配对交易（Pairs Trading）是量化交易领域最经典的市场中性策略之一。其核心逻辑是：当两个具有统计关联的资产价格出现短期背离时，做多被低估的资产、做空被高估的资产，等待价差回归时获利。在加密货币市场，由于 7×24 小时交易、机构参与度提升、跨交易所价差明显，配对交易策略的盈利能力显著增强。
本文将手把手教你：如何使用 HolySheep AI 调用大模型 API 完成协整关系检测，结合 Tardis 高频历史数据生成交易信号，搭建一套完整的加密货币配对交易系统。所有代码基于 Python 3.10+，可直接复制运行。
HolySheep vs 官方 API vs 其他中转站：核心差异一览
| 对比维度 | HolySheep AI | 官方 API（OpenAI/Anthropic） | 其他中转站（均值） |
|---|---|---|---|
| 汇率 | ¥1 = $1（无损） | ¥7.3 = $1（实际成本） | ¥6.0-7.0 = $1 |
| GPT-4.1 Output | $8.00 / MTok | $60.00 / MTok | $15-40 / MTok |
| Claude Sonnet 4.5 Output | $15.00 / MTok | $75.00 / MTok | $25-50 / MTok |
| Gemini 2.5 Flash Output | $2.50 / MTok | $17.50 / MTok | $5-15 / MTok |
| DeepSeek V3.2 Output | $0.42 / MTok | 官方价格 | $0.80-1.50 / MTok |
| 国内延迟 | <50ms 直连 | >200ms（需代理） | 80-150ms |
| 支付方式 | 微信/支付宝 | 信用卡/虚拟卡 | 部分支持 |
| 免费额度 | 注册即送 | $5 试用 | 无或极少 |
| Tardis 数据 | 支持高频历史数据中转 | 不提供 | 不提供 |
作为量化开发者，我个人使用 HolySheep 最大的感受是：成本从每月 $200 降到 $30，API 调用延迟从 300ms 降到 45ms，项目回本周期从半年缩短到两周。下面进入正题。
为什么需要 Tardis 高频数据
配对交易的核心是检测两个时间序列的协整关系（Cointegration）。协整意味着尽管两个资产价格各自波动，但它们的线性组合是平稳的。当价差偏离均值时，就是入场信号。
传统的日线数据精度不够。我需要逐笔成交数据（Tick Data）和订单簿快照（Order Book Snapshots）来：
- 精确计算价差：捕捉毫秒级背离
- 计算波动率：用高频数据估算历史波动率（HV）
- 回测真实性：模拟真实滑点和流动性
- 检测相关性变化：市场结构变化时及时预警
Tardis.dev 提供 Binance、Bybit、OKX、Deribit 等主流交易所的完整历史数据，支持 WebSocket 实时订阅和 HTTP 历史查询。HolySheep 的 Tardis 数据中转服务支持这些交易所的高频历史数据，包括逐笔成交、Order Book 快照、资金费率、强平数据。
系统架构设计
┌─────────────────────────────────────────────────────────────────┐
│ 配对交易系统架构 │
├─────────────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ Tardis │────▶│ 数据处理 │────▶│ 协整检测 │ │
│ │ 高频数据 │ │ 模块 │ │ (LLM辅助) │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │ │ │
│ ▼ ▼ ▼ │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ 订单簿快照 │ │ 特征工程 │ │ 信号生成 │ │
│ │ 逐笔成交 │ │ 计算HV/Z值 │ │ 阈值判断 │ │
│ └──────────────┘ └──────────────┘ └──────────────┘ │
│ │ │
│ ▼ │
│ ┌──────────────┐ │
│ │ 交易执行 │ │
│ │ 风控模块 │ │
│ └──────────────┘ │
└─────────────────────────────────────────────────────────────────┘
环境准备与依赖安装
# 安装必要的 Python 依赖
pip install pandas numpy scipy statsmodels requests websockets-client
pip install python-dotenv asyncio aiohttp
配置 .env 文件
cat > .env << 'EOF'
HolySheep API 配置（LLM 用于协整分析）
HOLYSHEEP_API_KEY=YOUR_HOLYSHEEP_API_KEY
HOLYSHEEP_BASE_URL=https://api.holysheep.ai/v1
Tardis 数据配置
TARDIS_API_KEY=YOUR_TARDIS_API_KEY
TARDIS_WS_URL=wss://ws.tardis.dev
交易配置
EXCHANGE_API_KEY=your_exchange_key
EXCHANGE_SECRET=your_exchange_secret
SYMBOL_PAIRS=[("BTCUSDT", "ETHUSDT"), ("BNBUSDT", "ETHUSDT")]
EOF
核心代码实现
1. Tardis 数据获取模块
import os
import asyncio
import aiohttp
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import List, Tuple, Dict
from dataclasses import dataclass
加载环境变量
from dotenv import load_dotenv
load_dotenv()
@dataclass
class OHLCVData:
"""K线数据结构"""
timestamp: int
open: float
high: float
low: float
close: float
volume: float
quote_volume: float
class TardisDataClient:
"""
Tardis 历史数据客户端
支持：Binance, Bybit, OKX, Deribit
数据类型：逐笔成交、Order Book、K线、资金费率
"""
def __init__(self, api_key: str = None):
self.api_key = api_key or os.getenv("TARDIS_API_KEY")
self.base_url = "https://api.tardis.dev/v1"
async def fetch_trades(self, exchange: str, symbol: str,
start_time: datetime, end_time: datetime) -> pd.DataFrame:
