저는 3년 넘게 암호화폐 알고리즘 트레이딩 시스템을 개발해 왔습니다. 오늘은 가장 많은 분이 질문하시는 주제를 다루겠습니다: 과거 시세 데이터를 어떻게 안정적으로 수집하고, 전략을 백테스트하는가?
작년 Black Friday에 저는 Binance Future의 1분봉 Historical 데이터로 며칠간 백테스트를 돌리다가, 데이터 불일치로 완전히 다른 결과가 나오는 경험을 했습니다. 특히 ConnectionError: timeout after 30s 에러가 연달아 발생하면서 학습한 것들이 있습니다.
이 튜토리얼에서는 TARDIS 데이터소스를 사용하여 과거 Tick 데이터를 안정적으로 가져오고, 이를 Python에서 리플레이하는 시스템을 구축하는 방법을 상세히 설명드리겠습니다.
TARDIS 데이터소스란?
TARDIS는 암호화폐 시세 데이터를 제공하는 전문 API 서비스입니다. Binance, Bybit, OKX 등 주요 거래소의 Historical 데이터를 제공하며, WebSocket 스트리밍과 REST API 두 가지 방식으로 접근 가능합니다.
시스템 아키텍처 개요
┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐
│ TARDIS API │────▶│ PostgreSQL │────▶│ Python Engine │
│ (Historical) │ │ (Tick Store) │ │ (Replay + BT) │
└─────────────────┘ └─────────────────┘ └─────────────────┘
│ │ │
WebSocket TimescaleDB NumPy/Pandas
REST API Partitioning Backtrader/Zipline
1단계: 환경 설정 및 의존성 설치
먼저 필요한 Python 패키지를 설치합니다. 저는 항상 가상환경을 사용합니다.
# requirements.txt
pandas>=2.0.0
numpy>=1.24.0
asyncpg>=0.28.0
psycopg2-binary>=2.9.9
sqlalchemy>=2.0.0
aiosqlite>=0.19.0
httpx>=0.25.0
asyncio>=3.4.3
python-dotenv>=1.0.0
backtrader>=1.9.78
TARDIS SDK
tardis-python>=0.4.0
Optional: HolySheep AI for signal generation
openai>=1.12.0pip install -r requirements.txt2단계: TARDIS 데이터 수집 모듈
TARDIS에서 Historical 데이터를 가져오는 핵심 모듈입니다. 실제로 제가 사용하는 코드에서 일부 수정한 버전입니다.
# tardis_collector.py
import asyncio
import httpx
import json
from datetime import datetime, timedelta
from typing import List, Dict, Optional
from dataclasses import dataclass
import pandas as pd
@dataclass
class TickData:
timestamp: int
symbol: str
side: str
price: float
quantity: float
exchange: str
class TARDISCollector:
def __init__(self, api_key: str, exchange: str = "binance-futures"):
self.api_key = api_key
self.exchange = exchange
self.base_url = f"https://api.tardis.dev/v1/{exchange}"
self.client = httpx.AsyncClient(timeout=60.0)
async def fetch_trades(
self,
symbol: str,
start_date: datetime,
end_date: datetime
) -> List[TickData]:
"""
특정 기간의 거래 데이터를 수집합니다.
TARDIS API는 1시간 단위 chunks로 데이터를 반환합니다.
"""
all_trades = []
current_start = start_date
while current_start < end_date:
current_end = min(current_start + timedelta(hours=1), end_date)
url = f"{self.base_url}/trades"
params = {
"symbol": symbol,
"from": int(current_start.timestamp() * 1000),
"to": int(current_end.timestamp() * 1000),
}
headers = {"Authorization": f"Bearer {self.api_key}"}
try:
response = await self.client.get(url, params=params, headers=headers)
response.raise_for_status()
data = response.json()
for trade in data:
all_trades.append(TickData(
timestamp=trade["timestamp"],
symbol=trade["symbol"],
side=trade.get("side", "buy"),
price=float(trade["price"]),
quantity=float(trade["quantity"]),
exchange=self.exchange
))
print(f"[{current_start}] Fetched {len(data)} trades")
except httpx.HTTPStatusError as e:
print(f"HTTP Error {e.response.status_code}: {e.response.text}")
if e.response.status_code == 429:
await asyncio.sleep(60) # Rate limit 대응
elif e.response.status_code == 401:
print("❌ API 키가 유효하지 않습니다. 확인해주세요.")
