我叫阿杰，去年在一家加密货币量化基金做策略开发。我们团队当时遇到一个头疼的问题：手动设定买卖价差要么太宽吃掉利润，要么太窄频繁被动成交。最关键的是，我们发现订单簿的微观结构其实藏着很多可以量化的信息——挂单密度、深度分布、波动率聚集……这些都需要实时的高质量数据来驱动。
后来我们接入 Tardis.dev 的逐笔成交和 Order Book 数据，配合 HolySheep AI 的 Claude 模型来做动态报价参数优化，3个月内把做市策略的年化收益率从 12% 提升到了 23%，而成交滑点降低了 60%。今天这篇文章，我就把整套方案完整拆解给你看。
一、为什么做市商需要订单簿数据
传统做市商策略往往依赖简单的固定价差或历史统计模型。但真实的订单簿是这样的：
- 深度在某几个价格区间会形成"厚墙"，突破需要更多流动性
- 大单会在特定价格积累，形成支撑或压力位
- 波动率高的时候合理价差应该更宽，低波动时可以做窄
- 交易所深度图的形态每天都在变化，静态策略注定跑输
我自己在测试中发现，用 5 分钟 K 线数据做的价差模型，最大回撤是实时订单簿驱动的 3 倍以上。所以核心问题变成了：如何实时获取并解析订单簿数据，然后用 AI 做出更智能的报价决策。
二、Tardis.dev 数据架构
2.1 核心数据类型
Tardis.dev 提供的数据类型非常适合做市商策略：
- Order Book 快照 + 增量更新：每个交易所的消息格式都已归一化
- 逐笔成交 (Trades)：含成交方向、量级、是否是主力单标记
- 资金费率 & 强平数据：Bybit/OKX 的合约数据直接影响价格波动
- 支持交易所：Binance/Bybit/OKX/Deribit 等主流合约交易所全覆盖
2.2 WebSocket 数据订阅代码
# tardis_websocket_collector.py
import asyncio
import json
from tardis_dev import TardisDevClient
from dataclasses import dataclass, field
from typing import Dict, List
from collections import defaultdict
import numpy as np
@dataclass
class OrderBookLevel:
"""订单簿价格档位"""
price: float
size: float
count: int = 1
@dataclass
class OrderBook:
"""订单簿状态"""
bids: List[OrderBookLevel] = field(default_factory=list) # 买单
asks: List[OrderBookLevel] = field(default_factory=list) # 卖单
last_update_id: int = 0
timestamp: float = 0.0
def get_mid_price(self) -> float:
"""计算中间价"""
if not self.bids or not self.asks:
return 0.0
return (self.bids[0].price + self.asks[0].price) / 2
def get_spread_bps(self) -> float:
"""计算价差（基点）"""
if not self.bids or not self.asks:
return 0.0
mid = self.get_mid_price()
if mid == 0:
return 0.0
return (self.asks[0].price - self.bids[0].price) / mid * 10000
def get_depth_ratio(self, depth_pct: float = 0.01) -> float:
"""计算指定深度区间内买卖量的比值"""
mid = self.get_mid_price()
if mid == 0:
return 1.0
bid_depth = sum(
lvl.size for lvl in self.bids
if (mid - lvl.price) / mid <= depth_pct
)
ask_depth = sum(
lvl.size for lvl in self.asks
if (lvl.price - mid) / mid <= depth_pct
)
return bid_depth / ask_depth if ask_depth > 0 else 1.0
def get_order_imbalance(self, levels: int = 10) -> float:
"""计算订单簿买卖不平衡度 (-1 到 1)"""
if not self.bids or not self.asks:
return 0.0
bid_volume = sum(lvl.size for lvl in self.bids[:levels])
ask_volume = sum(lvl.size for lvl in self.asks[:levels])
total = bid_volume + ask_volume
if total == 0:
return 0.0
return (bid_volume - ask_volume) / total
class TardisMarketDataCollector:
"""Tardis.dev 数据采集器"""
def __init__(self, exchange: str, symbol: str):
self.exchange = exchange
self.symbol = symbol
self.order_books: Dict[str, OrderBook] = {}
self.trade_buffer: List[dict] = []
self.liquidation_buffer: List[dict] = []
# 统计指标
self.price_volatility = 0.0
self.volume_ma5 = 0.0
self.volume_history: List[float] = []
def process_message(self, msg: dict):
"""处理 Tardis 归一化消息"""
msg_type = msg.get("type")
if msg_type == "book":
self._process_order_book_snapshot(msg)
elif msg_type == "book_snapshot":
self._process_order_book_snapshot(msg)
elif msg_type == "trade":
self._process_trade(msg)
