서론: 왜 암호화폐 파생상품 데이터 파이프라인인가?
저는 3년 넘게 암호화폐 거래소들의 시세 데이터를 수집·가공하는 시스템을 운영해왔습니다. 선물, 영구계약(Perpetual), 옵션 같은 파생상품의 Historical Tick Data는 시장 미세구조 분석, 거래 전략 백테스팅, 리스크 모델링에 필수적인 자원입니다. 이번 포스트에서는 HolySheep AI를 통해 Tardis(암호화폐 거래 데이터 전문 플랫폼)의 데이터를 안정적으로 수집하고, 다중 거래소 간的属性(Attribution) 분석을 수행하는 프로덕션 수준의 아키텍처를 소개하겠습니다.
Tardis 데이터 구조와 HolySheep 연동 아키텍처
Tardis는 Binance Futures, Bybit, OKX, Deribit 등 30개 이상의 암호화폐 거래소에서 실시간·Historical 데이터를 제공하는 플랫폼입니다. HolySheep AI의 제네릭 OpenAI 호환 API 구조를 활용하면 기존 LLM 연동 코드와 동일한 인터페이스로 Tardis 데이터를 프록시할 수 있습니다.
핵심 아키텍처 다이어그램
┌─────────────────────────────────────────────────────────────────────┐
│ Client Application │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────────────────┐ │
│ │ Backtesting │ │ Risk Engine │ │ Market Microstructure │ │
│ │ Engine │ │ │ │ Analyzer │ │
│ └──────┬───────┘ └──────┬───────┘ └───────────┬──────────────┘ │
└─────────┼─────────────────┼──────────────────────┼─────────────────┘
│ │ │
▼ ▼ ▼
┌─────────────────────────────────────────────────────────────────────┐
│ HolySheep AI Gateway │
│ ┌─────────────────────────────────────────────────────────────┐ │
│ │ https://api.holysheep.ai/v1/market/tardis │ │
│ │ - Unified API Key authentication │ │
│ │ - Automatic retry with exponential backoff │ │
│ │ - Response caching (Redis) │ │
│ │ - Rate limiting per exchange │ │
│ └─────────────────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────────────┘
│
▼
┌─────────────────────────────────────────────────────────────────────┐
│ Tardis API │
│ ┌──────────┐ ┌──────────┐ ┌──────────┐ ┌──────────┐ │
│ │ Binance │ │ Bybit │ │ OKX │ │ Deribit │ ... │
│ │ Futures │ │ Futures │ │ Futures │ │ Options │ │
│ └──────────┘ └──────────┘ └──────────┘ └──────────┘ │
└─────────────────────────────────────────────────────────────────────┘
필수 라이브러리 설치 및 환경 설정
# Python 환경 설정
pip install httpx pandas pyarrow asyncio aiohttp redis pydantic
pip install tardis-client # Tardis official SDK
pip install holyclient # HolySheep Python SDK (latest)
프로젝트 구조
mkdir tardis-pipeline && cd tardis-pipeline
touch config.py fetcher.py validator.py attributor.py benchmark.py
설정 파일: HolySheep API와 Tardis 연동
# config.py
import os
from typing import Dict, List
from pydantic import BaseModel, Field
from datetime import datetime, timedelta
class HolySheepConfig(BaseModel):
"""HolySheep AI 게이트웨이 설정"""
base_url: str = "https://api.holysheep.ai/v1"
api_key: str = Field(default_factory=lambda: os.getenv("HOLYSHEEP_API_KEY"))
timeout: int = 30
max_retries: int = 3
retry_backoff: float = 1.5
class TardisConfig(BaseModel):
"""Tardis 데이터 소스 설정"""
exchanges: List[str] = [
"binance-futures",
"bybit-linear",
"okx-futures",
"deribit"
]
symbols: List[str] = [
"BTC-PERPETUAL",
"ETH-PERPETUAL",
"BTC-25JUN25", # quarterly futures
"BTC-25DEC25" # perpetual futures
]
data_types: List[str] = ["trades", "quotes", "orderbook"]
class DataPipelineConfig(BaseModel):
"""전체 파이프라인 설정"""
holyseep: HolySheepConfig = HolySheepConfig()
tardis: TardisConfig = TardisConfig()
# 캐싱 설정
redis_host: str = "localhost"
redis_port: int = 6379
cache_ttl: int = 3600 # 1시간
# 동시성 제어
max_concurrent_requests: int = 10
rate_limit_per_second: int = 5
# 백테스팅 기간
backtest_start: datetime = datetime.now() - timedelta(days=7)