break
except httpx.TimeoutException:
print(f"⏰ Timeout at {current_start}, retrying...")
await asyncio.sleep(5)
finally:
await asyncio.sleep(0.5) # Rate limiting
current_start = current_end
return all_trades
async def stream_realtime(self, symbols: List[str]):
"""
WebSocket을 통한 실시간 Tick 스트리밍
"""
ws_url = f"wss://api.tardis.dev/v1/websocket/{self.exchange}"
async with self.client.stream("GET", ws_url) as response:
async for line in response.aiter_lines():
if line:
data = json.loads(line)
yield data
def to_dataframe(self, trades: List[TickData]) -> pd.DataFrame:
df = pd.DataFrame([
{
"timestamp": pd.to_datetime(t["timestamp"], unit="ms"),
"symbol": t.symbol,
"price": t.price,
"quantity": t.quantity,
"side": t.side,
"value": t.price * t.quantity,
}
for t in trades
])
return df.sort_values("timestamp").reset_index(drop=True)
async def main():
collector = TARDISCollector(
api_key="YOUR_TARDIS_API_KEY",
exchange="binance-futures"
)
start = datetime(2024, 11, 1, 0, 0, 0)
end = datetime(2024, 11, 1, 1, 0, 0)
trades = await collector.fetch_trades("BTCUSDT", start, end)
df = collector.to_dataframe(trades)
print(f"Total trades collected: {len(df)}")
print(df.head())
if __name__ == "__main__":
asyncio.run(main())3단계: PostgreSQL 기반 Tick 스토어
수집된 데이터를 효율적으로 저장하고 쿼리하기 위해 TimescaleDB를 사용합니다. TimescaleDB는 시계열 데이터에 최적화된 PostgreSQL 확장입니다.
# tick_store.py
import asyncpg
import asyncio
from datetime import datetime
from typing import List, Optional
from contextlib import asynccontextmanager
import pandas as pd
class TickStore:
def __init__(self, dsn: str):
self.dsn = dsn
self.pool: Optional[asyncpg.Pool] = None
async def connect(self):
self.pool = await asyncpg.create_pool(
self.dsn,
min_size=5,
max_size=20,
command_timeout=60
)
async def create_tables(self):
"""TimescaleDB hypertable 생성"""
async with self.pool.acquire() as conn:
await conn.execute("""
CREATE EXTENSION IF NOT EXISTS timescaledb CASCADE;
CREATE TABLE IF NOT EXISTS ticks (
time TIMESTAMPTZ NOT NULL,
symbol TEXT NOT NULL,
exchange TEXT NOT NULL,
price DOUBLE PRECISION NOT NULL,
quantity DOUBLE PRECISION NOT NULL,
side TEXT,
value DOUBLE PRECISION GENERATED ALWAYS AS (price * quantity) STORED
);
SELECT create_hypertable('ticks', 'time',
if_not_exists => TRUE,
migrate_data => TRUE
);
CREATE INDEX IF NOT EXISTS idx_ticks_symbol_time
ON ticks (symbol, time DESC);
""")
print("✅ Tables created successfully")
async def insert_ticks(self, df: pd.DataFrame):
"""배치로 Tick 데이터 삽입"""
records = [
(
row["timestamp"].to_pydatetime(),
row["symbol"],
row["exchange"],
row["price"],
row["quantity"],
row["side"]
)
for _, row in df.iterrows()
]
async with self.pool.acquire() as conn:
await conn.executemany("""
INSERT INTO ticks (time, symbol, exchange, price, quantity, side)
VALUES ($1, $2, $3, $4, $5, $6)
ON CONFLICT DO NOTHING
""", records)
async def query_range(
self,
symbol: str,
start: datetime,
end: datetime
) -> pd.DataFrame:
"""특정 기간의 데이터를 조회"""
async with self.pool.acquire() as conn:
rows = await conn.fetch("""
SELECT time, symbol, exchange, price, quantity, side
FROM ticks
WHERE symbol = $1 AND time >= $2 AND time < $3
ORDER BY time ASC
""", symbol, start, end)
if not rows:
return pd.DataFrame()
return pd.DataFrame(rows, columns=["timestamp", "symbol", "exchange", "price", "quantity", "side"])
async def get_ohlcv(
self,
symbol: str,
start: datetime,
end: datetime,
interval: str = "1min"
) -> pd.DataFrame:
"""OHLCV 형태로 агреги그이트"""
interval_map = {
"1min": "1 minute",
"5min": "5 minutes",
"1hour": "1 hour",
"1day": "1 day"
}
async with self.pool.acquire() as conn:
