股票交易有开发接口-利用ccxt爬取交易所的交易历史烛线图数据信息

利用ccxt爬取交易所的交易历史烛线图数据信息
2Python实现


import pandas as pd
import time
import os
import datetime
import ccxt


pd.set_option("expand_frame_repr", False)  #

TIMEOUT = 6  # 6 second
BITFINEX_LIMIT = 5000
BITMEX_LIMIT = 500
BINANCE_LIMIT = 500


def crawl_exchanges_datas(exchange_name, symbol, start_time, end_time):
    """
    爬取交易所数据的方法.
    :param exchange_name:  交易所名称.
    :param symbol: 请求的symbol: like BTC/USDT, ETH/USD等。
    :param start_time: like 2018-1-1
    :param end_time: like 2019-1-1
    :return:
    """

    exchange_class = getattr(ccxt, exchange_name)   # 获取交易所的名称 ccxt.binance
    exchange = exchange_class()  # 交易所的类. 类似 ccxt.bitfinex()
    print(exchange)
    # exit()

    current_path = os.getcwd()
    file_dir = os.path.join(current_path, exchange_name, symbol.replace("/", ""))

    if not os.path.exists(file_dir):
        os.makedirs(file_dir)


    start_time = datetime.datetime.strptime(start_time, "%Y-%m-%d")
    end_time = datetime.datetime.strptime(end_time, "%Y-%m-%d")

    start_time_stamp = int(time.mktime(start_time.timetuple())) * 1000
    end_time_stamp = int(time.mktime(end_time.timetuple())) * 1000

    print(start_time_stamp)  # 1529233920000
    print(end_time_stamp)

    limit_count = 500
    if exchange_name == "bitfinex":
        limit_count = BITFINEX_LIMIT
    elif exchange_name == "bitmex":
        limit_count = BITMEX_LIMIT
    elif exchange_name == "binance":
        limit_count = BINANCE_LIMIT

    while True:
        try:

            print(start_time_stamp)
            data = exchange.fetch_ohlcv(symbol, timeframe="1m", since=start_time_stamp, limit=limit_count)
            df = pd.DataFrame(data)
            df.rename(columns={0: "open_time", 1: "open", 2: "high", 3: "low", 4: "close", 5: "volume"}, inplace=True)

            start_time_stamp = int(df.iloc[-1]["open_time"])  # 获取下一个次请求的时间.

            filename = str(start_time_stamp) + ".csv"
            save_file_path = os.path.join(file_dir, filename)

            print("文件保存路径为：%s" % save_file_path)
            # exit()
            df.set_index("open_time", drop=True, inplace=True)
            df.to_csv(save_file_path)

            if start_time_stamp > end_time_stamp:
                print("完成数据的请求.")
                break

            time.sleep(3)

        except Exception as error:
            print(error)
            time.sleep(10)


def sample_datas(exchange_name, symbol):
    """

    :param exchange_name:
    :param symbol:
    :return:
    """
    path = os.path.join(os.getcwd(), exchange_name, symbol.replace("/", ""))
    print(path)

    # exit()

    file_paths = []
    for root, dirs, files in os.walk(path):
        if files:
            for file in files:
                if file.endswith(".csv"):
                    file_paths.append(os.path.join(path, file))

    file_paths = sorted(file_paths)
    all_df = pd.DataFrame()

    for file in file_paths:
        df = pd.read_csv(file)
        all_df = all_df.append(df, ignore_index=True)

    all_df = all_df.sort_values(by="open_time", ascending=True)

    print(all_df)

    return all_df


def clear_datas(exchange_name, symbol):
    df = sample_datas(exchange_name, symbol)
    df["open_time"] = df["open_time"].apply(lambda x: (x//60)*60)
    print(df)
    df["Datetime"] = pd.to_datetime(df["open_time"], unit="ms") + pd.Timedelta(hours=8)
    df.drop_duplicates(subset=["open_time"], inplace=True)
    df.set_index("Datetime", inplace=True)
    print("*" * 20)
    print(df)
    symbol_path = symbol.replace("/", "")
    df.to_csv(f"{exchange_name}_{symbol_path}_1min_data.csv")


if __name__ == "__main__":

    exchange_name = "mexc"
    # 初始化交易所对象
    exchange_class = getattr(ccxt, exchange_name)   # 获取交易所的名称 ccxt.binance
    exchange = exchange_class()  # 交易所的类. 类似 ccxt.bitfinex()
    # 输出交易所所有symbol
    # if exchange.has["fetchOHLCV"]:
    #     for symbol in exchange.load_markets():
    symbol = "BTC/USDT"
    crawl_exchanges_datas(exchange_name, symbol, "2022-8-1", "2022-10-25")
    # 合并文件
    # sample_datas(exchange_name, symbol)
文章为作者独立观点，不代表观点