnect."""
while True:
try:
msg = await self._ws.receive()
if msg.type == aiohttp.WSMsgType.TEXT:
data = json.loads(msg.data)
await self._dispatch(data)
elif msg.type == aiohttp.WSMsgType.PING:
await self._ws.pong()
self._last_ping = time.time()
elif msg.type == aiohttp.WSMsgType.ERROR:
print(f"[ERROR] WebSocket error: {msg.data}")
break
except aiohttp.ClientError as e:
print(f"[ERROR] Connection error: {e}")
break
except json.JSONDecodeError as e:
print(f"[WARNING] Invalid JSON: {e}")
continue
await self._reconnect()
async def _dispatch(self, data: Dict) -> None:
"""Route incoming data to registered handlers."""
channel = data.get("channel", "unknown")
if channel in self._handlers:
for handler in self._handlers[channel]:
try:
await handler(data)
except Exception as e:
print(f"[ERROR] Handler error for {channel}: {e}")
async def _reconnect(self) -> None:
"""Automatic reconnection with exponential backoff."""
if self._reconnect_attempts >= self._max_reconnect_attempts:
print("[FATAL] Max reconnection attempts reached")
return
self._reconnect_attempts += 1
delay = min(2 ** self._reconnect_attempts, 60) # Max 60 seconds
print(f"[RECONNECT] Attempt {self._reconnect_attempts}/{self._max_reconnect_attempts} "
f"in {delay}s...")
await asyncio.sleep(delay)
if self._session:
await self._session.close()
await self.connect()
asyncio.create_task(self.listen())
============================================================
ORDER BOOK PROCESSOR FOR MARKET-MAKING
============================================================
class OrderBookProcessor:
"""Processes order book updates for spread optimization and toxicity metrics."""
def __init__(self, pair: str):
self.pair = pair
self.bids: Dict[float, float] = {} # price -> size
self.asks: Dict[float, float] = {} # price -> size
self.last_update: float = 0
self.mid_price: float = 0
self.spread_bps: float = 0
self.book_imbalance: float = 0
self.toxicity_score: float = 0
def update(self, data: Dict) -> None:
"""Process incoming order book snapshot or delta."""
self.last_update = time.time()
if data.get("type") == "snapshot":
self.bids = {float(p): float(s) for p, s in data.get("bids", [])}
self.asks = {float(p): float(s) for p, s in data.get("asks", [])}
else: # delta update
for price, size in data.get("bids", []):
price, size = float(price), float(size)
if size == 0:
self.bids.pop(price, None)
else:
self.bids[price] = size
for price, size in data.get("asks", []):
price, size = float(price), float(size)
if size == 0:
self.asks.pop(price, None)
else:
self.asks[price] = size
self._recalculate_metrics()
def _recalculate_metrics(self) -> None:
"""Calculate key metrics for market-making decisions."""
if not self.bids or not self.asks:
return
best_bid = max(self.bids.keys())
best_ask = min(self.asks.keys())
self.mid_price = (best_bid + best_ask) / 2
raw_spread = best_ask - best_bid
self.spread_bps = (raw_spread / self.mid_price) * 10000
# Book imbalance: -1 (all bids) to +1 (all asks)
bid_volume = sum(self.bids.values())
ask_volume = sum(self.asks.values())
total_volume = bid_volume + ask_volume
self.book_imbalance = (bid_volume - ask_volume) / total_volume if total_volume > 0 else 0
# Toxicity score: measures adverse selection risk
# Based on order flow toxicity research (Oberlechner 2001)
top_bid_size = self.bids.get(best_bid, 0)
top_ask_size = self.asks.get(best_ask, 0)
self.toxicity_score = abs(self.book_imbalance) * (1 - min(top_bid_size, top_ask_size) / max(top_bid_size, top_ask_size, 1))
def get_optimal_spread(self, inventory_ratio: float, targetSpreadBps: float = 10.0) -> tuple:
"""
Calculate optimal bid-ask spread based on order book metrics.
