2017-06-02 13:44:28 +00:00
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{
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"cells": [
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{
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"cell_type": "code",
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2018-01-11 08:03:42 +00:00
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"execution_count": 1,
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2017-06-02 13:44:28 +00:00
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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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2018-01-08 09:28:30 +00:00
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"from vnpy.trader.app.ctaStrategy.strategy.strategyAtrRsi import AtrRsiStrategy\n",
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2017-12-15 15:05:22 +00:00
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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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2017-06-02 13:44:28 +00:00
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]
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},
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{
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"cell_type": "code",
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2017-12-15 15:05:22 +00:00
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"execution_count": 2,
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2017-06-02 13:44:28 +00:00
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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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2017-12-15 15:05:22 +00:00
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"execution_count": 3,
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2017-06-02 13:44:28 +00:00
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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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2018-01-08 09:28:30 +00:00
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"engine.setStartDate('20130101') # 设置回测用的数据起始日期"
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2017-06-02 13:44:28 +00:00
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]
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},
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{
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"cell_type": "code",
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2017-12-15 15:05:22 +00:00
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"execution_count": 4,
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2017-06-02 13:44:28 +00:00
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"metadata": {
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2018-01-08 09:28:30 +00:00
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"collapsed": false
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2017-06-02 13:44:28 +00:00
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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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2017-07-14 12:12:42 +00:00
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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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2017-06-02 13:44:28 +00:00
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]
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},
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{
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"cell_type": "code",
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2018-01-08 09:28:30 +00:00
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"execution_count": 5,
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2017-06-02 13:44:28 +00:00
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"metadata": {
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2017-10-07 14:36:23 +00:00
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"collapsed": false
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2017-06-02 13:44:28 +00:00
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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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2018-01-11 08:03:42 +00:00
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"engine.initStrategy(AtrRsiStrategy, d) # 创建策略对象\n",
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2017-12-15 15:05:22 +00:00
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"#ngine.initStrategy(MultiTimeframeStrategy, d) \n",
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2018-01-11 08:03:42 +00:00
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"#engine.initStrategy(MultiSignalStrategy, {}) "
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2017-06-02 13:44:28 +00:00
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]
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},
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{
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"cell_type": "code",
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2018-01-11 08:03:42 +00:00
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"execution_count": 6,
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2017-06-02 13:44:28 +00:00
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"metadata": {
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"collapsed": false
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},
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2018-01-11 08:03:42 +00:00
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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 14:17:58 +00:00
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"2018-02-16 22:10:48.332000\t开始载入数据\n",
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"2018-02-16 22:10:48.417000\t载入完成,数据量:91381\n",
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"2018-02-16 22:10:48.418000\t开始回测\n",
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"2018-02-16 22:10:48.463000\t策略初始化完成\n",
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"2018-02-16 22:10:48.463000\t策略启动完成\n",
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"2018-02-16 22:10:48.463000\t开始回放数据\n",
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"2018-02-16 22:10:56.057000\t数据回放结束\n"
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2018-01-11 08:03:42 +00:00
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]
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}
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],
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2017-06-02 13:44:28 +00:00
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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",
|
2018-01-11 08:03:42 +00:00
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"execution_count": 7,
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2017-06-02 13:44:28 +00:00
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"metadata": {
|
2018-02-16 14:17:58 +00:00
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"collapsed": false,
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"scrolled": false
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2017-06-02 13:44:28 +00:00
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},
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2018-01-11 08:03:42 +00:00
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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 14:17:58 +00:00
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"2018-02-16 22:10:56.062000\t计算按日统计结果\n",
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"2018-02-16 22:10:56.304000\t------------------------------\n",