elif msg_type == "liquidation":
self._process_liquidation(msg)
def _process_order_book_snapshot(self, msg: dict):
"""处理订单簿快照"""
exchange = msg.get("exchange", self.exchange)
bids = [
OrderBookLevel(price=float(p), size=float(s))
for p, s in msg.get("bids", [])
]
asks = [
OrderBookLevel(price=float(p), size=float(s))
for p, s in msg.get("asks", [])
]
self.order_books[exchange] = OrderBook(
bids=bids,
asks=asks,
last_update_id=msg.get("id", 0),
timestamp=msg.get("timestamp", 0) / 1000
)
def _process_trade(self, msg: dict):
"""处理成交数据"""
self.trade_buffer.append({
"price": float(msg["price"]),
"amount": float(msg["amount"]),
"side": msg.get("side", "buy"),
"timestamp": msg["timestamp"],
"is_buyer_maker": msg.get("isBuyerMaker", True)
})
# 更新波动率统计
if len(self.trade_buffer) > 100:
prices = [t["price"] for t in self.trade_buffer[-100:]]
self.price_volatility = np.std(prices) / np.mean(prices)
# 更新成交量均线
volume = float(msg["amount"])
self.volume_history.append(volume)
if len(self.volume_history) > 300: # 5分钟窗口
self.volume_history.pop(0)
self.volume_ma5 = np.mean(self.volume_history[-60:])
def _process_liquidation(self, msg: dict):
"""处理强平数据"""
self.liquidation_buffer.append({
"price": float(msg["price"]),
"size": float(msg["size"]),
"side": msg.get("side", "buy"),
"timestamp": msg["timestamp"]
})
def get_market_features(self) -> dict:
"""提取策略需要的特征"""
ob = self.order_books.get(self.exchange)
if not ob:
return {}
return {
"mid_price": ob.get_mid_price(),
"spread_bps": ob.get_spread_bps(),
"order_imbalance": ob.get_order_imbalance(levels=10),
"depth_ratio": ob.get_depth_ratio(depth_pct=0.005),
"volatility": self.price_volatility,
"volume_ratio": sum(t["amount"] for t in self.trade_buffer[-10:]) / max(self.volume_ma5, 0.001),
"recent_liquidation_size": sum(l["size"] for l in self.liquidation_buffer[-5:]),
"bid1_size": ob.bids[0].size if ob.bids else 0,
"ask1_size": ob.asks[0].size if ob.asks else 0,
"top10_bid_depth": sum(lvl.size for lvl in ob.bids[:10]),
"top10_ask_depth": sum(lvl.size for lvl in ob.asks[:10]),
}
async def main():
"""主函数：连接 Tardis 并实时处理数据"""
collector = TardisMarketDataCollector(
exchange="binance",
symbol="BTC-USDT-PERPETUAL"
)
# Tardis.dev API Token（从环境变量获取）
tardis_token = os.getenv("TARDIS_API_TOKEN")
client = TardisDevClient(auth_token=tardis_token)
# 订阅实时数据流
await client.stream(
exchange="binance",
symbols=["BTCUSDT"],
filters=["book", "trade", "liquidation"],
callback=collector.process_message
)
if __name__ == "__main__":
asyncio.run(main())三、做市商价差策略核心逻辑
3.1 价差模型公式
我做策略时总结的动态价差公式：
# market_making_pricing.py
import numpy as np
from dataclasses import dataclass
from typing import Optional
@dataclass
class PricingConfig:
"""报价参数配置"""
min_spread_bps: float = 2.0 # 最小价差（基点）
max_spread_bps: float = 20.0 # 最大价差（基点）
base_spread_bps: float = 5.0 # 基础价差
volatility_multiplier: float = 1.5 # 波动率乘数
imbalance_multiplier: float = 0.8 # 不平衡度影响系数
inventory_skew: float = 0.3 # 库存偏移惩罚
# 库存管理
max_inventory_pct: float = 0.05 # 最大持仓占比（总资金）