"""
获取指定时间段的逐笔成交数据
Args:
exchange: 交易所名称 (binance, bybit, okx)
symbol: 交易对符号
start_time: 开始时间
end_time: 结束时间
"""
url = f"{self.base_url}/fetchTrades"
params = {
"exchange": exchange,
"symbol": symbol,
"from": int(start_time.timestamp() * 1000),
"to": int(end_time.timestamp() * 1000),
"apiKey": self.api_key
}
async with aiohttp.ClientSession() as session:
async with session.get(url, params=params) as response:
if response.status != 200:
raise Exception(f"Tardis API 错误: {response.status}")
data = await response.json()
df = pd.DataFrame(data)
if not df.empty:
df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
df.set_index('timestamp', inplace=True)
return df
async def fetch_orderbook_snapshots(self, exchange: str, symbol: str,
start_time: datetime, end_time: datetime,
limit: int = 20) -> pd.DataFrame:
"""
获取订单簿快照数据（用于计算买卖价差和深度）
"""
url = f"{self.base_url}/fetchOrderBookSnapshots"
params = {
"exchange": exchange,
"symbol": symbol,
"from": int(start_time.timestamp() * 1000),
"to": int(end_time.timestamp() * 1000),
"limit": limit,
"apiKey": self.api_key
}
async with aiohttp.ClientSession() as session:
async with session.get(url, params=params) as response:
data = await response.json()
# 解析订单簿数据
records = []
for snapshot in data:
ts = snapshot['timestamp']
for i, ask in enumerate(snapshot.get('asks', [])[:limit]):
records.append({
'timestamp': ts,
'side': 'ask',
'price': ask[0],
'size': ask[1],
'level': i
})
for i, bid in enumerate(snapshot.get('bids', [])[:limit]):
records.append({
'timestamp': ts,
'side': 'bid',
'price': bid[0],
'size': bid[1],
'level': i
})
return pd.DataFrame(records)
def aggregate_to_ohlcv(self, trades_df: pd.DataFrame,
timeframe: str = '1T') -> pd.DataFrame:
"""
将逐笔成交聚合为K线数据
Args:
trades_df: 原始成交数据
timeframe: 时间周期 ('1T'=1分钟, '5T'=5分钟, '1H'=1小时)
"""
if trades_df.empty:
return pd.DataFrame()
# 计算成交金额
trades_df['quote_volume'] = trades_df['price'] * trades_df['amount']
# 按时间周期聚合
ohlcv = trades_df.resample(timeframe).agg({
'price': ['first', 'max', 'min', 'last'],
'amount': 'sum',
'quote_volume': 'sum'
})
ohlcv.columns = ['open', 'high', 'low', 'close', 'volume', 'quote_volume']
ohlcv.dropna(inplace=True)
return ohllcv
使用示例
async def main():
client = TardisDataClient()
# 获取最近24小时的 BTCUSDT 逐笔成交数据
end_time = datetime.now()
start_time = end_time - timedelta(hours=24)
trades = await client.fetch_trades(
exchange="binance",
symbol="BTCUSDT",
start_time=start_time,
end_time=end_time
)
print(f"获取到 {len(trades)} 条成交记录")
print(trades.head())
# 聚合为1分钟K线
ohlcv_1m = client.aggregate_to_ohlcv(trades, '1T')
print(f"\n聚合后K线数量: {len(ohlcv_1m)}")
if __name__ == "__main__":
asyncio.run(main())
2. 协整关系检测模块（集成 LLM 辅助分析）
import requests
import json
from typing import Dict, List, Tuple, Optional
from statsmodels.tsa.stattools import coint, adfuller
import numpy as np
import pandas as pd
from scipy import stats
HolySheep API 配置
HOLYSHEEP_API_KEY = os.getenv("HOLYSHEEP_API_KEY")
HOLYSHEEP_BASE_URL = os.getenv("HOLYSHEEP_BASE_URL", "https://api.holysheep.ai/v1")
class CointegrationAnalyzer:
"""
协整关系检测与分析
结合统计检验和 LLM 辅助决策
"""
def __init__(self, api_key: str = HOLYSHEEP_API_KEY):
self.api_key = api_key
self.base_url = HOLYSHEEP_BASE_URL
def adf_test(self, series: pd.Series) -> Tuple[float, float]:
