增加一篇关于Python性能提升的文章和相关代码
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chenxy123
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vn.how/README.md
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vn.how/README.md
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# vn.py项目的实战应用指南
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本文件夹下的内容主要是围绕vn.py在实际交易中的一系列具体应用，包括说明文档和代码例子。
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* performance：《百倍加速！Python量化策略的算法性能提升指南》
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vn.how/performance/Python Performance.ipynb
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vn.how/performance/Python Performance.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# 这个测试目标在于仿造一个类似于实盘中，不断有新的数据推送过来，\n",
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"# 然后需要计算移动平均线数值，这么一个比较常见的任务。\n",
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"\n",
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"from __future__ import division\n",
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"import time\n",
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"import random\n",
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"\n",
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"# 生成测试用的数据\n",
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"data = []\n",
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"data_length = 100000 # 总数据量\n",
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"ma_length = 500 # 移动均线的窗口\n",
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"test_times = 10 # 测试次数\n",
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"\n",
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"for i in range(data_length):\n",
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" data.append(random.randint(1, 100))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：1.16959998608秒\n",
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"单个数据点耗时：11.7547737294微秒\n",
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"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
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]
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}
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],
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"source": [
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"# 计算500期的移动均线，并将结果保存到一个列表里返回\n",
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"def ma_basic(data, ma_length):\n",
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" \n",
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" # 用于保存均线输出结果的列表\n",
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" ma = []\n",
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" \n",
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" # 计算均线用的数据窗口\n",
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" data_window = data[:ma_length]\n",
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" \n",
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" # 测试用数据（去除了之前初始化用的部分）\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" # 模拟实盘不断收到新数据推送的情景，遍历历史数据计算均线\n",
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" for new_tick in test_data:\n",
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" # 移除最老的数据点并增加最新的数据点\n",
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" data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" \n",
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" # 遍历求均线\n",
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" sum_tick = 0\n",
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" for tick in data_window:\n",
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" sum_tick += tick\n",
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" ma.append(sum_tick/ma_length)\n",
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" \n",
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" # 返回数据\n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_basic(data, ma_length)\n",
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"\n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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" \n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：2.11879999638秒\n",
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"单个数据点耗时：21.2944723254微秒\n",
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"最后10个移动平均值： [49.804000000000002, 49.832000000000001, 49.799999999999997, 49.899999999999999, 49.892000000000003, 49.887999999999998, 49.927999999999997, 50.052, 50.106000000000002, 49.981999999999999]\n"
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]
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}
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],
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"source": [
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"# 改用numpy（首先是一种常见的错误用法）\n",
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"import numpy as np\n",
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"\n",
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"def ma_numpy_wrong(data, ma_length):\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" for new_tick in test_data:\n",
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" data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" \n",
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" # 使用numpy求均线，注意这里本质上每次循环\n",
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" # 都在创建一个新的numpy数组对象，开销很大\n",
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" data_array = np.array(data_window)\n",
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" ma.append(data_array.mean())\n",
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" \n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_numpy_wrong(data, ma_length)\n",
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" \n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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" \n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：0.614300012589秒\n",
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"单个数据点耗时：6.17386947325微秒\n",
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"最后10个移动平均值： [49.804000000000002, 49.832000000000001, 49.799999999999997, 49.899999999999999, 49.892000000000003, 49.887999999999998, 49.927999999999997, 50.052, 50.106000000000002, 49.981999999999999]\n"
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]
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}
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],
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"source": [
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"# numpy的正确用法\n",
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"def ma_numpy_right(data, ma_length):\n",
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" ma = []\n",
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" \n",
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" # 用numpy数组来缓存计算窗口内的数据\n",
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" data_window = np.array(data[:ma_length])\n",
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" \n",
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" test_data = data[ma_length:]\n",