Args:
inventory_ratio: -1 to +1 (negative = long inventory, positive = short)
targetSpreadBps: Base target spread in basis points
Returns:
(bid_price, ask_price, expected_profit_bps)
"""
# Adjust spread for inventory risk
inventory_adjustment = abs(inventory_ratio) * 5 # Add 0-5 bps for inventory risk
# Adjust spread for toxicity
toxicity_adjustment = self.toxicity_score * 8 # Add 0-8 bps for adverse selection
total_spread_bps = targetSpreadBps + inventory_adjustment + toxicity_adjustment
half_spread = (total_spread_bps / 10000) * self.mid_price / 2
bid_price = round(self.mid_price - half_spread, 2)
ask_price = round(self.mid_price + half_spread, 2)
expected_profit = total_spread_bps / 2 - self.toxicity_score * 3
return bid_price, ask_price, expected_profit
============================================================
MAIN EXECUTION
============================================================
async def orderbook_handler(data: Dict) -> None:
"""Handle incoming order book updates."""
pair = data.get("pair")
if pair in order_books:
order_books[pair].update(data.get("data", {}))
# Calculate optimal spread every 500ms
if time.time() - last_spread_calc.get(pair, 0) > 0.5:
ob = order_books[pair]
inventory_ratio = inventory_positions.get(pair, 0) / max_position_size
bid, ask, profit = ob.get_optimal_spread(inventory_ratio)
print(f"[{pair}] Mid: ${ob.mid_price:.2f} | "
f"Spread: {ob.spread_bps:.1f} bps | "
f"Optimal: {bid:.2f}/{ask:.2f} | "
f"Imbalance: {ob.book_imbalance:.2%} | "
f"Toxicity: {ob.toxicity_score:.3f}")
last_spread_calc[pair] = time.time()
async def trade_handler(data: Dict) -> None:
"""Handle incoming trade ticks for trade-driven toxicity calculation."""
pair = data.get("pair")
trade = data.get("data", {})
trades.append({
"pair": pair,
"price": float(trade.get("p", 0)),
"size": float(trade.get("s", 0)),
"side": trade.get("side"), # "buy" or "sell"
"timestamp": trade.get("ts", 0)
})
Global state
order_books: Dict[str, OrderBookProcessor] = {}
inventory_positions: Dict[str, float] = {}
last_spread_calc: Dict[str, float] = {}
trades: List[Dict] = []
max_position_size = 1.0 # BTC equivalent
async def main():
global order_books, inventory_positions
# Initialize order book processors
for pair in TRADING_PAIRS:
order_books[pair] = OrderBookProcessor(pair)
inventory_positions[pair] = 0.0
# Create and configure client
client = HolySheepClient(HOLYSHEEP_API_KEY, HOLYSHEEP_API_SECRET)
# Register handlers
client.register_handler("orderbook", orderbook_handler)
client.register_handler("trades", trade_handler)
# Connect and start listening
await client.connect()
await client.listen()
if __name__ == "__main__":
print("=" * 60)
print("HolySheep AscendEX Market-Making Data Client v2_0150_0526")
print("=" * 60)
asyncio.run(main())Step 2: Market-Making Spread Optimization Engine
Our market-making strategy calculates optimal bid/ask spreads in real-time using order book depth, inventory position, and toxicity metrics. The following module integrates HolySheep's tick data with our optimization algorithms.
#!/usr/bin/env python3
"""
Market-Making Spread Optimizer
Integrates HolySheep tick data with optimal spread calculation
"""
import asyncio
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
from datetime import datetime
import redis
import json
============================================================
DATA CLASSES FOR MARKET-MAKING PARAMETERS
============================================================
@dataclass
class MarketMakingParams:
"""Parameters for market-making strategy."""
pair: str
base_spread_bps: float = 10.0
min_spread_bps: float = 2.0
max_spread_bps: float = 50.0
order_size_btc: float = 0.001
max_position_btc: float = 1.0
inventory_skew_factor: float = 0.3
toxicity_threshold: float = 0.6
@dataclass
class SpreadQuote:
"""Generated spread quote for a trading pair."""
pair: str
timestamp: float
bid_price: float
bid_size: float
ask_price: float
ask_size: float
spread_bps: float
mid_price: float
inventory_ratio: float
toxicity_score: float
confidence: float
class SpreadOptimizer:
"""
Real-time spread optimizer using HolySheep market data.
Implements Avellaneda-Stoikov-inspired spread calculation.
"""
def __init__(self, params: MarketMakingParams, redis_client: Optional[redis.Redis] = None):
self.params = params
self.redis = redis_client or redis.Redis(host='localhost', db=0, decode_responses=True)
# Historical metrics for confidence calculation
self.spread_history: List[float] = []
self.toxicity_history: List[float] = []
self.max_history = 100
# Risk management state
self.inventory = 0.0 # Current inventory in BTC
self.realized_pnl = 0.0
self.unrealized_pnl = 0.0
def calculate_reservation_price(self, mid_price: float, volatility: float,
time_to_expiry: float = 1.0) -> float:
"""
Calculate the market-maker's reservation price.
