Introduction: 왜 Perp 마켓메이킹인가
저는 3년째 크립토 시장制造团队에서 일하고 있는 엔지니어입니다. Perpetual futures는 현물 거래보다 10배 이상 높은 거래 빈도와 레버리지 구조 덕분에 market making 전략 구현에 최적화된 환경입니다. 이번 튜토리얼에서는 Tardis의 Kraken perp tick 데이터를 HolySheep AI 게이트웨이를 통해 안정적으로 수신하고, 주문서 마이크로스트럭처를 실시간 분석하며, 슬리피지 모델을 구축하는 전 과정을 다룹니다.
아키텍처 개요: Tardis → HolySheep → 마켓메이킹 엔진
프로덕션 환경에서 지연 시간 5ms 이하를 달성하기 위한 아키텍처 설계는 다음과 같습니다:
- 데이터 소스: Tardis Kraken Perp WebSocket feed (wss://tardis-devnet.tardis.dev/v1/stream)
- 게이트웨이: HolySheep AI 단일 엔드포인트로 다중 모델 라우팅
- 분석 엔진: Rust 기반 시그널 프로세서 + Python ML 슬리피지 예측기
- 거래 실행: Kraken Perp REST API via HolySheep
필수 의존성 및 환경 설정
# Python 3.11+ 환경
pip install tardis-client websockets asyncio aiohttp numpy pandas pydantic
pip install holy Sheep-holysheep # HolySheep 공식 SDK
Rust (高性能 경로용)
cargo add tokio async-trait serde_json rmp-serde
docker-compose.yml (Tardis 시뮬레이션)
version: '3.8'
services:
tardis:
image: tardis/tardis-devnet:latest
ports:
- "9000:9000"
environment:
- TARDIS_MODE=kraken_perp
market-maker:
build: .
depends_on:
- tardis
environment:
- HOLYSHEEP_API_KEY=${HOLYSHEEP_API_KEY}
- TARDIS_WS_URL=ws://tardis:9000/v1/stream1단계: HolySheep AI 게이트웨이 연결 설정
HolySheep AI는 단일 API 키로 여러 AI 모델을 라우팅할 수 있어 마켓메이킹 전략 최적화 요청과 실시간 주문서 분석 요청을 모두 처리합니다. 여기서는 DeepSeek V3.2로 비용을 최적화하면서必要时 Claude Sonnet으로 전환하는 전략을 사용합니다.
# config.py
import os
from holySheep_client import HolySheepClient, ModelConfig
class MarketMakerConfig:
"""마켓메이킹 설정"""
# HolySheep AI 게이트웨이
HOLYSHEEP_BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY")
# Tardis 연결 (perpetual tick)
TARDIS_WS_URL = os.getenv("TARDIS_WS_URL", "wss://tardis-devnet.tardis.dev/v1/stream")
# 모델 라우팅 전략
MODEL_CONFIG = {
"slippage_predictor": ModelConfig(
model="deepseek-v3.2",
max_tokens=512,
temperature=0.1,
cost_per_mtok=0.42 # DeepSeek V3.2: $0.42/MTok
),
"strategy_optimizer": ModelConfig(
model="claude-sonnet-4.5",
max_tokens=1024,
temperature=0.2,
cost_per_mtok=15.0 # Claude Sonnet 4.5: $15/MTok
),
"market_analyzer": ModelConfig(
model="gpt-4.1",
max_tokens=2048,
temperature=0.3,
cost_per_mtok=8.0 # GPT-4.1: $8/MTok
)
}
# Kraken Perp 설정
KRAKEN_PERP_SYMBOLS = ["XBT/USD", "ETH/USD", "SOL/USD"]
TARGET_SPREAD_BPS = 5 # 목표 스프레드 5 basis points
MAX_POSITION_SIZE = 100_000 # USD
holy Sheep_client.py
from openai import AsyncOpenAI
import json
from typing import Optional
class HolySheepClient:
"""HolySheep AI 게이트웨이 클라이언트"""
def __init__(self, api_key: str):
self.base_url = "https://api.holysheep.ai/v1"
self.client = AsyncOpenAI(
api_key=api_key,
base_url=self.base_url
)
async def chat_completion(
self,
model: str,
messages: list,
max_tokens: int = 1024,
temperature: float = 0.1
) -> str:
"""HolySheep AI를 통한 채팅 완성"""
response = await self.client.chat.completions.create(
model=model,
messages=messages,
max_tokens=max_tokens,
temperature=temperature
)
return response.choices[0].message.content
async def analyze_orderbook(
self,
bids: list[tuple[float, float]],
asks: list[tuple[float, float]],
symbol: str
) -> dict:
"""주문서 마이크로스트럭처 분석 via HolySheep AI"""
system_prompt = """당신은 크립토 마켓메이킹 전문가입니다.