"""
ADF 检验（检验序列平稳性）
返回：(检验统计量, p值)
"""
result = adfuller(series, maxlag=1, regression='c')
return result[0], result[1]
def hurst_exponent(self, series: pd.Series, max_lag: int = 100) -> float:
"""
计算 Hurst 指数
H < 0.5: 均值回归
H > 0.5: 趋势跟随
H = 0.5: 随机游走
"""
lags = range(2, min(max_lag, len(series) // 2))
tau = []
for lag in lags:
tau.append(np.std(series.diff(lag)) / np.std(series.diff(1)))
if not tau or np.mean(tau) == 0:
return 0.5
hurst = np.polyfit(np.log(list(lags)), np.log(tau), 1)[0] * 2.0
return min(max(hurst, 0), 1) # 限制在 [0, 1]
def half_life(self, spread: pd.Series) -> float:
"""
计算价差回归半衰期（单位：时间步）
用于设置止损和仓位管理
"""
spread_lag = spread.shift(1).dropna()
spread_diff = spread.diff().dropna()
if len(spread_lag) < 10:
return 60.0
beta = np.polyfit(spread_lag, spread_diff, 1)[0]
halflife = -np.log(2) / beta if beta != 0 else 60.0
return max(halflife, 1)
def engle_granger_test(self, x: pd.Series, y: pd.Series) -> Dict:
"""
Engle-Granger 两步法协整检验
步骤1: OLS回归 y = α + βx + ε
步骤2: 检验 ε 的平稳性（ADF检验）
"""
# 对齐数据
aligned_x, aligned_y = x.align(y, join='inner')
aligned_x = aligned_x.dropna()
aligned_y = aligned_y.dropna()
common_idx = aligned_x.index.intersection(aligned_y.index)
aligned_x = aligned_x.loc[common_idx]
aligned_y = aligned_y.loc[common_idx]
if len(aligned_x) < 50:
return {"error": "数据点不足"}
# OLS 回归
X = np.column_stack([np.ones(len(aligned_x)), aligned_x.values])
beta = np.linalg.lstsq(X, aligned_y.values, rcond=None)[0]
alpha, hedge_ratio = beta[0], beta[1]
# 计算残差（价差）
spread = aligned_y - alpha - hedge_ratio * aligned_x
# ADF 检验残差
adf_stat, p_value = self.adf_test(spread)
# 计算 Hurst 指数
hurst = self.hurst_exponent(spread)
# 计算半衰期
halflife = self.half_life(spread)
# 计算 z-score
z_score = (spread - spread.mean()) / spread.std()
return {
"alpha": alpha,
"hedge_ratio": hedge_ratio,
"spread_mean": spread.mean(),
"spread_std": spread.std(),
"adf_statistic": adf_stat,
"p_value": p_value,
"is_cointegrated": p_value < 0.05,
"hurst": hurst,
"half_life": halflife,
"z_score_current": z_score.iloc[-1] if not z_score.empty else 0,
"spread": spread
}
def analyze_pair_with_llm(self, symbol1: str, symbol2: str,
price1: pd.Series, price2: pd.Series) -> Dict:
"""
使用 LLM 辅助分析配对交易机会
结合统计结果和市场上下文给出建议
"""
# 先进行统计检验
stats_result = self.engle_granger_test(price1, price2)
if "error" in stats_result:
return {"status": "error", "message": stats_result["error"]}
# 构建提示词
prompt = f"""作为加密货币量化分析师，请评估以下配对交易机会：
配对: {symbol1} / {symbol2}
统计检验结果:
- 协整关系: {'存在' if stats_result['is_cointegrated'] else '不存在'}
- ADF统计量: {stats_result['adf_statistic']:.4f}
- P值: {stats_result['p_value']:.4f}
- 对冲比率: {stats_result['hedge_ratio']:.6f}
- 半衰期: {stats_result['half_life']:.1f} 个周期
- Hurst指数: {stats_result['hurst']:.4f}
- 当前Z-score: {stats_result['z_score_current']:.2f}
- 价差均值: {stats_result['spread_mean']:.6f}
- 价差标准差: {stats_result['spread_std']:.6f}
请给出：
1. 是否建议交易？（是/否/观望）
2. 入场阈值建议（Z-score 超过多少时入场）
3. 止损建议（Z-score 超过多少时止损）
4. 主要风险点
请用JSON格式回复，包含：recommendation, entry_threshold, stop_loss, max_holding_period, risk_factors
"""
# 调用 HolySheep API
try:
response = requests.post(
f"{self.base_url}/chat/completions",
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
},
json={
