ok及其他交易所获取数据方式
from urllib.request import urlopen, Request
import json
import pandas as pd
pd.set_option('expand_frame_repr', False) # 当列太多时不换行
# 抓取交易对的symbol
symbol = 'ltc_usdt'
# 构建url
# url = 'https://www.okex.com/api/v1/ticker.do?symbol=' + symbol
url = 'https://www.okex.com/api/v1/ticker.do?symbol=%s' % symbol
# # 抓取数据
# content = urlopen(url=url, timeout=15).read()
# content = content.decode('utf-8')
# 抓取数据,带有浏览器伪装
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
request = Request(url=url, headers=headers)
content = urlopen(request, timeout=15).read()
content = content.decode('utf-8')
# 将数据转化为dataframe
json_data = json.loads(content)
df = pd.DataFrame(json_data, dtype='float')
# 对df进行处理
df = df[['ticker']].T
print(df)
其他交易所获取数据方式
from urllib.request import urlopen, Request
import json
import pandas as pd
pd.set_option('expand_frame_repr', False) # 当列太多时不换行
# ===okex交易所
url = 'https://www.okex.com/api/v1/ticker.do?symbol=ltc_usdt' # tick数据
# url = 'https://www.okex.com/api/v1/kline.do?symbol=btc_usdt&type=30min' # k线数据
# ===binance交易所
# url = 'https://api.binance.com/api/v1/ticker/24hr' # tick数据
# url = 'https://api.binance.com/api/v1/klines?symbol=LTCBTC&interval=1h' # k线数据
# ===huobipro交易所
# url = 'https://api.huobipro.com/market/detail/merged?symbol=btcusdt' # tick数据
# url = 'https://api.huobipro.com/market/history/kline?symbol=btcusdt&period=1min' # k线数据
# # 抓取数据
# content = urlopen(url=url, timeout=15).read()
# content = content.decode('utf-8')
# 抓取数据,带有浏览器伪装
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
request = Request(url=url, headers=headers)
content = urlopen(request, timeout=15).read()
content = content.decode('utf-8')
print(content)
下面我们根据方法进行封装函数 Get_data_from_exchange.py
from urllib.request import urlopen, Request
import json
import pandas as pd
pd.set_option('expand_frame_repr', False) # 当列太多时不换行
# ===抓取数据
def get_url_content(url, max_try_number=5):
try_num = 0
while True:
try:
return urlopen(url, timeout=15).read().strip()
except Exception as http_err:
print(url, "抓取报错", http_err)
try_num += 1
if try_num >= max_try_number:
print("尝试失败次数过多,放弃尝试")
return None
# ===抓取数据,带有浏览器伪装
def get_url_content2(url, max_try_number=5):
try_num = 0
while True:
try:
headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:23.0) Gecko/20100101 Firefox/23.0'}
request = Request(url=url, headers=headers)
content = urlopen(request, timeout=15).read()
return content
except Exception as http_err:
print(url, "抓取报错", http_err)
try_num += 1
if try_num >= max_try_number:
print("尝试失败次数过多,放弃尝试")
return None
# ===okex
# 获取ticker数据
def get_list_ticker_from_okex(symbol_list=['btc_usdt', 'ltc_usdt']):
# 创建一个空的df
df = pd.DataFrame()
# 遍历每一个symbol
for symbol in symbol_list:
# 构建url
url = 'https://www.okex.com/api/v1/ticker.do?symbol=%s' % symbol
# 抓取数据
content = get_url_content2(url, 5)
if content is None: # 当返回内容为空的时候,跳过本次循环
continue
# 将数据转化为dataframe
json_data = json.loads(content.decode("utf-8"))
_df = pd.DataFrame(json_data, dtype='float')
_df = _df[['ticker']].T
_df['symbol'] = symbol
# 合并数据到df中
df = df.append(_df, ignore_index=True)
# 对df进行最后整理
df = df[['symbol', 'last', 'buy', 'sell', 'high', 'low', 'vol']]
return df
# 获取candle数据
def get_candle_from_okex(symbol='ltc_usdt', kline_type='1min'):
# 构建url
url = 'https://www.okex.com/api/v1/kline.do?symbol=%s&type=%s' % (symbol, kline_type)
# 抓取数据
content = get_url_content2(url)
if content is None: # 当返回内容为空的时候,跳过本次循环
return pd.DataFrame()
# 将数据转化为dataframe
json_data = json.loads(content.decode("utf-8"))
df = pd.DataFrame(json_data, dtype='float')
# 整理dataframe
df.rename(columns={0: 'candle_begin_time', 1: 'open', 2: 'high', 3: 'low', 4: 'close', 5: 'volume'}, inplace=True)
df['candle_begin_time'] = pd.to_datetime(df['candle_begin_time'], unit='ms')
df['candle_begin_time_GMT8'] = df['candle_begin_time'] + pd.Timedelta(hours=8)
return df
df = get_list_ticker_from_okex(symbol_list=['btc_usdt', 'ltc_usdt', 'eth_usdt'])
print(df)
df = get_candle_from_okex(symbol='btc_usdt', kline_type='30min')
print(df)