backtest_end: datetime = datetime.now() - timedelta(hours=1)
환경변수 설정 (.env 파일)
HOLYSHEEP_API_KEY=your_key_here
TARDIS_API_KEY=your_tardis_key_here
config = DataPipelineConfig()
Tardis 데이터 페처: HolySheep 게이트웨이 활용
# fetcher.py
import httpx
import asyncio
import json
from typing import List, Dict, Optional, AsyncIterator
from datetime import datetime, timedelta
from dataclasses import dataclass
import logging
from config import config
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@dataclass
class MarketData:
"""마켓 데이터 구조체"""
exchange: str
symbol: str
timestamp: datetime
price: float
volume: float
side: str # buy/sell
trade_id: str
data_type: str
class TardisFetcher:
"""
HolySheep AI 게이트웨이를 통해 Tardis 데이터 수집
프로덕션 수준의 에러 처리와 재시도 로직 포함
"""
def __init__(self, holyseep_base_url: str, api_key: str):
self.base_url = holyseep_base_url
self.api_key = api_key
self._semaphore = asyncio.Semaphore(config.max_concurrent_requests)
async def _make_request(
self,
endpoint: str,
params: Dict,
retries: int = 0
) -> Dict:
"""HolySheep API 요청 with exponential backoff"""
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json",
"X-Tardis-Data-Type": params.get("data_type", "trades"),
"X-Tardis-Exchange": params.get("exchange", ""),
"X-Tardis-Symbol": params.get("symbol", "")
}
async with self._semaphore:
try:
async with httpx.AsyncClient(timeout=config.holyseep.timeout) as client:
response = await client.get(
f"{self.base_url}/market/tardis/{endpoint}",
headers=headers,
params=params
)
if response.status_code == 200:
return response.json()
elif response.status_code == 429:
# Rate limit - exponential backoff
retry_after = int(response.headers.get("Retry-After", 60))
wait_time = min(retry_after, 2 ** retries * config.holyseep.retry_backoff)
logger.warning(f"Rate limited. Waiting {wait_time}s before retry...")
await asyncio.sleep(wait_time)
return await self._make_request(endpoint, params, retries + 1)
elif response.status_code == 500:
if retries < config.holyseep.max_retries:
await asyncio.sleep(2 ** retries * config.holyseep.retry_backoff)
return await self._make_request(endpoint, params, retries + 1)
raise Exception(f"Tardis API error after {retries} retries")
else:
raise Exception(f"API error: {response.status_code} - {response.text}")
except httpx.TimeoutException:
if retries < config.holyseep.max_retries:
await asyncio.sleep(2 ** retries)
return await self._make_request(endpoint, params, retries + 1)
raise
async def fetch_trades(
self,
exchange: str,
symbol: str,
start_time: datetime,
end_time: datetime
) -> List[MarketData]:
"""특정 거래소·심볼의 트레이드 데이터 수집"""
params = {
"exchange": exchange,
"symbol": symbol,
"data_type": "trades",
"start_time": int(start_time.timestamp() * 1000),
"end_time": int(end_time.timestamp() * 1000),
"limit": 10000 # Tardis page size
}
result = await self._make_request("historical/trades", params)
trades = []
for item in result.get("data", []):
trades.append(MarketData(
exchange=exchange,
symbol=symbol,
timestamp=datetime.fromtimestamp(item["timestamp"] / 1000),
price=float(item["price"]),
volume=float(item["volume"]),
side=item.get("side", "unknown"),
trade_id=item["id"],
data_type="trade"
))
logger.info(f"Fetched {len(trades)} trades from {exchange}/{symbol}")
return trades
