如何接入实时期货行情数据 - 详细操作步骤

期货市场通过标准化合约的交易，为投资者提供了在大宗商品、金融工具等方面进行风险对冲和投机的机会。量化交易以计算机模型为核心，通过历史数据和实时数据进行分析和策略执行，减少人为情绪对交易的干扰。由于期货市场的波动性强且价格变化迅速，量化交易者必须依赖实时准确的数据来做出交易决策。

期货行情数据接口是量化交易系统的基础模块，提供了交易者所需的实时价格、成交量、持仓量等关键市场数据。这些数据通过API自动传输给量化交易系统，使策略能够在毫秒级别内做出反应。API通常支持主流的期货交易所，如芝加哥商品交易所（CME）、上海期货交易所（SHFE）等，帮助量化交易者跨市场执行多资产策略。

注意延迟行据和实时数据的差别

目前你在市面上看到的几乎所有行情报价都存在延时，这里的延迟一般指的是交易的真实发生时间，到成交价格呈现在终端的时间，大概在15分钟左右。而实时数据则没有这个延迟，从交易发生到价格被传递出去，只有1秒以内的延迟。国内提供实时行情数据的供应商非常少，一般使用国外的产品。

实时行情数据接口一般用在哪些场景？

实时行情数据接口的应用场景主要包括：

量化交易策略执行

量化交易依赖于实时数据来识别市场上的价格微小变化并及时执行策略，例如套利策略、趋势跟随策略等。毫秒级别的价格信息可以让交易系统以最快的速度捕捉市场机会，优化交易收益。

交易平台的报价系统

在线期货交易平台需要为用户提供实时价格、深度图等市场信息，以支持用户下单。实时行情数据接口可以确保交易平台的数据准确性和及时性，提升用户体验，增加用户粘性。

算法交易

实时数据不仅用于执行交易，还用于监控市场风险和调整仓位。通过实时数据，量化交易系统可以设定动态风控模型，及时止损或止盈，确保投资组合的稳健运行。

期货市场对实时数据的需求非常高，使用高质量的实时行情数据接口可以帮助量化交易者和交易平台提高竞争优势，在快速变化的市场中占据领先地位。

如何接入期货实时行情API - 代码示例

下面以AllTick提供的期货数据接口为例，通过接口查询美股原油的实时报价：

import time
import requests
import json
 
# Extra headers
test_headers = {
    'Content-Type': 'application/json'
}
 
'''
# Special Note:
# GitHub: https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
# Token Application: https://alltick.co
# Replace "testtoken" in the URL below with your own token
# API address for futures (including commodities like crude oil):
# https://quote.tradeswitcher.com/quote-futures-b-ws-api
Encode the following JSON and copy it to the "query" field of the HTTP query string
{"trace": "python_http_test1", "data": {"code": "USOUSD", "kline_type": 1, "kline_timestamp_end": 0, "query_kline_num": 2, "adjust_type": 0}}
{"trace": "python_http_test2", "data": {"symbol_list": [{"code": "USOUSD"}]}}
{"trace": "python_http_test3", "data": {"symbol_list": [{"code": "USOUSD"}]}}
'''
test_url1 = 'https://quote.tradeswitcher.com/quote-futures-b-api/kline?token=testtoken&query=%7B%22trace%22%20%3A%20%22python_http_test1%22%2C%22data%22%20%3A%20%7B%22code%22%20%3A%20%22USOUSD%22%2C%22kline_type%22%20%3A%201%2C%22kline_timestamp_end%22%20%3A%200%2C%22query_kline_num%22%20%3A%202%2C%22adjust_type%22%3A%200%7D%7D'
test_url2 = 'https://quote.tradeswitcher.com/quote-futures-b-api/depth-tick?token=testtoken&query=%7B%22trace%22%20%3A%20%22python_http_test2%22%2C%22data%22%20%3A%20%7B%22symbol_list%22%3A%20%5B%7B%22code%22%3A%20%22USOUSD%22%7D%5D%7D%7D'
test_url3 = 'https://quote.tradeswitcher.com/quote-futures-b-api/trade-tick?token=testtoken&query=%7B%22trace%22%20%3A%20%22python_http_test3%22%2C%22data%22%20%3A%20%7B%22symbol_list%22%3A%20%5B%7B%22code%22%3A%20%22USOUSD%22%7D%5D%7D%7D'
 
resp1 = requests.get(url=test_url1, headers=test_headers)
time.sleep(1)
resp2 = requests.get(url=test_url2, headers=test_headers)
time.sleep(1)
resp3 = requests.get(url=test_url3, headers=test_headers)
 
# Decoded text returned by the request
text1 = resp1.text
print(text1)
 
text2 = resp2.text
print(text2)
 
text3 = resp3.text
print(text3)

也可以通过Websocket查询：

import json
import websocket    # pip install websocket-client
 
'''
# Special Note:
# GitHub: https://github.com/alltick/realtime-forex-crypto-stock-tick-finance-websocket-api
# Token Application: https://alltick.co
# Replace "testtoken" in the URL below with your own token
# API address for futures (including commodities like crude oil):
# wss://quote.tradeswitcher.com/quote-futures-b-ws-api
'''
 
class Feed(object):
 
    def __init__(self):
        self.url = 'wss://quote.tradeswitcher.com/quote-futures-b-ws-api?token=testtoken'  # Enter your websocket URL here
        self.ws = None
 
    def on_open(self, ws):
        """
        Callback object which is called at opening websocket.
        1 argument:
        @ ws: the WebSocketApp object
        """
        print('A new WebSocketApp is opened!')
 
        # Start subscribing (an example)
        sub_param = {
            "cmd_id": 22002,
            "seq_id": 123,
            "trace":"3baaa938-f92c-4a74-a228-fd49d5e2f8bc-1678419657806",
            "data":{
                "symbol_list":[
                    {
                        "code": "USOUSD",  # Changed to US crude oil symbol
                        "depth_level": 5,
                    }
                ]
            }
        }
 
        # If you want to run for a long time, you need to modify the code to send heartbeats periodically to avoid disconnection, please refer to the API documentation for details
        sub_str = json.dumps(sub_param)
        ws.send(sub_str)
        print("depth quote for USOUSD is subscribed!")
 
    def on_data(self, ws, string, type, continue_flag):
        """
        4 arguments.
        The 1st argument is this class object.
        The 2nd argument is utf-8 string which we get from the server.
        The 3rd argument is data type. ABNF.OPCODE_TEXT or ABNF.OPCODE_BINARY will be came.
        The 4th argument is continue flag. If 0, the data continue
        """
 
    def on_message(self, ws, message):
        """
        Callback object which is called when received data.
        2 arguments:
        @ ws: the WebSocketApp object
        @ message: utf-8 data received from the server
        """
        # Parse the received message
        result = eval(message)
        print(result)
 
    def on_error(self, ws, error):
        """
        Callback object which is called when got an error.
        2 arguments:
        @ ws: the WebSocketApp object
        @ error: exception object
        """
        print(error)
 
    def on_close(self, ws, close_status_code, close_msg):
        """
        Callback object which is called when the connection is closed.
        2 arguments:
        @ ws: the WebSocketApp object
        @ close_status_code
        @ close_msg
        """
        print('The connection is closed!')
 
    def start(self):
        self.ws = websocket.WebSocketApp(
            self.url,
            on_open=self.on_open,
            on_message=self.on_message,
            on_data=self.on_data,
            on_error=self.on_error,
            on_close=self.on_close,
        )
        self.ws.run_forever()
 
 
if __name__ == "__main__":
    feed = Feed()
    feed.start()