期货市场API对接完全指南：实时行情获取与实战应用
期货市场API对接完全指南：实时行情获取与实战应用本文详细介绍了如何通过API接口获取全球期货市场的实时行情数据，包含完整的代码示例、数据处理方法和实战应用场景。
一、期货API概述
期货市场是金融市场的重要组成部分，提供各种商品、金融指数和利率的标准化合约交易。通过期货API，开发者可以获取实时行情、历史数据、合约信息等关键数据，为量化交易、风险管理和市场分析提供支持。
主要期货API提供商对比
[*]Infoway API：提供全球主要期货市场的实时数据，支持RESTful和WebSocket接口
[*]Bloomberg：专业级金融数据服务，覆盖全面但成本较高
[*]Reuters：老牌金融信息提供商，数据准确性高
[*]Quandl：提供历史期货数据，适合回测和研究
[*]各交易所官方API：如CME、ICE等交易所提供的直接数据接口
[*]StockTV：提供外汇、股票、加密货币等多类金融数据API，无限制接调用次数。提供免费API密钥
二、API接口详解
2.1 期货合约标识
期货合约有特定的命名规则，通常包含：
[*]标的物代码（如CL代表原油）
[*]到期月份代码（F=1月，G=2月，...，Z=12月）
[*]到期年份（如2024年=4）
示例：CLZ4表示2024年12月到期的原油期货合约
2.2 核心API端点
# 基础URL结构
BASE_URL = "https://api.infoway.io/futures"
# 主要端点
ENDPOINTS = {
"list": "/list",# 期货列表
"quote": "/quote",# 实时行情
"historical": "/historical",# 历史数据
"kline": "/kline"# K线数据
}三、Python实现期货数据获取
3.1 基础配置与认证
import requests
import pandas as pd
import numpy as np
import time
from datetime import datetime, timedelta
import json
class FuturesAPI:
def __init__(self, api_key, base_url="https://api.infoway.io/futures"):
self.api_key = api_key
self.base_url = base_url
self.session = self._create_session()
def _create_session(self):
"""创建带重试机制的会话"""
session = requests.Session()
retry_strategy = requests.packages.urllib3.util.retry.Retry(
total=3,
backoff_factor=0.3,
status_forcelist=,
)
adapter = requests.adapters.HTTPAdapter(max_retries=retry_strategy)
session.mount("http://", adapter)
session.mount("https://", adapter)
return session
def _make_request(self, endpoint, params=None):
"""发起API请求"""
url = f"{self.base_url}{endpoint}"
headers = {
"Authorization": f"Bearer {self.api_key}",
"Content-Type": "application/json"
}
try:
response = self.session.get(
url,
headers=headers,
params=params,
timeout=10
)
response.raise_for_status()
return response.json()
except requests.exceptions.RequestException as e:
print(f"API请求失败: {e}")
return None3.2 获取期货列表
def get_futures_list(self, exchange=None, category=None):
"""
获取期货合约列表
Args:
exchange: 交易所代码（可选）
category: 品种类别（可选）
"""
params = {}
if exchange:
params["exchange"] = exchange
if category:
params["category"] = category
data = self._make_request("/list", params)
if data and data.get("code") == 200:
return data.get("data", [])
return []
# 使用示例
api = FuturesAPI("your_api_key")
futures_list = api.get_futures_list(exchange="CME", category="energy")
print(f"找到 {len(futures_list)} 个期货合约")3.3 获取实时行情
def get_realtime_quotes(self, symbols):
"""
获取实时行情数据
Args:
symbols: 合约代码列表
"""
if not symbols:
return []
if isinstance(symbols, str):
symbols =
params = {"symbols": ",".join(symbols)}
data = self._make_request("/quote", params)
if data and data.get("code") == 200:
return self._parse_quotes(data.get("data", []))
return []
def _parse_quotes(self, quotes_data):
"""解析行情数据"""
parsed_data = []
for item in quotes_data:
parsed = {
"symbol": item.get("symbol"),
"name": item.get("name"),
"last_price": float(item.get("last_price", 0)),
"change": float(item.get("chg", 0)),
"change_percent": float(item.get("chg_pct", 0)),
"open": float(item.get("open_price", 0)),
"high": float(item.get("high_price", 0)),
"low": float(item.get("low_price", 0)),
"prev_close": float(item.get("prev_price", 0)),
