vn.py
GitHub
commit
1b10ee4713
1
.flake8
1
.flake8
@ -5,3 +5,4 @@ ignore =
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W503 line break before binary operator
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W293 blank line contains whitespace
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W291 trailing whitespace
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W391 blank line at end of file
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@ -17,3 +17,4 @@ tigeropen
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rqdatac
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ta-lib
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ibapi
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deap
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@ -10,28 +10,19 @@
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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 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 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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"execution_count": null,
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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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"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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@ -51,20 +42,9 @@
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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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"execution_count": null,
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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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"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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@ -72,7 +52,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -83,40 +63,18 @@
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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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"execution_count": null,
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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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"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": 6,
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"execution_count": null,
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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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"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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@ -124,7 +82,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -168,27 +126,16 @@
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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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"execution_count": null,
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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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"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": 9,
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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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@ -203,7 +150,7 @@
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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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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -224,8 +171,8 @@
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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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" 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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@ -266,55 +213,11 @@
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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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"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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{
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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",
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"19 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"20 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"21 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"22 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"23 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"24 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"25 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"26 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"27 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"28 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"29 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"30 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
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"遗传算法优化完成，耗时309秒\n",
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"输出帕累托前沿解集：\n",
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"{'boll_window': 48, 'cci_window': 40, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n",
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"{'boll_window': 48, 'cci_window': 50, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n"
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]
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}
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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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27
tests/backtesting/run_ga.py
Normal file
27
tests/backtesting/run_ga.py
Normal file
@ -0,0 +1,27 @@
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from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
