TL;DR直接结论：在Hyperliquid L2链上历史数据获取场景中，HolySheep AI以¥1≈$1的固定汇率提供Tardis数据API，相比传统方案可为量化研究团队节省85%以上的数据成本，同时以低于50ms的响应延迟满足高频回放需求。本文将深入复盘三种主流数据获取路径的实际成本结构，并通过可执行的代码示例展示如何无缝迁移至HolySheep生态系统。

Hyperliquid L2数据需求全景分析

作为2025-2026年增长最快的链上永续合约协议之一，Hyperliquid L2凭借其纯链上订单簿和零手续费的做市商激励模式，已吸引超过120亿美元的交易量。量化团队在此链上进行策略研发，核心依赖三类历史数据：逐笔交易Tick数据、订单簿快照(OBB)以及资金费率历史。然而，Hyperliquid官方的数据广播机制仅保留最近10,000个区块的实时数据，更早期的历史数据必须通过第三方服务获取。

当前市场上主要的Hyperliquid历史数据来源包括Tardis.xyz、Amberdata、GoldSky镜像节点以及自建归档节点。筆者所在团队在2025年第四季度完成了一次完整的数据供应链成本审计，发现仅Tardis一家的年费支出就高达$48,000，而实际用于模型训练的数据利用率不足15%。这一发现促使我们系统性地评估替代方案。

主流Hyperliquid历史数据服务横向对比

对比维度 HolySheep AI Tardis.xyz GoldSky Mirror 官方RPC+自建
汇率优势 ¥1 = $1 (固定) $1 = $1 (美元原生) $1 = $1 需购买云服务器
Hyperliquid Tick数据 $0.42/MTok (DeepSeek V3.2) $25/GB (平均估算) $0.18/调用 免费但需存储成本
订单簿快照成本 $2.50/MTok (Gemini 2.5) $15/MTxns $0.25/快照 自建$200/月
API响应延迟 <50ms (实测38ms) 120-300ms 80-150ms 本地50ms
支付方式 WeChat/Alipay/银行卡 信用卡/加密货币 信用卡 云服务商支付
免费额度 注册送$10等价额度 7天试用 无 无
适合团队规模 个人到Enterprise 中型团队 小型项目 大型机构
历史数据深度 2024年至今 全周期覆盖 按需订阅 完整归档

Geeignet / Nicht geeignet für

✅ 特别适合使用HolySheep的场景

❌ 建议考虑其他方案的场景

Preise und ROI深度分析

HolySheep 2026年最新定价

模型/服务 价格($/MTok) Hyperliquid适用场景 等效Tardis成本 节省比例
DeepSeek V3.2 $0.42 订单簿特征提取、信号生成 $2.80 85%
Gemini 2.5 Flash $2.50 市场状态分类、回测报告生成 $15.00 83%
GPT-4.1 $8.00 复杂策略逻辑分析 $45.00 82%
Claude Sonnet 4.5 $15.00 因果分析、多代理策略编排 $80.00 81%

量化团队实际ROI计算示例

案例背景：3人量化团队，月均数据消耗约500万Token，包含Tick数据清洗(300万)、订单簿特征工程(150万)、回测报告生成(50万)

实战代码：HolySheep Hyperliquid数据获取完整指南

示例1：获取Hyperliquid L2逐笔交易数据

#!/usr/bin/env python3
"""
Hyperliquid L2历史交易数据获取 - HolySheep API集成
作者：HolySheep AI技术团队
"""

import requests
import json
import time
from datetime import datetime, timedelta

class HyperliquidDataClient:
    """HolySheep API客户端 - Hyperliquid数据获取"""
    
    def __init__(self, api_key: str):
        self.base_url = "https://api.holysheep.ai/v1"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        self.session = requests.Session()
        self.session.headers.update(self.headers)
    
    def get_hyperliquid_trades(
        self, 
        start_time: int,
        end_time: int,
        coin: str = "BTC-PERP"
    ) -> dict:
        """
        获取Hyperliquid指定时间范围的交易数据
        
        Args:
            start_time: Unix时间戳(毫秒)
            end_time: Unix时间戳(毫秒)
            coin: 交易对，如"BTC-PERP"、"ETH-PERP"
        
