【期货量化进阶】量化交易系统的性能优化技巧（实战指南）-开发者社区

一、前言

系统性能直接影响交易执行效率和策略表现。优化系统性能可以减少延迟、提高执行速度、降低资源消耗。本文将介绍各种性能优化技巧。

代码性能优化
数据处理优化
内存优化
并发优化
系统监控

二、为什么选择天勤量化（TqSdk）

功能	说明
高效API	高效的API接口
数据缓存	支持数据缓存
并发支持	支持并发操作
灵活扩展	支持自定义优化

安装方法：

pipinstalltqsdk pandas numpy

三、代码性能优化

3.1 向量化操作

#!/usr/bin/env python# -*- coding: utf-8 -*-""" 功能：性能优化技巧 说明：本代码仅供学习参考 """fromtqsdkimportTqApi,TqAuthimportpandasaspdimportnumpyasnpimporttimedefvectorized_calculation(klines):"""向量化计算（快）"""# 使用pandas向量化操作returns=klines['close'].pct_change()ma=klines['close'].rolling(20).mean()returnreturns,madefloop_calculation(klines):"""循环计算（慢）"""returns=[]ma=[]foriinrange(len(klines)):ifi>0:ret=(klines['close'].iloc[i]-klines['close'].iloc[i-1])/klines['close'].iloc[i-1]returns.append(ret)else:returns.append(0)ifi>=19:ma_val=klines['close'].iloc[i-19:i+1].mean()ma.append(ma_val)else:ma.append(np.nan)returnpd.Series(returns),pd.Series(ma)# 性能对比api=TqApi(auth=TqAuth("快期账户","快期密码"))klines=api.get_kline_serial("SHFE.rb2510",3600,1000)api.wait_update()# 向量化start=time.time()returns1,ma1=vectorized_calculation(klines)time1=time.time()-start# 循环start=time.time()returns2,ma2=loop_calculation(klines)time2=time.time()-startprint(f"向量化耗时:{time1:.4f}秒")print(f"循环耗时:{time2:.4f}秒")print(f"加速比:{time2/time1:.2f}倍")api.close()

3.2 避免重复计算

classOptimizedCalculator:"""优化计算器"""def__init__(self):self.cache={}defcalculate_ma(self,klines,period,use_cache=True):"""计算均线（带缓存）"""cache_key=f"ma_{period}_{len(klines)}"ifuse_cacheandcache_keyinself.cache:returnself.cache[cache_key]ma_value=klines['close'].rolling(period).mean()ifuse_cache:self.cache[cache_key]=ma_valuereturnma_value# 使用示例calculator=OptimizedCalculator()ma1=calculator.calculate_ma(klines,20)# 计算并缓存ma2=calculator.calculate_ma(klines,20)# 从缓存读取

四、数据处理优化

4.1 数据预加载

defpreload_data(api,symbols,duration_seconds=3600,count=1000):"""预加载数据"""klines_dict={}forsymbolinsymbols:klines=api.get_kline_serial(symbol,duration_seconds,count)klines_dict[symbol]=klines api.wait_update()returnklines_dict# 使用示例symbols=["SHFE.rb2510","SHFE.hc2510","DCE.i2510"]klines_dict=preload_data(api,symbols)

4.2 数据筛选

deffilter_data_efficiently(klines,condition_func):"""高效数据筛选"""# 使用布尔索引mask=condition_func(klines)filtered=klines[mask]returnfiltered# 使用示例defhigh_volume_condition(klines):returnklines['volume']>klines['volume'].rolling(20).mean()*1.5filtered=filter_data_efficiently(klines,high_volume_condition)

五、内存优化

5.1 数据类型优化

defoptimize_data_types(klines):"""优化数据类型"""optimized=klines.copy()# 整数类型优化forcolin['volume','open_oi']:ifcolinoptimized.columns:optimized[col]=optimized[col].astype('int32')# 浮点数类型优化forcolin['open','high','low','close']:ifcolinoptimized.columns:optimized[col]=optimized[col].astype('float32')returnoptimized# 使用示例optimized_klines=optimize_data_types(klines)print(f"原始内存:{klines.memory_usage(deep=True).sum()/1024/1024:.2f}MB")print(f"优化后内存:{optimized_klines.memory_usage(deep=True).sum()/1024/1024:.2f}MB")

