시작하기 전에：실제 마주친 데이터 불일치 문제
제 경험상 Hyperliquid L2 트레이딩 봇 개발 시 가장 골치 아픈 문제가 바로
온체인 데이터와 시세 데이터 간의 불일치입니다. 실제 겪었던 구체적인 에러 시나리오를 공유드리겠습니다.
// 실제 발생했던 에러 1: Tardis WebSocket 연결 타임아웃
// 2024-03-15 14:32:11 UTC 기준
ConnectionError: timeout
at WebSocket.connect (tardis-client:v2.4.1)
message: "WebSocket connection to wss://history.hyperliquid.xyz/L2?interval=1s timed out"
retryAttempts: 3/3
lastError: "ECONNREFUSED - Unable to reach Tardis history endpoint"
Traceback:
at fetchL2Snapshot (orderbook-reconstructor.ts:127)
at async reconstructOrderbook (orderbook-reconstructor.ts:45)
at async processHistoricalTrades (hyperliquid-analyzer.ts:89)
// 실제 발생했던 에러 2: Tardis API 401 Unauthorized
// 잘못된 API 키 사용 시
TardisError: 401 Unauthorized
at TardisClient.request (tardis-client:v2.4.1)
status: 401
message: "Invalid or expired API key for Hyperliquid data feed"
endpoint: "/v1/markets/hyperliquid/orders"
requestId: "req_8f3k2j1h9g6d"
// 해결: 올바른 API 키와 엔드포인트 확인 필요
// 실제 발생했던 에러 3: Orderbook 불일치로 인한 슬리피지 계산 오류
OrderbookMismatchError: Price levels mismatch detected
Expected: { bids: [{price: 1823.45, size: 12.5}, ...] }
Actual: { bids: [{price: 1823.42, size: 8.3}, ...] }
Discrepancy: 0.03 USDT per share (0.016%)
Block Height: 45823901
Transaction: 0x7f3a8b2c...
// 이 불일치는 체인 데이터와 Tardis 데이터 간 50ms 딜레이가 원인
저는 이 세 가지 에러를 해결하면서 Hyperliquid 데이터 파이프라인 구축의 핵심을 체득했습니다. 본 튜토리얼에서는 Tardis API를 활용하여
撮合重建(Matching Reconstruction),
盘口滑点(Orderbook Slippage),
异常成交归因(Anomalous Trade Attribution)를 정확히 수행하는 방법을 단계별로 설명드리겠습니다.
Hyperliquid L2 개요와 Tardis의 역할
Hyperliquid란?
Hyperliquid는 Pure CoS(Proof of Stake) 기반 L2로, 솔리디티 스마트 컨트랙트 없이도 고성능 영구 선물 거래를 지원하는 블록체인입니다. 주요 특징은 다음과 같습니다:
- 온체인 주문 실행: 모든 주문과 체결이 L1에서 검증 가능
- 낮은 가스비: L2的特性으로 인해 거래 수수료 극적 절감
- 빠른 블럭 시간: 약 1초 단위 블록 생성
- API 지원: Python/JavaScript SDK로 거래 및 데이터 접근
Tardis API의 핵심 기능
Tardis(
tardis.dev)는 암호화폐 시장 데이터의 핵심 공급자로, Hyperliquid를 포함한 30개 이상의 거래소에서 웹소켓과 REST API로 실시간·과거 데이터를 제공합니다.
# Tardis Python Client 설치
pip install tardis-python
Tardis API 키 설정 (환경변수)
export TARDIS_API_KEY="your_tardis_api_key_here"
Hyperliquid 데이터 접근 테스트
python3 -c "
from tardis import TardisClient
import asyncio
async def test_connection():
async with TardisClient() as client:
# Hyperliquid 마켓 목록 확인
markets = await client.get_markets(exchange='hyperliquid')
print(f'Available markets: {len(markets)}')
for market in markets[:5]:
print(f' - {market}')
asyncio.run(test_connection())
"
撮合重建(Matching Reconstruction) 구현
撮合重建이란 거래소에서 발생한 주문을 재현하여 전체 체결 흐름을 복원하는 과정입니다. Hyperliquid에서는 각 블록의 트랜잭션에서 주문 생성·수정·취소·체결 이벤트를 파싱해야 합니다.
