171 lines
5.5 KiB
Plaintext
171 lines
5.5 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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"#%%\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.atr_rsi_strategy import (\n",
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" AtrRsiStrategy,\n",
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")\n",
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"from datetime import datetime"
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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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"#%%\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(2019, 1, 1),\n",
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" end=datetime(2019, 4, 30),\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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"engine.add_strategy(AtrRsiStrategy, {})"
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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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"#%%\n",
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"engine.load_data()\n",
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"engine.run_backtesting()\n",
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"df = engine.calculate_result()\n",
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"engine.calculate_statistics()\n",
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"engine.show_chart()"
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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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"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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"2019-05-03 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': 38, 'atr_ma_length': 25}, 1.4676268402266743)]"
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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 = OptimizationSetting()\n",
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"setting.set_target(\"sharpe_ratio\")\n",
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"setting.add_parameter(\"atr_length\", 3, 39, 1)\n",
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"setting.add_parameter(\"atr_ma_length\", 10, 30, 1)\n",
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"\n",
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"engine.run_ga_optimization(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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"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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"print(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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}
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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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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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