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[Mod]add return value of run_ga_optimization

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vn.py 2019-05-03 16:10:11 +08:00
parent 6e3b7faae3
commit 4cd84b45a5
4 changed files with 153 additions and 202 deletions
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104
tests/backtesting/GA_Pre_Final.ipynb
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@ -20,17 +20,9 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据总体： 13824\n"
]
}
],
"outputs": [],
"source": [
"setting = OptimizationSetting()\n",
"#setting.add_parameter('atr_length', 10, 50, 2)\n",
@ -50,20 +42,9 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['boll_window', 'cci_window', 'atr_window'])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"setting_names = random.choice(local_setting).keys()\n",
"setting_names"
@ -71,7 +52,7 @@
},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -82,40 +63,18 @@
},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[48, 6, 26]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"parameter_generate()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'boll_window': 16, 'cci_window': 48, 'atr_window': 6}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"setting=dict(zip(setting_names,parameter_generate()))\n",
"setting"
@ -123,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": 7,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -167,41 +126,16 @@
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": null,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-05-03 15:02:07.528909\t开始加载历史数据\n",
"2019-05-03 15:02:34.854177\t历史数据加载完成，数据量：175440\n",
"2019-05-03 15:02:34.877616\t策略初始化完成\n",
"2019-05-03 15:02:34.877616\t开始回放历史数据\n",
"2019-05-03 15:02:37.000744\t历史数据回放结束\n",
"2019-05-03 15:02:37.000744\t开始计算逐日盯市盈亏\n",
"2019-05-03 15:02:37.012463\t逐日盯市盈亏计算完成\n",
"2019-05-03 15:02:37.012463\t开始计算策略统计指标\n"
]
},
{
"data": {
"text/plain": [
"(0.96, 0.38)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"object_func(parameter_generate())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -216,7 +150,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -283,15 +217,7 @@
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
}
],
"outputs": [],
"source": [
"optimize()"
]

27
tests/backtesting/run_ga.py Normal file
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@ -0,0 +1,27 @@
from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import (
AtrRsiStrategy,
)
from datetime import datetime
if __name__ == "__main__":
engine = BacktestingEngine()
engine.set_parameters(
vt_symbol="IF88.CFFEX",
interval="1m",
start=datetime(2019, 1, 1),
end=datetime(2019, 4, 30),
rate=0.3 / 10000,
slippage=0.2,
size=300,
pricetick=0.2,
capital=1_000_000,
)
engine.add_strategy(AtrRsiStrategy, {})
setting = OptimizationSetting()
setting.set_target("sharpe_ratio")
setting.add_parameter("atr_length", 3, 39, 1)
setting.add_parameter("atr_ma_length", 10, 30, 1)
engine.run_ga_optimization(setting)