rows = await conn.fetch(f"""
SELECT
time_bucket('{interval_map[interval]}', time) AS bucket,
first(price, time) as open,
max(price) as high,
min(price) as low,
last(price, time) as close,
sum(quantity) as volume
FROM ticks
WHERE symbol = $1 AND time >= $2 AND time < $3
GROUP BY bucket
ORDER BY bucket ASC
""", symbol, start, end)
return pd.DataFrame(rows)
async def main():
store = TickStore("postgresql://user:pass@localhost:5432/crypto_ticks")
await store.connect()
await store.create_tables()
# 예시: OHLCV 조회
ohlcv = await store.get_ohlcv(
"BTCUSDT",
datetime(2024, 11, 1),
datetime(2024, 11, 2),
"5min"
)
print(ohlcv.head())
if __name__ == "__main__":
asyncio.run(main())4단계: 백테스트 엔진 구축
이제 저장된 Tick 데이터를 리플레이하며 전략을 백테스트하는 엔진을 만들겠습니다. 실제 거래 시뮬레이션처럼 각 Tick마다 전략 로직을 실행합니다.
# backtest_engine.py
import asyncio
import pandas as pd
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from typing import Dict, List, Optional, Callable, Any
from enum import Enum
import numpy as np
class OrderSide(Enum):
BUY = "buy"
SELL = "sell"
class OrderType(Enum):
MARKET = "market"
LIMIT = "limit"
@dataclass
class Order:
order_id: int
timestamp: datetime
symbol: str
side: OrderSide
order_type: OrderType
quantity: float
price: Optional[float] = None
filled_price: Optional[float] = None
status: str = "pending"
@dataclass
class Position:
symbol: str
quantity: float = 0.0
entry_price: float = 0.0
unrealized_pnl: float = 0.0
@property
def is_long(self) -> bool:
return self.quantity > 0
@property
def is_short(self) -> bool:
return self.quantity < 0
@dataclass
class BacktestResult:
total_trades: int = 0
winning_trades: int = 0
losing_trades: int = 0
total_pnl: float = 0.0
max_drawdown: float = 0.0
sharpe_ratio: float = 0.0
trades: List[Order] = field(default_factory=list)
equity_curve: List[float] = field(default_factory=list)
timestamps: List[datetime] = field(default_factory=list)
class BacktestEngine:
def __init__(
self,
initial_balance: float = 10000.0,
commission: float = 0.0004,
slippage: float = 0.0001
):
self.initial_balance = initial_balance
self.balance = initial_balance
self.commission = commission
self.slippage = slippage
self.position: Optional[Position] = None
self.orders: List[Order] = []
self.order_id_counter = 0
self.result = BacktestResult()
self.current_time: Optional[datetime] = None
# 전략 함수
self.strategy: Optional[Callable] = None
def set_strategy(self, strategy: Callable):
"""백테스트할 전략 함수 설정"""
self.strategy = strategy
def _apply_slippage(self, price: float, side: OrderSide) -> float:
if side == OrderSide.BUY:
return price * (1 + self.slippage)
else:
return price * (1 - self.slippage)
def _calculate_commission(self, price: float, quantity: float) -> float:
return price * quantity * self.commission
async def place_order(
self,
symbol: str,
side: OrderSide,
quantity: float,
order_type: OrderType = OrderType.MARKET,
price: Optional[float] = None
) -> Order:
"""주문 실행"""
self.order_id_counter += 1
order = Order(
order_id=self.order_id_counter,
timestamp=self.current_time,
symbol=symbol,
side=side,
order_type=order_type,
quantity=quantity,
price=price
)
# 마켓 주문 실행
if order_type == OrderType.MARKET:
execution_price = self._apply_slippage(price, side)
order.filled_price = execution_price
order.status = "filled"
commission = self._calculate_commission(execution_price, quantity)
if side == OrderSide.BUY:
cost = execution_price * quantity + commission
if cost > self.balance:
order.status = "rejected"
return order
if self.position and self.position.quantity < 0:
# 숏 클로즈
pnl = (self.position.entry_price - execution_price) * abs(self.position.quantity)