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from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import (
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AtrRsiStrategy,
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)
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from datetime import datetime
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if __name__ == "__main__":
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engine = BacktestingEngine()
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engine.set_parameters(
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vt_symbol="IF88.CFFEX",
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interval="1m",
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start=datetime(2019, 1, 1),
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end=datetime(2019, 4, 30),
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rate=0.3 / 10000,
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slippage=0.2,
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size=300,
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pricetick=0.2,
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capital=1_000_000,
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)
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engine.add_strategy(AtrRsiStrategy, {})
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setting = OptimizationSetting()
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setting.set_target("sharpe_ratio")
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setting.add_parameter("atr_length", 3, 39, 1)
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setting.add_parameter("atr_ma_length", 10, 30, 1)
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engine.run_ga_optimization(setting)
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@ -16,7 +16,7 @@
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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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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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@ -54,115 +54,87 @@
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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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"execution_count": 3,
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"metadata": {
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"scrolled": true
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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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"2019-04-15 22:19:49.696835\t参数：{'atr_length': 22}, 目标：121.19996051999999\n",
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"2019-04-15 22:19:49.709531\t参数：{'atr_length': 23}, 目标：116.54901966000013\n",
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"2019-04-15 22:19:49.710507\t参数：{'atr_length': 24}, 目标：113.29820520000014\n"
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"2019-05-03 16:19:04.193703\t开始运行遗传算法，每代族群总数：11, 优良品种筛选个数：8，迭代次数：30，交叉概率：0.95，突变概率：0.050000000000000044\n",
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"gen\tnevals\tmean \tstd \tmin \tmax \n",
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"0 \t11 \t[0.58423524]\t[0.30377007]\t[0.13231977]\t[1.2382818]\n",
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"1 \t11 \t[0.90248989]\t[0.15747112]\t[0.68707859]\t[1.2382818]\n",
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"2 \t11 \t[1.09406088]\t[0.18860523]\t[0.86284921]\t[1.46762684]\n",
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"3 \t11 \t[1.21413386]\t[0.12138014]\t[1.02072108]\t[1.46762684]\n",
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"4 \t11 \t[1.29561806]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
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"5 \t11 \t[1.41029058]\t[0.09930932]\t[1.2382818] \t[1.46762684]\n",
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"6 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"7 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"8 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"9 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"10 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"11 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"12 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"13 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
|
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"14 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"15 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
|
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"16 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"17 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"18 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"19 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"20 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
|
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"21 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"22 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"23 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"24 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"25 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"26 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"27 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"28 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"29 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"30 \t11 \t[1.46762684]\t[0.] \t[1.46762684]\t[1.46762684]\n",
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"2019-05-03 16:19:58.256354\t遗传算法优化完成，耗时54秒\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"[(\"{'atr_length': 22}\",\n",
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" 121.19996051999999,\n",
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" {'start_date': datetime.date(2013, 1, 18),\n",
