增加交易策略、交易指标、量化库代码等文件夹
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from datetime import datetime\n",
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"\n",
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"from vnpy.trader.optimize import OptimizationSetting\n",
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"from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
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"# from vnpy_ctastrategy.strategies.vip13 import vip13\n",
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"from vip13 import vip13"
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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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"engine = BacktestingEngine()\n",
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"engine.set_parameters(\n",
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" vt_symbol=\"rbJQ00.SHFE\",\n",
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" interval=\"1h\",\n",
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" start=datetime(2020, 1, 1),\n",
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" end=datetime(2024, 3, 21),\n",
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" rate=1.5/10000,\n",
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" slippage=1,\n",
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" size=10,\n",
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" pricetick=1,\n",
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" capital=1_000_00,\n",
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")\n",
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"engine.add_strategy(vip13, {})"
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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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"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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"2025-01-30 22:14:21.167367\t开始加载历史数据\n",
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"2025-01-30 22:14:21.167367\t加载进度：# [0%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：# [10%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：## [20%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：### [30%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：#### [40%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：##### [50%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：###### [60%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：####### [70%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：######## [80%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：######### [90%]\n",
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"2025-01-30 22:14:21.167367\t加载进度：########## [100%]\n",
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"2025-01-30 22:14:21.167367\t历史数据加载完成，数据量：0\n",
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"2025-01-30 22:14:21.167367\t策略初始化完成\n",
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"2025-01-30 22:14:21.167367\t开始回放历史数据\n",
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"2025-01-30 22:14:21.167367\t历史数据回放结束\n",
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"2025-01-30 22:14:21.167367\t开始计算逐日盯市盈亏\n",
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"2025-01-30 22:14:21.167367\t回测成交记录为空\n"
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]
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},
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{
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"ename": "KeyError",
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"evalue": "\"None of ['date'] are in the columns\"",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[10], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m engine\u001b[38;5;241m.\u001b[39mload_data()\n\u001b[0;32m 2\u001b[0m engine\u001b[38;5;241m.\u001b[39mrun_backtesting()\n\u001b[1;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m engine\u001b[38;5;241m.\u001b[39mcalculate_statistics()\n\u001b[0;32m 5\u001b[0m engine\u001b[38;5;241m.\u001b[39mshow_chart()\n",
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"File \u001b[1;32mc:\\veighna_studio\\lib\\site-packages\\vnpy_ctastrategy\\backtesting.py:288\u001b[0m, in \u001b[0;36mBacktestingEngine.calculate_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m daily_result\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 286\u001b[0m results[key]\u001b[38;5;241m.\u001b[39mappend(value)\n\u001b[1;32m--> 288\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdaily_df \u001b[38;5;241m=\u001b[39m \u001b[43mDataFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_index\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(_(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m逐日盯市盈亏计算完成\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m 291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdaily_df\n",
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"File \u001b[1;32mc:\\veighna_studio\\lib\\site-packages\\pandas\\core\\frame.py:6109\u001b[0m, in \u001b[0;36mDataFrame.set_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m 6106\u001b[0m missing\u001b[38;5;241m.\u001b[39mappend(col)\n\u001b[0;32m 6108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing:\n\u001b[1;32m-> 6109\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmissing\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are in the columns\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m 6112\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n",
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"\u001b[1;31mKeyError\u001b[0m: \"None of ['date'] are in the columns\""
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]
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}
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],
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"source": [
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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": 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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"# '''用于参数优化'''\n",
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"# setting = OptimizationSetting()\n",
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"# setting.set_target(\"sharpe_ratio\")\n",
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"# setting.add_parameter(\"boll_n\", 5, 60, 5)\n",
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"# setting.add_parameter(\"offset\", 0.5,3, 0.5)\n",
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"# setting.add_parameter(\"trailing_stop_rate\", 5, 100, 5)\n",
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"# setting.add_parameter(\"x\", 5, 80, 5)\n",
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"# from multiprocessing import cpu_count\n",
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"# # 获取 CPU 核心数量\n",
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"# num_cores = cpu_count()\n",
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"# print(f\"获取 CPU 核心数量:\",round(num_cores/2))\n",
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"# engine.run_ga_optimization(setting, max_workers=round(num_cores/2))"
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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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"# engine.run_bf_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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}
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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": "3.10.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "1b43cb0bd93d5abbadd54afed8252f711d4681fe6223ad6b67ffaee289648f85"
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}
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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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