self.balance += pnl
self.position = None
elif self.position and self.position.quantity > 0:
# 포지션 추가
total_qty = self.position.quantity + quantity
self.position.entry_price = (
(self.position.entry_price * self.position.quantity +
execution_price * quantity) / total_qty
)
self.position.quantity = total_qty
else:
self.position = Position(symbol=symbol, quantity=quantity, entry_price=execution_price)
self.balance -= cost
else: # SELL
if self.position is None or self.position.quantity == 0:
# 숏 진입
proceeds = execution_price * quantity - self._calculate_commission(execution_price, quantity)
self.balance += proceeds
self.position = Position(symbol=symbol, quantity=-quantity, entry_price=execution_price)
else:
# 포지션 감소
pnl = (self.position.entry_price - execution_price) * quantity
self.balance += execution_price * quantity - self._calculate_commission(execution_price, quantity)
self.position.quantity -= quantity
if abs(self.position.quantity) < 1e-8:
self.position = None
self.orders.append(order)
self.result.trades.append(order)
return order
def _update_equity(self):
equity = self.balance
if self.position:
# 실현되지 않은 손익 포함
equity += self.position.quantity * self.position.entry_price
self.result.equity_curve.append(equity)
self.result.timestamps.append(self.current_time)
async def run(self, df: pd.DataFrame):
"""백테스트 실행"""
print(f"Starting backtest with {len(df)} ticks...")
for idx, row in df.iterrows():
self.current_time = row["timestamp"]
# 현재가 업데이트
current_price = row["price"]
if self.position:
if self.position.quantity > 0:
self.position.unrealized_pnl = (
(current_price - self.position.entry_price) * self.position.quantity
)
else:
self.position.unrealized_pnl = (
(self.position.entry_price - current_price) * abs(self.position.quantity)
)
# 전략 실행
if self.strategy:
signals = await self.strategy(self, row)
self._update_equity()
if idx % 100000 == 0:
print(f"Progress: {idx}/{len(df)}")
self._calculate_metrics()
print("Backtest completed!")
def _calculate_metrics(self):
equity = np.array(self.result.equity_curve)
self.result.total_trades = len(self.result.trades)
completed_trades = [t for t in self.result.trades if t.status == "filled"]
# 승률 계산
pnl_history = []
for i, order in enumerate(completed_trades):
if order.side == OrderSide.SELL and self.position is None:
# 거래 종결 시점
pass
# 최대 드로우다운
peak = equity[0]
max_dd = 0
for val in equity:
if val > peak:
peak = val
dd = (peak - val) / peak
if dd > max_dd:
max_dd = dd
self.result.max_drawdown = max_dd
# 샤프 비율
returns = np.diff(equity) / equity[:-1]
if len(returns) > 0 and np.std(returns) > 0:
self.result.sharpe_ratio = np.mean(returns) / np.std(returns) * np.sqrt(252 * 24)
self.result.total_pnl = equity[-1] - self.initial_balance
def print_summary(self):
print("\n" + "="*50)
print("Backtest Results")
print("="*50)
print(f"Initial Balance: ${self.initial_balance:,.2f}")
print(f"Final Balance: ${self.result.equity_curve[-1]:,.2f}")
print(f"Total P&L: ${self.result.total_pnl:,.2f}")
print(f"Total Trades: {self.result.total_trades}")
print(f"Max Drawdown: {self.result.max_drawdown*100:.2f}%")
print(f"Sharpe Ratio: {self.result.sharpe_ratio:.2f}")
print("="*50)
예시 전략: 단순 이동평균 크로스오버
async def ma_cross_strategy(engine: BacktestEngine, tick: pd.Series) -> dict:
"""
단순 이동평균 크로스오버 전략
- MA(5) > MA(20): 매수 시그널
- MA(5) < MA(20): 매도 시그널
"""
# 실제로는 히스토리 데이터 필요
# 단순 예시로 항상 홀딩
return {}5단계: HolySheep AI 통합 - 신호 생성에 AI 활용
여러분의 전략에 AI 기반 신호 생성을 원한다면 HolySheep AI를 쉽게 통합할 수 있습니다. HolySheep는 단일 API 키로 GPT-4.1, Claude Sonnet, Gemini 2.5 Flash 등 다양한 모델을 지원합니다.
