247 lines
7.8 KiB
Python
247 lines
7.8 KiB
Python
"""
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Binance 数据服务 - 获取加密货币 K 线数据和技术指标
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"""
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import pandas as pd
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import numpy as np
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from typing import Dict, List, Optional, Any
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from binance.client import Client
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from binance.enums import (
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KLINE_INTERVAL_5MINUTE,
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KLINE_INTERVAL_15MINUTE,
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KLINE_INTERVAL_1HOUR,
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KLINE_INTERVAL_4HOUR
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)
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from app.utils.logger import logger
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class BinanceService:
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"""Binance 数据服务"""
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# K线周期映射
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INTERVALS = {
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'5m': KLINE_INTERVAL_5MINUTE,
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'15m': KLINE_INTERVAL_15MINUTE,
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'1h': KLINE_INTERVAL_1HOUR,
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'4h': KLINE_INTERVAL_4HOUR
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}
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def __init__(self, api_key: str = "", api_secret: str = ""):
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"""
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初始化 Binance 客户端
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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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"""
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self.client = Client(api_key=api_key, api_secret=api_secret)
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logger.info("Binance 服务初始化完成")
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def get_klines(self, symbol: str, interval: str, limit: int = 100) -> 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
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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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binance_interval = self.INTERVALS.get(interval, interval)
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klines = self.client.get_klines(
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symbol=symbol,
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interval=binance_interval,
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limit=limit
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)
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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) -> 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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Returns:
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包含 5m, 15m, 1h, 4h 数据的字典
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"""
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data = {}
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for interval in ['5m', '15m', '1h', '4h']:
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df = self.get_klines(symbol, interval, limit=100)
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if not df.empty:
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df = self.calculate_indicators(df)
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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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"""解析 K 线数据为 DataFrame"""
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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', 'volume',
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'close_time', 'quote_volume', 'trades',
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'taker_buy_base', 'taker_buy_quote', 'ignore'
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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'], unit='ms')
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df['close_time'] = pd.to_datetime(df['close_time'], unit='ms')
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for col in ['open', 'high', 'low', 'close', 'volume', 'quote_volume']:
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df[col] = df[col].astype(float)
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df['trades'] = df['trades'].astype(int)
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# 只保留需要的列
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df = df[['open_time', 'open', 'high', 'low', 'close', 'volume', 'trades']]
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return df
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def calculate_indicators(self, df: pd.DataFrame) -> 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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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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# 移动平均线
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df['ma5'] = self._calculate_ma(df['close'], 5)
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df['ma10'] = self._calculate_ma(df['close'], 10)
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df['ma20'] = self._calculate_ma(df['close'], 20)
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df['ma50'] = self._calculate_ma(df['close'], 50)
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# EMA
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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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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"""
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delta = data.diff()
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gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
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loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
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rs = gain / 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) -> Optional[float]:
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"""获取当前价格"""
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try:
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ticker = self.client.get_symbol_ticker(symbol=symbol)
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return float(ticker['price'])
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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_24h_stats(self, symbol: str) -> Optional[Dict[str, Any]]:
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"""获取 24 小时统计数据"""
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try:
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stats = self.client.get_ticker(symbol=symbol)
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return {
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'price': float(stats['lastPrice']),
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'price_change': float(stats['priceChange']),
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'price_change_percent': float(stats['priceChangePercent']),
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'high': float(stats['highPrice']),
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'low': float(stats['lowPrice']),
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'volume': float(stats['volume']),
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'quote_volume': float(stats['quoteVolume'])
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}
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except Exception as e:
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logger.error(f"获取 {symbol} 24h 统计失败: {e}")
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return None
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# 全局实例
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binance_service = BinanceService()
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