702 lines
23 KiB
Python
702 lines
23 KiB
Python
"""
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Bitget UTA 数据服务 - 获取加密货币 K 线数据和技术指标
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使用 requests 直接调用 REST API
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"""
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import pandas as pd
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import numpy as np
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import requests
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from typing import Dict, List, Optional, Any
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from app.utils.logger import logger
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class BitgetService:
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"""Bitget UTA 数据服务（使用 requests 直接调用 REST API）"""
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# K线周期映射 - 注意 Bitget 使用大写 H
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INTERVALS = {
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'1m': '1m',
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'5m': '5m',
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'15m': '15m',
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'30m': '30m',
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'1h': '1H', # Bitget 大写
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}
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# Bitget API 基础 URL
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BASE_URL = "https://api.bitget.com"
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TESTNET_URL = "https://api-testnet.bitget.com"
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# 产品类型
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CATEGORY_SPOT = 'SPOT'
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CATEGORY_USDT_FUTURES = 'USDT-FUTURES'
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CATEGORY_COIN_FUTURES = 'COIN-FUTURES'
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CATEGORY_USDC_FUTURES = 'USDC-FUTURES'
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def __init__(self, api_key: str = "", api_secret: str = "", use_testnet: bool = False):
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"""
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初始化 Bitget 服务
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Args:
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api_key: API 密钥（可选，公开数据不需要）
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api_secret: API 密钥（可选）
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use_testnet: 是否使用测试网
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"""
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self._api_key = api_key
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self._api_secret = api_secret
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self._base_url = self.TESTNET_URL if use_testnet else self.BASE_URL
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self._session = requests.Session()
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if api_key:
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self._session.headers.update({
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'ACCESS-KEY': api_key,
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'ACCESS-SIGN': api_secret
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})
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logger.info(f"Bitget 服务初始化完成 ({'测试网' if use_testnet else '生产网'})")
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def get_klines(self, symbol: str, interval: str, limit: int = 100,
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category: str = None) -> pd.DataFrame:
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"""
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获取 K 线数据
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Args:
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symbol: 交易对，如 'BTCUSDT'
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interval: K线周期，如 '5m', '15m', '1h', '4h'
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limit: 获取数量（最大1000）
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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DataFrame 包含 OHLCV 数据
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"""
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try:
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if category is None:
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category = self.CATEGORY_USDT_FUTURES
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bitget_interval = self.INTERVALS.get(interval, interval)
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url = f"{self._base_url}/api/v3/market/candles"
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params = {
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'category': category,
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'symbol': symbol,
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'interval': bitget_interval,
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'limit': str(min(limit, 1000)) # Bitget 最大 1000
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}
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response = self._session.get(url, params=params, timeout=10)
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response.raise_for_status()
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result = response.json()
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# Bitget 返回格式: {"code": "00000", "msg": "success", "requestTime": ..., "data": [...]}
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if result.get('code') != '00000':
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logger.error(f"Bitget API 错误: {result.get('msg')}")
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return pd.DataFrame()
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klines = result.get('data', [])
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return self._parse_klines(klines)