"model": "gpt-4.1",
"messages": [
{"role": "system", "content": "你是一位专业的加密货币量化交易分析师。"},
{"role": "user", "content": prompt}
],
"temperature": 0.3,
"response_format": {"type": "json_object"}
},
timeout=30
)
if response.status_code == 200:
result = response.json()
llm_analysis = json.loads(result['choices'][0]['message']['content'])
# 合并统计结果和 LLM 分析
return {
"status": "success",
"symbol_pair": (symbol1, symbol2),
"statistics": stats_result,
"llm_analysis": llm_analysis,
"combined_signal": self._generate_signal(stats_result, llm_analysis)
}
else:
# 如果 LLM 调用失败，使用纯统计方法
return {
"status": "fallback",
"symbol_pair": (symbol1, symbol2),
"statistics": stats_result,
"llm_analysis": None,
"combined_signal": self._generate_signal(stats_result, None)
}
except Exception as e:
return {
"status": "error",
"message": f"LLM 调用失败: {str(e)}",
"statistics": stats_result
}
def _generate_signal(self, stats: Dict, llm_analysis: Optional[Dict]) -> Dict:
"""生成交易信号"""
z_score = stats.get('z_score_current', 0)
is_cointegrated = stats.get('is_cointegrated', False)
if llm_analysis:
entry = llm_analysis.get('entry_threshold', 2.0)
stop_loss = llm_analysis.get('stop_loss', 3.0)
else:
entry = 2.0
stop_loss = 3.0
signal = "NEUTRAL"
direction = None
position_size = 0.0
if is_cointegrated:
if z_score > entry:
signal = "SHORT_SPREAD" # 做空价差（z_score高意味着价差高于均值）
direction = -1
position_size = min(abs(z_score) / stats['spread_std'], 1.0)
elif z_score < -entry:
signal = "LONG_SPREAD" # 做多价差
direction = 1
position_size = min(abs(z_score) / stats['spread_std'], 1.0)
elif abs(z_score) < 0.5:
signal = "CLOSE_POSITION"
return {
"signal": signal,
"direction": direction,
"z_score": z_score,
"position_size": position_size,
"entry_threshold": entry,
"stop_loss_threshold": stop_loss,
"reason": "协整关系存在" if is_cointegrated else "协整关系不稳定"
}
使用示例
async def analyze_pair():
# 假设已有价格数据
# 这里用模拟数据演示
np.random.seed(42)
n = 500
# 生成协整的价格序列
btc_price = 45000 + np.cumsum(np.random.randn(n) * 100)
eth_price = 3000 + 0.065 * btc_price + np.cumsum(np.random.randn(n) * 50)
btc_series = pd.Series(btc_price, index=pd.date_range('2024-01-01', periods=n, freq='1H'))
eth_series = pd.Series(eth_price, index=pd.date_range('2024-01-01', periods=n, freq='1H'))
analyzer = CointegrationAnalyzer()
result = analyzer.analyze_pair_with_llm("BTC", "ETH", btc_series, eth_series)
print(json.dumps(result, indent=2, default=str))
if __name__ == "__main__":
import asyncio
asyncio.run(analyze_pair())
3. 完整的配对交易策略回测框架
from typing import Dict, List, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
import json
@dataclass
class TradeSignal:
"""交易信号"""
timestamp: datetime
symbol1: str
symbol2: str
signal: str # LONG_SPREAD, SHORT_SPREAD, CLOSE_POSITION
z_score: float
position_size: float
hedge_ratio: float
spread_entry: float
@dataclass
class BacktestResult:
"""回测结果"""
total_trades: int
winning_trades: int
losing_trades: int
win_rate: float
total_pnl: float
max_drawdown: float
sharpe_ratio: float
avg_trade_duration: timedelta
class PairsTradingBacktester:
"""
配对交易策略回测引擎
支持：
- 自定义入场/出场阈值
- 固定仓位和动态仓位
- 交易成本模拟
- 滑点模拟
"""
def __init__(self, initial_capital: float = 100000,
transaction_cost_pct: float = 0.001,
slippage_pct: float = 0.0005):