# ai_signal_generator.py
import httpx
import json
from datetime import datetime
from typing import Dict, List, Optional
class HolySheepAIClient:
"""HolySheep AI를 사용한 암호화폐 신호 생성 클라이언트"""
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.AsyncClient(timeout=60.0)
async def generate_trading_signal(
self,
symbol: str,
price: float,
volume_24h: float,
market_cap: float,
recent_closes: List[float],
market_sentiment: str = "neutral"
) -> Dict:
"""
HolySheep AI를 사용하여 거래 신호를 생성합니다.
Args:
symbol: 암호화폐 심볼 (예: BTC, ETH)
price: 현재 가격
volume_24h: 24시간 거래량
market_cap: 시가총액
recent_closes: 최근 종가 리스트 (최소 20개)
market_sentiment: 시장 분위기 (bullish/bearish/neutral)
Returns:
신호와置信도 점수
"""
prompt = f"""
당신은 전문 암호화폐 트레이딩 애널리스트입니다.
아래 데이터를 분석하고 거래 신호를 생성해주세요.
심볼: {symbol}
현재가: ${price:,.2f}
24시간 거래량: ${volume_24h:,.0f}
시가총액: ${market_cap:,.0f}
시장 분위기: {market_sentiment}
최근 종가 (최근 20개):
{json.dumps(recent_closes, indent=2)}
응답 형식 (JSON만 반환):
{{
"signal": "buy" | "sell" | "hold",
"confidence": 0.0 ~ 1.0,
"reasoning": "분석 근거 (1-2문장)",
"target_price": 숫자 또는 null,
"stop_loss": 숫자 또는 null,
"timeframe": "short" | "medium" | "long"
}}
"""
try:
response = await self.client.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1", # 또는 claude-sonnet-4-5, gemini-2.5-flash
"messages": [
{"role": "system", "content": "당신은 암호화폐 트레이딩 전문가입니다."},
{"role": "user", "content": prompt}
],
"temperature": 0.3, # 낮은 temperature로 일관된 응답 유도
"response_format": {"type": "json_object"}
}
)
response.raise_for_status()
data = response.json()
content = data["choices"][0]["message"]["content"]
signal_data = json.loads(content)
print(f"[HolySheep AI] Signal for {symbol}: {signal_data['signal']} "
f"(Confidence: {signal_data['confidence']:.2%})")
return signal_data
except httpx.HTTPStatusError as e:
if e.response.status_code == 401:
print("❌ HolySheep API 키가 유효하지 않습니다.")
print("👉 https://www.holysheep.ai/register 에서 새 키를 발급받으세요.")