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" 'end_date': datetime.date(2019, 4, 11),\n",
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" 'total_days': 1514,\n",
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" 'profit_days': 763,\n",
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" 'loss_days': 750,\n",
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" 'capital': 1000000,\n",
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" 'end_balance': 2211999.6052,\n",
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" 'max_drawdown': -248787.6971999996,\n",
|
||||
" 'max_ddpercent': -12.636908338002794,\n",
|
||||
" 'total_net_pnl': 1211999.6052000003,\n",
|
||||
" 'daily_net_pnl': 800.5281408190227,\n",
|
||||
" 'total_commission': 242400.39479999998,\n",
|
||||
" 'daily_commission': 160.10594108322323,\n",
|
||||
" 'total_slippage': 481860.0,\n",
|
||||
" 'daily_slippage': 318.2694848084544,\n",
|
||||
" 'total_turnover': 8080013160.0,\n",
|
||||
" 'daily_turnover': 5336864.702774108,\n",
|
||||
" 'total_trade_count': 8031,\n",
|
||||
" 'daily_trade_count': 5.30449141347424,\n",
|
||||
" 'total_return': 121.19996051999999,\n",
|
||||
" 'annual_return': 19.212675379656538,\n",
|
||||
" 'daily_return': 0.052348808029058974,\n",
|
||||
" 'return_std': 0.9487639654919149,\n",
|
||||
" 'sharpe_ratio': 0.854779772691872,\n",
|
||||
" 'return_drawdown_ratio': 9.590950355754112}),\n",
|
||||
" (\"{'atr_length': 23}\",\n",
|
||||
" 116.54901966000013,\n",
|
||||
" {'start_date': datetime.date(2013, 1, 18),\n",
|
||||
" 'end_date': datetime.date(2019, 4, 11),\n",
|
||||
" 'total_days': 1514,\n",
|
||||
" 'profit_days': 759,\n",
|
||||
" 'loss_days': 754,\n",
|
||||
" 'capital': 1000000,\n",
|
||||
" 'end_balance': 2165490.1966000013,\n",
|
||||
" 'max_drawdown': -232904.1239999996,\n",
|
||||
" 'max_ddpercent': -13.536251422505968,\n",
|
||||
" 'total_net_pnl': 1165490.1966000004,\n",
|
||||
" 'daily_net_pnl': 769.8085842800531,\n",
|
||||
" 'total_commission': 242769.80339999998,\n",
|
||||
" 'daily_commission': 160.34993619550858,\n",
|
||||
" 'total_slippage': 482700.0,\n",
|
||||
" 'daily_slippage': 318.82430647291943,\n",
|
||||
" 'total_turnover': 8092326780.0,\n",
|
||||
" 'daily_turnover': 5344997.873183619,\n",
|
||||
" 'total_trade_count': 8045,\n",
|
||||
" 'daily_trade_count': 5.313738441215324,\n",
|
||||
" 'total_return': 116.54901966000013,\n",
|
||||
" 'annual_return': 18.475406022721288,\n",
|
||||
" 'daily_return': 0.0509452313711608,\n",
|
||||
" 'return_std': 0.961380153488665,\n",
|
||||
" 'sharpe_ratio': 0.8209448965768181,\n",
|
||||
" 'return_drawdown_ratio': 8.610139987960078}),\n",
|
||||
" (\"{'atr_length': 24}\",\n",
|
||||
" 113.29820520000014,\n",
|
||||
" {'start_date': datetime.date(2013, 1, 18),\n",
|
||||
" 'end_date': datetime.date(2019, 4, 11),\n",
|
||||
" 'total_days': 1514,\n",
|
||||
" 'profit_days': 760,\n",
|
||||
" 'loss_days': 753,\n",
|
||||
" 'capital': 1000000,\n",
|
||||
" 'end_balance': 2132982.0520000015,\n",
|
||||
" 'max_drawdown': -236503.9475999996,\n",
|
||||
" 'max_ddpercent': -13.23872340727957,\n",
|
||||
" 'total_net_pnl': 1132982.0520000013,\n",
|
||||
" 'daily_net_pnl': 748.3368903566719,\n",
|
||||
" 'total_commission': 242817.948,\n",
|
||||
" 'daily_commission': 160.3817357992074,\n",
|
||||
" 'total_slippage': 482700.0,\n",
|
||||
" 'daily_slippage': 318.82430647291943,\n",
|
||||
" 'total_turnover': 8093931600.0,\n",
|
||||
" 'daily_turnover': 5346057.85997358,\n",
|
||||
" 'total_trade_count': 8045,\n",
|
||||
" 'daily_trade_count': 5.313738441215324,\n",
|
||||
" 'total_return': 113.29820520000014,\n",
|
||||
" 'annual_return': 17.96008536856013,\n",
|
||||
" 'daily_return': 0.049946173936258026,\n",
|
||||
" 'return_std': 0.959328411709829,\n",
|
||||
" 'sharpe_ratio': 0.8065671672003681,\n",
|
||||
" 'return_drawdown_ratio': 8.558091419728651})]"
|
||||
"[({'atr_length': 38, 'atr_ma_length': 25}, 1.4676268402266743)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"setting = OptimizationSetting()\n",
|
||||
"setting.set_target(\"total_return\")\n",
|
||||
"setting.add_parameter(\"atr_length\", 22, 24, 1)\n",
|
||||
"setting.set_target(\"sharpe_ratio\")\n",
|
||||
"setting.add_parameter(\"atr_length\", 3, 39, 1)\n",
|
||||
"setting.add_parameter(\"atr_ma_length\", 10, 30, 1)\n",
|
||||
"\n",
|
||||
"engine.run_optimization(setting)"
|
||||
"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)"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
||||
@ -222,20 +222,25 @@ class BacktesterEngine(BaseEngine):
|
||||
return strategy_class.get_class_parameters()
|
||||
|
||||
def run_optimization(
|
||||
self,
|
||||
class_name: str,
|
||||
vt_symbol: str,
|
||||
interval: str,
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
rate: float,
|
||||
slippage: float,
|
||||
size: int,
|
||||
pricetick: float,
|
||||
capital: int,
|
||||
optimization_setting: OptimizationSetting):
|
||||
self,
|
||||
class_name: str,
|
||||
vt_symbol: str,
|
||||
interval: str,
|
||||
start: datetime,
|
||||
end: datetime,
|
||||
rate: float,
|
||||
slippage: float,
|
||||
size: int,
|
||||
pricetick: float,
|
||||
capital: int,
|
||||
optimization_setting: OptimizationSetting,
|
||||
use_ga: bool
|
||||
):
|
||||
""""""
|
||||
self.write_log("开始多进程参数优化")
|
||||
if use_ga:
|
||||
self.write_log("开始遗传算法参数优化")
|
||||
else:
|
||||
self.write_log("开始多进程参数优化")
|
||||
|
||||
self.result_values = None
|
||||
|
||||
@ -260,10 +265,16 @@ class BacktesterEngine(BaseEngine):
|
||||
{}
|
||||
)
|
||||
|
||||
self.result_values = engine.run_optimization(
|
||||
optimization_setting,
|
||||
output=False
|
||||
)
|
||||
if use_ga:
|
||||
self.result_values = engine.run_ga_optimization(
|
||||
optimization_setting,
|
||||
output=False
|
||||
)
|
||||
else:
|
||||
self.result_values = engine.run_optimization(
|
||||
optimization_setting,
|
||||
output=False
|
||||
)