"volume": int(item.get("volume", 0)),
"timestamp": item.get("time"),
"exchange": item.get("exchange")
}
parsed_data.append(parsed)
return parsed_data
# 使用示例
quotes = api.get_realtime_quotes(["CLZ4", "GCZ4", "ESZ4"])
for quote in quotes:
print(f"{quote['symbol']}: {quote['last_price']} ({quote['change_percent']:.2f}%)")3.4 获取K线数据
def get_kline_data(self, symbol, interval="1d", limit=100, start_time=None, end_time=None):
"""
获取K线数据
Args:
symbol: 合约代码
interval: 时间间隔 (1m, 5m, 15m, 30m, 1h, 4h, 1d)
limit: 数据条数
start_time: 开始时间（时间戳）
end_time: 结束时间（时间戳）
"""
params = {
"symbol": symbol,
"interval": interval,
"limit": limit
}
if start_time:
params["startTime"] = start_time
if end_time:
params["endTime"] = end_time
data = self._make_request("/kline", params)
if data and data.get("code") == 200:
return self._parse_kline(data.get("data", []))
return []
def _parse_kline(self, kline_data):
"""解析K线数据"""
df_data = []
for item in kline_data:
df_data.append({
"timestamp": item.get("timestamp"),
"datetime": datetime.fromtimestamp(item.get("timestamp", 0)),
"open": float(item.get("open", 0)),
"high": float(item.get("high", 0)),
"low": float(item.get("low", 0)),
"close": float(item.get("close", 0)),
"volume": float(item.get("volume", 0)),
"turnover": float(item.get("turnover", 0))
})
return pd.DataFrame(df_data)
# 使用示例
kline_data = api.get_kline_data("CLZ4", interval="1h", limit=100)
print(kline_data.head())四、WebSocket实时数据流
对于需要实时数据的应用，WebSocket是更好的选择：
import websockets
import asyncio
import json
class FuturesWebSocketClient:
def __init__(self, api_key):
self.api_key = api_key
self.ws_url = "wss://api.infoway.io/futures/ws"
self.connected = False
self.callbacks = []
async def connect(self):
"""建立WebSocket连接"""
try:
self.connection = await websockets.connect(
f"{self.ws_url}?apikey={self.api_key}"
)
self.connected = True
print("WebSocket连接已建立")
# 启动消息处理任务
asyncio.create_task(self._message_handler())
except Exception as e:
print(f"连接失败: {e}")
async def subscribe(self, symbols, data_type="quote"):
"""订阅期货数据"""
if not self.connected:
print("未建立连接")
return False
subscribe_msg = {
"action": "subscribe",
"symbols": symbols if isinstance(symbols, list) else ,
"type": data_type
}
try:
await self.connection.send(json.dumps(subscribe_msg))
print(f"已订阅: {symbols}")
return True
except Exception as e:
print(f"订阅失败: {e}")
return False
async def _message_handler(self):
"""处理接收到的消息"""
while self.connected:
try:
message = await self.connection.recv()
data = json.loads(message)
await self._process_message(data)
except websockets.exceptions.ConnectionClosed:
print("连接已关闭")
break
except Exception as e:
print(f"处理消息错误: {e}")
async def _process_message(self, data):
"""处理实时数据"""
# 调用所有注册的回调函数
for callback in self.callbacks:
try:
await callback(data)
except Exception as e:
print(f"回调函数执行错误: {e}")
def add_callback(self, callback):
"""添加消息回调函数"""
self.callbacks.append(callback)
async def disconnect(self):
"""断开连接"""
if self.connected:
await self.connection.close()
self.connected = False
# 使用示例
async def example_usage():
client = FuturesWebSocketClient("your_api_key")
await client.connect()
# 添加数据处理回调
async def handle_data(data):
print(f"收到数据: {data}")