inventory_target: float = 0.0 # 库存目标（中性）
class DynamicSpreadCalculator:
"""动态价差计算器"""
def __init__(self, config: PricingConfig):
self.config = config
self.current_inventory = 0.0 # 当前持仓（合约数）
self.total_balance = 1.0 # 总资金（归一化）
def calculate_spread(
self,
features: dict,
current_price: float
) -> tuple[float, float, float]:
"""
计算买卖价
返回: (买价, 卖价, 中间价)
"""
mid_price = features.get("mid_price", current_price)
volatility = features.get("volatility", 0.001)
imbalance = features.get("order_imbalance", 0.0)
spread_bps = features.get("spread_bps", 5.0)
# 1. 基础价差：根据当前市场价差调整
base_spread = max(
self.config.min_spread_bps,
min(self.config.max_spread_bps, spread_bps * 1.2)
)
# 2. 波动率调整：波动越大，价差越宽
volatility_adj = self.config.volatility_multiplier * volatility * 10000
# 3. 订单簿不平衡调整：卖方压力大时，降低卖价吸引力
if imbalance < -0.2: # 卖方占优
sell_adjustment = imbalance * self.config.imbalance_multiplier
elif imbalance > 0.2: # 买方占优
sell_adjustment = imbalance * self.config.imbalance_multiplier * 0.5
else:
sell_adjustment = 0
# 4. 库存管理惩罚
inventory_pct = self.current_inventory / (self.total_balance * current_price)
inventory_penalty = self._calculate_inventory_penalty(inventory_pct)
# 5. 最终价差
final_spread = (
base_spread
+ volatility_adj
+ inventory_penalty
)
final_spread = max(
self.config.min_spread_bps,
min(self.config.max_spread_bps, final_spread)
)
# 6. 计算买卖价（考虑库存偏移）
half_spread = final_spread / 2 / 10000 * mid_price
# 库存偏移：多头时买价更低卖价更低，空头时买价更高卖价更高
skew_adjustment = (
self.config.inventory_skew
* (self.current_inventory / (self.total_balance * current_price))
* half_spread
)
bid_price = mid_price - half_spread + skew_adjustment
ask_price = mid_price + half_spread + skew_adjustment
return bid_price, ask_price, mid_price
def _calculate_inventory_penalty(self, inventory_pct: float) -> float:
"""
计算库存惩罚（基点）
持仓越多，惩罚越大，价差越宽
"""
if abs(inventory_pct) <= self.config.max_inventory_pct:
return 0
excess = abs(inventory_pct) - self.config.max_inventory_pct
# 惩罚曲线：指数增长
penalty = 10 * (excess ** 0.5) * 100 # 转为基点
return min(penalty, 50) # 最大惩罚50基点
def update_inventory(self, filled_bids: list, filled_asks: list):
"""更新持仓"""
for fill in filled_bids:
self.current_inventory += fill["size"]
for fill in filled_asks:
self.current_inventory -= fill["size"]
def should_cancel_order(
self,
order_price: float,
current_mid: float,
max_deviation_bps: float = 20.0
) -> bool:
"""
判断是否应该撤单
价格偏离太大时撤单重挂
"""
deviation = abs(order_price - current_mid) / current_mid * 10000
return deviation > max_deviation_bps3.2 仓位管理与风控
我踩过的坑：只关注价差而忽略仓位管理，2022年某次波动直接爆仓。从那以后我的风控规则是：
- 单边持仓不超过总资金的 5%
- 浮亏超过 2% 自动降仓 50%
- 资金费率大于 0.1%/4h 时停止做多
- 强平订单超过历史均值 3 倍时暂停策略
四、AI 驱动的报价参数优化
4.1 为什么需要 AI 来优化报价
我最初用固定参数跑策略，回测看起来很美。但实盘第一个月就发现：
- 不同交易对需要完全不同的价差参数
- 亚盘和欧美盘的波动特征差异巨大
- 新闻事件前后需要即时调整风控参数
手动调参根本跟不上市场变化，所以后来我接入了 HolySheep AI，用 Claude Sonnet 4.5 模型来实时分析市场状态，生成参数调整建议。
4.2 HolySheep AI 集成代码
# ai_optimizer.py
import httpx
import json
from typing import Optional
from dataclasses import dataclass
from datetime import datetime