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"2018-02-16 22:10:56.305000\t首个交易日:\t2013-01-11\n",
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"2018-02-16 22:10:56.305000\t最后交易日:\t2014-06-04\n",
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"2018-02-16 22:10:56.305000\t总交易日:\t334\n",
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"2018-02-16 22:10:56.305000\t盈利交易日\t167\n",
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"2018-02-16 22:10:56.305000\t亏损交易日:\t167\n",
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"2018-02-16 22:10:56.305000\t起始资金:\t1000000\n",
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"2018-02-16 22:10:56.305000\t结束资金:\t1,283,473.32\n",
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"2018-02-16 22:10:56.305000\t总收益率:\t28.35%\n",
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"2018-02-16 22:10:56.305000\t年化收益:\t20.37%\n",
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"2018-02-16 22:10:56.305000\t总盈亏:\t283,473.32\n",
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"2018-02-16 22:10:56.305000\t最大回撤: \t-101,191.41\n",
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"2018-02-16 22:10:56.305000\t百分比最大回撤: -9.23%\n",
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"2018-02-16 22:10:56.305000\t总手续费:\t33,266.68\n",
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"2018-02-16 22:10:56.305000\t总滑点:\t94,260.0\n",
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"2018-02-16 22:10:56.305000\t总成交金额:\t1,108,889,460.0\n",
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"2018-02-16 22:10:56.305000\t总成交笔数:\t1,571.0\n",
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"2018-02-16 22:10:56.305000\t日均盈亏:\t848.72\n",
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"2018-02-16 22:10:56.305000\t日均手续费:\t99.6\n",
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"2018-02-16 22:10:56.305000\t日均滑点:\t282.22\n",
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"2018-02-16 22:10:56.305000\t日均成交金额:\t3,320,028.32\n",
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"2018-02-16 22:10:56.305000\t日均成交笔数:\t4.7\n",
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"2018-02-16 22:10:56.305000\t日均收益率:\t0.07%\n",
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"2018-02-16 22:10:56.305000\t收益标准差:\t0.87%\n",
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"2018-02-16 22:10:56.305000\tSharpe Ratio:\t1.3\n"
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2018-01-11 08:03:42 +00:00
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]
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},
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{
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"data": {
|
2018-02-16 14:17:58 +00:00
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"image/png": "iVBORw0KGgoAAAANSUhEUgAAAoEAAAOlCAYAAAASGT0sAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nk40ffvjBmDp1aua9Jk6
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2018-01-11 08:03:42 +00:00
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"text/plain": [
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2018-02-16 14:17:58 +00:00
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"<matplotlib.figure.Figure at 0xccfd390>"
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2018-01-11 08:03:42 +00:00
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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2017-06-02 13:44:28 +00:00
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"source": [
|
2017-07-14 12:12:42 +00:00
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|
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"# 显示逐日回测结果\n",
|
2017-10-07 15:25:59 +00:00
|
|
|
|
"engine.showDailyResult()"
|
2017-06-02 13:44:28 +00:00
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|
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]
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},
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{
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"cell_type": "code",
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2017-10-07 14:36:23 +00:00
|
|
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|
"execution_count": null,
|
2017-06-02 13:44:28 +00:00
|
|
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"metadata": {
|
|
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"collapsed": false
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},
|
2017-10-07 14:36:23 +00:00
|
|
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"outputs": [],
|
2017-07-14 12:12:42 +00:00
|
|
|
|
"source": [
|
|
|
|
|
"# 显示逐笔回测结果\n",
|
|
|
|
|
"engine.showBacktestingResult()"
|
|
|
|
|
]
|
|
|
|
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},
|
|
|
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|
{
|
|
|
|
|
"cell_type": "code",
|
2017-10-07 14:36:23 +00:00
|
|
|
|
"execution_count": null,
|
2017-07-14 12:12:42 +00:00
|
|
|
|
"metadata": {
|
|
|
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|
"collapsed": false
|
|
|
|
|
},
|
2017-10-07 14:36:23 +00:00
|
|
|
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"outputs": [],
|
2017-06-02 13:44:28 +00:00
|
|
|
|
"source": [
|
|
|
|
|
"# 显示前10条成交记录\n",
|
|
|
|
|
"for i in range(10):\n",
|
2017-09-25 05:51:24 +00:00
|
|
|
|
" d = engine.tradeDict[str(i+1)].__dict__\n",
|
|
|
|
|
" print 'TradeID: %s, Time: %s, Direction: %s, Price: %s, Volume: %s' %(d['tradeID'], d['dt'], d['direction'], d['price'], d['volume'])"
|
2017-06-02 13:44:28 +00:00
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
2018-01-11 08:03:42 +00:00
|
|
|
|
"execution_count": null,
|
2017-06-02 13:44:28 +00:00
|
|
|
|
"metadata": {
|
2017-07-14 12:12:42 +00:00
|
|
|
|
"collapsed": false
|
2017-06-02 13:44:28 +00:00
|
|
|
|
},
|
2018-01-11 08:03:42 +00:00
|
|
|
|
"outputs": [],
|
2017-06-02 13:44:28 +00:00
|
|
|
|
"source": [
|
|
|
|
|
"# 优化配置\n",
|
|
|
|
|
"setting = OptimizationSetting() # 新建一个优化任务设置对象\n",
|
2018-01-08 09:28:30 +00:00
|
|
|
|
"setting.setOptimizeTarget('totalNetPnl') # 设置优化排序的目标是策略净盈利\n",
|
|
|
|
|
"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",
|
2018-02-16 14:17:58 +00:00
|
|
|
|
"#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",
|
|
|
|
|
"#setting.addParameter('rsiLength', 5) # 增加一个固定数值的参数\n",
|
2017-06-02 13:44:28 +00:00
|
|
|
|
"\n",
|
|
|
|
|
"# 执行多进程优化\n",
|
|
|
|
|
"import time\n",
|
2017-09-25 05:51:24 +00:00
|
|
|
|
"start = time.time()\n",
|
2018-02-16 14:17:58 +00:00
|
|
|
|
"#resultList = engine.runParallelOptimization(AtrRsiStrategy, setting)\n",
|
|
|
|
|
"resultList = engine.runOptimization(AtrRsiStrategy, setting)\n",
|
2017-06-02 13:44:28 +00:00
|
|
|
|
"print u'耗时:%s' %(time.time()-start)"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {
|
2018-01-11 08:03:42 +00:00
|
|
|
|
"collapsed": false
|
2017-06-02 13:44:28 +00:00
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
2018-01-08 09:28:30 +00:00
|
|
|
|
"source": [
|
|
|
|
|
"# 显示优化的所有统计数据\n",
|
|
|
|
|
"for result in resultList:\n",
|
2018-01-11 08:03:42 +00:00
|
|
|
|
" print '-' * 30\n",
|
|
|
|
|
" print u'参数:%s,目标:%s' %(result[0], result[1])\n",
|
|
|
|
|
" print u'统计数据:'\n",
|
|
|
|
|
" for k, v in result[2].items():\n",
|
|
|
|
|
" print u'%s:%s' %(k, v)"
|
2018-01-08 09:28:30 +00:00
|
|
|
|
]
|
2018-01-11 08:03:42 +00:00
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": null,
|
|
|
|
|
"metadata": {
|
|
|
|
|
"collapsed": true
|
|
|
|
|
},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"source": []
|
2017-06-02 13:44:28 +00:00
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"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": "2.7.13"
|
|
|
|
|
}
|
|
|
|
|
},
|
|
|
|
|
"nbformat": 4,
|
|
|
|
|
"nbformat_minor": 0
|
|
|
|
|
}
|