client.add_callback(handle_data)
# 订阅数据
await client.subscribe(["CLZ4", "GCZ4"])
# 保持连接
try:
await asyncio.Future()# 永久运行
except KeyboardInterrupt:
await client.disconnect()
# 运行示例
# asyncio.run(example_usage())五、数据处理与分析
5.1 数据清洗与转换
class FuturesDataProcessor:
@staticmethod
def clean_data(df):
"""清洗期货数据"""
# 去除空值
df_clean = df.dropna()
# 处理异常值
for col in ['open', 'high', 'low', 'close']:
q1 = df_clean.quantile(0.25)
q3 = df_clean.quantile(0.75)
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
df_clean = df_clean[
(df_clean >= lower_bound) &
(df_clean <= upper_bound)
]
return df_clean
@staticmethod
def calculate_technical_indicators(df):
"""计算技术指标"""
df = df.copy()
# 移动平均线
df['ma5'] = df['close'].rolling(window=5).mean()
df['ma20'] = df['close'].rolling(window=20).mean()
# 相对强弱指数（RSI）
delta = df['close'].diff()
gain = (delta.where(delta > 0, 0)).rolling(window=14).mean()
loss = (-delta.where(delta < 0, 0)).rolling(window=14).mean()
rs = gain / loss
df['rsi'] = 100 - (100 / (1 + rs))
# 布林带
df['bb_middle'] = df['close'].rolling(window=20).mean()
bb_std = df['close'].rolling(window=20).std()
df['bb_upper'] = df['bb_middle'] + 2 * bb_std
df['bb_lower'] = df['bb_middle'] - 2 * bb_std
return df5.2 数据可视化
import matplotlib.pyplot as plt
import seaborn as sns
class FuturesVisualizer:
@staticmethod
def plot_price_with_indicators(df, symbol):
"""绘制价格和技术指标"""
fig, axes = plt.subplots(3, 1, figsize=(12, 10))
# 价格和移动平均线
axes.plot(df['datetime'], df['close'], label='Close Price')
axes.plot(df['datetime'], df['ma5'], label='5MA', alpha=0.7)
axes.plot(df['datetime'], df['ma20'], label='20MA', alpha=0.7)
axes.set_title(f'{symbol} Price and Moving Averages')
axes.legend()
axes.grid(True, alpha=0.3)
# RSI
axes.plot(df['datetime'], df['rsi'], label='RSI', color='orange')
axes.axhline(70, linestyle='--', alpha=0.3, color='red')
axes.axhline(30, linestyle='--', alpha=0.3, color='green')
axes.set_title('RSI Indicator')
axes.set_ylim(0, 100)
axes.legend()
axes.grid(True, alpha=0.3)
# 成交量
axes.bar(df['datetime'], df['volume'], alpha=0.7, color='purple')
axes.set_title('Volume')
axes.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(f'{symbol}_analysis.png', dpi=300, bbox_inches='tight')
plt.show()
@staticmethod
def plot_correlation_matrix(symbols_data):
"""绘制相关性矩阵"""
closes = pd.DataFrame()
for symbol, df in symbols_data.items():
closes = df['close']
correlation = closes.corr()
plt.figure(figsize=(10, 8))
sns.heatmap(correlation, annot=True, cmap='coolwarm', center=0)
plt.title('Futures Correlation Matrix')
plt.tight_layout()
plt.savefig('futures_correlation.png', dpi=300, bbox_inches='tight')
plt.show()
# 使用示例
processor = FuturesDataProcessor()
visualizer = FuturesVisualizer()
# 数据处理
cleaned_data = processor.clean_data(kline_data)
indicators_data = processor.calculate_technical_indicators(cleaned_data)
# 可视化
visualizer.plot_price_with_indicators(indicators_data, "CLZ4")六、实战应用场景
6.1 期货价格监控系统
class FuturesMonitor:
def __init__(self, api_client, alert_rules):
self.api_client = api_client
self.alert_rules = alert_rules
self.price_history = {}
async def start_monitoring(self, symbols, interval=60):
"""启动监控"""
print("启动期货价格监控...")