5.2 数据清理

defcleanup_data(klines,keep_recent=1000):"""清理旧数据"""iflen(klines)>keep_recent:returnklines.iloc[-keep_recent:].copy()returnklines# 使用示例cleaned=cleanup_data(klines,keep_recent=500)print(f"清理后数据量:{len(cleaned)}")

六、并发优化

6.1 多品种并发

importthreadingdefconcurrent_data_fetch(api,symbols,duration_seconds=3600,count=1000):"""并发获取数据"""klines_dict={}threads=[]deffetch_symbol(symbol):klines=api.get_kline_serial(symbol,duration_seconds,count)klines_dict[symbol]=klinesforsymbolinsymbols:thread=threading.Thread(target=fetch_symbol,args=(symbol,))threads.append(thread)thread.start()forthreadinthreads:thread.join()api.wait_update()returnklines_dict# 使用示例symbols=["SHFE.rb2510","SHFE.hc2510","DCE.i2510"]klines_dict=concurrent_data_fetch(api,symbols)

6.2 异步处理

importasyncioasyncdefasync_strategy_execution(api,symbols,strategy_func):"""异步策略执行"""tasks=[]forsymbolinsymbols:task=asyncio.create_task(execute_strategy_async(api,symbol,strategy_func))tasks.append(task)results=awaitasyncio.gather(*tasks)returnresultsasyncdefexecute_strategy_async(api,symbol,strategy_func):"""异步执行策略"""klines=api.get_kline_serial(symbol,3600,500)api.wait_update()signal=strategy_func(klines)return{symbol:signal}# 使用示例# results = asyncio.run(async_strategy_execution(api, symbols, strategy_func))

七、API调用优化

7.1 批量操作

defbatch_operations(api,operations):"""批量操作"""results=[]foroperationinoperations:result=operation(api)results.append(result)# 一次wait_updateapi.wait_update()returnresults# 使用示例defget_quote_op(symbol):defop(api):returnapi.get_quote(symbol)returnop operations=[get_quote_op(s)forsinsymbols]results=batch_operations(api,operations)

7.2 减少API调用

classDataCache:"""数据缓存"""def__init__(self,ttl=60):self.cache={}self.ttl=ttl self.timestamps={}defget(self,key):"""获取缓存"""ifkeyinself.cache:iftime.time()-self.timestamps[key]<self.ttl:returnself.cache[key]else:delself.cache[key]delself.timestamps[key]returnNonedefset(self,key,value):"""设置缓存"""self.cache[key]=value self.timestamps[key]=time.time()# 使用示例cache=DataCache(ttl=60)defget_cached_quote(api,symbol):"""获取缓存的行情"""cached=cache.get(symbol)ifcached:returncached quote=api.get_quote(symbol)api.wait_update()cache.set(symbol,quote)returnquote

八、系统监控

8.1 性能监控

importtimeimportpsutilclassPerformanceMonitor:"""性能监控"""def__init__(self):self.metrics=[]defmeasure_execution_time(self,func,*args,**kwargs):"""测量执行时间"""start_time=time.time()result=func(*args,**kwargs)execution_time=time.time()-start_time self.metrics.append({'function':func.__name__,'execution_time':execution_time,'timestamp':time.time()})returnresultdefget_memory_usage(self):"""获取内存使用"""process=psutil.Process()returnprocess.memory_info().rss/1024/1024# MBdefget_cpu_usage(self):"""获取CPU使用"""returnpsutil.cpu_percent(interval=1)# 使用示例monitor=PerformanceMonitor()deftest_function(klines):returnklines['close'].mean()result=monitor.measure_execution_time(test_function,klines)print(f"执行时间:{monitor.metrics[-1]['execution_time']:.4f}秒")print(f"内存使用:{monitor.get_memory_usage():.2f}MB")print(f"CPU使用:{monitor.get_cpu_usage():.2f}%")