HolySheep AI API 配置（国内直连 <50ms）
BASE_URL = "https://api.holysheep.ai/v1"
HOLYSHEEP_API_KEY = "YOUR_HOLYSHEEP_API_KEY" # 替换为你的 API Key
@dataclass
class MarketContext:
"""市场上下文"""
symbol: str
current_time: str
price: float
spread_bps: float
order_imbalance: float
volatility: float
volume_ratio: float
recent_liquidation: float
bid_depth_10: float
ask_depth_10: float
def to_prompt_context(self) -> str:
return f"""
交易对: {self.symbol}
时间: {self.current_time}
当前价格: ${self.price:.2f}
市场价差: {self.spread_bps:.1f} bps
订单簿不平衡度: {self.order_imbalance:.3f} (范围 -1 到 1)
波动率: {self.volatility:.5f}
成交量比(短期/均值): {self.volume_ratio:.2f}
近期强平量: {self.recent_liquidation:.2f}
买一深度(10档): {self.bid_depth_10:.4f}
卖一深度(10档): {self.ask_depth_10:.4f}
"""
class AIParameterOptimizer:
"""AI 驱动的参数优化器"""
SYSTEM_PROMPT = """你是一位专业的加密货币做市商策略师。
你的任务是分析当前市场状态，给出最优的报价参数调整建议。
请基于以下原则给出建议：
1. 波动率高时适当扩大价差保护自己
2. 订单簿严重不平衡时，调整报价方向引导市场
3. 大额强平信号预示波动加剧，建议谨慎
4. 深度不对称时，合理利用信息优势
5. 始终控制风险，留有余地
请以 JSON 格式输出分析和建议："""
def __init__(self, api_key: str = HOLYSHEEP_API_KEY):
self.api_key = api_key
self.client = httpx.AsyncClient(
base_url=BASE_URL,
timeout=30.0,
headers={
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
)
# 建议缓存，避免频繁调用
self._cache: dict = {}
self._cache_ttl = 10 # 缓存 10 秒
async def get_optimization_advice(
self,
context: MarketContext
) -> Optional[dict]:
"""
获取 AI 参数优化建议
返回格式:
{
"spread_adjustment": 1.2, # 价差调整系数
"risk_level": "normal", # risk_level: low/normal/high
"direction_bias": "neutral", # neutral/buy_heavy/sell_heavy
"max_position_pct": 0.03, # 建议最大持仓
"reasoning": "...",
"confidence": 0.85
}
"""
# 检查缓存
cache_key = f"{context.symbol}_{int(context.price)}"
if cache_key in self._cache:
cached = self._cache[cache_key]
if datetime.now().timestamp() - cached["ts"] < self._cache_ttl:
return cached["data"]
user_prompt = f"""市场状态:
{context.to_prompt_context()}
请分析以上市场数据，输出 JSON 格式的参数建议："""
try:
response = await self.client.post(
"/chat/completions",
json={
"model": "claude-sonnet-4.5",
"messages": [
{"role": "system", "content": self.SYSTEM_PROMPT},
{"role": "user", "content": user_prompt}
],
"temperature": 0.3,
"max_tokens": 500
}
)
if response.status_code == 200:
result = response.json()
content = result["choices"][0]["message"]["content"]
# 解析 JSON
advice = json.loads(content)
self._cache[cache_key] = {
"data": advice,
"ts": datetime.now().timestamp()
}
return advice
else:
print(f"AI API 请求失败: {response.status_code}")
return None
except Exception as e:
print(f"AI 优化器错误: {e}")
return None
async def analyze_market_sentiment(self, features: dict) -> str:
"""
快速情绪分析（用更便宜的模型）
返回: "bullish" / "bearish" / "neutral"
"""
prompt = f"""
当前市场数据：
- 订单簿不平衡度: {features.get('order_imbalance', 0):.3f}
- 成交量比: {features.get('volume_ratio', 1):.2f}
- 波动率: {features.get('volatility', 0):.5f}
快速判断短期情绪（仅输出 bullish/bearish/neutral）："""
try:
response = await self.client.post(
"/chat/completions",
json={
"model": "gpt-4.1", # $8/MTok，更经济
"messages": [{"role": "user", "content": prompt}],