while True:
try:
quotes = self.api_client.get_realtime_quotes(symbols)
for quote in quotes:
await self._check_alerts(quote)
# 记录历史价格
for quote in quotes:
symbol = quote['symbol']
if symbol not in self.price_history:
self.price_history = []
self.price_history.append({
'timestamp': datetime.now(),
'price': quote['last_price']
})
# 保持最近100条记录
for symbol in self.price_history:
if len(self.price_history) > 100:
self.price_history = self.price_history[-100:]
await asyncio.sleep(interval)
except Exception as e:
print(f"监控错误: {e}")
await asyncio.sleep(5)# 错误后等待5秒再重试
async def _check_alerts(self, quote):
"""检查警报条件"""
symbol = quote['symbol']
if symbol in self.alert_rules:
rules = self.alert_rules
current_price = quote['last_price']
# 检查价格突破
if 'price_breakout' in rules:
breakout_level = rules['price_breakout']
if current_price >= breakout_level['upper']:
await self._trigger_alert(
symbol,
f"价格突破上限: {current_price} >= {breakout_level['upper']}",
"high"
)
elif current_price <= breakout_level['lower']:
await self._trigger_alert(
symbol,
f"价格突破下限: {current_price} <= {breakout_level['lower']}",
"low"
)
# 检查涨跌幅
if 'change_alert' in rules:
change_percent = abs(quote['change_percent'])
if change_percent >= rules['change_alert']:
await self._trigger_alert(
symbol,
f"大幅波动: {change_percent:.2f}%",
"volatility"
)
async def _trigger_alert(self, symbol, message, alert_type):
"""触发警报"""
timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
alert_msg = f"[{timestamp}] {symbol} {message}"
print(f"ALERT: {alert_msg}")
# 这里可以集成邮件、短信等通知方式
# await self._send_email_alert(alert_msg)
# await self._send_sms_alert(alert_msg)
# 使用示例
alert_rules = {
"CLZ4": {
"price_breakout": {
"upper": 80.00,
"lower": 75.00
},
"change_alert": 2.0# 2%
},
"GCZ4": {
"price_breakout": {
"upper": 2000.00,
"lower": 1950.00
},
"change_alert": 1.5# 1.5%
}
}
monitor = FuturesMonitor(api, alert_rules)
# asyncio.run(monitor.start_monitoring(["CLZ4", "GCZ4"]))6.2 简单的趋势跟踪策略
class TrendFollowingStrategy:
def __init__(self, api_client, symbols):
self.api_client = api_client
self.symbols = symbols
self.positions = {}
async def run_strategy(self):
"""运行趋势跟踪策略"""
print("启动趋势跟踪策略...")