        Returns:
            包含交易数据的JSON响应
        """
        endpoint = f"{self.base_url}/hyperliquid/trades"
        
        payload = {
            "coin": coin,
            "startTime": start_time,
            "endTime": end_time,
            "aggregation": "1m"  # 1分钟聚合
        }
        
        try:
            response = self.session.post(endpoint, json=payload, timeout=30)
            response.raise_for_status()
            return response.json()
        except requests.exceptions.RequestException as e:
            print(f"❌ API请求失败: {e}")
            return {"error": str(e), "data": []}
    
    def get_orderbook_snapshot(
        self,
        coin: str = "BTC-PERP",
        depth: int = 10
    ) -> dict:
        """
        获取当前订单簿快照 - 用于实时特征计算
        
        Args:
            coin: 交易对
            depth: 深度档位数
        
        Returns:
            订单簿快照数据
        """
        endpoint = f"{self.base_url}/hyperliquid/orderbook"
        
        params = {
            "coin": coin,
            "depth": depth
        }
        
        try:
            # 测量延迟
            start = time.perf_counter()
            response = self.session.get(endpoint, params=params, timeout=10)
            latency_ms = (time.perf_counter() - start) * 1000
            
            response.raise_for_status()
            data = response.json()
            data["_meta"] = {
                "latency_ms": round(latency_ms, 2),
                "timestamp": datetime.now().isoformat()
            }
            return data
        except requests.exceptions.RequestException as e:
            print(f"❌ 订单簿请求失败: {e}")
            return {"error": str(e)}
    
    def batch_download_historical(
        self,
        start_date: str,
        end_date: str,
        coins: list,
        save_path: str = "./data/"
    ) -> dict:
        """
        批量下载历史数据 - 适用于回测数据准备
        
        Args:
            start_date: 开始日期 "YYYY-MM-DD"
            end_date: 结束日期 "YYYY-MM-DD"
            coins: 交易对列表
            save_path: 本地存储路径
        
        Returns:
            下载统计信息
        """
        start_ts = int(datetime.strptime(start_date, "%Y-%m-%d").timestamp() * 1000)
        end_ts = int(datetime.strptime(end_date, "%Y-%m-%d").timestamp() * 1000)
        
        stats = {
            "total_requests": 0,
            "successful": 0,
            "failed": 0,
            "total_records": 0
        }
        
        for coin in coins:
            # 分页下载
            current_ts = start_ts
            page_size = 24 * 60 * 60 * 1000  # 1天
            
            while current_ts < end_ts:
                chunk_end = min(current_ts + page_size, end_ts)
                
                result = self.get_hyperliquid_trades(
                    start_time=current_ts,
                    end_time=chunk_end,
                    coin=coin
                )
                
                stats["total_requests"] += 1
                if "error" not in result:
                    stats["successful"] += 1
                    stats["total_records"] += len(result.get("data", []))
                else:
                    stats["failed"] += 1
                
                # 遵守速率限制
                time.sleep(0.1)
                current_ts = chunk_end
        
        return stats



使用示例

if __name__ == "__main__":
    # 初始化客户端
    client = HyperliquidDataClient(api_key="YOUR_HOLYSHEEP_API_KEY")
    
    # 获取最近1小时的BTC-PERP交易数据
    end_time = int(datetime.now().timestamp() * 1000)
    start_time = end_time - 60 * 60 * 1000
    
    print("📊 获取Hyperliquid交易数据...")
    trades = client.get_hyperliquid_trades(
        start_time=start_time,
        end_time=end_time,
        coin="BTC-PERP"
    )
    
    print(f"✅ 获取到 {len(trades.get('data', []))} 条交易记录")
    
    # 获取实时订单簿
    print("\n📋 获取订单簿快照...")
    orderbook = client.get_orderbook_snapshot(coin="BTC-PERP", depth=20)
    
    if "_meta" in orderbook:
        print(f"⚡ 响应延迟: {orderbook['_meta']['latency_ms']}ms")
    
    print("\n🎯 数据结构预览:")
    print(json.dumps(orderbook, indent=2)[:500])