주문서 데이터를 분석하고 다음을 제공하세요:
1. liquidity_score: 0-100 (유동성 점수)
2. imbalance_ratio: 매수/매도 비율
3. recommended_spread_bps: 권장 스프레드 (bps)
4. risk_factors: 위험 요소 배열"""
orderbook_summary = {
"symbol": symbol,
"bid_levels": len(bids),
"ask_levels": len(asks),
"top_bid": bids[0] if bids else None,
"top_ask": asks[0] if asks else None,
"spread_bps": ((asks[0][0] - bids[0][0]) / bids[0][0] * 10000) if bids and asks else 0
}
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"주문서 데이터: {json.dumps(orderbook_summary)}"}
]
result = await self.chat_completion(
model="deepseek-v3.2",
messages=messages,
max_tokens=512,
temperature=0.1
)
return json.loads(result)
async def predict_slippage(
self,
orderbook: dict,
trade_size: float,
side: str # "buy" or "sell"
) -> dict:
"""AI 기반 슬리피지 예측"""
system_prompt = """ perp 시장에서의 예상 슬리피지를 basis points로 계산하세요.
입력: 현재 주문서 상태, 주문 크기, 매수/매도 방향
출력 형식: {"expected_slippage_bps": float, "confidence": float, "reasoning": str}"""
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"orderbook={json.dumps(orderbook)}, size={trade_size}, side={side}"}
]
result = await self.chat_completion(
model="deepseek-v3.2",
messages=messages,
max_tokens=256,
temperature=0.0
)
return json.loads(result)2단계: Tardis Kraken Perp Tick 데이터 수신
# tardis_consumer.py
import asyncio
import json
from dataclasses import dataclass, field
from typing import Optional
from tardis_client import TardisClient, TardisReplay, Channel
from tardis_client.exceptions import TardisError
@dataclass
class PerpTick:
"""Perpetual tick 데이터"""
timestamp: int
symbol: str
side: str # buy/sell
price: float
size: float
order_id: str
@dataclass
class OrderBookSnapshot:
"""주문서 스냅샷"""
symbol: str
timestamp: int
bids: list[tuple[float, float]] = field(default_factory=list) # (price, size)
asks: list[tuple[float, float]] = field(default_factory=list)
last_trade_id: int = 0
class TardisKrakenPerpConsumer:
"""Tardis Kraken Perp WebSocket consumer"""
def __init__(self, ws_url: str, symbols: list[str]):
self.ws_url = ws_url
self.symbols = symbols
self.orderbooks: dict[str, OrderBookSnapshot] = {}
self.tick_buffer: asyncio.Queue[PerpTick] = asyncio.Queue(maxsize=10000)
self._running = False
async def start_live(self):
"""라이브 모드: 실시간 데이터 수신"""
self._running = True
client = TardisClient(url=self.ws_url)
channels = [
Channel(name=symbol, types=["trade", "book_snapshot"])
for symbol in self.symbols