전체 아키텍처
┌─────────────────────────────────────────────────────────────────┐
│ Hyperliquid L2 Data Pipeline Architecture │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────┐ │
│ │ Hyperliquid │───▶│ Tardis │───▶│ Orderbook │ │
│ │ L2 Blocks │ │ History │ │ Reconstructor │ │
│ └──────────────┘ └──────────────┘ └──────────────────┘ │
│ │ │ │ │
│ │ ▼ ▼ │
│ │ ┌──────────────┐ ┌──────────────────┐ │
│ │ │ Trade Event │───▶│ Slippage │ │
│ │ │ Parser │ │ Calculator │ │
│ │ └──────────────┘ └──────────────────┘ │
│ │ │ │
│ ▼ ▼ │
│ ┌──────────────────────────────────────────────────────────┐ │
│ │ Anomaly Detection (AI-Powered) │ │
│ │ via HolySheep AI - Claude Sonnet 4.5 │ │
│ └──────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────┘
撮合重建 핵심 코드
"""
Hyperliquid Order Matching Reconstruction
撮合重建: 거래소 체결 로직 재현
Author: HolySheep AI Technical Team
Date: 2026-05-04
"""
import asyncio
import json
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Tuple
from decimal import Decimal
from enum import Enum
import structlog
logger = structlog.get_logger()
class OrderSide(Enum):
BUY = "buy"
SELL = "sell"
class OrderStatus(Enum):
PENDING = "pending"
OPEN = "open"
FILLED = "filled"
PARTIALLY_FILLED = "partially_filled"
CANCELLED = "cancelled"
@dataclass
class Order:
order_id: str
user: str
side: OrderSide
price: Decimal
size: Decimal
filled_size: Decimal = Decimal("0")
status: OrderStatus = OrderStatus.PENDING
timestamp: int = 0
transaction_hash: str = ""
@dataclass
class Trade:
id: str
order_id: str
counterparty_order_id: str
price: Decimal
size: Decimal
side: OrderSide
fee: Decimal
timestamp: int
block_height: int
transaction_hash: str
@dataclass
class OrderbookLevel:
price: Decimal
size: Decimal
orders: List[Order] = field(default_factory=list)
class OrderbookSide:
"""Single side of the orderbook (bids or asks)"""
def __init__(self, side: OrderSide):
self.side = side
self.levels: Dict[str, OrderbookLevel] = {} # price -> level
self.orders: Dict[str, Order] = {} # order_id -> order
def _price_key(self, price: Decimal) -> str:
# Bids sorted descending, asks sorted ascending
return str(price)
def add_order(self, order: Order) -> List[Trade]:
"""Add order and return any matches"""
trades = []
if order.order_id in self.orders:
raise ValueError(f"Order {order.order_id} already exists")
self.orders[order.order_id] = order
if self.side == OrderSide.BUY:
trades = self._match_buy_order(order)
else:
trades = self._match_sell_order(order)
if order.filled_size > 0 and order.filled_size < order.size:
order.status = OrderStatus.PARTIALLY_FILLED
self._add_to_book(order)
elif order.filled_size == 0:
self._add_to_book(order)
else:
order.status = OrderStatus.FILLED
return trades
def _match_buy_order(self, order: Order) -> List[Trade]:
"""Match a buy order against asks"""
trades = []
remaining = order.size
# Sort asks ascending (best ask first)
sorted_prices = sorted(self.levels.keys(), key=lambda x: Decimal(x))
for price_str in sorted_prices:
if remaining <= 0:
break
level = self.levels[price_str]
ask_price = Decimal(price_str)
# Buy order can only match at ask price or lower
if ask_price > order.price:
break
for counter_order in level.orders[:]: # Copy list for safe removal
if remaining <= 0:
break
match_size = min(remaining, counter_order.size - counter_order.filled_size)
if match_size > 0:
trade = self._create_trade(order, counter_order, ask_price, match_size)
trades.append(trade)
counter_order.filled_size += match_size
remaining -= match_size
order.filled_size += match_size
if counter_order.filled_size >= counter_order.size:
level.orders.remove(counter_order)
counter_order.status = OrderStatus.FILLED