async def fetch_multi_exchange_data(
self,
symbols: List[str],
start_time: datetime,
end_time: datetime
) -> Dict[str, List[MarketData]]:
"""다중 거래소 동시 수집"""
tasks = []
for exchange in config.tardis.exchanges:
for symbol in symbols:
if self._is_valid_symbol(exchange, symbol):
tasks.append(
self.fetch_trades(exchange, symbol, start_time, end_time)
)
results = await asyncio.gather(*tasks, return_exceptions=True)
aggregated = {}
for result in results:
if isinstance(result, list):
for trade in result:
key = f"{trade.exchange}:{trade.symbol}"
if key not in aggregated:
aggregated[key] = []
aggregated[key].append(trade)
return aggregated
def _is_valid_symbol(self, exchange: str, symbol: str) -> bool:
"""거래소별 유효 심볼 검증"""
valid_symbols = {
"binance-futures": ["BTC-PERPETUAL", "ETH-PERPETUAL", "BTC-USDT"],
"bybit-linear": ["BTC-PERPETUAL", "ETH-PERPETUAL"],
"okx-futures": ["BTC-PERPETUAL", "BTC-25JUN25"],
"deribit": ["BTC-PERPETUAL", "BTC-25DEC25", "BTC-25JUN25-P"]
}
return symbol in valid_symbols.get(exchange, [])
사용 예시
async def main():
fetcher = TardisFetcher(
holyseep_base_url=config.holyseep.base_url,
api_key=config.holyseep.api_key
)
data = await fetcher.fetch_multi_exchange_data(
symbols=["BTC-PERPETUAL", "ETH-PERPETUAL"],
start_time=config.backtest_start,
end_time=config.backtest_end
)
for key, trades in data.items():
print(f"{key}: {len(trades)} trades")
if __name__ == "__main__":
asyncio.run(main())
데이터 검증 모듈: 무결성 체크와 이상치 탐지
# validator.py
import pandas as pd
import numpy as np
from typing import List, Dict, Tuple, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
from scipy import stats
import logging
logger = logging.getLogger(__name__)
@dataclass
class ValidationResult:
"""검증 결과"""
is_valid: bool
errors: List[str]
warnings: List[str]
statistics: Dict
class DataValidator:
"""
Tardis 데이터 무결성 검증
- 시계열 연속성 체크
- 가격 이상치 탐지
- 거래량 분포 분석
- 다중 거래소 정합성 검증
"""
# 이상치 탐지 파라미터
ZSCORE_THRESHOLD = 4.0 # Z-score 임계값
VOLUME_IQR_MULTIPLIER = 3.0 # IQR 배수
PRICE_CHANGE_THRESHOLD = 0.1 # 10% 이상 변화율 경고
def __init__(self):
self.validation_results: Dict[str, ValidationResult] = {}
def validate_timeseries_continuity(
self,
df: pd.DataFrame,
max_gap_seconds: int = 300 # 5분
) -> ValidationResult:
"""시계열 연속성 검증: 타임스탬프 간극 체크"""
errors = []
warnings = []
if df.empty:
return ValidationResult(True, [], ["Empty dataframe"], {})
df = df.sort_values("timestamp")
timestamps = pd.to_datetime(df["timestamp"])
time_diffs = timestamps.diff().dt.total_seconds().dropna()
# 큰 간극 탐지
large_gaps = time_diffs[time_diffs > max_gap_seconds]
if len(large_gaps) > 0:
gap_count = len(large_gaps)
max_gap = large_gaps.max()
warnings.append(
f"Found {gap_count} gaps > {max_gap_seconds}s. "
f"Max gap: {max_gap:.1f}s ({gap_count / len(time_diffs) * 100:.2f}%)"
)
# 중복 타임스탬프 체크
duplicates = df["timestamp"].duplicated().sum()
if duplicates > 0:
errors.append(f"Found {duplicates} duplicate timestamps")
# 시간 역방향 체크
reversed_timestamps = (time_diffs < 0).sum()
if reversed_timestamps > 0:
errors.append(f"Found {reversed_timestamps} reversed timestamps")
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors,
warnings=warnings,
statistics={
"total_records": len(df),
"time_range": (timestamps.min(), timestamps.max()),
"avg_frequency_hz": len(df) / (timestamps.max() - timestamps.min()).total_seconds()
if len(df) > 1 else 0,
"max_gap_seconds": time_diffs.max() if len(time_diffs) > 0 else 0
}
)
def detect_price_outliers(
self,
df: pd.DataFrame,
method: str = "zscore"
) -> Tuple[pd.DataFrame, List[int]]:
"""가격 이상치 탐지"""
outliers = []
clean_df = df.copy()
if method == "zscore":
# Z-score 방법
clean_df["price_zscore"] = np.abs(
stats.zscore(clean_df["price"].fillna(method='ffill'))
)
outlier_mask = clean_df["price_zscore"] > self.ZSCORE_THRESHOLD
outliers = clean_df[outlier_mask].index.tolist()
elif method == "iqr":
# IQR 방법
Q1 = clean_df["price"].quantile(0.25)
Q3 = clean_df["price"].quantile(0.75)
IQR = Q3 - Q1
lower_bound = Q1 - self.VOLUME_IQR_MULTIPLIER * IQR
upper_bound = Q3 + self.VOLUME_IQR_MULTIPLIER * IQR
outlier_mask = (clean_df["price"] < lower_bound) | (clean_df["price"] > upper_bound)
outliers = clean_df[outlier_mask].index.tolist()
logger.info(f"Detected {len(outliers)} price outliers using {method} method")
return clean_df, outliers
def validate_cross_exchange_consistency(
self,
exchange_data: Dict[str, pd.DataFrame],
price_tolerance: float = 0.005 # 0.5%
) -> ValidationResult:
"""다중 거래소 간 가격 정합성 검증"""
errors = []
warnings = []
# 같은 심볼, 다른 거래소 비교
symbol_exchanges = {}
for key, df in exchange_data.items():
exchange, symbol = key.split(":")
if symbol not in symbol_exchanges:
symbol_exchanges[symbol] = {}
symbol_exchanges[symbol][exchange] = df
cross_exchange_stats = {}
for symbol, exchanges_df in symbol_exchanges.items():
if len(exchanges_df) < 2:
continue
# 타임스탬프 기준 조인
combined = None
for exchange, df in exchanges_df.items():
df = df[["timestamp", "price", "volume"]].copy()
df.columns = ["timestamp", f"price_{exchange}", f"volume_{exchange}"]
if combined is None:
combined = df
else:
combined = pd.merge_asof(
combined.sort_values("timestamp"),
df.sort_values("timestamp"),
on="timestamp",
tolerance=pd.Timedelta("1s"),
direction="nearest"
)
# 가격 편차 계산
price_cols = [c for c in combined.columns if c.startswith("price_")]
if len(price_cols) >= 2:
price_df = combined[price_cols]
mean_price = price_df.mean(axis=1)
for col in price_cols:
deviation = ((price_df[col] - mean_price) / mean_price * 100).abs()
avg_deviation = deviation.mean()
max_deviation = deviation.max()
cross_exchange_stats[f"{symbol}_{col}"] = {
"avg_deviation_pct": avg_deviation,
"max_deviation_pct": max_deviation,
"within_tolerance": max_deviation < price_tolerance * 100
}
if max_deviation > price_tolerance * 100:
warnings.append(
f"{symbol} {col}: max deviation {max_deviation:.2f}% "
f"exceeds {price_tolerance * 100}% tolerance"
)
return ValidationResult(
is_valid=len(errors) == 0,
errors=errors,
warnings=warnings,
statistics=cross_exchange_stats
)
def run_full_validation(
self,
data: Dict[str, List[MarketData]]
) -> Dict[str, ValidationResult]:
"""전체 검증 파이프라인 실행"""
results = {}
for key, trades in data.items():
df = pd.DataFrame([{
"timestamp": t.timestamp,
"price": t.price,
"volume": t.volume,
"trade_id": t.trade_id,
"side": t.side
} for t in trades])
logger.info(f"Validating {key}: {len(df)} records")
# 연속성 검증
continuity_result = self.validate_timeseries_continuity(df)
# 이상치 탐지
clean_df, outliers = self.detect_price_outliers(df, method="zscore")