60
tests/backtesting/turtle.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -16,7 +16,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
@ -54,44 +54,38 @@
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2019-05-03 14:58:44.510371\t开始运行遗传算法，每代族群总数：20, 优良品种筛选个数：16，迭代次数：300，交叉概率：0.95，突变概率：0.05\n"
]
},
{
"ename": "AttributeError",
"evalue": "Can't pickle local object 'create_ga_optimize.<locals>.ga_optimize'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-3-d83ea019c683>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[0msetting\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_parameter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"atr_ma_length\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m80\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 6\u001b[1;33m \u001b[0mengine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun_ga_optimization\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msetting\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32mC:\\Github\\vnpy\\vnpy\\app\\cta_strategy\\backtesting.py\u001b[0m in \u001b[0;36mrun_ga_optimization\u001b[1;34m(self, optimization_setting, output)\u001b[0m\n\u001b[0;32m 601\u001b[0m \u001b[0mngen\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 602\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 603\u001b[1;33m \u001b[0mhalloffame\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhof\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 604\u001b[0m ) \n\u001b[0;32m 605\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\site-packages\\deap\\algorithms.py\u001b[0m in \u001b[0;36meaMuPlusLambda\u001b[1;34m(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats, halloffame, verbose)\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[1;31m# Evaluate the individuals with an invalid fitness\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 302\u001b[0m \u001b[0minvalid_ind\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mind\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mind\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mpopulation\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalid\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 303\u001b[1;33m \u001b[0mfitnesses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtoolbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoolbox\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mevaluate\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minvalid_ind\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 304\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfit\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minvalid_ind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfitnesses\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 305\u001b[0m \u001b[0mind\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfitness\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mmap\u001b[1;34m(self, func, iterable, chunksize)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;32min\u001b[0m \u001b[0ma\u001b[0m \u001b[0mlist\u001b[0m \u001b[0mthat\u001b[0m \u001b[1;32mis\u001b[0m \u001b[0mreturned\u001b[0m\u001b[1;33m.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 289\u001b[0m '''\n\u001b[1;32m--> 290\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_map_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmapstar\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 291\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 292\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mstarmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0miterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m 681\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 682\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 683\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 685\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_set\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\pool.py\u001b[0m in \u001b[0;36m_handle_tasks\u001b[1;34m(taskqueue, put, outqueue, pool, cache)\u001b[0m\n\u001b[0;32m 455\u001b[0m \u001b[1;32mbreak\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 456\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 457\u001b[1;33m \u001b[0mput\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 458\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 459\u001b[0m \u001b[0mjob\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtask\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\connection.py\u001b[0m in \u001b[0;36msend\u001b[1;34m(self, obj)\u001b[0m\n\u001b[0;32m 204\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 205\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_writable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 206\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_send_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_ForkingPickler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 207\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 208\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mrecv_bytes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmaxlength\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32mc:\\miniconda3\\lib\\multiprocessing\\reduction.py\u001b[0m in \u001b[0;36mdumps\u001b[1;34m(cls, obj, protocol)\u001b[0m\n\u001b[0;32m 49\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mdumps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 50\u001b[0m \u001b[0mbuf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 51\u001b[1;33m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbuf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mprotocol\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 52\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mbuf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetbuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 53\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mAttributeError\u001b[0m: Can't pickle local object 'create_ga_optimize.<locals>.ga_optimize'"
]
}
],
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"setting = OptimizationSetting()\n",
"setting.set_target(\"sharpe_ratio\")\n",
"setting.add_parameter(\"atr_length\", 3, 105, 1)\n",
"setting.add_parameter(\"atr_ma_length\", 10, 80, 1)\n",
"setting.add_parameter(\"atr_length\", 3, 39, 1)\n",
"setting.add_parameter(\"atr_ma_length\", 10, 30, 1)\n",
"\n",
"engine.run_ga_optimization(setting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"result = _"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"print(result)"
]
},
{
"cell_type": "code",
"execution_count": null,