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except Exception as e:
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logger.error(f"获取 {symbol} {interval} K线数据失败: {e}")
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return pd.DataFrame()
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def get_multi_timeframe_data(self, symbol: str,
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category: str = None) -> Dict[str, pd.DataFrame]:
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"""
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获取多周期 K 线数据
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Args:
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symbol: 交易对
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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包含 1m, 5m, 15m, 30m, 1h 数据的字典
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"""
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# 不同周期使用不同的数据量，平衡分析深度和性能
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# 1m: 200根 = 3.3小时（超短线精确入场）
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# 5m: 200根 = 16.7小时（日内分析）
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# 15m: 200根 = 2.1天（短线分析）
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# 30m: 200根 = 4.2天（日内趋势）
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# 1h: 300根 = 12.5天（日内主趋势）
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limits = {
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'1m': 200,
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'5m': 200,
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'15m': 200,
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'30m': 200,
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'1h': 300
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}
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data = {}
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for interval in ['1m', '5m', '15m', '30m', '1h']:
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df = self.get_klines(symbol, interval, limit=limits.get(interval, 100),
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category=category)
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if not df.empty:
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df = self.calculate_indicators(df, interval)
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data[interval] = df
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logger.info(f"获取 {symbol} 多周期数据完成")
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return data
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def _parse_klines(self, klines: List) -> pd.DataFrame:
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"""
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解析 K 线数据为 DataFrame
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Bitget 返回格式: [timestamp, open, high, low, close, base_volume, quote_volume]
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"""
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if not klines:
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return pd.DataFrame()
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df = pd.DataFrame(klines, columns=[
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'open_time', 'open', 'high', 'low', 'close',
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'base_volume', 'quote_volume'
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])
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# 转换数据类型
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df['open_time'] = pd.to_datetime(df['open_time'].astype(int), unit='ms')
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for col in ['open', 'high', 'low', 'close', 'base_volume', 'quote_volume']:
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df[col] = df[col].astype(float)
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# 重命名以匹配 Binance 格式
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df = df.rename(columns={'base_volume': 'volume'})
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# 只保留需要的列（与 Binance 保持一致）
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df = df[['open_time', 'open', 'high', 'low', 'close', 'volume']]
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# 按时间排序（Bitget 返回的数据可能是倒序）
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df = df.sort_values('open_time').reset_index(drop=True)
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return df
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def calculate_indicators(self, df: pd.DataFrame, interval: str = '1h') -> pd.DataFrame:
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"""
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计算技术指标
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Args:
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df: K线数据 DataFrame
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interval: K线周期，用于调整 MA 参数
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Returns:
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添加了技术指标的 DataFrame
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"""
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if df.empty:
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return df
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# 根据周期调整 MA 参数
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ma_config = {
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'1m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
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'5m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
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'15m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
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'30m': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
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'1h': {'ma_short': 5, 'ma_mid': 10, 'ma_long': 20, 'ma_extra': 50},
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}
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config = ma_config.get(interval, ma_config['1h'])