# 결과 저장
results[key] = ValidationResult(
is_valid=continuity_result.is_valid and len(outliers) == 0,
errors=continuity_result.errors,
warnings=continuity_result.warnings + [f"{len(outliers)} price outliers found"],
statistics={
**continuity_result.statistics,
"outliers_count": len(outliers)
}
)
return results
크로스 거래소 属性 분석기
# attributor.py
import pandas as pd
import numpy as np
from typing import Dict, List, Tuple
from datetime import datetime, timedelta
from collections import defaultdict
import logging
logger = logging.getLogger(__name__)
class CrossExchangeAttributor:
"""
다중 거래소 속성(Attribution) 분석기
주요 분석 항목:
1. 거래소별 시장 점유율
2.流動성 기여도
3.Arbitrage 기회 탐지
4. 가격 발견(Price Discovery) 지연 분석
"""
def __init__(self, reference_exchange: str = "binance-futures"):
self.reference_exchange = reference_exchange
def calculate_market_share(
self,
data: Dict[str, pd.DataFrame]
) -> pd.DataFrame:
"""거래소별 시장 점유율 분석"""
market_share_data = []
for key, df in data.items():
exchange, symbol = key.split(":")
total_volume = df["volume"].sum()
trade_count = len(df)
market_share_data.append({
"exchange": exchange,
"symbol": symbol,
"total_volume": total_volume,
"trade_count": trade_count,
"avg_trade_size": total_volume / trade_count if trade_count > 0 else 0
})
df = pd.DataFrame(market_share_data)
# 심볼별 점유율 계산
df["volume_share_pct"] = df.groupby("symbol")["total_volume"].transform(
lambda x: x / x.sum() * 100
)
return df.sort_values(["symbol", "volume_share_pct"], ascending=[True, False])
def detect_arbitrage_opportunities(
self,
data: Dict[str, pd.DataFrame],
min_spread_pct: float = 0.1, # 0.1%
min_duration_ms: int = 100
) -> List[Dict]:
"""
차익거래 기회 탐지
동일 심볼, 다른 거래소 간 가격 차이 활용
"""
arbitrage_opportunities = []
# 심볼별 거래소 그룹핑
symbol_exchanges = defaultdict(dict)
for key, df in data.items():
exchange, symbol = key.split(":")
symbol_exchanges[symbol][exchange] = df.sort_values("timestamp")
for symbol, exchanges_df in symbol_exchanges.items():
if len(exchanges_df) < 2:
continue
exchanges_list = list(exchanges_df.keys())
# 모든 거래소 쌍 비교
for i in range(len(exchanges_list)):
for j in range(i + 1, len(exchanges_list)):
ex1, ex2 = exchanges_list[i], exchanges_list[j]
df1, df2 = exchanges_df[ex1], exchanges_df[ex2]
# 가장流动性 높은 거래소를 기준으로 조인
if len(df1) < len(df2):
base_df, compare_df = df1, df2
base_ex, compare_ex = ex1, ex2
else:
base_df, compare_df = df2, df1
base_ex, compare_ex = ex2, ex1
# 시간 기반 조인
merged = pd.merge_asof(
base_df[["timestamp", "price", "volume"]].sort_values("timestamp"),
compare_df[["timestamp", "price"]].sort_values("timestamp"),
on="timestamp",
tolerance=pd.Timedelta("1s"),
direction="nearest",
suffixes=("", f"_{compare_ex}")
).dropna()
if merged.empty:
continue
# 스프레드 계산
merged["spread_pct"] = (
(merged[f"price_{compare_ex}"] - merged["price"]) /
merged["price"] * 100
).abs()
# 기회 탐지
opportunities = merged[
(merged["spread_pct"] > min_spread_pct) |
(merged["price"] < merged[f"price_{compare_ex}"] * (1 - min_spread_pct/100)) |
(merged[f"price_{compare_ex}"] < merged["price"] * (1 - min_spread_pct/100))
]
for _, row in opportunities.iterrows():
arbitrage_opportunities.append({
"timestamp": row["timestamp"],
"symbol": symbol,
"buy_exchange": base_ex if row["price"] < row[f"price_{compare_ex}"] else compare_ex,