164
vnpy/app/cta_strategy/backtesting.py
View File

@ -6,6 +6,7 @@ from functools import lru_cache
from time import time
import multiprocessing
import random
import math
import numpy as np
import matplotlib.pyplot as plt
@ -29,6 +30,8 @@ from .base import (
from .template import CtaTemplate
sns.set_style("whitegrid")
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
class OptimizationSetting:
@ -537,44 +540,52 @@ class BacktestingEngine:
return list(random.choice(settings).values())
# Create ga object function
object_func = create_ga_optimize(
target_name,
self.strategy_class,
settings[0],
self.vt_symbol,
self.interval,
self.start,
self.rate,
self.slippage,
self.size,
self.pricetick,
self.capital,
self.end,
self.mode
)
global ga_target_name
global ga_strategy_class
global ga_setting
global ga_vt_symbol
global ga_interval
global ga_start
global ga_rate
global ga_slippage
global ga_size
global ga_pricetick
global ga_capital
global ga_end
global ga_mode
ga_target_name = target_name
ga_strategy_class = self.strategy_class
ga_setting = settings[0]
ga_vt_symbol = self.vt_symbol
ga_interval = self.interval
ga_start = self.start
ga_rate = self.rate
ga_slippage = self.slippage
ga_size = self.size
ga_pricetick = self.pricetick
ga_capital = self.capital
ga_end = self.end
ga_mode = self.mode
# Set up genetic algorithem
creator.create("FitnessMax", base.Fitness, weights=(1.0,))
creator.create("Individual", list, fitness=creator.FitnessMax)
toolbox = base.Toolbox()
toolbox.register("individual", tools.initIterate, creator.Individual, generate_parameter)
toolbox.register("population", tools.initRepeat, list, toolbox.individual)
toolbox.register("mate", tools.cxTwoPoint)
toolbox.register("mutate", tools.mutUniformInt, low=4, up=40, indpb=1)
toolbox.register("evaluate", object_func)
toolbox.register("evaluate", ga_optimize)
toolbox.register("select", tools.selNSGA2)
pool = multiprocessing.Pool(multiprocessing.cpu_count())
toolbox.register("map", pool.map)
mu = 16 # number of individuals to select for the next generation
lambda_ = 20 # number of children to produce at each generation
cxpb = 0.95 # probability that an offspring is produced by crossover
mutpb = 0.05 # probability that an offspring is produced by mutation
ngen = 300 # number of generation
pop_size = 20 # number of individuals in each generation
total_size = len(settings)
pop_size = int(pow(total_size, 1 / math.e)) # number of individuals in each generation
lambda_ = pop_size # number of children to produce at each generation
mu = int(pop_size * 0.8) # number of individuals to select for the next generation
cxpb = 0.95 # probability that an offspring is produced by crossover
mutpb = 1 - cxpb # probability that an offspring is produced by mutation
ngen = 30 # number of generation
pop = toolbox.population(pop_size)
hof = tools.ParetoFront() # end result of pareto front
@ -584,11 +595,15 @@ class BacktestingEngine:
stats.register("std", np.std, axis=0)
stats.register("min", np.min, axis=0)
stats.register("max", np.max, axis=0)
msg = "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_size, mu, ngen, cxpb, mutpb)
self.output(msg)
# Multiprocessing is not supported yet.
# pool = multiprocessing.Pool(multiprocessing.cpu_count())
# toolbox.register("map", pool.map)
# Run ga optimization
msg = "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_size, mu, ngen, cxpb, mutpb)
self.output(msg)
start = time()
algorithms.eaMuPlusLambda(
@ -607,9 +622,17 @@ class BacktestingEngine:
cost = int((end - start))
self.output(f"遗传算法优化完成，耗时{cost}秒")
self.output("输出帕累托前沿解集：")
return hof
# Return result list
results = []
parameter_keys = list(ga_setting.keys())
for parameter_values in hof:
setting = dict(zip(parameter_keys, parameter_values))
target_value = ga_optimize(parameter_values)[0]
results.append((setting, target_value))
return results
def update_daily_close(self, price: float):
""""""
@ -1065,52 +1088,33 @@ def optimize(
return (str(setting), target_value, statistics)
def create_ga_optimize(
target_name: str,
strategy_class: CtaTemplate,
setting: dict,
vt_symbol: str,
interval: Interval,
start: datetime,
rate: float,
slippage: float,
size: float,
pricetick: float,
capital: int,
end: datetime,
mode: BacktestingMode,
):
"""
Function for running in multiprocessing.pool
"""
parameter_keys = list(setting.keys())
@lru_cache(maxsize=1000000)
def _ga_optimizae(parameter_values: tuple):
""""""
parameter_keys = list(ga_setting.keys())
setting = dict(zip(parameter_keys, parameter_values))
@lru_cache(maxsize=1000000)
def _optimizae(parameter_values: tuple):
""""""
setting = dict(zip(parameter_keys, parameter_values))
result = optimize(
target_name,
strategy_class,
setting,
vt_symbol,
interval,
start,
rate,
slippage,
size,
pricetick,
capital,
end,
mode
)
return (result[1],)
result = optimize(
ga_target_name,
ga_strategy_class,
setting,
ga_vt_symbol,
ga_interval,
ga_start,
ga_rate,
ga_slippage,
ga_size,
ga_pricetick,
ga_capital,
ga_end,
ga_mode
)
return (result[1],)
def ga_optimize(parameter_values: list):
""""""
return _optimizae(tuple(parameter_values))
return ga_optimize
def ga_optimize(parameter_values: list):
""""""
return _ga_optimizae(tuple(parameter_values))
@lru_cache(maxsize=10)
@ -1141,6 +1145,8 @@ def load_tick_data(
# GA related global value
ga_end = None
ga_mode = None
ga_target_name = None
ga_strategy_class = None
ga_setting = None
@ -1151,6 +1157,4 @@ ga_rate = None
ga_slippage = None
ga_size = None
ga_pricetick = None
ga_capital = None
ga_end = None
ga_mode = None
ga_capital = None