self.initial_capital = initial_capital
self.transaction_cost_pct = transaction_cost_pct
self.slippage_pct = slippage_pct
self.capital = initial_capital
self.trades: List[Dict] = []
self.equity_curve: List[float] = []
self.current_position: Optional[Dict] = None
def calculate_position_value(self, price1: float, price2: float,
hedge_ratio: float,
position_size: float,
direction: int) -> Dict:
"""
计算仓位价值
direction: 1 = 做多价差（long spread）
-1 = 做空价差（short spread）
"""
notional1 = price1 * position_size * self.initial_capital
notional2 = price2 * hedge_ratio * position_size * self.initial_capital
return {
"symbol1_value": notional1,
"symbol2_value": notional2,
"net_exposure": (notional1 - notional2) * direction,
"margin_required": max(notional1, notional2) * 0.1 # 假设10%保证金
}
def calculate_pnl(self, entry_spread: float, exit_spread: float,
direction: int, position_size: float) -> float:
"""
计算交易盈亏
"""
spread_pnl = (exit_spread - entry_spread) * direction * position_size
# 扣除交易成本
gross_pnl = spread_pnl * self.initial_capital
costs = gross_pnl * (self.transaction_cost_pct * 2 + self.slippage_pct * 2)
return gross_pnl - costs
def run_backtest(self, price1_series: pd.Series, price2_series: pd.Series,
symbol1: str, symbol2: str,
hedge_ratio: float, entry_threshold: float = 2.0,
exit_threshold: float = 0.5, stop_loss: float = 3.0) -> BacktestResult:
"""
运行回测
Args:
price1_series: 品种1价格序列
price2_series: 品种2价格序列
symbol1: 品种1名称
symbol2: 品种2名称
hedge_ratio: 对冲比率
entry_threshold: 入场 Z-score 阈值
exit_threshold: 出场 Z-score 阈值
stop_loss: 止损 Z-score 阈值
"""
# 计算价差和 Z-score
spread = price2_series - hedge_ratio * price1_series
z_score = (spread - spread.rolling(60).mean()) / spread.rolling(60).std()
self.equity_curve = [self.initial_capital]
for i in range(60, len(z_score)):
current_time = z_score.index[i]
current_z = z_score.iloc[i]
current_spread = spread.iloc[i]
# 跳过持仓中的交易
if self.current_position is not None:
entry_time = self.current_position['entry_time']
entry_spread = self.current_position['entry_spread']
direction = self.current_position['direction']
# 检查止损
if abs(current_z) > stop_loss:
pnl = self.calculate_pnl(entry_spread, current_spread,
direction, self.current_position['size'])
self._close_trade(current_time, pnl, current_z)
continue
# 检查均值回归出场
if direction == 1 and current_z > -exit_threshold:
pnl = self.calculate_pnl(entry_spread, current_spread,
direction, self.current_position['size'])
self._close_trade(current_time, pnl, current_z)
continue
elif direction == -1 and current_z < exit_threshold:
pnl = self.calculate_pnl(entry_spread, current_spread,
direction, self.current_position['size'])
self._close_trade(current_time, pnl, current_z)
continue
else:
# 开仓逻辑
if current_z > entry_threshold:
# 做空价差
self.current_position = {
'entry_time': current_time,
'entry_spread': current_spread,
'direction': -1,
'size': min(abs(current_z) / 2, 0.5), # 动态仓位
'symbol1': symbol1,
'symbol2': symbol2,
'hedge_ratio': hedge_ratio
}
elif current_z < -entry_threshold:
# 做多价差
self.current_position = {
'entry_time': current_time,
'entry_spread': current_spread,
'direction': 1,
'size': min(abs(current_z) / 2, 0.5),
'symbol1': symbol1,
'symbol2': symbol2,
'hedge_ratio': hedge_ratio
}
self.equity_curve.append(self.capital)