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# EMA（日内交易使用 EMA 反应更快）
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df['ema5'] = self._calculate_ema(df['close'], config['ma_short'])
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df['ema10'] = self._calculate_ema(df['close'], config['ma_mid'])
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df['ema20'] = self._calculate_ema(df['close'], config['ma_long'])
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df['ema50'] = self._calculate_ema(df['close'], config['ma_extra'])
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# 兼容：保留 ma5/ma10/ma20/ma50 作为 EMA 的别名
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df['ma5'] = df['ema5']
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df['ma10'] = df['ema10']
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df['ma20'] = df['ema20']
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df['ma50'] = df['ema50']
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# MACD EMA（保持不变）
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df['ema12'] = self._calculate_ema(df['close'], 12)
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df['ema26'] = self._calculate_ema(df['close'], 26)
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# RSI
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df['rsi'] = self._calculate_rsi(df['close'], 14)
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# MACD
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df['macd'], df['macd_signal'], df['macd_hist'] = self._calculate_macd(df['close'])
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# 布林带
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df['bb_upper'], df['bb_middle'], df['bb_lower'] = self._calculate_bollinger(df['close'])
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# KDJ
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df['k'], df['d'], df['j'] = self._calculate_kdj(df['high'], df['low'], df['close'])
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# ATR
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df['atr'] = self._calculate_atr(df['high'], df['low'], df['close'])
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# 成交量均线
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df['volume_ma5'] = self._calculate_ma(df['volume'], 5)
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df['volume_ma20'] = self._calculate_ma(df['volume'], 20)
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df['volume_ratio'] = df['volume'] / df['volume_ma20']
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return df
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@staticmethod
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def _calculate_ma(data: pd.Series, period: int) -> pd.Series:
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"""简单移动平均线"""
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return data.rolling(window=period).mean()
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@staticmethod
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def _calculate_ema(data: pd.Series, period: int) -> pd.Series:
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"""指数移动平均线"""
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return data.ewm(span=period, adjust=False).mean()
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@staticmethod
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def _calculate_rsi(data: pd.Series, period: int = 14) -> pd.Series:
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"""RSI 指标 - 使用 Wilder's Smoothing 方法"""
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delta = data.diff()
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gain = delta.where(delta > 0, 0)
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loss = -delta.where(delta < 0, 0)
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avg_gain = gain.ewm(alpha=1/period, adjust=False).mean()
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avg_loss = loss.ewm(alpha=1/period, adjust=False).mean()
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rs = avg_gain / avg_loss
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rsi = 100 - (100 / (1 + rs))
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return rsi
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@staticmethod
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def _calculate_macd(data: pd.Series, fast: int = 12, slow: int = 26, signal: int = 9):
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"""MACD 指标"""
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ema_fast = data.ewm(span=fast, adjust=False).mean()
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ema_slow = data.ewm(span=slow, adjust=False).mean()
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macd = ema_fast - ema_slow
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signal_line = macd.ewm(span=signal, adjust=False).mean()
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histogram = macd - signal_line
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return macd, signal_line, histogram
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@staticmethod
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def _calculate_bollinger(data: pd.Series, period: int = 20, std_dev: float = 2.0):
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"""布林带"""
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middle = data.rolling(window=period).mean()
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std = data.rolling(window=period).std()
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upper = middle + (std * std_dev)
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lower = middle - (std * std_dev)
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return upper, middle, lower
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@staticmethod
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def _calculate_kdj(high: pd.Series, low: pd.Series, close: pd.Series,
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period: int = 9, k_period: int = 3, d_period: int = 3):