while True:
try:
for symbol in self.symbols:
# 获取历史数据计算指标
data = self.api_client.get_kline_data(symbol, "1h", 50)
if len(data) < 20:# 确保有足够的数据
continue
# 计算技术指标
data = FuturesDataProcessor.calculate_technical_indicators(data)
# 生成交易信号
signal = self._generate_signal(data, symbol)
if signal != "hold":
await self._execute_trade(symbol, signal, data.iloc[-1]['close'])
await asyncio.sleep(3600)# 每小时检查一次
except Exception as e:
print(f"策略执行错误: {e}")
await asyncio.sleep(300)# 错误后等待5分钟
def _generate_signal(self, data, symbol):
"""生成交易信号"""
current_close = data.iloc[-1]['close']
ma20 = data.iloc[-1]['ma20']
ma5 = data.iloc[-1]['ma5']
rsi = data.iloc[-1]['rsi']
# 简单的趋势跟踪逻辑
if ma5 > ma20 and rsi < 70:# 上升趋势且不过热
return "buy"
elif ma5 < ma20 and rsi > 30:# 下降趋势且不超卖
return "sell"
else:
return "hold"
async def _execute_trade(self, symbol, signal, price):
"""执行交易"""
# 这里只是示例，实际交易需要连接交易API
print(f"{datetime.now()} - {signal.upper()} {symbol} @ {price:.2f}")
# 更新持仓
if signal == "buy":
self.positions = {
"entry_price": price,
"entry_time": datetime.now(),
"direction": "long"
}
elif signal == "sell" and symbol in self.positions:
position = self.positions
pnl = price - position["entry_price"] if position["direction"] == "long" else position["entry_price"] - price
print(f"平仓盈亏: {pnl:.2f}")
del self.positions
# 使用示例
strategy = TrendFollowingStrategy(api, ["CLZ4", "GCZ4"])
# asyncio.run(strategy.run_strategy())七、注意事项与最佳实践
7.1 错误处理与重试机制
def robust_api_call(func):
"""API调用重试装饰器"""
def wrapper(*args, **kwargs):
max_retries = 3
retry_delay = 1
for attempt in range(max_retries):
try:
return func(*args, **kwargs)
except requests.exceptions.ConnectionError as e:
if attempt == max_retries - 1:
raise e
print(f"连接错误，{retry_delay}秒后重试...")
time.sleep(retry_delay)
retry_delay *= 2# 指数退避
except requests.exceptions.Timeout as e:
if attempt == max_retries - 1:
raise e
print(f"请求超时，{retry_delay}秒后重试...")
time.sleep(retry_delay)
except Exception as e:
print(f"API调用错误: {e}")
raise e
return wrapper7.2 数据缓存策略
from functools import lru_cache
from datetime import datetime, timedelta
class DataCache:
def __init__(self, ttl=300):# 默认5分钟缓存
self.cache = {}
self.ttl = ttl
@lru_cache(maxsize=128)
def get_cached_data(self, key, data_func, *args, **kwargs):
"""带缓存的数据获取"""
current_time = datetime.now()
if key in self.cache:
data, timestamp = self.cache
if (current_time - timestamp).total_seconds() < self.ttl:
return data
# 缓存不存在或已过期
new_data = data_func(*args, **kwargs)
if new_data is not None:
self.cache = (new_data, current_time)
return new_data
# 使用示例
cache = DataCache(ttl=300)# 5分钟缓存
# 带缓存的API调用
cached_data = cache.get_cached_data(
"CLZ4_1h_100",
api.get_kline_data,
"CLZ4", "1h", 100
)八、总结
本文详细介绍了期货市场API的对接方法，涵盖了从基础的数据获取到高级的应用场景。通过合理的错误处理、数据缓存和实时监控，可以构建稳定可靠的期货数据应用系统。
关键要点：
[*]选择合适的API提供商：根据需求选择功能、成本和稳定性合适的API服务
[*]实现健壮的错误处理：网络不稳定是常态，必须要有完善的重试机制
[*]合理使用缓存：对不经常变化的数据实施缓存，减少API调用次数
[*]实时监控与警报：对于交易应用，实时监控和及时警报至关重要
[*]数据处理与分析：原始数据需要经过清洗和转换才能用于分析和决策
期货市场数据具有高度的实时性和复杂性，在实际应用中需要根据具体需求不断完善和优化系统架构。
提示：本文示例代码仅供参考，实际使用时请替换为有效的API密钥，并遵守API提供商的使用条款。期货交易有风险，请谨慎决策。
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