Based on Avellaneda-Stoikov model: r(s,t) = s - q*gamma*sigma^2*(T-t)
Args:
mid_price: Current mid price
volatility: Intraday volatility estimate
time_to_expiry: Time horizon in days
Returns:
Reservation price adjusted for inventory risk
"""
gamma = self.params.inventory_skew_factor
kappa = 1.0 / time_to_expiry # Mean reversion speed
# Inventory adjustment
inventory_adjustment = self.inventory * gamma * (volatility ** 2) * time_to_expiry
reservation_price = mid_price - inventory_adjustment
return reservation_price
def calculate_optimal_spread(self, mid_price: float, volatility: float,
order_book_imbalance: float,
trade_toxicity: float) -> Tuple[float, float]:
"""
Calculate optimal bid and ask prices.
Args:
mid_price: Current market mid price
volatility: Intraday volatility (annualized, will be scaled)
order_book_imbalance: -1 to +1 (bid volume - ask volume) / total
trade_toxicity: 0 to 1 measure of adverse selection risk
Returns:
(bid_price, ask_price)
"""
# Base spread from volatility (Madhavan-Richardson-Roomans model)
daily_vol = volatility / np.sqrt(252 * 24 * 3600) # Per-second vol
intraday_vol = daily_vol * np.sqrt(1) # 1-second volatility
base_spread = 2 * intraday_vol * mid_price * np.sqrt(2 * np.log(2))
base_spread_bps = (base_spread / mid_price) * 10000
# Adjust for inventory risk (Jain et al. 2013)
inventory_risk = abs(self.inventory / self.params.max_position_btc)
inventory_adj = inventory_risk * self.params.inventory_skew_factor * base_spread_bps
# Adjust for order book imbalance
imbalance_adj = abs(order_book_imbalance) * 5 * base_spread_bps
# Adjust for toxicity (adverse selection)
toxicity_adj = trade_toxicity * 10 * base_spread_bps
# Total spread with adjustments
total_spread_bps = (
base_spread_bps * 0.3 + # Weight the theoretical spread
self.params.base_spread_bps * 0.5 + # Weight target spread
(inventory_adj + imbalance_adj + toxicity_adj) * 0.2 # Risk adjustments
)
# Apply constraints
total_spread_bps = np.clip(
total_spread_bps,
self.params.min_spread_bps,
self.params.max_spread_bps
)
# Reservation price as mid-point for symmetric spread
reservation = self.calculate_reservation_price(mid_price, volatility)
half_spread = (total_spread_bps / 10000) * mid_price / 2
# For inventory skew: widen one side
if self.inventory > 0: # Long inventory - make bid tighter, ask wider
bid_price = round(reservation - half_spread * 0.8, 2)
ask_price = round(reservation + half_spread * 1.2, 2)
elif self.inventory < 0: # Short inventory - make ask tighter, bid wider
bid_price = round(reservation - half_spread * 1.2, 2)
ask_price = round(reservation + half_spread * 0.8, 2)
else:
bid_price = round(reservation - half_spread, 2)
ask_price = round(reservation + half_spread, 2)
return bid_price, ask_price
def calculate_confidence(self, spread_bps: float, toxicity: float) -> float:
"""
Calculate confidence score for the generated quote.
Higher confidence = more aggressive sizing.
"""
# Normalize spread vs historical average
if self.spread_history:
mean_spread = np.mean(self.spread_history)
std_spread = np.std(self.spread_history) + 1e-6
spread_zscore = abs(spread_bps - mean_spread) / std_spread
spread_confidence = np.exp(-spread_zscore * 0.5) # Decay with z-score
else:
spread_confidence = 0.7
# Reduce confidence for high toxicity
toxicity_confidence = 1 - toxicity
# Combined confidence
confidence = 0.6 * spread_confidence + 0.4 * toxicity_confidence
return np.clip(confidence, 0.1, 1.0)
def update_inventory(self, trade_price: float, trade_size: float, side: str) -> None:
"""Update inventory position after a fill."""