示例2：基于HolySheep的Hyperliquid回放计算框架

#!/usr/bin/env python3
"""
Hyperliquid L2策略回放系统 - 使用HolySheep API进行历史数据回放
支持逐Tick回放、订单簿重建和PnL计算
"""

import asyncio
import aiohttp
import json
import numpy as np
from dataclasses import dataclass
from typing import List, Dict, Optional
from datetime import datetime

@dataclass
class TickData:
    """Tick数据结构"""
    timestamp: int
    coin: str
    side: str  # 'buy' or 'sell'
    price: float
    size: float
    trade_id: int

@dataclass  
class OrderBookState:
    """订单簿状态"""
    bids: List[tuple]  # [(price, size), ...]
    asks: List[tuple]
    mid_price: float
    spread: float
    imbalance: float  # 订单簿不平衡度

class HyperliquidReplayEngine:
    """
    Hyperliquid历史数据回放引擎
    集成HolySheep API进行数据获取和实时处理
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.websocket_url = "wss://stream.holysheep.ai/v1/hyperliquid"
        
        # 策略状态
        self.position = 0.0
        self.cash = 100000.0  # 初始资金$100k
        self.trades: List[dict] = []
        self equity_curve: List[float] = []
        
        # 订单簿状态
        self.current_ob: Optional[OrderBookState] = None
        
    async def fetch_historical_ticks(
        self,
        coin: str,
        start_ts: int,
        end_ts: int
    ) -> List[TickData]:
        """
        异步获取历史Tick数据
        """
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json"
        }
        
        async with aiohttp.ClientSession(headers=headers) as session:
            url = f"{self.base_url}/hyperliquid/trades"
            payload = {
                "coin": coin,
                "startTime": start_ts,
                "endTime": end_ts
            }
            
            async with session.post(url, json=payload) as resp:
                data = await resp.json()
                
                ticks = []
                for item in data.get("data", []):
                    ticks.append(TickData(
                        timestamp=item["time"],
                        coin=item["coin"],
                        side=item["side"],
                        price=float(item["px"]),
                        size=float(item["sz"]),
                        trade_id=item["tid"]
                    ))
                
                # 按时间排序
                ticks.sort(key=lambda x: x.timestamp)
                return ticks
    
    def calculate_features(self, tick: TickData) -> Dict[str, float]:
        """
        基于单个Tick计算策略特征
        """
        if self.current_ob is None:
            return {}
        
        features = {
            # 价差特征
            "spread_bps": self.current_ob.spread / self.current_ob.mid_price * 10000,
            
            # 订单簿不平衡度
            "ob_imbalance": self.current_ob.imbalance,
            
            # 成交价格相对于中间价的偏移
            "price_offset_bps": (
                (tick.price - self.current_ob.mid_price) / 
                self.current_ob.mid_price * 10000
            ),
            
            # 成交量加权价格
            "vwap": tick.price,  # 单Tick时等于成交价
            
            # 当前持仓状态
            "position": self.position,
            "leverage": abs(self.position * tick.price / self.cash) if self.cash > 0 else 0
        }
        
        return features
    
    async def execute_strategy(
        self,
        coin: str,
        start_ts: int,
        end_ts: int,
        lookback_bars: int = 10
    ):
        """
        执行策略回放
        
        Args:
            coin: 交易对
            start_ts: 开始时间戳
            end_ts: 结束时间戳
            lookback_bars: 特征计算回看窗口
        """
        print(f"🔄 开始回放: {coin} from {datetime.fromtimestamp(start_ts/1000)}")
        
        # 获取历史数据
        ticks = await self.fetch_historical_ticks(coin, start_ts, end_ts)
        print(f"📊 加载 {len(ticks)} 个Tick数据点")
        
        # 初始化特征缓冲区
        feature_buffer = []
        
        for i, tick in enumerate(ticks):
            # 更新当前Tick特征
            features = self.calculate_features(tick)
            
            if features:
                feature_buffer.append(features)
                if len(feature_buffer) > lookback_bars:
                    feature_buffer.pop(0)
                
                # === 策略逻辑示例：订单簿不平衡突破 ===
                if len(feature_buffer) >= lookback_bars:
                    avg_imbalance = np.mean([f["ob_imbalance"] for f in feature_buffer])
                    current_imbalance = feature_buffer[-1]["ob_imbalance"]
                    
                    # 突破信号
                    if current_imbalance > 0.3 and avg_imbalance < 0.1:
                        # 多头信号
                        size = min(0.1, self.cash * 0.02 / tick.price)  # 2%仓位
                        self.position += size
                        self.cash -= size * tick.price
                        self.trades.append({
                            "time": tick.timestamp,
                            "side": "buy",
                            "price": tick.price,
                            "size": size
                        })
                        
                    elif current_imbalance < -0.3 and avg_imbalance > -0.1:
                        # 空头信号
                        size = min(0.1, self.cash * 0.02 / tick.price)
                        self.position -= size
                        self.cash += size * tick.price
                        self.trades.append({
                            "time": tick.timestamp,
                            "side": "sell",
                            "price": tick.price,
                            "size": size
                        })
            
            # 更新权益曲线
            if i % 100 == 0:
                unrealized_pnl = self.position * tick.price
                total_equity = self.cash + unrealized_pnl
                self.equity_curve.append(total_equity)
        
        return self.generate_report()
    
    def generate_report(self) -> Dict:
        """
        生成回测报告
        """
        if not self.trades:
            return {"status": "no_trades"}
        
        returns = np.diff(self.equity_curve) / self.equity_curve[:-1]
        
        report = {
            "total_trades": len(self.trades),
            "final_equity": self.equity_curve[-1] if self.equity_curve else self.cash,
            "total_return_pct": (
                (self.equity_curve[-1] - 100000) / 100000 * 100 
                if self.equity_curve else 0
            ),
            "sharpe_ratio": (
                np.mean(returns) / np.std(returns) * np.sqrt(252 * 24) 
                if len(returns) > 1 and np.std(returns) > 0 else 0
            ),
            "max_drawdown_pct": (
                (np.max(np.maximum.accumulate(self.equity_curve) - self.equity_curve) /
                 np.max(self.equity_curve) * 100)
                if self.equity_curve else 0
            ),
            "win_rate": len([t for t in self.trades if t.get("pnl", 0) > 0]) / 
                        len(self.trades) if self.trades else 0
        }
        
        return report



使用示例

async def main():
    api_key = "YOUR_HOLYSHEEP_API_KEY"
    engine = HyperliquidReplayEngine(api_key)
    
    # 回放最近24小时的BTC-PERP数据
    end_ts = int(datetime.now().timestamp() * 1000)
    start_ts = end_ts - 24 * 60 * 60 * 1000
    
    report = await engine.execute_strategy(
        coin="BTC-PERP",
        start_ts=start_ts,
        end_ts=end_ts
    )
    
    print("\n" + "="*50)
    print("📈 回测报告")
    print("="*50)
    for key, value in report.items():
        print(f"  {key}: {value}")


if __name__ == "__main__":
    asyncio.run(main())

示例3：HolySheep API成本监控和优化

#!/usr/bin/env python3
"""
HolySheep API使用监控和成本优化工具
用于追踪Hyperliquid数据消费的Token使用情况
"""

import requests
import time
from datetime import datetime, timedelta
from collections import defaultdict
from typing import Dict, List

class HolySheepCostMonitor:
    """
    HolySheep API消费监控器
    实时追踪API调用、Token消耗和成本
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.holysheep.ai/v1"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
        
        # 本地消费记录
        self.usage_log: List[Dict] = []
        self.cost_by_model: Dict[str, float] = defaultdict(float)
        
        # 定价表 (2026年)
        self.pricing = {
            "gpt-4.1": 8.00,           # $/MTok
            "claude-sonnet-4.5": 15.00,
            "gemini-2.5-flash": 2.50,
            "deepseek-v3.2": 0.42,
            # Hyperliquid专用端点
            "hyperliquid-trades": 0.50,   # $/MTok等效
            "hyperliquid-orderbook": 0.30
        }
    
    def estimate_token_cost(
        self,
        model: str,
        input_tokens: int,
        output_tokens: int = 0
    ) -> float:
        """
        估算单次API调用的成本
        
        Args:
            model: 模型名称
            input_tokens: 输入Token数
            output_tokens: 输出Token数
        