]
await client.subscribe(channels=channels)
print(f"[Tardis] Connected to {self.ws_url}")
print(f"[Tardis] Subscribed to {len(channels)} channels")
try:
async for ts, channel, message in client.get_messages():
await self._process_message(channel.name, message)
except TardisError as e:
print(f"[Tardis] Error: {e}")
await self._reconnect()
async def start_replay(
self,
from_timestamp: int,
to_timestamp: int
):
"""리플레이 모드: 과거 데이터 백테스트"""
self._running = True
replay = TardisReplay(url=self.ws_url)
await replay.subscribe(
channels=[
Channel(name=symbol, types=["trade", "book_snapshot"])
for symbol in self.symbols
],
from_timestamp=from_timestamp,
to_timestamp=to_timestamp
)
async for ts, channel, message in replay.get_messages():
await self._process_message(channel.name, message)
async def _process_message(self, symbol: str, message: dict):
"""메시지 처리"""
msg_type = message.get("type")
if msg_type == "book_snapshot":
self.orderbooks[symbol] = OrderBookSnapshot(
symbol=symbol,
timestamp=message["timestamp"],
bids=[(b["price"], b["size"]) for b in message["bids"][:20]],
asks=[(a["price"], a["size"]) for a in message["asks"][:20]]
)
elif msg_type == "trade":
tick = PerpTick(
timestamp=message["timestamp"],
symbol=symbol,
side=message["side"],
price=message["price"],
size=message["size"],
order_id=message.get("orderId", "")
)
await self.tick_buffer.put(tick)
async def _reconnect(self):
"""자동 재연결 (지수 백오프)"""
delay = 1
max_delay = 60
while self._running:
print(f"[Tardis] Reconnecting in {delay}s...")
await asyncio.sleep(delay)
try:
await self.start_live()
except Exception as e:
print(f"[Tardis] Reconnect failed: {e}")
delay = min(delay * 2, max_delay)
async def get_orderbook(self, symbol: str) -> Optional[OrderBookSnapshot]:
"""현재 주문서 조회"""
return self.orderbooks.get(symbol)
사용 예제
async def main():
consumer = TardisKrakenPerpConsumer(
ws_url="wss://tardis-devnet.tardis.dev/v1/stream",
symbols=["XBT/USD", "ETH/USD"]
)
# 실시간 데이터 처리 태스크
async def process_ticks():
while True:
tick = await consumer.tick_buffer.get()
# 슬리피지 계산, 전략 실행 등
print(f"[Tick] {tick.symbol}: {tick.side} {tick.size} @ {tick.price}")
# 동시 실행
await asyncio.gather(
consumer.start_live(),
process_ticks()
)
if __name__ == "__main__":
asyncio.run(main())3단계: 주문서 마이크로스트럭처 분석
마켓메이킹에서 핵심은 주문서의 미세한 변화를 포착하여 최적 스프레드와 크기를 결정하는 것입니다. HolySheep AI를 활용하면 규칙 기반 분석보다 유연하고 상황 인식적인 결정을 내릴 수 있습니다.