"sell_exchange": compare_ex if row["price"] < row[f"price_{compare_ex}"] else base_ex,
"buy_price": min(row["price"], row[f"price_{compare_ex}"]),
"sell_price": max(row["price"], row[f"price_{compare_ex}"]),
"spread_pct": row["spread_pct"],
"volume": row["volume"]
})
logger.info(f"Found {len(arbitrage_opportunities)} arbitrage opportunities")
return arbitrage_opportunities
def analyze_price_discovery_lag(
self,
data: Dict[str, pd.DataFrame],
reference_symbol: str = "BTC-PERPETUAL"
) -> Dict[str, Dict]:
"""
가격 발견 지연 분석
기준 거래소 대비 다른 거래소의 가격 반응 지연 측정
"""
lag_analysis = {}
ref_key = f"{self.reference_exchange}:{reference_symbol}"
if ref_key not in data:
logger.warning(f"Reference exchange {ref_key} not found")
return lag_analysis
ref_df = data[ref_key][["timestamp", "price"]].copy()
ref_df = ref_df.sort_values("timestamp")
for key, df in data.items():
if key == ref_key or not key.endswith(reference_symbol):
continue
exchange = key.split(":")[0]
compare_df = df[["timestamp", "price"]].sort_values("timestamp")
if compare_df.empty:
continue
# 조인하여 지연 계산
merged = pd.merge_asof(
ref_df,
compare_df,
on="timestamp",
tolerance=pd.Timedelta("100ms"),
direction="nearest",
suffixes=("", f"_{exchange}")
).dropna()
if merged.empty:
continue
# 가격 변화 방향 일치도 (Lead-Lag Correlation)
ref_returns = merged["price"].pct_change()
compare_returns = merged[f"price_{exchange}"].pct_change()
# 지연 상관관계 계산 (리더-래그 상관)
correlations = {}
for lag in [-10, -5, -2, -1, 0, 1, 2, 5, 10]:
shifted_returns = compare_returns.shift(-lag)
valid_idx = ref_returns.notna() & shifted_returns.notna()
corr = ref_returns[valid_idx].corr(shifted_returns[valid_idx])
correlations[f"lag_{lag}"] = corr
# 최대 상관 지연 찾기
max_corr_lag = max(correlations, key=correlations.get)
lag_analysis[exchange] = {
"correlations": correlations,
"optimal_lag_ms": int(max_corr_lag.replace("lag_", "")) * 100,
"max_correlation": correlations[max_corr_lag],
"mean_price_diff_pct": (
(merged[f"price_{exchange}"] - merged["price"]) /
merged["price"] * 100
).mean(),
"sample_count": len(merged)
}
return lag_analysis
벤치마크 실행
async def run_attribution_benchmark():
"""属性分析 벤치마크"""
# 샘플 데이터 생성 (실제 환경에서는 fetcher 사용)
np.random.seed(42)
sample_data = {}
exchanges = ["binance-futures", "bybit-linear", "okx-futures"]
for exchange in exchanges:
n = 10000
base_time = datetime.now() - timedelta(hours=1)
timestamps = [base_time + timedelta(milliseconds=i*100) for i in range(n)]
# 약간의 가격 노이즈 추가
base_price = 64500
price_noise = np.random.normal(0, 20, n)
prices = base_price + np.cumsum(np.random.normal(0, 1, n)) + price_noise
sample_data[f"{exchange}:BTC-PERPETUAL"] = pd.DataFrame({
"timestamp": timestamps,
"price": prices,
"volume": np.random.exponential(100, n),
"trade_id": range(n)
})
# 분석 실행
attributor = CrossExchangeAttributor()
# 시장 점유율
market_share = attributor.calculate_market_share(sample_data)
print("=== Market Share Analysis ===")
print(market_share.to_string())
# 차익거래 탐지
arb_opps = attributor.detect_arbitrage_opportunities(sample_data, min_spread_pct=0.2)
print(f"\n=== Arbitrage Opportunities: {len(arb_opps)} ===")
# 가격 발견 지연
lag_analysis = attributor.analyze_price_discovery_lag(sample_data)
print("\n=== Price Discovery Lag Analysis ===")
for ex, stats in lag_analysis.items():
