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Quant_Code/1.交易策略/1.CTA策略/1.松鼠策略/4.松鼠SF11__MACD震荡与沙夫趋势/使用文档/vnpy/vip11.ipynb

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"from vnpy.trader.optimize import OptimizationSetting\n",
"from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
"from vnpy_ctastrategy.strategies.vip11 import vip11\n",
"#from vip11 import vip11"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"engine = BacktestingEngine()\n",
"engine.set_parameters(\n",
" vt_symbol=\"rb888.SHFE\",\n",
" interval=\"1h\",\n",
" start=datetime(2020, 1, 1),\n",
" end=datetime(2024, 3, 21),\n",
" rate=1.5/10000,\n",
" slippage=1,\n",
" size=10,\n",
" pricetick=1,\n",
" capital=1_000_00,\n",
")\n",
"engine.add_strategy(vip11, {})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
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"2024-05-09 19:26:09.616694\t开始加载历史数据\n",
"2024-05-09 19:26:09.616694\t加载进度：# [0%]\n",
"2024-05-09 19:26:09.725625\t加载进度：# [10%]\n",
"2024-05-09 19:26:09.769620\t加载进度：## [20%]\n",
"2024-05-09 19:26:09.847551\t加载进度：### [30%]\n",
"2024-05-09 19:26:09.893522\t加载进度：#### [40%]\n",
"2024-05-09 19:26:09.934502\t加载进度：##### [50%]\n",
"2024-05-09 19:26:09.978477\t加载进度：###### [60%]\n",
"2024-05-09 19:26:10.023447\t加载进度：####### [70%]\n",
"2024-05-09 19:26:10.069431\t加载进度：######## [80%]\n",
"2024-05-09 19:26:10.117395\t加载进度：######### [90%]\n",
"2024-05-09 19:26:10.161375\t加载进度：########## [100%]\n",
"2024-05-09 19:26:10.162367\t历史数据加载完成，数据量：6969\n",
"2024-05-09 19:26:10.163367\t策略初始化完成\n",
"2024-05-09 19:26:10.163367\t开始回放历史数据\n",
"2024-05-09 19:26:10.184365\t回放进度：= [0%]\n",
"2024-05-09 19:26:10.220336\t回放进度：== [10%]\n",
"2024-05-09 19:26:10.257314\t回放进度：=== [20%]\n",
"2024-05-09 19:26:10.298297\t回放进度：==== [30%]\n",
"2024-05-09 19:26:10.338276\t回放进度：===== [40%]\n",
"2024-05-09 19:26:10.375255\t回放进度：====== [50%]\n",
"2024-05-09 19:26:10.411236\t回放进度：======= [60%]\n",
"2024-05-09 19:26:10.447205\t回放进度：======== [70%]\n",
"2024-05-09 19:26:10.483185\t回放进度：========= [80%]\n",
"2024-05-09 19:26:10.521173\t回放进度：========== [90%]\n",
"2024-05-09 19:26:10.521173\t回放进度：=========== [100%]\n",
"2024-05-09 19:26:10.521173\t历史数据回放结束\n",
"2024-05-09 19:26:10.522174\t开始计算逐日盯市盈亏\n",
"2024-05-09 19:26:10.532178\t逐日盯市盈亏计算完成\n",
"2024-05-09 19:26:10.532178\t开始计算策略统计指标\n",
"2024-05-09 19:26:10.541201\t------------------------------\n",
"2024-05-09 19:26:10.541201\t首个交易日：\t2020-01-03\n",
"2024-05-09 19:26:10.541201\t最后交易日：\t2024-03-20\n",
"2024-05-09 19:26:10.541201\t总交易日：\t1020\n",
"2024-05-09 19:26:10.541201\t盈利交易日：\t278\n",
"2024-05-09 19:26:10.541201\t亏损交易日：\t277\n",
"2024-05-09 19:26:10.541201\t起始资金：\t100,000.00\n",
"2024-05-09 19:26:10.541201\t结束资金：\t153,324.56\n",
"2024-05-09 19:26:10.541201\t总收益率：\t53.32%\n",
"2024-05-09 19:26:10.541201\t年化收益：\t12.55%\n",
"2024-05-09 19:26:10.541201\t最大回撤: \t-7,287.31\n",
"2024-05-09 19:26:10.541201\t百分比最大回撤: -4.66%\n",
"2024-05-09 19:26:10.541201\t最长回撤天数: \t91\n",
"2024-05-09 19:26:10.541201\t总盈亏：\t53,324.56\n",
"2024-05-09 19:26:10.541201\t总手续费：\t1,275.44\n",
"2024-05-09 19:26:10.541201\t总滑点：\t1,420.00\n",
"2024-05-09 19:26:10.541201\t总成交金额：\t8,502,940.00\n",
"2024-05-09 19:26:10.541201\t总成交笔数：\t142\n",
"2024-05-09 19:26:10.541201\t日均盈亏：\t52.28\n",
"2024-05-09 19:26:10.541201\t日均手续费：\t1.25\n",
"2024-05-09 19:26:10.541201\t日均滑点：\t1.39\n",
"2024-05-09 19:26:10.541201\t日均成交金额：\t8,336.22\n",
"2024-05-09 19:26:10.541201\t日均成交笔数：\t0.1392156862745098\n",
"2024-05-09 19:26:10.541201\t日均收益率：\t0.04%\n",
"2024-05-09 19:26:10.541201\t收益标准差：\t0.50%\n",
"2024-05-09 19:26:10.541201\tSharpe Ratio：\t1.30\n",
"2024-05-09 19:26:10.541201\t收益回撤比：\t11.44\n",
"2024-05-09 19:26:10.542153\t策略统计指标计算完成\n"
]
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"source": [
"engine.load_data()\n",
"engine.run_backtesting()\n",
"df = engine.calculate_result()\n",
"engine.calculate_statistics()\n",
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},
{
"cell_type": "code",
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{
"data": {
"text/plain": [
"'用于参数优化'"
]
},
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"source": [
"'''用于参数优化'''\n",
"# setting = OptimizationSetting()\n",
"# setting.set_target(\"sharpe_ratio\")\n",
"# setting.add_parameter(\"length\", 5, 80, 5)\n",
"# setting.add_parameter(\"M\", 5,100, 5)\n",
"# setting.add_parameter(\"smoothingFactor\", 0.1, 0.9, 0.5)\n",
"# setting.add_parameter(\"TS\", 5, 80, 5)\n",
"# from multiprocessing import cpu_count\n",
"# # 获取 CPU 核心数量\n",
"# num_cores = cpu_count()\n",
"# print(f\"获取 CPU 核心数量:\",round(num_cores/2))\n",
"# engine.run_ga_optimization(setting, max_workers=round(num_cores/2))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# engine.run_bf_optimization(setting)"
]
},
{
"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": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "1b43cb0bd93d5abbadd54afed8252f711d4681fe6223ad6b67ffaee289648f85"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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