# orderbook_analyzer.py
import numpy as np
from dataclasses import dataclass
from typing import Optional
import asyncio
@dataclass
class MicrostructureMetrics:
"""마이크로스트럭처 메트릭스"""
spread_bps: float
mid_price: float
bid_depth_10: float # 상위 10단계 매수 총량
ask_depth_10: float # 상위 10단계 매도 총량
imbalance_ratio: float # >1이면 매수 과점, <1이면 매도 과점
vwap_spread: float # VWAP 기반 스프레드
liquidity_score: float # 0-100
class OrderBookAnalyzer:
"""주문서 마이크로스트럭처 분석기"""
def __init__(self, holysheep_client, lookback_trades: int = 100):
self.client = holysheep_client
self.lookback_trades = lookback_trades
self.trade_history: dict[str, list] = {}
def compute_metrics(
self,
orderbook: OrderBookSnapshot
) -> MicrostructureMetrics:
"""기본 메트릭스 계산"""
bids = orderbook.bids[:10]
asks = orderbook.asks[:10]
if not bids or not asks:
return None
best_bid, best_bid_size = bids[0]
best_ask, best_ask_size = asks[0]
mid_price = (best_bid + best_ask) / 2
spread = best_ask - best_bid
spread_bps = (spread / mid_price) * 10000
bid_depth = sum(size for _, size in bids)
ask_depth = sum(size for _, size in asks)
imbalance_ratio = bid_depth / ask_depth if ask_depth > 0 else 1.0
# VWAP 스프레드 계산
vwap_bid = sum(price * size for price, size in bids) / bid_depth if bid_depth > 0 else best_bid
vwap_ask = sum(price * size for price, size in asks) / ask_depth if ask_depth > 0 else best_ask
vwap_spread = (vwap_ask - vwap_bid) / mid_price * 10000
# 유동성 점수 (단순화)
depth_score = min(bid_depth / 1000000, 1.0) * 50
spread_score = max(0, 50 - spread_bps * 10)
liquidity_score = depth_score + spread_score
return MicrostructureMetrics(
spread_bps=spread_bps,
mid_price=mid_price,
bid_depth_10=bid_depth,
ask_depth_10=ask_depth,
imbalance_ratio=imbalance_ratio,
vwap_spread=vwap_spread,
liquidity_score=liquidity_score
)
async def get_ai_enhanced_analysis(
self,
orderbook: OrderBookSnapshot,
recent_metrics: Optional[MicrostructureMetrics] = None
) -> dict:
"""HolySheep AI 기반 강화 분석"""
bids = orderbook.bids[:20]
asks = orderbook.asks[:20]
# 기본 메트릭스 계산
metrics = self.compute_metrics(orderbook)
# HolySheep AI를 통한 고급 분석
ai_analysis = await self.client.analyze_orderbook(
bids=bids,
asks=asks,
symbol=orderbook.symbol
)
# 트렌드 분석 (과거 데이터 대비)
trend_indicators = {}
if recent_metrics:
trend_indicators = {
"spread_change_bps": metrics.spread_bps - recent_metrics.spread_bps,
"imbalance_change": metrics.imbalance_ratio - recent_metrics.imbalance_ratio,
"liquidity_trend": "improving" if metrics.liquidity_score > recent_metrics.liquidity_score else "deteriorating"
}
return {
"symbol": orderbook.symbol,
"basic_metrics": {
"spread_bps": round(metrics.spread_bps, 2),
"mid_price": round(metrics.mid_price, 2),
"imbalance_ratio": round(metrics.imbalance_ratio, 3),
"liquidity_score": round(metrics.liquidity_score, 1)
},
"ai_analysis": ai_analysis,
"trend_indicators": trend_indicators,
"recommended_action": self._determine_action(metrics, ai_analysis)
}
def _determine_action(
self,
metrics: MicrostructureMetrics,
ai_analysis: dict
) -> dict:
"""권장 행동 결정"""
# AI 분석 결과 통합
ai_liquidity = ai_analysis.get("liquidity_score", 50)
ai_imbalance = ai_analysis.get("imbalance_ratio", 1.0)
# 종합 점수
combined_liquidity = (metrics.liquidity_score * 0.6 + ai_liquidity * 0.4)
# 스프레드 결정
if combined_liquidity > 80:
target_spread = metrics.spread_bps * 0.9 # 유동성 높으면 스프레드 축소
position_size = min(100000, metrics.bid_depth_10 * 0.1)
elif combined_liquidity > 50:
target_spread = metrics.spread_bps * 1.0
position_size = min(50000, metrics.bid_depth_10 * 0.05)
else:
target_spread = metrics.spread_bps * 1.3 # 유동성 낮으면 스프레드 확대
position_size = min(20000, metrics.bid_depth_10 * 0.02)
# 방향 결정
if ai_imbalance > 1.2:
action = "quote_bid_favorable" # 매수压力 강함
elif ai_imbalance < 0.8:
action = "quote_ask_favorable" # 매도压力 강함
else:
action = "quote_both_sides"
return {
"action": action,
"target_spread_bps": round(target_spread, 2),
"max_position_size": round(position_size, 2),
"confidence": min(combined_liquidity / 100, 1.0)
}4단계: 슬리피지 모델링实战
슬리피지는 마켓메이킹 수익성에 직접적인 영향을 미칩니다. 저는 두 가지 접근법을 결합하여 예측 정확도를 높이고 있습니다:
- 고전적 모델: Avellaneda-Stoikov 모델 기반 마크업 계산
- AI 모델: HolySheep AI 기반 실시간 슬리피지 예측
# slippage_model.py
import numpy as np
from typing import Optional
import math
class SlippageModel:
"""슬리피지 예측 모델"""
def __init__(
self,
holysheep_client,
gamma: float = 0.1, # risk aversion
T: float = 1.0, # time horizon (hours)
sigma: float = 0.02 # volatility
):
self.client = holysheep_client
self.gamma = gamma
self.T = T
self.sigma = sigma
self._volatility_cache: dict[str, float] = {}
def compute_avellaneda_markup(
self,
orderbook: OrderBookSnapshot,
trade_size: float,
side: str,
k: float = 0.1 # 주문서 민감도 파라미터
) -> dict:
"""Avellaneda-Stoikov 기반 마크업 계산"""
bids = orderbook.bids[:10]
asks = orderbook.asks[:10]
if not bids or not asks:
return {"markup_bps": 0, "expected_fill_price": 0}
best_bid = bids[0][0]
best_ask = asks[0][0]
mid_price = (best_bid + best_ask) / 2
# 잔량 기반 시뮬레이션
if side == "buy":
levels = asks
expected_price = self._simulate_fill_price(levels, trade_size)
else:
levels = bids
expected_price = self._simulate_fill_price(levels, trade_size)
# 슬리피지 계산
if side == "buy":
slippage = (expected_price - best_ask) / best_ask * 10000
base_price = best_ask
else:
slippage = (best_bid - expected_price) / best_bid * 10000
base_price = best_bid
# Avellaneda 마크업
q = trade_size / 10000 # 포지션 (USD 단위 정규화)
t = 0.5 # 현재 시간 (중간값)
# 최적 스프레드 공식: s* = (2*sigma / gamma) * asin(gamma * T / 2)
optimal_spread = (2 * self.sigma / self.gamma) * math.asin(self.gamma * self.T / 2)
markup = optimal_spread * 10000 # bps로 변환
#inventory risk adjustment
inventory_adjustment = self.gamma * q * self.sigma * self.T
total_markup_bps = markup - inventory_adjustment * 10000
return {
"slippage_bps": round(slippage, 2),
"markup_bps": round(max(0, total_markup_bps), 2),
"expected_fill_price": round(expected_price, 4),
"base_price": round(base_price, 4),
"inventory_risk": round(inventory_adjustment * 10000, 2)
}
def _simulate_fill_price(
self,
levels: list[tuple[float, float]],
size: float
) -> float:
"""주문서 레벨별 체결 시뮬레이션"""
remaining = size
total_cost = 0
for price, level_size in levels:
fill_size = min(remaining, level_size)
total_cost += fill_size * price
remaining -= fill_size
if remaining <= 0:
break
if remaining > 0:
# 완전히 체결되지 않으면 마지막 가격 사용
return levels[-1][0]
return total_cost / (size - remaining) if remaining < size else total_cost / size
async def predict_with_ai(
self,
orderbook: OrderBookSnapshot,
trade_size: float,
side: str
) -> dict:
"""HolySheep AI 기반 슬리피지 예측"""
# 기본 모델 결과
classical = self.compute_avellaneda_markup(orderbook, trade_size, side)
# AI 예측
ai_prediction = await self.client.predict_slippage(
orderbook={
"bids": orderbook.bids[:10],
"asks": orderbook.asks[:10],
"mid_price": (orderbook.bids[0][0] + orderbook.asks[0][0]) / 2
},
trade_size=trade_size,
side=side
)
# 앙상블: 가중 평균 (고전적 40%, AI 60%)
classical_slippage = classical["slippage_bps"]
ai_slippage = ai_prediction.get("expected_slippage_bps", classical_slippage)
confidence = ai_prediction.get("confidence", 0.5)
# 신뢰도에 따라 가중치 조절
weight = 0.4 + 0.2 * confidence
final_slippage = weight * ai_slippage + (1 - weight) * classical_slippage
return {
"predicted_slippage_bps": round(final_slippage, 2),
"classical_slippage_bps": round(classical_slippage, 2),
"ai_slippage_bps": round(ai_slippage, 2),
"ai_confidence": confidence,
"ensemble_weight": round(weight, 2),
"recommended_markup_bps": round(classical["markup_bps"] + final_slippage, 2),
"reasoning": ai_prediction.get("reasoning", "")
}
def estimate_execution_probability(
self,
orderbook: OrderBookSnapshot,
price: float,
side: str
) -> float:
"""주문 체결 확률 추정"""
levels = orderbook.bids if side == "sell" else orderbook.asks
if not levels:
return 0.0
# price 이상(또는 이하)의 레벨 총량
cumulative_size = 0
for level_price, level_size in levels:
if side == "buy" and level_price <= price:
cumulative_size += level_size
elif side == "sell" and level_price >= price:
cumulative_size += level_size
# Market depth ratio (초과 시그니피던스)
total_depth = sum(size for _, size in levels)
return min(cumulative_size / total_depth, 1.0) if total_depth > 0 else 0.0
종합 마켓메이킹 엔진
class MarketMakingEngine:
"""마켓메이킹 실행 엔진"""
def __init__(
self,
holysheep_client,
slippage_model: SlippageModel,
analyzer: OrderBookAnalyzer
):
self.client = holysheep_client
self.slippage_model = slippage_model
self.analyzer = analyzer
self.position = 0.0
async def generate_quotes(
self,
symbol: str,
orderbook: OrderBookSnapshot
) -> dict:
"""호가 생성"""
# 1. AI 분석
analysis = await self.analyzer.get_ai_enhanced_analysis(orderbook)
# 2. 슬리피지 예측 (양방향)
buy_size = analysis["recommended_action"]["max_position_size"] * 0.5
sell_size = analysis["recommended_action"]["max_position_size"] * 0.5
buy_prediction = await self.slippage_model.predict_with_ai(
orderbook, buy_size, "buy"
)
sell_prediction = await self.slippage_model.predict_with_ai(
orderbook, sell_size, "sell"
)
# 3. 호가 가격 결정
mid_price = (orderbook.bids[0][0] + orderbook.asks[0][0]) / 2
spread = analysis["recommended_action"]["target_spread_bps"] / 10000 * mid_price
bid_price = mid_price - spread / 2 - buy_prediction["recommended_markup_bps"] / 10000 * mid_price
ask_price = mid_price + spread / 2 + sell_prediction["recommended_markup_bps"] / 10000 * mid_price
return {
"symbol": symbol,
"bid_price": round(bid_price, 4),
"bid_size": round(buy_size, 2),
"ask_price": round(ask_price, 4),
"ask_size": round(sell_size, 2),
"expected_slippage_bps": {
"buy": buy_prediction["predicted_slippage_bps"],
"sell": sell_prediction["predicted_slippage_bps"]
},
"confidence": analysis["recommended_action"]["confidence"],
"timestamp": orderbook.timestamp
}5단계: 벤치마크 및 성능 측정
# benchmark.py
import asyncio
import time
import statistics
from dataclasses import dataclass
@dataclass
class BenchmarkResult:
"""벤치마크 결과"""
operation: str
avg_latency_ms: float
p50_ms: float
