280 lines
9.2 KiB
Plaintext
280 lines
9.2 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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"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,OptimizationSetting\n",
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"from vnpy.app.cta_strategy.strategies.boll_channel_strategy import BollChannelStrategy\n",
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"from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import AtrRsiStrategy\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 functools import lru_cache"
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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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"setting = OptimizationSetting()\n",
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"#setting.add_parameter('atr_length', 10, 50, 2)\n",
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"#setting.add_parameter('atr_ma_length', 10, 50, 2)\n",
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"#setting.add_parameter('rsi_length', 4, 50, 2)\n",
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"#setting.add_parameter('rsi_entry', 4, 30, 1)\n",
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"setting.add_parameter('boll_window', 4, 50, 2)\n",
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"#setting.add_parameter('boll_dev', 4, 50, 2)\n",
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"setting.add_parameter('cci_window', 4, 50, 2)\n",
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"setting.add_parameter('atr_window', 4, 50, 2)\n",
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"\n",
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"\n",
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"local_setting = setting.generate_setting()\n",
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"total_sample = len(local_setting)\n",
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"print(\"数据总体：\",total_sample)"
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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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"setting_names = random.choice(local_setting).keys()\n",
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"setting_names"
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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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" setting_param = list(random.choice(local_setting).values())\n",
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" return setting_param"
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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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"parameter_generate()"
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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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"setting=dict(zip(setting_names,parameter_generate()))\n",
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"setting"
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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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" return run_backtesting(tuple(strategy_avg))\n",
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" #return run_backtesting(strategy_avg)\n",
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" \n",
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"\n",
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"@lru_cache(maxsize=1000000)\n",
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"def run_backtesting(strategy_avg):\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(2016, 1, 1),\n",
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" end=datetime(2019, 1,1),\n",
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" rate=0.3/10000,\n",
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" slippage=0.2,\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=dict(zip(setting_names,strategy_avg))\n",
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" \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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"object_func(parameter_generate())"
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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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"target_names = [\"return_drawdown_ratio\" , \"sharpe_ratio\"]\n",
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"def show_result(hof):\n",
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" for i in range(len(hof)):\n",
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" solution = hof[i] \n",
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" parameter=dict(zip(setting_names,solution))\n",
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" result=dict(zip(target_names,list(object_func(solution))))\n",
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" print({**parameter, **result})"
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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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"from time import time\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(\"FitnessMax\", 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.FitnessMax)\n",
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"\n",
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"def optimize(population=None):\n",
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" \"\"\"\"\"\" \n",
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" start = time() \n",
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" toolbox = base.Toolbox() \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) \n",
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" toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual) \n",
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" toolbox.register(\"mate\", tools.cxTwoPoint) \n",
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" toolbox.register(\"mutate\", tools.mutUniformInt,low = 4,up = 40,indpb=1) \n",
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" toolbox.register(\"evaluate\", object_func) \n",
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" toolbox.register(\"select\", tools.selNSGA2) \n",
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" pool = multiprocessing.Pool(multiprocessing.cpu_count())\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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" \n",
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" #遗传算法参数设置\n",
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" MU = 80 #设置每一代选择的个体数\n",
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" LAMBDA = 100 #设置每一代产生的子女数\n",
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" POP=100\n",
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" CXPB, MUTPB, NGEN = 0.95, 0.05,30 #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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" \n",
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" if population==None:\n",
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" LAMBDA = POP = int(pow(total_sample, 1/2.7))\n",
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" MU = int(0.8*POP) \n",
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" \n",
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" pop = toolbox.population(POP) #设置族群里面的个体数量\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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" 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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" print(\"开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s\" %(POP,MU,NGEN,CXPB,MUTPB))\n",
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" \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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" end = time()\n",
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" cost = int((end - start))\n",
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"\n",
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" print(\"遗传算法优化完成，耗时%s秒\"% (cost))\n",
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" print(\"输出帕累托前沿解集：\")\n",
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" show_result(hof)\n",
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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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"execution_count": null,
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"metadata": {
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"scrolled": false
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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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"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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" MU = 80 #设置每一代选择的个体数\n",
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" POP = 100 #设置每一代产生的子女数\n",
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" CXPB, MUTPB, NGEN = 0.95, 0.05,20 \n",
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" print(\"开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s\" %(POP,MU,NGEN,CXPB,MUTPB))"
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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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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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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": 3
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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": "ipython3",
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"version": "3.7.1"
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},
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"toc": {
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"base_numbering": 1,
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"nav_menu": {},
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"number_sections": true,
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"sideBar": true,
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"skip_h1_title": false,
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"title_cell": "Table of Contents",
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"title_sidebar": "Contents",
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"toc_cell": false,
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"toc_position": {},
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"toc_section_display": true,
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"toc_window_display": false
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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