"temperature": 0.1,
"max_tokens": 10
}
)
if response.status_code == 200:
result = response.json()
return result["choices"][0]["message"]["content"].strip().lower()
except Exception:
pass
return "neutral"
async def close(self):
"""关闭连接"""
await self.client.aclose()
使用示例
async def example_usage():
optimizer = AIParameterOptimizer()
context = MarketContext(
symbol="BTC-USDT-PERPETUAL",
current_time=datetime.now().isoformat(),
price=67500.0,
spread_bps=3.5,
order_imbalance=0.15,
volatility=0.0008,
volume_ratio=1.3,
recent_liquidation=250000,
bid_depth_10=15.5,
ask_depth_10=14.2
)
advice = await optimizer.get_optimization_advice(context)
if advice:
print(f"AI 建议价差调整系数: {advice['spread_adjustment']}")
print(f"风险等级: {advice['risk_level']}")
print(f"方向偏好: {advice['direction_bias']}")
print(f"建议最大持仓: {advice['max_position_pct']*100:.1f}%")
await optimizer.close()4.3 策略主循环
# market_maker_main.py
import asyncio
from market_making_pricing import DynamicSpreadCalculator, PricingConfig
from tardis_websocket_collector import TardisMarketDataCollector
from ai_optimizer import AIParameterOptimizer, MarketContext
from datetime import datetime
import os
class MarketMakerStrategy:
"""做市商策略主类"""
def __init__(self, symbol: str):
# 初始化组件
self.collector = TardisMarketDataCollector(
exchange="binance",
symbol=symbol
)
self.pricing = DynamicSpreadCalculator(
config=PricingConfig(
min_spread_bps=2.0,
max_spread_bps=15.0,
base_spread_bps=4.0,
volatility_multiplier=1.2,
max_inventory_pct=0.04
)
)
# HolySheep AI 优化器
self.optimizer = AIParameterOptimizer(
api_key=os.getenv("HOLYSHEEP_API_KEY")
)
# 状态
self.active_orders = []
self.running = False
async def on_tick(self, features: dict):
"""每个数据更新周期调用"""
if not features or features.get("mid_price", 0) == 0:
return
# 1. 获取 AI 建议（每 10 秒最多一次）
ai_advice = await self.optimizer.get_optimization_advice(
MarketContext(
symbol=self.collector.symbol,
current_time=datetime.now().isoformat(),
price=features["mid_price"],
spread_bps=features["spread_bps"],
order_imbalance=features["order_imbalance"],
volatility=features["volatility"],
volume_ratio=features["volume_ratio"],
recent_liquidation=features["recent_liquidation_size"],
bid_depth_10=features["top10_bid_depth"],
ask_depth_10=features["top10_ask_depth"]
)
)
# 2. 动态调整参数
if ai_advice:
risk_level = ai_advice.get("risk_level", "normal")
if risk_level == "high":
self.pricing.config.max_spread_bps = 25.0
self.pricing.config.max_inventory_pct = 0.02
elif risk_level == "low":
self.pricing.config.max_spread_bps = 12.0
self.pricing.config.max_inventory_pct = 0.06
else:
self.pricing.config.max_spread_bps = 18.0
self.pricing.config.max_inventory_pct = 0.04
# 3. 计算报价
bid_price, ask_price, mid = self.pricing.calculate_spread(
features, features["mid_price"]
)
# 4. 下单/撤单逻辑（简化版）
await self._manage_orders(bid_price, ask_price, mid, features)
# 5. 风控检查
await self._risk_check(features)
async def _manage_orders(self, bid: float, ask: float, mid: float, features: dict):
"""订单管理"""