p95_ms: float
p99_ms: float
throughput_rps: float
error_rate: float
async def benchmark_slippage_model():
"""슬리피지 모델 벤치마크"""
# 테스트 설정
from tardis_consumer import OrderBookSnapshot
from slippage_model import SlippageModel
from holy Sheep_client import HolySheepClient
client = HolySheepClient(api_key="YOUR_HOLYSHEEP_API_KEY")
model = SlippageModel(client)
# 테스트 데이터 생성
test_orderbook = OrderBookSnapshot(
symbol="XBT/USD",
timestamp=int(time.time() * 1000),
bids=[(100000 - i * 10, 1000 + i * 100) for i in range(10)],
asks=[(100000 + i * 10, 1000 + i * 100) for i in range(10)]
)
latencies = []
errors = 0
iterations = 100
start_time = time.time()
for _ in range(iterations):
try:
iter_start = time.time()
result = await model.predict_with_ai(
test_orderbook,
trade_size=50000,
side="buy"
)
latencies.append((time.time() - iter_start) * 1000)
except Exception as e:
errors += 1
print(f"Error: {e}")
total_time = time.time() - start_time
latencies.sort()
n = len(latencies)
return BenchmarkResult(
operation="Slippage Prediction (AI)",
avg_latency_ms=statistics.mean(latencies),
p50_ms=latencies[n // 2],
p95_ms=latencies[int(n * 0.95)],
p99_ms=latencies[int(n * 0.99)],
throughput_rps=iterations / total_time,
error_rate=errors / iterations * 100
)
async def benchmark_ai_analysis():
"""AI 분석 벤치마크"""
from holy Sheep_client import HolySheepClient
from orderbook_analyzer import OrderBookAnalyzer
from tardis_consumer import OrderBookSnapshot
client = HolySheepClient(api_key="YOUR_HOLYSHEEP_API_KEY")
analyzer = OrderBookAnalyzer(client)
test_orderbook = OrderBookSnapshot(
symbol="ETH/USD",
timestamp=int(time.time() * 1000),
bids=[(3000 - i * 2, 5000 + i * 50) for i in range(20)],
asks=[(3000 + i * 2, 5000 + i * 50) for i in range(20)]
)
latencies = []
iterations = 50
for _ in range(iterations):
start = time.time()
result = await analyzer.get_ai_enhanced_analysis(test_orderbook)
latencies.append((time.time() - start) * 1000)
latencies.sort()
n = len(latencies)
return BenchmarkResult(
operation="OrderBook AI Analysis",
avg_latency_ms=statistics.mean(latencies),
p50_ms=latencies[n // 2],
p95_ms=latencies[int(n * 0.95)],
p99_ms=latencies[int(n * 0.99)],
throughput_rps=iterations / sum(latencies) * 1000,
error_rate=0.0
)
async def main():
print("=" * 60)
print("HolySheep AI 마켓메이킹 벤치마크")
print("=" * 60)
# 슬리피지 모델 벤치마크
print("\n[1/2] Sl ippage Model Benchmark...")
slippage_result = await benchmark_slippage_model()
print(f"\n슬리피지 예측 결과:")
print(f" 평균 지연: {slippage_result.avg_latency_ms:.2f} ms")
print(f" P50 지연: {slippage_result.p50_ms:.2f} ms")
print(f" P95 지연: {slippage_result.p95_ms:.2f} ms")
print(f" P99 지연: {slippage_result.p99_ms:.2f} ms")
print(f" 처리량: {slippage_result.throughput_rps:.2f} req/s")
print(f" 에러율: {slippage_result.error_rate:.2f}%")
# AI 분석 벤치마크
print("\n[2/2] AI Analysis Benchmark...")
analysis_result = await benchmark_ai_analysis()
print(f"\nAI 분석 결과:")
print(f" 평균 지연: {analysis_result.avg_latency_ms:.2f} ms")
print(f" P50 지연: {analysis_result.p50_ms:.2f} ms")
print(f" P95 지연: {analysis_result.p95_ms:.2f} ms")
# 비용 추정
print("\n" + "=" * 60)
print("비용 추정 (1일 operations)")
print("=" * 60)
daily_predictions = 86400 / (slippage_result.avg_latency_ms / 1000)
daily_analyses = 86400 / (analysis_result.avg_latency_ms / 1000)
# DeepSeek V3.2: $0.