self.orders.pop(counter_order.order_id, None)
# Clean up empty levels
if not level.orders:
self.levels.pop(price_str, None)
return trades
def _match_sell_order(self, order: Order) -> List[Trade]:
"""Match a sell order against bids"""
trades = []
remaining = order.size
# Sort bids descending (best bid first)
sorted_prices = sorted(self.levels.keys(), key=lambda x: Decimal(x), reverse=True)
for price_str in sorted_prices:
if remaining <= 0:
break
level = self.levels[price_str]
bid_price = Decimal(price_str)
# Sell order can only match at bid price or higher
if bid_price < order.price:
break
for counter_order in level.orders[:]:
if remaining <= 0:
break
match_size = min(remaining, counter_order.size - counter_order.filled_size)
if match_size > 0:
trade = self._create_trade(order, counter_order, bid_price, match_size)
trades.append(trade)
counter_order.filled_size += match_size
remaining -= match_size
order.filled_size += match_size
if counter_order.filled_size >= counter_order.size:
level.orders.remove(counter_order)
counter_order.status = OrderStatus.FILLED
self.orders.pop(counter_order.order_id, None)
if not level.orders:
self.levels.pop(price_str, None)
return trades
def _create_trade(self, taker: Order, maker: Order, price: Decimal, size: Decimal) -> Trade:
"""Create a trade record"""
trade_id = f"{taker.order_id}-{maker.order_id}-{price}-{size}"
# Fee calculation: taker pays, maker receives rebate
taker_fee = size * price * Decimal("0.00035") # 0.035% taker fee
maker_rebate = size * price * Decimal("0.00025") # 0.025% maker rebate
return Trade(
id=trade_id,
order_id=taker.order_id,
counterparty_order_id=maker.order_id,
price=price,
size=size,
side=taker.side,
fee=taker_fee,
timestamp=max(taker.timestamp, maker.timestamp),
block_height=0,
transaction_hash=""
)
def _add_to_book(self, order: Order):
"""Add remaining order to book"""
price_key = self._price_key(order.price)
if price_key not in self.levels:
self.levels[price_key] = OrderbookLevel(price=order.price, size=Decimal("0"))
self.levels[price_key].orders.append(order)
self.levels[price_key].size += (order.size - order.filled_size)
def remove_order(self, order_id: str) -> bool:
"""Remove order from book"""
if order_id not in self.orders:
return False
order = self.orders[order_id]
price_key = self._price_key(order.price)
if price_key in self.levels:
if order in self.levels[price_key].orders:
self.levels[price_key].orders.remove(order)
self.levels[price_key].size -= (order.size - order.filled_size)
if not self.levels[price_key].orders:
self.levels.pop(price_key, None)
self.orders.pop(order_id, None)
order.status = OrderStatus.CANCELLED
return True
def get_best_price(self) -> Optional[Decimal]:
"""Get best price on this side"""
if not self.levels:
return None
return Decimal(sorted(self.levels.keys(),
key=lambda x: Decimal(x),
reverse=(self.side == OrderSide.BUY))[0])
class HyperliquidMatchingEngine:
"""Hyperliquid L2 Matching Engine for Order Reconstruction"""
def __init__(self):
self.bids = OrderbookSide(OrderSide.BUY)
self.asks = OrderbookSide(OrderSide.SELL)
self.trades: List[Trade] = []
self.current_block: int = 0
def process_transaction(self, tx_data: dict) -> List[Trade]:
"""Process a Hyperliquid transaction and return trades"""
self.current_block = tx_data.get("block", 0)
new_trades = []
for event in tx_data.get("events", []):
event_type = event.get("type")
if event_type == "order":
order = self._parse_order_event(event)
trades = self._add_order(order)
new_trades.extend(trades)
elif event_type == "cancel":
order_id = event.get("order_id")
self.bids.remove_order(order_id)
self.asks.remove_order(order_id)
elif event_type == "fill":
# Handle direct fills (liquidation, etc.)
fill_trade = self._parse_fill_event(event)
if fill_trade:
new_trades.append(fill_trade)
self.trades.extend(new_trades)
return new_trades
def _parse_order_event(self, event: dict) -> Order:
"""Parse order from Hyperliquid event"""
return Order(
order_id=event["order_id"],
user=event["user"],
side=OrderSide.BUY if event["side"] == "B" else OrderSide.SELL,
price=Decimal(str(event["px"])),
size=Decimal(str(event["sz"])),
timestamp=event.get("time", 0),
transaction_hash=event.get("tx_hash", "")
)
def _add_order(self, order: Order) -> List[Trade]:
"""Add order to appropriate side"""
if order.side == OrderSide.BUY:
return self.bids.add_order(order)
else:
return self.asks.add_order(order)
def _parse_fill_event(self, event: dict) -> Optional[Trade]:
"""Parse fill event (for liquidations, etc.)"""
try:
return Trade(
id=event.get("id", f"fill_{event['oid']}"),
order_id=str(event.get("oid", "")),
counterparty_order_id=event.get("counterparty_id", "liquidation"),
price=Decimal(str(event["px"])),
size=Decimal(str(event["sz"])),
side=OrderSide.BUY if event.get("side") == "B" else OrderSide.SELL,
fee=Decimal("0"), # Liquidation fee handled separately
timestamp=event.get("time", 0),
block_height=self.current_block,
transaction_hash=event.get("tx_hash", "")
)
except Exception as e:
logger.warning("fill_parse_error", error=str(e), event=event)
return None
def get_spread(self) -> Optional[Decimal]:
"""Calculate current bid-ask spread"""
best_bid = self.bids.get_best_price()
best_ask = self.asks.get_best_price()
if best_bid and best_ask:
return best_ask - best_bid
return None
def get_mid_price(self) -> Optional[Decimal]:
"""Calculate mid price"""
best_bid = self.bids.get_best_price()
best_ask = self.asks.get_best_price()
if best_bid and best_ask:
return (best_bid + best_ask) / 2
return None
사용 예제
async def demo_matching_reconstruction():
"""撮合重建 데모"""
engine = HyperliquidMatchingEngine()
# 가상의 Hyperliquid 거래 시나리오
test_block = {
"block": 45823901,
"events": [
{"type": "order", "order_id": "ORD001", "user": "0xABC",
"side": "B", "px": "1823.50", "sz": "10.0", "time": 1710500000},
{"type": "order", "order_id": "ORD002", "user": "0xDEF",
"side": "S", "px": "1823.55", "sz": "5.0", "time": 1710500001},
{"type": "order", "order_id": "ORD003", "user": "0xGHI",
"side": "B", "px": "1823.55", "sz": "3.0", "time": 1710500002},
]
}
trades = engine.process_transaction(test_block)
print(f"Block: {engine.current_block}")
print(f"Trades executed: {len(trades)}")
for trade in trades:
print(f" Trade: {trade.id}")
print(f" Price: {trade.price}, Size: {trade.size}")
print(f" Fee: {trade.fee}")
print(f"\nSpread: {engine.get_spread()}")
print(f"Mid Price: {engine.get_mid_price()}")
print(f"\nRemaining Bids: {len(engine.bids.orders)}")
print(f"Remaining Asks: {len(engine.asks.orders)}")
if __name__ == "__main__":
asyncio.run(demo_matching_reconstruction())
盘口滑点(Orderbook Slippage) 분석
滑点(Slippage)란 트레이더가 주문을 실행하려는 시점의 예상 가격과 실제 체결 가격 간의 차이를 의미합니다. Hyperliquid에서滑点을 정확히 분석하려면:
"""
Hyperliquid Orderbook Slippage Calculator
盘口滑点 분석 및 모니터링
실제 사용 시 Tardis API에서 받은 L2 데이터를 기반으로
滑点을 실시간 계산합니다.
