190 lines
7.3 KiB
Plaintext
190 lines
7.3 KiB
Plaintext
{
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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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"outputs": [],
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"source": [
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"import random\n",
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"import multiprocessing\n",
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"import numpy as np\n",
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"from deap import creator, base, tools, algorithms\n",
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"from vnpy.app.cta_strategy.backtesting import BacktestingEngine\n",
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"from boll_channel_strategy import BollChannelStrategy\n",
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"from datetime import datetime\n",
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"import multiprocessing #多进程\n",
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"from scoop import futures #多进程"
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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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"outputs": [],
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"source": [
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"def parameter_generate():\n",
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" '''\n",
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" 根据设置的起始值，终止值和步进，随机生成待优化的策略参数\n",
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" '''\n",
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" parameter_list = []\n",
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" p1 = random.randrange(4,50,2) #布林带窗口\n",
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" p2 = random.randrange(4,50,2) #布林带通道阈值\n",
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" p3 = random.randrange(4,50,2) #CCI窗口\n",
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" p4 = random.randrange(18,40,2) #ATR窗口 \n",
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"\n",
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" parameter_list.append(p1)\n",
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" parameter_list.append(p2)\n",
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" parameter_list.append(p3)\n",
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" parameter_list.append(p4)\n",
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"\n",
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" return parameter_list"
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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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"outputs": [],
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"source": [
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"def object_func(strategy_avg):\n",
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" \"\"\"\n",
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" 本函数为优化目标函数，根据随机生成的策略参数，运行回测后自动返回2个结果指标：收益回撤比和夏普比率\n",
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" \"\"\"\n",
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" # 创建回测引擎对象\n",
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" engine = BacktestingEngine()\n",
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" engine.set_parameters(\n",
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" vt_symbol=\"IF88.CFFEX\",\n",
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" interval=\"1m\",\n",
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" start=datetime(2018, 9, 1),\n",
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" end=datetime(2019, 1,1),\n",
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" rate=0,\n",
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" slippage=0,\n",
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" size=300,\n",
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" pricetick=0.2,\n",
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" capital=1_000_000,\n",
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" )\n",
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"\n",
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" setting = {'boll_window': strategy_avg[0], #布林带窗口\n",
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" 'boll_dev': strategy_avg[1], #布林带通道阈值\n",
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" 'cci_window': strategy_avg[2], #CCI窗口\n",
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" 'atr_window': strategy_avg[3],} #ATR窗口 \n",
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"\n",
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" #加载策略 \n",
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" #engine.initStrategy(TurtleTradingStrategy, setting)\n",
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" engine.add_strategy(BollChannelStrategy, setting)\n",
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" engine.load_data()\n",
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" engine.run_backtesting()\n",
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" engine.calculate_result()\n",
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" result = engine.calculate_statistics(Output=False)\n",
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"\n",
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" return_drawdown_ratio = round(result['return_drawdown_ratio'],2) #收益回撤比\n",
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" sharpe_ratio= round(result['sharpe_ratio'],2) #夏普比率\n",
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" return return_drawdown_ratio , sharpe_ratio"
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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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"outputs": [],
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"source": [
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"\n",
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"#设置优化方向：最大化收益回撤比，最大化夏普比率\n",
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"creator.create(\"FitnessMulti\", base.Fitness, weights=(1.0, 1.0)) # 1.0 求最大值；-1.0 求最小值\n",
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"creator.create(\"Individual\", list, fitness=creator.FitnessMulti)\n",
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"\n",
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"def optimize():\n",
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" \"\"\"\"\"\" \n",
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" toolbox = base.Toolbox() #Toolbox是deap库内置的工具箱，里面包含遗传算法中所用到的各种函数\n",
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"\n",
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" # 初始化 \n",
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" toolbox.register(\"individual\", tools.initIterate, creator.Individual,parameter_generate) # 注册个体：随机生成的策略参数parameter_generate() \n",
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" toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual) #注册种群：个体形成种群 \n",
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" toolbox.register(\"mate\", tools.cxTwoPoint) #注册交叉：两点交叉 \n",
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" toolbox.register(\"mutate\", tools.mutUniformInt,low = 4,up = 40,indpb=0.6) #注册变异：随机生成一定区间内的整数\n",
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" toolbox.register(\"evaluate\", object_func) #注册评估：优化目标函数object_func() \n",
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" toolbox.register(\"select\", tools.selNSGA2) #注册选择:NSGA-II(带精英策略的非支配排序的遗传算法)\n",
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" #pool = multiprocessing.Pool()\n",
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" #toolbox.register(\"map\", pool.map)\n",
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" #toolbox.register(\"map\", futures.map)\n",
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"\n",
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" #遗传算法参数设置\n",
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" MU = 40 #设置每一代选择的个体数\n",
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" LAMBDA = 160 #设置每一代产生的子女数\n",
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" pop = toolbox.population(400) #设置族群里面的个体数量\n",
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" CXPB, MUTPB, NGEN = 0.5, 0.35,40 #分别为种群内部个体的交叉概率、变异概率、产生种群代数\n",
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" hof = tools.ParetoFront() #解的集合：帕累托前沿(非占优最优集)\n",
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"\n",
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" #解的集合的描述统计信息\n",
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" #集合内平均值，标准差，最小值，最大值可以体现集合的收敛程度\n",
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" #收敛程度低可以增加算法的迭代次数\n",
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" stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
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" np.set_printoptions(suppress=True) #对numpy默认输出的科学计数法转换\n",
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" stats.register(\"mean\", np.mean, axis=0) #统计目标优化函数结果的平均值\n",
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" stats.register(\"std\", np.std, axis=0) #统计目标优化函数结果的标准差\n",
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" stats.register(\"min\", np.min, axis=0) #统计目标优化函数结果的最小值\n",
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" stats.register(\"max\", np.max, axis=0) #统计目标优化函数结果的最大值\n",
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"\n",
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" #运行算法\n",
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" algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n",
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" halloffame=hof) #esMuPlusLambda是一种基于(μ+λ)选择策略的多目标优化分段遗传算法\n",
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"\n",
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" return pop"
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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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"scrolled": true
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},
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"outputs": [],
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"source": [
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"optimize()"
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]
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},
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{
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},
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"title_cell": "Table of Contents",
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