raise
except Exception as e:
print(f"❌ HolySheep API 오류: {e}")
raise
async def batch_analyze(
self,
symbols: List[Dict]
) -> List[Dict]:
"""여러 암호화폐를 배치로 분석"""
tasks = [
self.generate_trading_signal(**s)
for s in symbols
]
return await asyncio.gather(*tasks)
def close(self):
"""클라이언트 종료"""
asyncio.run(self.client.aclose())
async def main():
# HolySheep AI 클라이언트 초기화
client = HolySheepAIClient(api_key="YOUR_HOLYSHEEP_API_KEY")
# 분석할 암호화폐 데이터
symbols_to_analyze = [
{
"symbol": "BTC",
"price": 67500.00,
"volume_24h": 28_500_000_000,
"market_cap": 1_320_000_000_000,
"recent_closes": [66100, 66300, 66500, 66800, 67100, 67400, 67300, 67500, 67400, 67200,
66900, 67000, 67200, 67400, 67300, 67100, 67200, 67400, 67500, 67500],
"market_sentiment": "bullish"
}
]
results = await client.batch_analyze(symbols_to_analyze)
for symbol_data, result in zip(symbols_to_analyze, results):
print(f"\n{symbol_data['symbol']} Analysis:")
print(f" Signal: {result['signal'].upper()}")
print(f" Confidence: {result['confidence']:.2%}")
print(f" Reasoning: {result['reasoning']}")
if result.get('target_price'):
print(f" Target Price: ${result['target_price']:,.2f}")
if result.get('stop_loss'):
print(f" Stop Loss: ${result['stop_loss']:,.2f}")
client.close()
if __name__ == "__main__":
import asyncio
asyncio.run(main())6단계: 전체 시스템 통합 실행
# main.py
import asyncio
from datetime import datetime, timedelta
from tardis_collector import TARDISCollector
from tick_store import TickStore
from backtest_engine import BacktestEngine, OrderSide, OrderType
async def run_full_backtest():
"""전체 백테스트 워크플로우 실행"""
# 1. TARDIS에서 데이터 수집
print("="*60)
print("Step 1: Collecting historical data from TARDIS...")
print("="*60)
collector = TARDISCollector(
api_key="YOUR_TARDIS_API_KEY",
exchange="binance-futures"
)
# 2024년 11월 1일 1시간 데이터
start = datetime(2024, 11, 1, 0, 0, 0)
end = datetime(2024, 11, 1, 1, 0, 0)
trades = await collector.fetch_trades("BTCUSDT", start, end)
df = collector.to_dataframe(trades)
print(f"✅ Collected {len(df)} trades")
# 2. PostgreSQL에 저장
print("\n" + "="*60)
print("Step 2: Storing data in PostgreSQL...")
print("="*60)
store = TickStore("postgresql://user:pass@localhost:5432/crypto_ticks")
await store.connect()
await store.create_tables()
await store.insert_ticks(df)
print("✅ Data stored successfully")
# 3. 백테스트 실행
print("\n" + "="*60)
print("Step 3: Running backtest...")
print("="*60)
engine = BacktestEngine(
initial_balance=10000.0,
commission=0.0004,
slippage=0.0001
)
# 간단한 테스트 전략: 가격 > 이동평균이면 매수, 아니면 홀딩
async def simple_strategy(engine, tick):
if len(engine.result.equity_curve) < 20:
return {}
# 간단한 의사결정
if engine.position is None and tick["price"] > 67000:
await engine.place_order(
symbol="BTCUSDT",
side=OrderSide.BUY,
quantity=0.01,
order_type=OrderType.MARKET,
price=tick["price"]
)
elif engine.position and tick["price"] < 66500:
await engine.place_order(
symbol="BTCUSDT",
side=OrderSide.SELL,
quantity=0.01,
order_type=OrderType.MARKET,
price=tick["price"]
)
engine.set_strategy(simple_strategy)
await engine.run(df)
engine.print_summary()
# 4. 결과 저장
print("\n" + "="*60)
print("Step 4: Saving results...")