# 检查是否需要撤单
for order in self.active_orders:
if self.pricing.should_cancel_order(order["price"], mid):
await self._cancel_order(order)
# 保持双向各挂一单
if len(self.active_orders) < 2:
await self._place_order("buy", bid)
await self._place_order("sell", ask)
async def _place_order(self, side: str, price: float):
"""下单接口（需对接交易所 API）"""
# TODO: 实现实际下单逻辑
self.active_orders.append({
"side": side,
"price": price,
"size": 0.01, # 最小交易单位
"order_id": f"local_{side}_{price}_{datetime.now().timestamp()}"
})
print(f"[下单] {side.upper()} @ {price}")
async def _cancel_order(self, order: dict):
"""撤单"""
self.active_orders = [
o for o in self.active_orders
if o["order_id"] != order["order_id"]
]
print(f"[撤单] {order['side'].upper()} @ {order['price']}")
async def _risk_check(self, features: dict):
"""风控检查"""
# 检查强平信号
liq_size = features.get("recent_liquidation_size", 0)
if liq_size > 500000: # 超过 50 万 USDT 强平
print("[风控] 检测到大额强平，暂停策略 5 分钟")
await self._pause_strategy(300)
# 检查持仓超限
inv_pct = abs(self.pricing.current_inventory) / (self.pricing.total_balance * features["mid_price"])
if inv_pct > self.pricing.config.max_inventory_pct * 1.5:
print(f"[风控] 持仓超限 {inv_pct:.2%}，强制平仓")
await self._force_close()
async def _pause_strategy(self, seconds: int):
"""暂停策略"""
self.running = False
await asyncio.sleep(seconds)
self.running = True
async def _force_close(self):
"""强制平仓"""
for order in self.active_orders[:]:
await self._cancel_order(order)
# TODO: 执行市价平仓
async def run(self):
"""主运行循环"""
self.running = True
print(f"[启动] 做市商策略开始运行 - {self.collector.symbol}")
while self.running:
features = self.collector.get_market_features()
if features:
await self.on_tick(features)
await asyncio.sleep(1) # 每秒更新一次
async def main():
strategy = MarketMakerStrategy(symbol="BTC-USDT-PERPETUAL")
await strategy.run()
if __name__ == "__main__":
asyncio.run(main())五、Tardis.dev vs 其他数据源对比
我用过多家加密货币数据提供商，下面是真实的横向对比：
| 对比项 | Tardis.dev | 交易所官方 WebSocket | CryptoCompare | CoinGecko |
|---|---|---|---|---|
| 数据完整性 | ⭐⭐⭐⭐⭐ 归一化全量数据 | ⭐⭐⭐⭐ 原始格式需转换 | ⭐⭐⭐ 仅 REST，历史有限 | ⭐⭐ 仅现货，深度差 |
| 延迟 | <100ms | <50ms | 500ms+ | 1s+ |
| 订单簿历史 | ⭐⭐⭐⭐⭐ 支持回放 | ❌ 无 | ❌ 无 | ❌ 无 |
| API 易用性 | ⭐⭐⭐⭐ 统一格式 | ⭐⭐ 需多交易所适配 | ⭐⭐⭐ | ⭐⭐⭐ |
| 价格（月付） | $99 起 | 免费 | $79 起 | $0（有限制） |
| 适合做市商 | ⭐⭐⭐⭐⭐ 强烈推荐 | ⭐⭐⭐ 可用但不推荐 | ⭐ 不适合 | ❌ 不适合 |
Tardis.dev 订阅计划
| 计划 | 价格 | 数据权限 | 适用场景 |
|---|---|---|---|
| Starter | $99/月 | 1 个交易所 | 个人策略测试 |
| Pro | $299/月 | 3 个交易所 | 中小型量化团队 |
| Enterprise | $999/月起 | 全交易所 + 专属支持 | 机构级做市商 |
六、适合谁与不适合谁
适合使用这套方案的人
- 有技术背景的独立开发者：能写 Python，了解 WebSocket 和订单簿概念
- 量化交易团队：已有交易执行系统，只需数据层和策略层
- 做市商新手：想从数据层面理解订单簿微观结构
- 有稳定 IT 预算：能承担 $99-299/月的 Tardis + HolySheep 费用
不适合的情况
- 纯小白用户：连 Python 都写不利索，建议先学习基础
- 高频交易 (HFT)：延迟要求 <1ms 的场景需要 C++/FPGA 方案
- 资金量小于 $10,000：手续费和价差可能吃掉大部分利润
- 没有技术团队：遇到交易所 API 变更或数据问题时无法快速响应
七、价格与回本测算
我自己跑这套方案的月度成本：
| 成本项 | 供应商 | 月费（美元） | 备注 |
|---|---|---|---|
| 实时数据 | Tardis.dev Starter | $99 | 1 个交易所 |
| AI 参数优化 | HolySheep AI | ~$30 | Claude Sonnet 4.5，约 200 万 Token/月 |
| 服务器 | AWS Tokyo / 阿里云 | $50 |
相关资源相关文章 |