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" \n",
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" for new_tick in test_data:\n",
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" # 使用numpy数组的底层数据偏移来实现数据更新\n",
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" data_window[0:ma_length-1] = data_window[1:ma_length]\n",
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" data_window[-1] = new_tick\n",
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" ma.append(data_window.mean())\n",
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" \n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_numpy_right(data, ma_length)\n",
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" \n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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" \n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：0.043700003624秒\n",
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"单个数据点耗时：0.439196016321微秒\n",
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"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
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]
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}
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],
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"source": [
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"# 使用numba加速，ma_numba函数和ma_basic完全一样\n",
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"import numba\n",
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"\n",
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"@numba.jit\n",
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"def ma_numba(data, ma_length):\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" for new_tick in test_data:\n",
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" data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" sum_tick = 0\n",
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" for tick in data_window:\n",
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" sum_tick += tick\n",
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" ma.append(sum_tick/ma_length)\n",
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"\n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_numba(data, ma_length)\n",
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"\n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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" \n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：0.0348000049591秒\n",
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"单个数据点耗时：0.349748793559微秒\n",
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"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
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]
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}
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],
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"source": [
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"# 将均线计算改写为高速算法\n",
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"def ma_online(data, ma_length):\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" # 缓存的窗口内数据求和结果\n",
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" sum_buffer = 0\n",
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" \n",
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" for new_tick in test_data:\n",
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" old_tick = data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" \n",
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" # 如果缓存结果为空，则先通过遍历求第一次结果\n",
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" if not sum_buffer:\n",
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" sum_tick = 0\n",
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" for tick in data_window:\n",
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" sum_tick += tick\n",
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" ma.append(sum_tick/ma_length)\n",
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" \n",
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" # 将求和结果缓存下来\n",
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" sum_buffer = sum_tick\n",
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" else:\n",
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" # 这里的算法将计算复杂度从O(n)降低到了O(1)\n",
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" sum_buffer = sum_buffer - old_tick + new_tick\n",
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" ma.append(sum_buffer/ma_length)\n",
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" \n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_online(data, ma_length)\n",
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" \n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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"\n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"单次耗时：0.0290000200272秒\n",
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"单个数据点耗时：0.29145748771微秒\n",
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"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
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]
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}
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],
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"source": [
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"# 高速算法和numba结合，ma_online_numba函数和ma_online完全一样\n",
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"@numba.jit\n",
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"def ma_online_numba(data, ma_length):\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" sum_buffer = 0\n",
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" \n",
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" for new_tick in test_data:\n",
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" old_tick = data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" \n",
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" if not sum_buffer:\n",
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" sum_tick = 0\n",
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" for tick in data_window:\n",
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" sum_tick += tick\n",
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" ma.append(sum_tick/ma_length)\n",
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" sum_buffer = sum_tick\n",
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" else:\n",
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" sum_buffer = sum_buffer - old_tick + new_tick\n",
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" ma.append(sum_buffer/ma_length)\n",
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"\n",
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" return ma\n",
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"\n",
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"# 运行测试\n",
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"start = time.time()\n",
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"\n",
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"for i in range(test_times):\n",