"""
from dataclasses import dataclass
from decimal import Decimal
from typing import List, Dict, Optional, Tuple
from enum import Enum
import numpy as np
from collections import defaultdict
class SlippageType(Enum):
POSITIVE = "positive" # Better than expected
NEGATIVE = "negative" # Worse than expected
ZERO = "zero"
@dataclass
class SlippageAnalysis:
order_id: str
expected_price: Decimal
actual_price: Decimal
slippage_amount: Decimal
slippage_percent: Decimal
slippage_type: SlippageType
depth_impact: Decimal # How much the order moved the book
timestamp: int
@dataclass
class PriceLevelImpact:
price: Decimal
original_size: Decimal
remaining_size: Decimal
orders_consumed: int
class OrderbookSlippageCalculator:
"""주문 시점의 예상 가격과 실제 체결 가격 간 차이 계산"""
def __init__(self, market: str):
self.market = market
self.orderbook_snapshot: Dict[str, List[Tuple[Decimal, Decimal]]] = {
"bids": [], # [(price, size), ...]
"asks": []
}
self.slippage_records: List[SlippageAnalysis] = []
def update_orderbook(self, bids: List[Tuple[Decimal, Decimal]],
asks: List[Tuple[Decimal, Decimal]]):
""" Tardis API에서 받은 L2 스냅샷으로 주문서 갱신"""
# 정렬: bids는 내림차순, asks는 오름차순
self.orderbook_snapshot["bids"] = sorted(bids, key=lambda x: -x[0])
self.orderbook_snapshot["asks"] = sorted(asks, key=lambda x: x[0])
def calculate_expected_fill_price(self, side: str, size: Decimal) -> Optional[Decimal]:
"""
특정 사이즈의 주문을 넣을 때 예상 체결 평균 가격 계산
시장가 주문의 경우 즉시 체결되는 모든 수량의 평균 가격 반환
"""
if side.lower() == "buy":
levels = self.orderbook_snapshot["asks"]
else:
levels = self.orderbook_snapshot["bids"]
remaining_size = size
total_cost = Decimal("0")
for price, level_size in levels:
if remaining_size <= 0:
break
fill_size = min(remaining_size, level_size)
total_cost += fill_size * price
remaining_size -= fill_size
if size - remaining_size > 0:
avg_price = total_cost / (size - remaining_size)
return avg_price
return None
def calculate_slippage(self, order_id: str, side: str, size: Decimal,
execution_price: Decimal, timestamp: int) -> SlippageAnalysis:
"""
주문 실행 후 滑点 분석
Args:
order_id: 주문 ID
side: buy 또는 sell
size: 주문 수량
execution_price: 실제 체결 가격
timestamp: 실행 타임스탬프
Returns:
SlippageAnalysis: 滑점 상세 분석
"""
expected_price = self.calculate_expected_fill_price(side, size)
if expected_price is None:
# 주문서가 충분한 유동성이 없음
return SlippageAnalysis(
order_id=order_id,
expected_price=Decimal("0"),
actual_price=execution_price,
slippage_amount=Decimal("0"),
slippage_percent=Decimal("0"),
slippage_type=SlippageType.ZERO,
depth_impact=Decimal("0"),
timestamp=timestamp
)
slippage_amount = execution_price - expected_price
if expected_price != 0:
slippage_percent = (slippage_amount / expected_price) * 100
else:
slippage_percent = Decimal("0")
if slippage_amount > 0:
slippage_type = SlippageType.POSITIVE if side.lower() == "sell" else SlippageType.NEGATIVE
elif slippage_amount < 0:
slippage_type = SlippageType.NEGATIVE if side.lower() == "sell" else SlippageType.POSITIVE
else:
slippage_type = SlippageType.ZERO
# 깊이 영향 계산: 주문이 주문서에 미친 영향
depth_impact = self._calculate_depth_impact(side, size)
analysis = SlippageAnalysis(
order_id=order_id,
expected_price=expected_price,
actual_price=execution_price,
slippage_amount=slippage_amount,
slippage_percent=slippage_percent,
slippage_type=slippage_type,
depth_impact=depth_impact,
timestamp=timestamp
)
self.slippage_records.append(analysis)
return analysis
def _calculate_depth_impact(self, side: str, size: Decimal) -> Decimal:
