377 lines
14 KiB
Plaintext
377 lines
14 KiB
Plaintext
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{
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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 backtesting import BacktestingEngine,OptimizationSetting\n",
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"from boll_channel_strategy import BollChannelStrategy\n",
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"from 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 scoop import futures #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": 2,
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"metadata": {},
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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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"数据总体： 13824\n"
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]
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}
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],
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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": 3,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"dict_keys(['boll_window', 'cci_window', 'atr_window'])"
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]
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},
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"execution_count": 3,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 4,
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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": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[22, 28, 22]"
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]
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},
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"execution_count": 5,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"{'boll_window': 24, 'cci_window': 14, 'atr_window': 28}"
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]
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},
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"execution_count": 6,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 7,
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(-0.51, -0.28)"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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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": 9,
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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": 10,
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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()\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": 11,
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"metadata": {
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"scrolled": 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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"开始运行遗传算法，每代族群总数：34, 优良品种筛选个数：27，迭代次数：30，交叉概率：0.95，突变概率：0.05\n",
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"gen\tnevals\tmean \tstd \tmin \tmax \n",
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"0 \t34 \t[0.08852941 0.00352941]\t[0.5373362 0.29107188]\t[-0.7 -0.63]\t[1.51 0.5 ]\n",
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"1 \t34 \t[0.60148148 0.27518519]\t[0.31013383 0.08573734]\t[0.32 0.18] \t[1.51 0.5 ]\n",
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"2 \t34 \t[0.79333333 0.33851852]\t[0.27758215 0.06742369]\t[0.47 0.25] \t[1.54 0.5 ]\n",
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"3 \t34 \t[1.00888889 0.39777778]\t[0.3147525 0.06214281]\t[0.7 0.33] \t[1.54 0.5 ]\n",
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"4 \t34 \t[1.41074074 0.47444444]\t[0.22881217 0.04661373]\t[0.96 0.36] \t[1.92 0.57]\n",
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"5 \t34 \t[1.59666667 0.51222222]\t[0.14714568 0.0255797 ]\t[1.51 0.49] \t[1.92 0.57]\n",
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"6 \t34 \t[1.66259259 0.52185185]\t[0.16585564 0.02981884]\t[1.52 0.49] \t[1.92 0.57]\n",
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"7 \t34 \t[1.8737037 0.55666667]\t[0.07713135 0.01763834]\t[1.75 0.53] \t[1.95 0.57]\n",
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"8 \t34 \t[1.93666667 0.57 ]\t[0.01490712 0. ]\t[1.92 0.57] \t[1.95 0.57]\n",
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"9 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"10 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"11 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"12 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"13 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"14 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"15 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"16 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"17 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"18 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"19 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"20 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"21 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"22 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"23 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"24 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"25 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"26 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"27 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"28 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"29 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"30 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
|
|||
|
|
"遗传算法优化完成，耗时309秒\n",
|
|||
|
|
"输出帕累托前沿解集：\n",
|
|||
|
|
"{'boll_window': 48, 'cci_window': 40, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n",
|
|||
|
|
"{'boll_window': 48, 'cci_window': 50, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n"
|
|||
|
|
]
|
|||
|
|
}
|
|||
|
|
],
|
|||
|
|
"source": [
|
|||
|
|
"optimize()"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"cell_type": "code",
|
|||
|
|
"execution_count": null,
|
|||
|
|
"metadata": {},
|
|||
|
|
"outputs": [],
|
|||
|
|
"source": [
|
|||
|
|
" MU = 80 #设置每一代选择的个体数\n",
|
|||
|
|
" POP = 100 #设置每一代产生的子女数\n",
|
|||
|
|
" CXPB, MUTPB, NGEN = 0.95, 0.05,20 \n",
|
|||
|
|
" 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))"
|
|||
|
|
]
|
|||
|
|
},
|
|||
|
|
{
|
|||
|
|
"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
|
|||
|
|
}
|