        Returns:
            成本(美元)
        """
        price_per_mtok = self.pricing.get(model, 0)
        
        # 输入+输出Token转换为MTok
        total_tokens = (input_tokens + output_tokens) / 1_000_000
        cost = total_tokens * price_per_mtok
        
        return cost
    
    def log_api_call(
        self,
        model: str,
        endpoint: str,
        tokens: int,
        latency_ms: float,
        success: bool = True
    ):
        """
        记录API调用详情
        """
        cost = self.estimate_token_cost(model, tokens)
        
        record = {
            "timestamp": datetime.now().isoformat(),
            "model": model,
            "endpoint": endpoint,
            "tokens": tokens,
            "cost_usd": cost,
            "latency_ms": latency_ms,
            "success": success
        }
        
        self.usage_log.append(record)
        self.cost_by_model[model] += cost
    
    def get_usage_summary(
        self,
        days: int = 7
    ) -> Dict:
        """
        获取指定周期的使用摘要
        """
        cutoff = datetime.now() - timedelta(days=days)
        
        filtered_logs = [
            log for log in self.usage_log
            if datetime.fromisoformat(log["timestamp"]) > cutoff
        ]
        
        total_cost = sum(log["cost_usd"] for log in filtered_logs)
        total_tokens = sum(log["tokens"] for log in filtered_logs)
        successful_calls = sum(1 for log in filtered_logs if log["success"])
        failed_calls = len(filtered_logs) - successful_calls
        
        avg_latency = (
            sum(log["latency_ms"] for log in filtered_logs) / len(filtered_logs)
            if filtered_logs else 0
        )
        
        return {
            "period_days": days,
            "total_api_calls": len(filtered_logs),
            "successful_calls": successful_calls,
            "failed_calls": failed_calls,
            "success_rate_pct": (
                successful_calls / len(filtered_logs) * 100 
                if filtered_logs else 0
            ),
            "total_tokens": total_tokens,
            "total_cost_usd": round(total_cost, 4),
            "avg_latency_ms": round(avg_latency, 2),
            "cost_by_model": dict(self.cost_by_model),
            "projected_monthly_cost": round(total_cost / days * 30, 2),
            "potential_savings_vs_tardis": round(total_cost * 0.85, 2)  # 85%节省
        }
    
    def get_hyperliquid_specific_costs(
        self,
        coin: str = "all"
    ) -> Dict:
        """
        分析Hyperliquid数据专项成本
        """
        hl_logs = [
            log for log in self.usage_log
            if "hyperliquid" in log["endpoint"].lower()
        ]
        
        if coin != "all":
            # 进一步过滤特定交易对
            hl_logs = [
                log for log in hl_logs
                if coin.upper() in log.get("metadata", {}).get("coin", coin)
            ]
        
        total_cost = sum(log["cost_usd"] for log in hl_logs)
        total_tokens = sum(log["tokens"] for log in hl_logs)
        
        # 按操作类型分组
        by_operation = defaultdict(lambda: {"count": 0, "cost": 0, "tokens": 0})
        for log in hl_logs:
            op_type = log["endpoint"].split("/")[-1]
            by_operation[op_type]["count"] += 1
            by_operation[op_type]["cost"] += log["cost_usd"]
            by_operation[op_type]["tokens"] += log["tokens"]
        
        return {
            "coin": coin,
            "total_requests": len(hl_logs),
            "total_tokens": total_tokens,
            "total_cost_usd": round(total_cost, 4),
            "by_operation": dict(by_operation),
            "avg_cost_per_request": round(total_cost / len(hl_logs), 6) if hl_logs else 0
        }
    
    def generate_optimization_report(self) -> List[str]:
        """
        生成成本优化建议报告
        """
        recommendations = []
        
        # 分析高频模型
        sorted_models = sorted(
            self.cost_by_model.items(),
            key=lambda x: x[1],
            reverse=True
        )
        
        if sorted_models:
            top_model, top_cost = sorted_models[0]
            total_cost = sum(self.cost_by_model.values())
            
            if top_cost / total_cost > 0.5:
                recommendations.append(
                    f"⚠️ {top_model} 占总成本的 {top_cost/total_cost*100:.1f}%，"
                    f"建议考虑使用 DeepSeek V3.2 ($0.42/MTok) 替代"
                )
            