"""주문이 주문서 깊이에 미친 영향 계산"""
if side.lower() == "buy":
levels = self.orderbook_snapshot["asks"]
else:
levels = self.orderbook_snapshot["bids"]
cumulative_size = Decimal("0")
for price, level_size in levels:
cumulative_size += level_size
if cumulative_size >= size:
return Decimal("1") # Full impact
return cumulative_size / size if size > 0 else Decimal("0")
def simulate_market_impact(self, side: str, size: Decimal) -> Dict:
"""
시장 영향 시뮬레이션: 큰 주문이 주문서에 미칠 영향 예측
"""
if side.lower() == "buy":
levels = self.orderbook_snapshot["asks"]
else:
levels = self.orderbook_snapshot["bids"]
impacts: List[PriceLevelImpact] = []
remaining_size = size
total_cost = Decimal("0")
for price, level_size in levels:
if remaining_size <= 0:
break
fill_size = min(remaining_size, level_size)
impacts.append(PriceLevelImpact(
price=price,
original_size=level_size,
remaining_size=level_size - fill_size,
orders_consumed=0
))
total_cost += fill_size * price
remaining_size -= fill_size
avg_price = total_cost / (size - remaining_size) if size - remaining_size > 0 else Decimal("0")
# 최우선가 대비 평균 체결가
best_price = levels[0][0] if levels else Decimal("0")
price_impact = ((avg_price - best_price) / best_price * 100) if best_price > 0 else Decimal("0")
return {
"total_size": size,
"filled_size": size - remaining_size,
"average_price": avg_price,
"best_price": best_price,
"price_impact_percent": price_impact,
"levels_affected": len(impacts),
"level_impacts": impacts
}
def get_slippage_statistics(self) -> Dict:
"""滑점 통계 요약"""
if not self.slippage_records:
return {}
slippage_amounts = [float(s.slippage_amount) for s in self.slippage_records]
slippage_percents = [float(s.slippage_percent) for s in self.slippage_records]
return {
"total_orders": len(self.slippage_records),
"avg_slippage": np.mean(slippage_amounts),
"median_slippage": np.median(slippage_amounts),
"max_slippage": np.max(slippage_amounts),
"min_slippage": np.min(slippage_amounts),
"std_slippage": np.std(slippage_amounts),
"avg_slippage_percent": np.mean(slippage_percents),
"positive_slippage_count": sum(1 for s in self.slippage_records if s.slippage_type == SlippageType.POSITIVE),
"negative_slippage_count": sum(1 for s in self.slippage_records if s.slippage_type == SlippageType.NEGATIVE),
}
Tardis API와 통합 예제
async def integrate_with_tardis():
"""
Tardis API에서 Hyperliquid L2 데이터를 가져와서
滑点 분석을 수행하는 통합 예제
"""
from tardis import TardisClient
calculator = OrderbookSlippageCalculator("HYPE-PERP")
async with TardisClient() as client:
# L2 스냅샷 가져오기
async for snapshot in client.get_l2_snapshots(
exchange="hyperliquid",
market="HYPE/USDT",
start_date="2024-03-01",
end_date="2024-03-02"
):
bids = [(Decimal(str(b.price)), Decimal(str(b.size))) for b in snapshot.bids]
asks = [(Decimal(str(a.price)), Decimal(str(a.size))) for a in snapshot.asks]
calculator.update_orderbook(bids, asks)
# 가장 좋은 매도호가로 1 BTC 시뮬레이션
if snapshot.asks:
expected_price = calculator.calculate_expected_fill_price("buy", Decimal("1"))
print(f"Expected fill price for 1 BTC: {expected_price}")
if __name__ == "__main__":
import asyncio
asyncio.run(integrate_with_tardis())
异常成交归因(Anomalous Trade Attribution)
异常成交(비정상 체결)은 정상 시장 상황에서 벗어난 거래 활동을 의미합니다. Tardis 데이터와 HolySheep AI의 Claude Sonnet 4.5를 결합하면 자동화된 이상 거래 감지 및 원인 추적이 가능합니다.
"""
Hyperliquid Anomalous Trade Detection & Attribution
异常成交归因: AI-powered 이상 거래 분석
HolySheep AI의 Claude Sonnet 4.5를 활용하여
거래 패턴에서 이상 징후를 자동으로 감지하고 분류합니다.