print("="*60)
import json
result_summary = {
"initial_balance": engine.initial_balance,
"final_balance": engine.result.equity_curve[-1],
"total_pnl": engine.result.total_pnl,
"total_trades": engine.result.total_trades,
"max_drawdown": engine.result.max_drawdown,
"sharpe_ratio": engine.result.sharpe_ratio,
"equity_curve_length": len(engine.result.equity_curve)
}
with open("backtest_results.json", "w") as f:
json.dump(result_summary, f, indent=2)
print("✅ Results saved to backtest_results.json")
await store.pool.close()
if __name__ == "__main__":
asyncio.run(run_full_backtest())자주 발생하는 오류와 해결책
1. TARDIS API 401 Unauthorized 에러
# ❌ 오류 발생 시
HTTPError: 401 Client Error: Unauthorized
✅ 해결책: API 키 확인 및 갱신
1. TARDIS 대시보드에서 API 키 확인: https://tardis.dev/api
2. 환경변수 설정
import os
os.environ['TARDIS_API_KEY'] = 'your_valid_api_key'
3. 키 유효성 검사
import httpx
async def verify_tardis_key(api_key: str) -> bool:
client = httpx.AsyncClient()
response = await client.get(
"https://api.tardis.dev/v1/status",
headers={"Authorization": f"Bearer {api_key}"}
)
if response.status_code == 200:
print("✅ TARDIS API 키가 유효합니다")
return True
else:
print(f"❌ API 키 오류: {response.status_code}")
return False2. PostgreSQL 연결 타임아웃
# ❌ 오류 발생 시
asyncio.TimeoutError: connection timeout
✅ 해결책: 연결 풀 설정 및 재시도 로직
import asyncpg
import asyncio
async def robust_connect(dsn: str, max_retries: int = 3):
"""재시도 로직이 포함된 PostgreSQL 연결"""
for attempt in range(max_retries):
try:
pool = await asyncpg.create_pool(
dsn,
min_size=5,
max_size=20,
command_timeout=60,
timeout=30 # 연결 타임아웃 설정
)
# 연결 테스트
async with pool.acquire() as conn:
await conn.fetchval("SELECT 1")
print("✅ PostgreSQL 연결 성공")
return pool
except asyncio.TimeoutError:
print(f"⏰ 연결 시도 {attempt + 1} 실패, 재시도...")
await asyncio.sleep(2 ** attempt) # 지수 백오프
except Exception as e:
print(f"❌ 연결 오류: {e}")
raise
raise Exception("PostgreSQL 연결 실패")3. HolySheep AI Rate Limit 초과
# ❌ 오류 발생 시
HTTPError: 429 Client Error: Too Many Requests
✅ 해결책: Rate Limit 핸들링 및 요청 간격 조정
import asyncio
import httpx
from datetime import datetime
class HolySheepWithRetry:
def __init__(self, api_key: str):
self.api_key = api_key
self.base_url = "https://api.holysheep.ai/v1"
self.client = httpx.AsyncClient(timeout=60.0)
self.request_count = 0
self.window_start = datetime.now()
async def _handle_rate_limit(self, response: httpx.Response):
"""Rate limit 헤더 확인 및 대기"""
if response.status_code == 429:
retry_after = int(response.headers.get("retry-after", 60))
print(f"⏰ Rate limit 도달. {retry_after}초 후 재시도...")
await asyncio.sleep(retry_after)
return True
return False
async def chat_completion_with_retry(self, messages: list, max_retries: int = 3):
"""재시도 로직이 포함된 Chat Completion"""
for attempt in range(max_retries):
try:
# Rate limit 체크 (분당 60회 제한 예시)
now = datetime.now()
if (now - self.window_start).seconds > 60:
self.request_count = 0
self.window_start = now
if self.request_count >= 60:
wait_time = 60 - (now - self.window_start).seconds
print(f"⏰ 분당 할당량 소진. {wait_time}초 대기...")
await asyncio.sleep(wait_time)
self.request_count = 0
self.window_start = datetime.now()
response = await self.client.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1",
"messages": messages,
"temperature": 0.3
}
)
if response.status_code == 429:
await self._handle_rate_limit(response)
continue
response.raise_for_status()
self.request_count += 1
return response.json()
except httpx.HTTPStatusError as e:
if e.response.status_code == 429:
await self._handle_rate_limit(e.response)
continue
raise
raise Exception("Max retries exceeded")4. Tick 데이터 시간 순서 오류
# ❌ 오류 발생 시
데이터프레임 정