242 lines
7.5 KiB
Plaintext
242 lines
7.5 KiB
Plaintext
{
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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": false
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},
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"outputs": [],
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"source": [
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"%matplotlib inline\n",
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"\n",
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"from vnpy.trader.app.ctaStrategy.ctaBacktesting import BacktestingEngine, OptimizationSetting, MINUTE_DB_NAME\n",
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"from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n",
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"#from vnpy.trader.app.ctaStrategy.strategy.strategyMultiTimeframe import MultiTimeframeStrategy\n",
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"from vnpy.trader.app.ctaStrategy.strategy.strategyMultiSignal import MultiSignalStrategy"
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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": true
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},
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"outputs": [],
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"source": [
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"# 创建回测引擎对象\n",
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"engine = BacktestingEngine()"
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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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"source": [
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"# 设置回测使用的数据\n",
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"engine.setBacktestingMode(engine.BAR_MODE) # 设置引擎的回测模式为K线\n",
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"engine.setDatabase(MINUTE_DB_NAME, 'IF0000') # 设置使用的历史数据库\n",
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"engine.setStartDate('20130101') # 设置回测用的数据起始日期"
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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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"source": [
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"# 配置回测引擎参数\n",
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"engine.setSlippage(0.2) # 设置滑点为股指1跳\n",
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"engine.setRate(0.3/10000) # 设置手续费万0.3\n",
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"engine.setSize(300) # 设置股指合约大小 \n",
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"engine.setPriceTick(0.2) # 设置股指最小价格变动 \n",
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"engine.setCapital(1000000) # 设置回测本金"
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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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"# 在引擎中创建策略对象\n",
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"d = {'atrLength': 11} # 策略参数配置\n",
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"engine.initStrategy(AtrRsiStrategy, d) # 创建策略对象\n",
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"#ngine.initStrategy(MultiTimeframeStrategy, d) \n",
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"#engine.initStrategy(MultiSignalStrategy, {}) "
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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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"# 运行回测\n",
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"engine.runBacktesting() # 运行回测"
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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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"scrolled": false
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},
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"outputs": [],
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"source": [
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"# 显示逐日回测结果\n",
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"engine.showDailyResult()"
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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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"# 显示逐笔回测结果\n",
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"engine.showBacktestingResult()"
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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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"# 显示前10条成交记录\n",
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"for i in range(10):\n",
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" d = engine.tradeDict[str(i+1)].__dict__\n",
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" print 'TradeID: %s, Time: %s, Direction: %s, Price: %s, Volume: %s' %(d['tradeID'], d['dt'], d['direction'], d['price'], d['volume'])"
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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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"2018-02-16 22:15:18.251000\t------------------------------\n",
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"2018-02-16 22:15:18.251000\tsetting: {'atrLength': 12}\n",
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"2018-02-16 22:15:18.256000\t开始载入数据\n",
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"2018-02-16 22:15:18.319000\t载入完成，数据量：91381\n",
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"2018-02-16 22:15:18.319000\t开始回测\n",
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"2018-02-16 22:15:18.361000\t策略初始化完成\n",
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"2018-02-16 22:15:18.361000\t策略启动完成\n",
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"2018-02-16 22:15:18.361000\t开始回放数据\n",
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"2018-02-16 22:15:25.992000\t数据回放结束\n",
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"2018-02-16 22:15:25.992000\t计算按日统计结果\n",
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"2018-02-16 22:15:26.085000\t------------------------------\n",
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"2018-02-16 22:15:26.085000\tsetting: {'atrLength': 14}\n",
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"2018-02-16 22:15:26.087000\t开始载入数据\n",
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"2018-02-16 22:15:26.142000\t载入完成，数据量：91381\n",
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"2018-02-16 22:15:26.142000\t开始回测\n",
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"2018-02-16 22:15:26.183000\t策略初始化完成\n",
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"2018-02-16 22:15:26.183000\t策略启动完成\n",
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"2018-02-16 22:15:26.183000\t开始回放数据\n",
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"2018-02-16 22:15:34.799000\t数据回放结束\n",
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"2018-02-16 22:15:34.799000\t计算按日统计结果\n",
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"2018-02-16 22:15:34.881000\t------------------------------\n",
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"2018-02-16 22:15:34.881000\tsetting: {'atrLength': 16}\n",
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"2018-02-16 22:15:34.883000\t开始载入数据\n",
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"2018-02-16 22:15:34.935000\t载入完成，数据量：91381\n",
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"2018-02-16 22:15:34.935000\t开始回测\n",
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"2018-02-16 22:15:34.981000\t策略初始化完成\n",
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"2018-02-16 22:15:34.981000\t策略启动完成\n",
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"2018-02-16 22:15:34.981000\t开始回放数据\n",
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"2018-02-16 22:15:43.120000\t数据回放结束\n",
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"2018-02-16 22:15:43.120000\t计算按日统计结果\n",
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"2018-02-16 22:15:43.151000\t------------------------------\n",
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"2018-02-16 22:15:43.151000\t优化结果：\n",
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"2018-02-16 22:15:43.151000\t参数：[\"{'atrLength': 16}\"]，目标：819765.66\n",
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"2018-02-16 22:15:43.151000\t参数：[\"{'atrLength': 14}\"]，目标：534310.005\n",
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"2018-02-16 22:15:43.151000\t参数：[\"{'atrLength': 12}\"]，目标：278612.3988\n",
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"耗时：24.9000000954\n"
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]
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}
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],
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"source": [
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"# 优化配置\n",
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"setting = OptimizationSetting() # 新建一个优化任务设置对象\n",
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"setting.setOptimizeTarget('totalNetPnl') # 设置优化排序的目标是策略净盈利\n",
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"setting.addParameter('atrLength', 12, 16, 2) # 增加第一个优化参数atrLength，起始12，结束20，步进2\n",
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"#setting.addParameter('atrMa', 20, 30, 5) # 增加第二个优化参数atrMa，起始20，结束30，步进5\n",
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"#setting.addParameter('rsiLength', 5) # 增加一个固定数值的参数\n",
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"\n",
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"# 执行多进程优化\n",
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"import time\n",
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"start = time.time()\n",
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"#resultList = engine.runParallelOptimization(AtrRsiStrategy, setting)\n",
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"resultList = engine.runOptimization(AtrRsiStrategy, setting)\n",
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"print u'耗时：%s' %(time.time()-start)"
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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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"# 显示优化的所有统计数据\n",
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"for result in resultList:\n",
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" print '-' * 30\n",
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" print u'参数：%s，目标：%s' %(result[0], result[1])\n",
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" print u'统计数据：'\n",
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" for k, v in result[2].items():\n",
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" print u'%s：%s' %(k, v)"
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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": true
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},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 2",
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"language": "python",
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"name": "python2"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 2
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython2",
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"version": "2.7.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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