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"""KDJ 指标"""
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low_min = low.rolling(window=period).min()
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high_max = high.rolling(window=period).max()
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rsv = (close - low_min) / (high_max - low_min) * 100
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k = rsv.ewm(com=k_period - 1, adjust=False).mean()
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d = k.ewm(com=d_period - 1, adjust=False).mean()
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j = 3 * k - 2 * d
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return k, d, j
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@staticmethod
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def _calculate_atr(high: pd.Series, low: pd.Series, close: pd.Series, period: int = 14):
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"""ATR 平均真实波幅"""
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tr1 = high - low
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tr2 = abs(high - close.shift())
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tr3 = abs(low - close.shift())
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tr = pd.concat([tr1, tr2, tr3], axis=1).max(axis=1)
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atr = tr.rolling(window=period).mean()
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return atr
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def get_current_price(self, symbol: str, category: str = None) -> Optional[float]:
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"""
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获取当前价格
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Args:
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symbol: 交易对
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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当前价格
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"""
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try:
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if category is None:
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category = self.CATEGORY_USDT_FUTURES
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url = f"{self._base_url}/api/v3/market/tickers"
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params = {
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'category': category,
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'symbol': symbol
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}
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response = self._session.get(url, params=params, timeout=10)
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response.raise_for_status()
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result = response.json()
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if result.get('code') != '00000':
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logger.error(f"Bitget API 错误: {result.get('msg')}")
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return None
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data = result.get('data', [])
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if not data:
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return None
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# 返回第一个（也是唯一的）ticker 的最新价格
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return float(data[0].get('lastPrice', 0))
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except Exception as e:
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logger.error(f"获取 {symbol} 当前价格失败: {e}")
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return None
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def get_ticker(self, symbol: str, category: str = None) -> Optional[Dict[str, Any]]:
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"""
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获取完整的 Ticker 数据
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Args:
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symbol: 交易对
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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完整的 ticker 数据
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"""
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try:
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if category is None:
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category = self.CATEGORY_USDT_FUTURES
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url = f"{self._base_url}/api/v3/market/tickers"
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params = {
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'category': category,
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'symbol': symbol
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}
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response = self._session.get(url, params=params, timeout=10)
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response.raise_for_status()
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result = response.json()
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if result.get('code') != '00000':
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logger.error(f"Bitget API 错误: {result.get('msg')}")
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return None
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data = result.get('data', [])
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if not data:
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return None
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return data[0]
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except Exception as e:
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logger.error(f"获取 {symbol} ticker 失败: {e}")
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return None
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def get_symbol_info(self, symbol: str, category: str = None) -> Optional[Dict[str, Any]]:
