122 lines
5.9 KiB
Python
122 lines
5.9 KiB
Python
import time
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from datetime import datetime, timedelta
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import logging
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from typing import Optional
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import pandas as pd
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import okx.MarketData as Market
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logging.basicConfig(level=logging.INFO, format='%(asctime)s %(levelname)s: %(message)s')
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class DataMonitor:
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def __init__(self,
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api_key: str,
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secret_key: str,
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passphrase: str,
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sandbox: bool = True):
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flag = "1" if sandbox else "0" # 0:实盘环境 1:沙盒环境
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self.market_api = Market.MarketAPI(
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api_key=api_key, api_secret_key=secret_key, passphrase=passphrase,
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flag=flag
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)
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def get_historical_kline_data(self, symbol: str = None, start: str = None, bar: str = '1m', limit: int = 100, end_time: int = None) -> Optional[pd.DataFrame]:
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"""
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获取历史K线数据，支持start为北京时间字符串（%Y-%m-%d %H:%M:%S）或UTC毫秒级时间戳
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:param symbol: 交易对
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:param start: 起始时间（北京时间字符串或UTC毫秒级时间戳）
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:param bar: K线周期
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:param limit: 每次请求数量
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:param end_time: 结束时间（毫秒级timestamp），默认当前时间
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:return: pd.DataFrame
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"""
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from core.utils import datetime_to_timestamp
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if symbol is None:
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symbol = "XCH-USDT"
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if end_time is None:
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end_time = int(time.time() * 1000) # 当前时间（毫秒）
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# 处理start参数
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if start is None:
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# 默认两个月前
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two_months_ago = datetime.now() - timedelta(days=60)
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start_time = int(two_months_ago.timestamp() * 1000)
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else:
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try:
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# 判断是否就是timestamp整型数据
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if isinstance(start, int):
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start_time = start
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# 判断是否为纯数字（UTC毫秒级timestamp）
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elif start.isdigit():
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start_time = int(start)
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else:
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# 按北京时间字符串处理，转换为毫秒级timestamp
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start_time = datetime_to_timestamp(start)
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except Exception as e:
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logging.error(f"start参数解析失败: {e}")
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return None
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columns = ["timestamp", "open", "high", "low", "close", "volume", "volCcy", "volCCyQuote", "confirm"]
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all_data = []
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latest_timestamp = -1
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while start_time < end_time:
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try:
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# after，真实逻辑是获得指定时间之前的数据 ！！！
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response = self.market_api.get_history_candlesticks(
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instId=symbol,
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after=end_time, # 获取指定时间之前的数据,
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bar=bar,
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limit=str(limit)
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)
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if response["code"] != "0" or not response["data"]:
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logging.warning(f"请求失败或无数据: {response.get('msg', 'No message')}")
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break
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candles = response["data"]
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from_time = int(candles[-1][0])
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to_time = int(candles[0][0])
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if latest_timestamp == -1:
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latest_timestamp = from_time
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else:
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if latest_timestamp > from_time:
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latest_timestamp = from_time
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else:
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logging.warning(f"上一次数据最早时间戳 {latest_timestamp} 小于等于 from_time {from_time}， 停止获取数据")
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break
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from_time_str = pd.to_datetime(from_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
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to_time_str = pd.to_datetime(to_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
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logging.info(f"已获取 {len(candles)} 条数据，从: {from_time_str} 到: {to_time_str}")
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if from_time < start_time:
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start_time_str = pd.to_datetime(start_time, unit='ms', utc=True).tz_convert('Asia/Shanghai')
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logging.warning(f"本轮获取数据最早时间 {from_time_str} 早于 此次数据获取起始时间 {start_time_str}， 停止获取数据")
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# candels中仅保留start_time之后的数据
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candles = [candle for candle in candles if int(candle[0]) >= start_time]
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all_data.extend(candles)
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break
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else:
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# 更新 end_time 为本次请求中最早的时间戳
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end_time = from_time - 1 # 减 1 毫秒以避免重复
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all_data.extend(candles)
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time.sleep(0.2)
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except Exception as e:
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logging.error(f"请求出错: {e}")
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break
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if all_data:
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df = pd.DataFrame(all_data, columns=columns)
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df = df[df['confirm'] == '1']
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for col in ['open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote']:
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df[col] = pd.to_numeric(df[col], errors='coerce')
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dt_series = pd.to_datetime(df['timestamp'].astype(int), unit='ms', utc=True, errors='coerce').dt.tz_convert('Asia/Shanghai')
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df['date_time'] = dt_series.dt.strftime('%Y-%m-%d %H:%M:%S')
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# 将timestamp转换为整型
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df['timestamp'] = df['timestamp'].astype(int)
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# 添加虚拟货币名称列，内容为symbol
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df['symbol'] = symbol
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# 添加bar列，内容为bar
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df['bar'] = bar
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df = df[['symbol', 'bar', 'timestamp', 'date_time', 'open', 'high', 'low', 'close', 'volume', 'volCcy', 'volCCyQuote']]
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df.sort_values('timestamp', inplace=True)
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df.reset_index(drop=True, inplace=True)
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logging.info(f"总计获取 {len(df)} 条 K 线数据（仅confirm=1）")
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return df
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else:
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logging.warning("未获取到数据")
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return None |