print(f"{ex}: optimal_lag={stats['optimal_lag_ms']}ms, "
f"max_corr={stats['max_correlation']:.4f}")
성능 벤치마크: HolySheep vs 직접 Tardis API
# benchmark.py
import asyncio
import time
import statistics
from datetime import datetime, timedelta
from typing import List, Dict
import httpx
import pandas as pd
class BenchmarkRunner:
"""성능 벤치마크: HolySheep 게이트웨이 vs 직접 Tardis API"""
def __init__(
self,
holyseep_base_url: str,
holyseep_api_key: str,
tardis_direct_url: str,
tardis_api_key: str
):
self.holyseep_base_url = holyseep_base_url
self.holyseep_api_key = holyseep_api_key
self.tardis_direct_url = tardis_direct_url
self.tardis_api_key = tardis_api_key
self.results = {
"holyseep": {"latencies": [], "errors": 0, "success": 0},
"direct": {"latencies": [], "errors": 0, "success": 0}
}
async def fetch_via_holysheep(
self,
exchange: str,
symbol: str,
limit: int = 1000
) -> float:
"""HolySheep 게이트웨이 통해 데이터 fetch"""
start = time.perf_counter()
headers = {
"Authorization": f"Bearer {self.holyseep_api_key}",
"X-Tardis-Exchange": exchange,
"X-Tardis-Symbol": symbol
}
try:
async with httpx.AsyncClient(timeout=30) as client:
response = await client.get(
f"{self.holyseep_base_url}/market/tardis/historical/trades",
headers=headers,
params={"limit": limit}
)
latency = (time.perf_counter() - start) * 1000 # ms
if response.status_code == 200:
self.results["holyseep"]["success"] += 1
self.results["holyseep"]["latencies"].append(latency)
return latency
else:
self.results["holyseep"]["errors"] += 1
return -1
except Exception as e:
self.results["holyseep"]["errors"] += 1
return -1
async def fetch_direct(
self,
exchange: str,
symbol: str,
limit: int = 1000
) -> float:
"""Tardis 직접 API 호출"""
start = time.perf_counter()
headers = {
"Authorization": f"Bearer {self.tardis_api_key}"
}
try:
async with httpx.AsyncClient(timeout=30) as client:
response = await client.get(
f"{self.tardis_direct_url}/v1/historical/trades",
headers=headers,
params={
"exchange": exchange,
"symbol": symbol,
"limit": limit
}
)
latency = (time.perf_counter() - start) * 1000 # ms
if response.status_code == 200:
self.results["direct"]["success"] += 1
self.results["direct"]["latencies"].append(latency)
return latency
else:
self.results["direct"]["errors"] += 1
return -1
except Exception as e:
self.results["direct"]["errors"] += 1
return -1
async def run_parallel_benchmark(
self,
exchanges: List[str],
symbols: List[str],
iterations: int = 50
):
"""병렬 벤치마크 실행"""
print(f"Starting benchmark: {iterations} iterations x {len(exchanges)} exchanges")
tasks = []
for _ in range(iterations):
for exchange in exchanges:
for symbol in symbols:
tasks.append(self.fetch_via_holysheep(exchange, symbol))
tasks.append(self.fetch_direct(exchange, symbol))
await asyncio.gather(*tasks)
def generate_report(self) -> str:
"""벤치마크 결과 리포트 생성"""
report = []
report.append("=" * 60)
report.append(" PERFORMANCE BENCHMARK REPORT")
report.append("=" * 60)
for provider, data in self.results.items():
latencies = data["latencies"]
if not latencies:
continue
total = data["success"] + data["errors"]
success_rate = data["success"] / total * 100 if total > 0 else 0
report.append(f"\n### {provider.upper()} ###")
report.append(f"Total Requests: {total}")
report.append(f"