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"""
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获取交易对信息（包含精度配置）
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Args:
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symbol: 交易对
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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交易对信息，包含 pricePrecision 和 quantityPrecision
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"""
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try:
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if category is None:
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category = self.CATEGORY_USDT_FUTURES
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url = f"{self._base_url}/api/v3/market/symbols"
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params = {
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'category': category,
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'symbol': symbol
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}
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response = self._session.get(url, params=params, timeout=10)
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response.raise_for_status()
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result = response.json()
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if result.get('code') != '00000':
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logger.error(f"Bitget API 错误: {result.get('msg')}")
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return None
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data = result.get('data', [])
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if not data:
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return None
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symbol_data = data[0]
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# 返回精度信息
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return {
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'symbol': symbol_data.get('symbol'),
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'status': symbol_data.get('status'),
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'pricePrecision': int(symbol_data.get('pricePrecision', 2)),
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'quantityPrecision': int(symbol_data.get('quantityPrecision', 2)),
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'minTradeAmount': float(symbol_data.get('minTradeAmount', 0)),
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'maxTradeAmount': float(symbol_data.get('maxTradeAmount', 0)),
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'takerFeeRate': float(symbol_data.get('takerFeeRate', 0.001)),
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'makerFeeRate': float(symbol_data.get('makerFeeRate', 0.001)),
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}
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except Exception as e:
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logger.error(f"获取 {symbol} 交易对信息失败: {e}")
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return None
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# 缓存交易对精度信息
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_symbol_precision_cache: Dict[str, Dict[str, int]] = {}
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def get_precision(self, symbol: str, category: str = None) -> Dict[str, int]:
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"""
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获取交易对价格和数量精度（带缓存）
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Args:
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symbol: 交易对
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category: 产品类型，默认 USDT-FUTURES
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Returns:
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{'pricePrecision': 2, 'quantityPrecision': 2}
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"""
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# 检查缓存
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if symbol in self._symbol_precision_cache:
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return self._symbol_precision_cache[symbol]
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# 获取交易对信息
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info = self.get_symbol_info(symbol, category)
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if info:
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precision = {
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'pricePrecision': info['pricePrecision'],
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'quantityPrecision': info['quantityPrecision']
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}
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# 缓存结果
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self._symbol_precision_cache[symbol] = precision
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return precision
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# 默认精度
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return {'pricePrecision': 2, 'quantityPrecision': 2}
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def round_price(self, symbol: str, price: float, category: str = None) -> float:
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"""
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根据交易对精度四舍五入价格
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Args:
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symbol: 交易对
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price: 原始价格
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category: 产品类型
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Returns:
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四舍五入后的价格
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"""
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precision = self.get_precision(symbol, category)
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return round(price, precision['pricePrecision'])