|
||||
|
||||
# Clear thread object handler.
|
||||
self.thread = None
|
||||
@ -285,7 +296,8 @@ class BacktesterEngine(BaseEngine):
|
||||
size: int,
|
||||
pricetick: float,
|
||||
capital: int,
|
||||
optimization_setting: OptimizationSetting
|
||||
optimization_setting: OptimizationSetting,
|
||||
use_ga: bool
|
||||
):
|
||||
if self.thread:
|
||||
self.write_log("已有任务在运行中，请等待完成")
|
||||
@ -305,7 +317,8 @@ class BacktesterEngine(BaseEngine):
|
||||
size,
|
||||
pricetick,
|
||||
capital,
|
||||
optimization_setting
|
||||
optimization_setting,
|
||||
use_ga
|
||||
)
|
||||
)
|
||||
self.thread.start()
|
||||
|
||||
@ -240,7 +240,7 @@ class BacktesterManager(QtWidgets.QWidget):
|
||||
if i != dialog.Accepted:
|
||||
return
|
||||
|
||||
optimization_setting = dialog.get_setting()
|
||||
optimization_setting, use_ga = dialog.get_setting()
|
||||
self.target_display = dialog.target_display
|
||||
|
||||
self.backtester_engine.start_optimization(
|
||||
@ -254,7 +254,8 @@ class BacktesterManager(QtWidgets.QWidget):
|
||||
size,
|
||||
pricetick,
|
||||
capital,
|
||||
optimization_setting
|
||||
optimization_setting,
|
||||
use_ga
|
||||
)
|
||||
|
||||
self.result_button.setEnabled(False)
|
||||
@ -592,6 +593,7 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
|
||||
self.edits = {}
|
||||
|
||||
self.optimization_setting = None
|
||||
self.use_ga = False
|
||||
|
||||
self.init_ui()
|
||||
|
||||
@ -642,12 +644,27 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
|
||||
|
||||
row += 1
|
||||
|
||||
button = QtWidgets.QPushButton("确定")
|
||||
button.clicked.connect(self.generate_setting)
|
||||
grid.addWidget(button, row, 0, 1, 4)
|
||||
parallel_button = QtWidgets.QPushButton("多进程优化")
|
||||
parallel_button.clicked.connect(self.generate_parallel_setting)
|
||||
grid.addWidget(parallel_button, row, 0, 1, 4)
|
||||
|
||||
row += 1
|
||||
ga_button = QtWidgets.QPushButton("遗传算法优化")
|
||||
ga_button.clicked.connect(self.generate_ga_setting)
|
||||
grid.addWidget(ga_button, row, 0, 1, 4)
|
||||
|
||||
self.setLayout(grid)
|
||||
|
||||
def generate_ga_setting(self):
|
||||
""""""
|
||||
self.use_ga = True
|
||||
self.generate_setting()
|
||||
|
||||
def generate_parallel_setting(self):
|
||||
""""""
|
||||
self.use_ga = False
|
||||
self.generate_setting()
|
||||
|
||||
def generate_setting(self):
|
||||
""""""
|
||||
self.optimization_setting = OptimizationSetting()
|
||||
@ -676,7 +693,7 @@ class OptimizationSettingEditor(QtWidgets.QDialog):
|
||||
|
||||
def get_setting(self):
|
||||
""""""
|
||||
return self.optimization_setting
|
||||
return self.optimization_setting, self.use_ga
|
||||
|
||||
|
||||
class OptimizationResultMonitor(QtWidgets.QDialog):
|
||||
|
||||
@ -3,12 +3,16 @@ from datetime import date, datetime
|
||||
from typing import Callable
|
||||
from itertools import product
|
||||
from functools import lru_cache
|
||||
from time import time
|
||||
import multiprocessing
|
||||
import random
|
||||
import math
|
||||
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
from pandas import DataFrame
|
||||
from deap import creator, base, tools, algorithms
|
||||
|
||||
from vnpy.trader.constant import (Direction, Offset, Exchange,
|
||||
Interval, Status)
|
||||
@ -26,6 +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:
|
||||
@ -514,6 +520,124 @@ class BacktestingEngine:
|
||||
|
||||
return result_values
|
||||
|
||||
def run_ga_optimization(self, optimization_setting: OptimizationSetting, output=True):
|
||||
""""""
|
||||
# Get optimization setting and target
|
||||
settings = optimization_setting.generate_setting()
|
||||
target_name = optimization_setting.target_name
|
||||
|
||||
if not settings:
|
||||
self.output("优化参数组合为空，请检查")
|
||||
return
|
||||
|
||||
if not target_name:
|
||||
self.output("优化目标未设置，请检查")
|
||||
return
|
||||
|
||||
# Define parameter generation function
|
||||
def generate_parameter():
|
||||
""""""
|
||||
return list(random.choice(settings).values())
|
||||
|
||||
# Create ga object function
|
||||
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
|
||||
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", ga_optimize)
|
||||
toolbox.register("select", tools.selNSGA2)
|
||||
|
||||
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
|
||||
|
||||
stats = tools.Statistics(lambda ind: ind.fitness.values)
|
||||
np.set_printoptions(suppress=True)
|
||||
stats.register("mean", np.mean, axis=0)
|
||||
stats.register("std", np.std, axis=0)
|
||||
stats.register("min", np.min, axis=0)
|
||||
stats.register("max", np.max, axis=0)