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" result = ma_online_numba(data, ma_length)\n",
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" \n",
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"time_per_test = (time.time()-start)/test_times\n",
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"time_per_point = time_per_test/(data_length - ma_length)\n",
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"\n",
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"print u'单次耗时：%s秒' %time_per_test\n",
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"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
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"print u'最后10个移动平均值：', result[-10:]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"\"\"\"\n",
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"# 基础的cython加速\n",
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"def ma_cython(data, ma_length):\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" \n",
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" for new_tick in test_data:\n",
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" data_window.pop(0)\n",
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" data_window.append(new_tick)\n",
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" \n",
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" sum_tick = 0\n",
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" for tick in data_window:\n",
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" sum_tick += tick\n",
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" ma.append(sum_tick/ma_length)\n",
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" \n",
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" return ma\n",
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" \n",
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"\n",
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"# cython和高速算法\n",
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"def ma_cython_online(data, ma_length):\n",
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" # 静态声明变量\n",
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" cdef int sum_buffer, sum_tick, old_tick, new_tick\n",
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"\n",
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" ma = []\n",
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" data_window = data[:ma_length]\n",
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" test_data = data[ma_length:]\n",
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" sum_buffer = 0\n",
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" \n",
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" for new_tick in test_data:\n",
|
||||
" old_tick = data_window.pop(0)\n",
|
||||
" data_window.append(new_tick)\n",
|
||||
" \n",
|
||||
" if not sum_buffer:\n",
|
||||
" sum_tick = 0\n",
|
||||
" for tick in data_window:\n",
|
||||
" sum_tick += tick\n",
|
||||
" ma.append(sum_tick/ma_length)\n",
|
||||
" \n",
|
||||
" sum_buffer = sum_tick\n",
|
||||
" else:\n",
|
||||
" sum_buffer = sum_buffer - old_tick + new_tick\n",
|
||||
" ma.append(sum_buffer/ma_length)\n",
|
||||
" \n",
|
||||
" return ma\n",
|
||||
"\"\"\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"单次耗时：0.600800013542秒\n",
|
||||
"单个数据点耗时：6.03819109088微秒\n",
|
||||
"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 基础cython加速\n",
|
||||
"from test import ma_cython\n",
|
||||
"\n",
|
||||
"start = time.time()\n",
|
||||
"\n",
|
||||
"for i in range(test_times):\n",
|
||||
" result = ma_cython(data, ma_length)\n",
|
||||
" \n",
|
||||
"time_per_test = (time.time()-start)/test_times\n",
|
||||
"time_per_point = time_per_test/(data_length - ma_length)\n",
|
||||
"\n",
|
||||
"print u'单次耗时：%s秒' %time_per_test\n",
|
||||
"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
|
||||
"print u'最后10个移动平均值：', result[-10:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"单次耗时：0.00980000495911秒\n",
|
||||
"单个数据点耗时：0.0984925121518微秒\n",
|
||||
"最后10个移动平均值： [49.804, 49.832, 49.8, 49.9, 49.892, 49.888, 49.928, 50.052, 50.106, 49.982]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 高速算法和cython结合\n",
|
||||
"from test import ma_cython_online\n",
|
||||
"\n",
|
||||
"start = time.time()\n",
|
||||
"\n",
|
||||
"for i in range(test_times):\n",
|
||||
" result = ma_cython_online(data, ma_length)\n",
|
||||
"\n",
|
||||
"time_per_test = (time.time()-start)/test_times\n",
|
||||
"time_per_point = time_per_test/(data_length - ma_length)\n",
|
||||
"\n",
|
||||
"print u'单次耗时：%s秒' %time_per_test\n",
|
||||
"print u'单个数据点耗时：%s微秒' %(time_per_point*1000000)\n",
|
||||
"print u'最后10个移动平均值：', result[-10:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 2",
|
||||
"language": "python",
|
||||
"name": "python2"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 2
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython2",
|
||||
"version": "2.7.11"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
16
vn.how/performance/README.md
Normal file
16
vn.how/performance/README.md
Normal file
@ -0,0 +1,16 @@
|
||||
# 使用说明
|
||||
|
||||
### 使用步骤
|
||||
1. 在当前文件夹下打开cmd窗口
|
||||
2. 输入ipython notebook运行
|
||||
3. 打开Python Performance笔记本，使用Shift+回车逐个Cell运行
|
||||
|
||||
### 编译Cython
|
||||
打开cmd，输入运行：
|
||||
> python setup.py build_ext --inplace
|
||||
|
||||
### 文件说明
|
||||
* Python Performance.ipynb：Jupyter Notebook笔记本
|
||||
* test.pyx：Cython模块的源代码
|
||||
* test_setup.py：编译test.pyx所需的配置文件
|
||||
* test.pyd：编译好的Cython模块，可以在Python里直接import
|
||||
BIN
vn.how/performance/test.pyd
Normal file
BIN
vn.how/performance/test.pyd
Normal file
Binary file not shown.
48
vn.how/performance/test.pyx
Normal file
48
vn.how/performance/test.pyx
Normal file
@ -0,0 +1,48 @@
|
||||
#encoding:utf-8
|
||||
|
||||
from __future__ import division
|
||||
|
||||
# 基础的cython加速
|
||||
def ma_cython(data, ma_length):
|
||||
ma = []
|
||||
data_window = data[:ma_length]
|
||||
test_data = data[ma_length:]
|
||||
|
||||
for new_tick in test_data:
|
||||
data_window.pop(0)
|
||||
data_window.append(new_tick)
|
||||
|
||||
sum_tick = 0
|
||||
for tick in data_window:
|
||||
sum_tick += tick
|
||||
ma.append(sum_tick/ma_length)
|
||||
|
||||
return ma
|
||||
|
||||
|
||||
# cython和高速算法
|
||||
def ma_cython_online(data, ma_length):
|
||||
# 静态声明变量
|
||||
cdef int sum_buffer, sum_tick, old_tick, new_tick
|
||||
|
||||
ma = []
|
||||
data_window = data[:ma_length]
|
||||
test_data = data[ma_length:]
|
||||
sum_buffer = 0
|
||||
|
||||
for new_tick in test_data:
|
||||
old_tick = data_window.pop(0)
|
||||
data_window.append(new_tick)
|
||||
|
||||
if not sum_buffer:
|
||||
sum_tick = 0
|
||||
for tick in data_window:
|
||||
sum_tick += tick
|
||||
ma.append(sum_tick/ma_length)
|
||||
|
||||
sum_buffer = sum_tick
|
||||
else:
|
||||
sum_buffer = sum_buffer - old_tick + new_tick
|
||||
ma.append(sum_buffer/ma_length)
|
||||
|
||||
return ma
|
||||
7
vn.how/performance/test_setup.py
Normal file
7
vn.how/performance/test_setup.py
Normal file
@ -0,0 +1,7 @@
|
||||
from distutils.core import setup
|
||||
from Cython.Build import cythonize
|
||||
|
||||
setup(
|
||||
name = 'cython test',
|
||||
ext_modules = cythonize("test.pyx"),
|
||||
)
|
||||
Loading…
Reference in New Issue
Block a user