if side == "buy":
self.inventory += trade_size
else:
self.inventory -= trade_size
# Clamp to max position
self.inventory = np.clip(
self.inventory,
-self.params.max_position_btc,
self.params.max_position_btc
)
def generate_quote(self, mid_price: float, volatility: float,
order_book_imbalance: float, trade_toxicity: float) -> SpreadQuote:
"""Generate a complete spread quote with all metadata."""
bid_price, ask_price = self.calculate_optimal_spread(
mid_price, volatility, order_book_imbalance, trade_toxicity
)
spread_bps = ((ask_price - bid_price) / mid_price) * 10000
inventory_ratio = self.inventory / self.params.max_position_btc
confidence = self.calculate_confidence(spread_bps, trade_toxicity)
# Dynamic order sizing based on confidence
size_multiplier = confidence * (1 - abs(inventory_ratio) * 0.5)
order_size = self.params.order_size_btc * size_multiplier
# Update history
self.spread_history.append(spread_bps)
self.toxicity_history.append(trade_toxicity)
if len(self.spread_history) > self.max_history:
self.spread_history.pop(0)
if len(self.toxicity_history) > self.max_history:
self.toxicity_history.pop(0)
return SpreadQuote(
pair=self.params.pair,
timestamp=datetime.utcnow().timestamp(),
bid_price=bid_price,
bid_size=round(order_size, 6),
ask_price=ask_price,
ask_size=round(order_size, 6),
spread_bps=round(spread_bps, 2),
mid_price=mid_price,
inventory_ratio=round(inventory_ratio, 4),
toxicity_score=round(trade_toxicity, 4),
confidence=round(confidence, 4)
)
def cache_quote(self, quote: SpreadQuote) -> None:
"""Cache latest quote in Redis for downstream consumption."""
try:
key = f"quote:{quote.pair.replace('/', '_')}"
self.redis.setex(
key,
2, # 2 second TTL
json.dumps({
"bid": quote.bid_price,
"ask": quote.ask_price,
"mid": quote.mid_price,
"spread_bps": quote.spread_bps,
"ts": quote.timestamp
})
)
except redis.RedisError as e:
print(f"[WARNING] Redis cache error: {e}")
class TradeReplayProcessor:
"""
Processes historical trade data for backtesting and model calibration.
Integrates with HolySheep's replay functionality.
"""
def __init__(self, holysheep_client, output_file: str = "replay_data.jsonl"):
self.client = holysheep_client
self.output_file = output_file
self.trade_buffer: List[Dict] = []
self.buffer_size = 1000
async def replay_trades(self, start_time: int, end_time: int,
pairs: List[str]) -> List[Dict]:
"""
Replay historical trades between start and end timestamps.
Args:
start_time: Unix timestamp in milliseconds
end_time: Unix timestamp in milliseconds
pairs: List of trading pairs to replay
Returns:
List of reconstructed trade events
"""
print(f"[REPLAY] Fetching trades from {start_time} to {end_time}")
# HolySheep historical data endpoint
async with self.client._session.get(
f"{self.client.base_url}/history/trades",
params={
"exchange": "ascendex",
"pairs": ",".join(pairs),
"start": start_time,
"end": end_time
},
headers=self.client._generate_auth_headers("/v1/history/trades")
) as response:
if response.status != 200:
print(f"[ERROR] History fetch failed: {response.status}")
return []
trades = await response.json()
print(f"[REPLAY] Retrieved {len(trades)} historical trades")
# Process and reconstruct order flow
processed_trades = []
for trade in trades:
processed = self._process_trade(trade)
processed_trades.append(processed)
# Buffer for batch writing
self.trade_buffer.append(processed)
if len(self.trade_buffer) >= self.buffer_size:
self._flush_buffer()
if self.trade_buffer:
self._flush_buffer()
return processed_trades
def _process_trade(self, trade: Dict) -> Dict:
"""Process individual trade into standardized format."""
return {
"timestamp": trade.get("ts", 0),
"pair": trade.get("s", ""),
"price": float(trade.get("p", 0)),
"size": float(trade.get("v", 0)),
"side": "buy" if trade.get("bm", False) else "sell",
"fee": float(trade.get("fee", 0)),
"trade_id": trade.get("tid", "")
}
def _flush_buffer(self) -> None:
"""Write buffered trades to output file."""
with open(self.output_file, 'a') as f:
for trade in self.trade_buffer:
f.write(json.dumps(trade) + "\n")
print(f"[REPLAY] Flushed {len(self.trade_buffer)} trades