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def round_quantity(self, symbol: str, quantity: float, category: str = None) -> float:
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"""
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根据交易对精度四舍五入数量
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||
|
||
Args:
|
||
symbol: 交易对
|
||
quantity: 原始数量
|
||
category: 产品类型
|
||
|
||
Returns:
|
||
四舍五入后的数量
|
||
"""
|
||
precision = self.get_precision(symbol, category)
|
||
return round(quantity, precision['quantityPrecision'])
|
||
|
||
def get_funding_rate(self, symbol: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
获取资金费率（包含标记价格和指数价格）
|
||
|
||
Args:
|
||
symbol: 交易对，如 'BTCUSDT'
|
||
|
||
Returns:
|
||
包含资金费率、标记价格、指数价格的字典
|
||
"""
|
||
try:
|
||
# 同时获取 ticker 数据（包含标记价格和指数价格）
|
||
ticker = self.get_ticker(symbol, self.CATEGORY_USDT_FUTURES)
|
||
if not ticker:
|
||
logger.error(f"获取 {symbol} ticker 数据失败")
|
||
return None
|
||
|
||
# 获取资金费率
|
||
url = f"{self._base_url}/api/v3/market/current-fund-rate"
|
||
params = {'symbol': symbol}
|
||
|
||
response = self._session.get(url, params=params, timeout=10)
|
||
response.raise_for_status()
|
||
result = response.json()
|
||
|
||
if result.get('code') != '00000':
|
||
logger.error(f"Bitget API 错误: {result.get('msg')}")
|
||
return None
|
||
|
||
data = result.get('data', [])
|
||
if not data:
|
||
return None
|
||
|
||
funding_data = data[0]
|
||
|
||
# 解析资金费率
|
||
funding_rate = float(funding_data.get('fundingRate', 0))
|
||
next_update = int(funding_data.get('nextUpdate', 0))
|
||
min_rate = float(funding_data.get('minFundingRate', 0))
|
||
max_rate = float(funding_data.get('maxFundingRate', 0))
|
||
|
||
# 从 ticker 获取标记价格和指数价格
|
||
mark_price = float(ticker.get('markPrice', 0))
|
||
index_price = float(ticker.get('indexPrice', 0))
|
||
|
||
# 判断市场情绪
|
||
if funding_rate > 0.01: # > 0.1%
|
||
sentiment = "极度贪婪"
|
||
sentiment_level = "extreme_greed"
|
||
elif funding_rate > 0.0005: # > 0.05%
|
||
sentiment = "贪婪"
|
||
sentiment_level = "greed"
|
||
elif funding_rate < -0.01: # < -0.1%
|
||
sentiment = "极度恐惧"
|
||
sentiment_level = "extreme_fear"
|
||
elif funding_rate < -0.0005: # < -0.05%
|
||
sentiment = "恐惧"
|
||
sentiment_level = "fear"
|
||
else:
|
||
sentiment = "中性"
|
||
sentiment_level = "neutral"
|
||
|
||
return {
|
||
'funding_rate': funding_rate,
|
||
'funding_rate_percent': funding_rate * 100,
|
||
'next_funding_time': next_update,
|
||
'min_funding_rate': min_rate,
|
||
'max_funding_rate': max_rate,
|
||
'mark_price': mark_price,
|
||
'index_price': index_price,
|
||
'sentiment': sentiment,
|
||
'sentiment_level': sentiment_level
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取 {symbol} 资金费率失败: {e}")
|
||
return None
|
||
|
||
def get_open_interest(self, symbol: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
获取持仓量
|
||
|
||
Args:
|
||
symbol: 交易对，如 'BTCUSDT'
|
||
|
||
Returns:
|
||
包含持仓量信息的字典
|
||
"""
|
||
try:
|
||
# 从 ticker 获取持仓量
|
||
ticker = self.get_ticker(symbol, self.CATEGORY_USDT_FUTURES)
|
||
if not ticker:
|
||
return None
|
||
|
||
open_interest = float(ticker.get('openInterest', 0))
|
||
|
||
return {
|
||
'open_interest': open_interest,
|
||
'timestamp': int(ticker.get('ts', 0))
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取 {symbol} 持仓量失败: {e}")
|
||
return None
|
||
|
||
def get_futures_market_data(self, symbol: str) -> Optional[Dict[str, Any]]:
|
||
"""
|
||
获取合约市场综合数据（资金费率 + 持仓量）
|
||
|
||
Args:
|
||
symbol: 交易对
|
||
|
||
Returns:
|
||
综合市场数据
|
||
"""
|
||
try:
|
||
# 获取数据
|
||
funding_rate = self.get_funding_rate(symbol)
|
||
open_interest = self.get_open_interest(symbol)
|
||
ticker = self.get_ticker(symbol, self.CATEGORY_USDT_FUTURES)
|
||
|
||
if not funding_rate or not open_interest or not ticker:
|
||
logger.warning(f"获取 {symbol} 合约数据不完整")
|
||
return None
|
||
|
||
# 计算溢价率
|
||
premium_rate = 0
|
||
index_price = float(ticker.get('indexPrice', 0))
|
||
mark_price = float(ticker.get('markPrice', 0))
|
||
|
||
if index_price > 0:
|
||
premium_rate = ((mark_price - index_price) / index_price * 100)
|
||
|
||
return {
|
||
'funding_rate': funding_rate,
|
||
'open_interest': open_interest,
|
||
'premium_rate': premium_rate,
|
||
'market_sentiment': funding_rate.get('sentiment', ''),
|
||
'sentiment_level': funding_rate.get('sentiment_level', ''),
|
||
'mark_price': mark_price,
|
||
'index_price': index_price
|
||
}
|
||
|
||
except Exception as e:
|
||
logger.error(f"获取 {symbol} 合约市场数据失败: {e}")
|
||
return None
|
||
|
||
def format_futures_data_for_llm(self, symbol: str,
|
||
market_data: Dict[str, Any]) -> str:
|
||
"""
|
||
格式化合约数据供 LLM 分析
|
||
|
||
Args:
|
||
symbol: 交易对
|
||
market_data: 合约市场数据
|
||
|
||
Returns:
|
||
格式化的文本
|
||
"""
|
||
if not market_data:
|
||
return ""
|
||
|
||
lines = [f"\n## {symbol} 合约市场数据\n"]
|
||
|
||
# 资金费率
|
||
funding = market_data.get('funding_rate', {})
|
||
if funding:
|
||
fr = funding.get('funding_rate_percent', 0)
|
||
sentiment = funding.get('sentiment', '')
|
||
lines.append(f"### 资金费率")
|
||
lines.append(f"• 当前费率: {fr:.4f}%")
|
||
lines.append(f"• 市场情绪: {sentiment}")
|
||
lines.append(f"• 标记价格: ${market_data.get('mark_price', 0):,.2f}")
|
||
lines.append(f"• 指数价格: ${market_data.get('index_price', 0):,.2f}")
|
||
|
||
# 资金费率分析
|
||
if fr > 0.1:
|
||
lines.append(f"• ⚠️ 极高费率，多头过度杠杆，警惕回调风险")
|
||
elif fr > 0.05:
|
||
lines.append(f"• 正费率，多头占优但未极端")
|
||
elif fr < -0.1:
|
||
lines.append(f"• ⚠️ 极低费率，空头过度杠杆，可能反弹")
|
||
elif fr < -0.05:
|
||
lines.append(f"• 负费率，空头占优但未极端")
|
||
|
||
# 持仓量
|
||
oi = market_data.get('open_interest', {})
|
||
if oi:
|
||
lines.append(f"\n### 持仓量")
|
||
lines.append(f"• 当前持仓: {oi.get('open_interest', 0):,.0f} 张")
|
||
|
||
# 溢价率
|
||
premium = market_data.get('premium_rate', 0)
|
||
if premium != 0:
|
||
lines.append(f"\n### 溢价分析")
|
||
lines.append(f"• 现货溢价: {premium:+.2f}%")
|
||
if premium > 1:
|
||
lines.append(f"• ⚠️ 高溢价，市场过热")
|
||
elif premium < -1:
|
||
lines.append(f"• ⚠️ 负溢价，市场偏冷")
|
||
|
||
return "\n".join(lines)
|
||
|
||
|
||
# 全局实例
|
||
bitget_service = BitgetService()
|