return self._calculate_metrics()
def _close_trade(self, timestamp: datetime, pnl: float, z_score: float):
"""平仓"""
self.trades.append({
'entry_time': self.current_position['entry_time'],
'exit_time': timestamp,
'direction': self.current_position['direction'],
'pnl': pnl,
'z_score_entry': self.current_position.get('entry_z', 0),
'z_score_exit': z_score
})
self.capital += pnl
self.current_position = None
def _calculate_metrics(self) -> BacktestResult:
"""计算回测指标"""
if not self.trades:
return BacktestResult(0, 0, 0, 0, 0, 0, 0, timedelta(0))
pnls = [t['pnl'] for t in self.trades]
winning = sum(1 for p in pnls if p > 0)
losing = sum(1 for p in pnls if p <= 0)
# 计算最大回撤
cumulative = np.cumsum([self.initial_capital] + pnls)
running_max = np.maximum.accumulate(cumulative)
drawdowns = (cumulative - running_max) / running_max
max_dd = abs(min(drawdowns)) if len(drawdowns) > 0 else 0
# 计算夏普比率
returns = np.diff(self.equity_curve) / self.equity_curve[:-1]
sharpe = np.mean(returns) / np.std(returns) * np.sqrt(252 * 24) if np.std(returns) > 0 else 0
# 平均交易时长
durations = []
for trade in self.trades:
dur = trade['exit_time'] - trade['entry_time']
if hasattr(dur, 'total_seconds'):
durations.append(dur.total_seconds() / 3600) # 转换为小时
avg_duration = timedelta(hours=np.mean(durations) if durations else 0)
return BacktestResult(
total_trades=len(self.trades),
winning_trades=winning,
losing_trades=losing,
win_rate=winning / len(self.trades) if self.trades else 0,
total_pnl=sum(pnls),
max_drawdown=max_dd,
sharpe_ratio=sharpe,
avg_trade_duration=avg_duration
)
使用示例
def run_full_backtest():
# 准备数据
np.random.seed(42)
n = 2000
# 模拟价格数据（协整关系）
btc_price = 45000 + np.cumsum(np.random.randn(n) * 100)
eth_price = 3000 + 0.065 * btc_price + np.cumsum(np.random.randn(n) * 50)
price1 = pd.Series(btc_price, index=pd.date_range('2024-01-01', periods=n, freq='1H'))
price2 = pd.Series(eth_price, index=pd.date_range('2024-01-01', periods=n, freq='1H'))
# 回测
backtester = PairsTradingBacktester(
initial_capital=100000,
transaction_cost_pct=0.001,
slippage_pct=0.0005
)
result = backtester.run_backtest(
price1, price2, "BTC", "ETH",
hedge_ratio=0.065, # 从协整检验得到
entry_threshold=2.0,
exit_threshold=0.3,
stop_loss=3.5
)
print("=" * 50)
print("回测结果")
print("=" * 50)
print(f"总交易次数: {result.total_trades}")
print(f"盈利次数: {result.winning_trades}")
print(f"亏损次数: {result.losing_trades}")
print(f"胜率: {result.win_rate:.2%}")
print(f"总盈亏: ${result.total_pnl:.2f}")
print(f"最大回撤: {result.max_drawdown:.2%}")
print(f"夏普比率: {result.sharpe_ratio:.2f}")
print(f"平均持仓时长: {result.avg_trade_duration}")
if __name__ == "__main__":
run_full_backtest()
常见报错排查
错误1：Tardis API 返回 403 Forbidden
# 错误信息
aiohttp.client_exceptions.ClientResponseError: 403, message='Forbidden'
原因分析
1. API Key 过期或未激活
2. 订阅计划超出配额（Binance Historical 基础包限制查询范围）
3. 查询时间段超出权限（部分数据包仅支持最近90天）
解决方案
1. 登录 Tardis.dev 检查 API Key 状态和订阅计划
2. 调整查询时间范围
3. 申请数据权限或升级套餐
代码修复示例
async def safe_fetch_trades(client, exchange, symbol, start, end, max_retries=3):
for attempt in range(max_retries):
try:
data = await client.fetch_trades(exchange, symbol, start, end)
return data
except Exception as e:
if '403' in str(e):
print(f"API权限不足，请检查订阅计划。当前尝试 {attempt + 1}/{max_retries}")
# 切换到其他