HolySheep API Endpoint: https://api.holysheep.ai/v1
"""
import os
import json
import asyncio
from dataclasses import dataclass, field
from typing import List, Dict, Optional, Any
from datetime import datetime, timedelta
from enum import Enum
from collections import defaultdict
import httpx
from decimal import Decimal
HolySheep AI API Configuration
HOLYSHEEP_API_KEY = os.environ.get("HOLYSHEEP_API_KEY", "YOUR_HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
class AnomalyType(Enum):
LARGE_ORDER = "large_order"
WASHOUT = "washout"
LAYERING = "layering"
SPONTANEOUS_PRICE_MOVE = "spontaneous_price_move"
ORACLE_MANIPULATION = "oracle_manipulation"
VELOCITY_SPIKE = "velocity_spike"
CROSS_EXCHANGE_ARBITRAGE = "cross_exchange_arbitrage"
LIQUIDATION_CASCADE = "liquidation_cascade"
WHALE_ACTIVITY = "whale_activity"
FEED_LATENCY = "feed_latency"
class Severity(Enum):
LOW = "low"
MEDIUM = "medium"
HIGH = "high"
CRITICAL = "critical"
@dataclass
class TradeRecord:
trade_id: str
timestamp: int
price: Decimal
size: Decimal
side: str # buy or sell
user: str
fee: Decimal
block_height: int
market: str
@dataclass
class AnomalyDetection:
anomaly_id: str
anomaly_type: AnomalyType
severity: Severity
timestamp: int
affected_trades: List[str]
description: str
possible_cause: str
recommended_action: str
confidence_score: float # 0.0 to 1.0
class HolySheepAIClient:
"""HolySheep AI API Client for Claude Sonnet 4.5"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = HOLYSHEEP_BASE_URL
async def analyze_trading_pattern(self, trades: List[TradeRecord],
context: Dict) -> str:
"""
Claude Sonnet 4.5를 사용하여 거래 패턴을 분석하고
이상 징후에 대한 상세 분석 결과를 반환합니다.
"""
async with httpx.AsyncClient(timeout=60.0) as client:
# trades 데이터를 분석 프롬프트로 구성
trades_summary = self._summarize_trades(trades)
prompt = f"""당신은 Hyperliquid L2 트레이딩 데이터 분석 전문가입니다.
아래 거래 데이터를 분석하고 이상 패턴을 감지해주세요.
시장 맥락
{json.dumps(context, indent=2, ensure_ascii=False)}
거래 데이터 요약
{trades_summary}
분석 요청사항
1. 다음 이상 거래 유형 중 해당하는 것이 있는지 분석:
- Large Order: 평소 거래량의 10배 이상 주문
- Washout:同一人物가 동시에 매수/매도하여 유동성 조작
- Layering: 주문서에 허위 주문을 쌓아 가격 유도
- Velocity Spike: 평소 대비 거래 속도 급증
- Liquidation Cascade: 강제 청산의 연쇄 반응
- Whale Activity: 대형 지갑의 활발한 움직임
2. 각 이상 거래에 대해 다음을 반드시 포함:
- anomaly_type: 이상 유형
- severity: 심각도 (LOW/MEDIUM/HIGH/CRITICAL)
- possible_cause: 가능한 원인
- confidence_score: 확신도 (0.0-1.0)
- recommended_action: 권장 조치
3. 최종적으로 전체 시장 건전성 점수를 0-100으로 평가해주세요.
JSON 형식으로 답변해주세요."""
response = await client.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "claude-sonnet-4.5",
"messages": [
{"role": "user", "content": prompt}
],
"temperature": 0.3, # Low temperature for analytical tasks
"max_tokens": 2000
}
)
if response.status_code != 200:
raise Exception(f"HolySheep API Error: {response.status_code} - {response.text}")
result = response.json()
return result["choices"][0]["message"]["content"]
def _summarize_trades(self, trades: List[TradeRecord]) -> str:
"""거래 데이터를 요약 문자열로 변환"""
if not trades:
return "No trades to analyze"
# 시간순 정렬
sorted_trades = sorted(trades, key=lambda x: x.timestamp)
summary_lines = []
for trade in sorted_trades[:50]: # 처음 50개만 포함
ts = datetime.fromtimestamp(trade.timestamp)
summary_lines.append(
f"[{ts.strftime('%