|
||||
|
||||
# Multiprocessing is not supported yet.
|
||||
# pool = multiprocessing.Pool(multiprocessing.cpu_count())
|
||||
# toolbox.register("map", pool.map)
|
||||
|
||||
# Run ga optimization
|
||||
self.output(f"参数优化空间：{total_size}")
|
||||
self.output(f"每代族群总数：{pop_size}")
|
||||
self.output(f"优良筛选个数：{mu}")
|
||||
self.output(f"迭代次数：{ngen}")
|
||||
self.output(f"交叉概率：{cxpb:.0%}")
|
||||
self.output(f"突变概率：{mutpb:.0%}")
|
||||
|
||||
start = time()
|
||||
|
||||
algorithms.eaMuPlusLambda(
|
||||
pop,
|
||||
toolbox,
|
||||
mu,
|
||||
lambda_,
|
||||
cxpb,
|
||||
mutpb,
|
||||
ngen,
|
||||
stats,
|
||||
halloffame=hof
|
||||
)
|
||||
|
||||
end = time()
|
||||
cost = int((end - start))
|
||||
|
||||
self.output(f"遗传算法优化完成，耗时{cost}秒")
|
||||
|
||||
# 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):
|
||||
""""""
|
||||
d = self.datetime.date()
|
||||
@ -939,12 +1063,13 @@ def optimize(
|
||||
pricetick: float,
|
||||
capital: int,
|
||||
end: datetime,
|
||||
mode: BacktestingMode,
|
||||
mode: BacktestingMode
|
||||
):
|
||||
"""
|
||||
Function for running in multiprocessing.pool
|
||||
"""
|
||||
engine = BacktestingEngine()
|
||||
|
||||
engine.set_parameters(
|
||||
vt_symbol=vt_symbol,
|
||||
interval=interval,
|
||||
@ -968,6 +1093,35 @@ def optimize(
|
||||
return (str(setting), target_value, statistics)
|
||||
|
||||
|
||||
@lru_cache(maxsize=1000000)
|
||||
def _ga_optimizae(parameter_values: tuple):
|
||||
""""""
|
||||
parameter_keys = list(ga_setting.keys())
|
||||
setting = dict(zip(parameter_keys, parameter_values))
|
||||
|
||||
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 _ga_optimizae(tuple(parameter_values))
|
||||
|
||||
|
||||
@lru_cache(maxsize=10)
|
||||
def load_bar_data(
|
||||
symbol: str,
|
||||
@ -993,3 +1147,19 @@ def load_tick_data(
|
||||
return database_manager.load_tick_data(
|
||||
symbol, exchange, start, end
|
||||
)
|
||||
|
||||
|
||||
# GA related global value
|
||||
ga_end = None
|
||||
ga_mode = None
|
||||
ga_target_name = None
|
||||
ga_strategy_class = None
|
||||
ga_setting = None
|
||||
ga_vt_symbol = None
|
||||
ga_interval = None
|
||||
ga_start = None
|
||||
ga_rate = None
|
||||
ga_slippage = None
|
||||
ga_size = None
|
||||
ga_pricetick = None
|
||||
ga_capital = None
|
||||
Loading…
Reference in New Issue
Block a user