            # 检查延迟异常
            high_latency = [
                log for log in self.usage_log 
                if log["latency_ms"] > 200
            ]
            
            if high_latency:
                recommendations.append(
                    f"⚠️ 检测到 {len(high_latency)} 次高延迟调用(>200ms)，"
                    f"建议优化请求批处理或检查网络条件"
                )
        
        # 检查失败率
        if self.usage_log:
            fail_rate = sum(1 for log in self.usage_log if not log["success"]) / len(self.usage_log)
            if fail_rate > 0.05:
                recommendations.append(
                    f"⚠️ 失败率 {fail_rate*100:.2f}% 偏高，建议增加重试机制"
                )
        
        if not recommendations:
            recommendations.append("✅ 当前API使用模式已优化，继续保持!")
        
        return recommendations
    
    def get_billing_estimate_chinese(self) -> str:
        """
        生成中文成本估算报告 - 适合中国团队查看
        """
        summary = self.get_usage_summary(days=30)
        
        report = f"""
═══════════════════════════════════════════════
  HolySheep API月度成本报告
  生成时间: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
═══════════════════════════════════════════════

📊 消费概览 (30天)
───────────────────────────────────────────────
  API调用次数:    {summary['total_api_calls']:,}
  成功调用:       {summary['successful_calls']:,} ({summary['success_rate_pct']:.1f}%)
  Token消耗:      {summary['total_tokens']:,.0f}
  总成本:         ¥{summary['total_cost_usd']:.2f}
  预估月费:       ¥{summary['projected_monthly_cost']:.2f}

💰 相对于Tardis节省
───────────────────────────────────────────────
  本期节省:       ¥{summary['potential_savings_vs_tardis']:.2f}
  节省比例:       85%+

⚡ 性能指标
───────────────────────────────────────────────
  平均延迟:       {summary['avg_latency_ms']:.2f}ms
  延迟SLA:        {'✅ 达标' if summary['avg_latency_ms'] < 50 else '⚠️ 需优化'}

📈 分模型消费
───────────────────────────────────────────────"""
        
        for model, cost in summary['cost_by_model'].items():
            report += f"\n  {model}:         ¥{cost:.2f}"
        
        return report



使用示例

if __name__ == "__main__":
    monitor = HolySheepCostMonitor(api_key="YOUR_HOLYSHEEP_API_KEY")
    
    # 模拟一些API调用记录
    for i in range(100):
        monitor.log_api_call(
            model="deepseek-v3.2",
            endpoint="/hyperliquid/trades",
            tokens=5000,
            latency_ms=35.5 + (i % 20),
            success=(i % 50 != 0)
        )
    
    # 生成报告
    summary = monitor.get_usage_summary(days=30)
    print(f"总成本: ${summary['total_cost_usd']}")
    print(f"预估月费: ${summary['projected_monthly_cost']}")
    print(f"节省: ${summary['potential_savings_vs_tardis']}")
    
    # 中文报告
    print(monitor.get_billing_estimate_chinese())

Häufige Fehler und Lösungen

Fehler 1：API Key配置错误导致认证失败

错误信息：{"error": "Invalid API key or unauthorized access"}

常见原因：

Lösung：

# 错误示例
api_key = " YOUR_HOLYSHEEP_API_KEY "  # 前后有空格!
headers = {"Authorization": f"Bearer {api_key}"}


正确示例

api_key = "YOUR_HOLYSHEEP_API_KEY".strip()  # 去除首尾空格
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json"
}


验证Key有效性

import requests
response = requests.get(
    "https://api.holysheep.ai/v1/auth/verify",
    headers={"Authorization": f"Bearer {api_key}"}
)
if response.status_code == 200:
    print("✅ API Key验证成功")
else:
    print(f"❌ 认证失败: {response.json()}")

Fehler 2：时间戳格式导致数据查询范围错误

错误信息：{"error": "startTime must be less than endTime"} 或获取的数据为空

常见原因：

Lösung：

from datetime import datetime, timezone

def get_hyperliquid_timestamp(dt: datetime = None) -> int:
    """
    转换datetime为Hyperliquid所需的

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