{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import random\n", "import multiprocessing\n", "import numpy as np\n", "from deap import creator, base, tools, algorithms\n", "from vnpy.app.cta_strategy.backtesting import BacktestingEngine\n", "from boll_channel_strategy import BollChannelStrategy\n", "from datetime import datetime\n", "import multiprocessing #多进程\n", "from scoop import futures #多进程" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def parameter_generate():\n", " '''\n", " 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", " '''\n", " parameter_list = []\n", " p1 = random.randrange(4,50,2) #布林带窗口\n", " p2 = random.randrange(4,50,2) #布林带通道阈值\n", " p3 = random.randrange(4,50,2) #CCI窗口\n", " p4 = random.randrange(18,40,2) #ATR窗口 \n", "\n", " parameter_list.append(p1)\n", " parameter_list.append(p2)\n", " parameter_list.append(p3)\n", " parameter_list.append(p4)\n", "\n", " return parameter_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "def object_func(strategy_avg):\n", " \"\"\"\n", " 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", " \"\"\"\n", " # 创建回测引擎对象\n", " engine = BacktestingEngine()\n", " engine.set_parameters(\n", " vt_symbol=\"IF88.CFFEX\",\n", " interval=\"1m\",\n", " start=datetime(2018, 9, 1),\n", " end=datetime(2019, 1,1),\n", " rate=0,\n", " slippage=0,\n", " size=300,\n", " pricetick=0.2,\n", " capital=1_000_000,\n", " )\n", "\n", " setting = {'boll_window': strategy_avg[0], #布林带窗口\n", " 'boll_dev': strategy_avg[1], #布林带通道阈值\n", " 'cci_window': strategy_avg[2], #CCI窗口\n", " 'atr_window': strategy_avg[3],} #ATR窗口 \n", "\n", " #加载策略 \n", " #engine.initStrategy(TurtleTradingStrategy, setting)\n", " engine.add_strategy(BollChannelStrategy, setting)\n", " engine.load_data()\n", " engine.run_backtesting()\n", " engine.calculate_result()\n", " result = engine.calculate_statistics(Output=False)\n", "\n", " return_drawdown_ratio = round(result['return_drawdown_ratio'],2) #收益回撤比\n", " sharpe_ratio= round(result['sharpe_ratio'],2) #夏普比率\n", " return return_drawdown_ratio , sharpe_ratio" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "\n", "#设置优化方向：最大化收益回撤比，最大化夏普比率\n", "creator.create(\"FitnessMulti\", base.Fitness, weights=(1.0, 1.0)) # 1.0 求最大值；-1.0 求最小值\n", "creator.create(\"Individual\", list, fitness=creator.FitnessMulti)\n", "\n", "def optimize():\n", " \"\"\"\"\"\" \n", " 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", "\n", " # 初始化 \n", " 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", " toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual) #注册种群：个体形成种群 \n", " toolbox.register(\"mate\", tools.cxTwoPoint) #注册交叉：两点交叉 \n", " 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", " toolbox.register(\"evaluate\", object_func) #注册评估：优化目标函数object_func() \n", " 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", " #pool = multiprocessing.Pool()\n", " #toolbox.register(\"map\", pool.map)\n", " #toolbox.register(\"map\", futures.map)\n", "\n", " #遗传算法参数设置\n", " MU = 40 #设置每一代选择的个体数\n", " LAMBDA = 160 #设置每一代产生的子女数\n", " pop = toolbox.population(400) #设置族群里面的个体数量\n", " 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", " hof = tools.ParetoFront() #解的集合：帕累托前沿(非占优最优集)\n", "\n", " #解的集合的描述统计信息\n", " #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", " #收敛程度低可以增加算法的迭代次数\n", " stats = tools.Statistics(lambda ind: ind.fitness.values)\n", " np.set_printoptions(suppress=True) #对numpy默认输出的科学计数法转换\n", " stats.register(\"mean\", np.mean, axis=0) #统计目标优化函数结果的平均值\n", " stats.register(\"std\", np.std, axis=0) #统计目标优化函数结果的标准差\n", " stats.register(\"min\", np.min, axis=0) #统计目标优化函数结果的最小值\n", " stats.register(\"max\", np.max, axis=0) #统计目标优化函数结果的最大值\n", "\n", " #运行算法\n", " algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n", " 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", "\n", " return pop" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "optimize()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.1" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }