{ "cells": [ { "cell_type": "code", "execution_count": 8, "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.vip13 import vip13\n", "from vip13 import vip13" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "engine = BacktestingEngine()\n", "engine.set_parameters(\n", " vt_symbol=\"rbJQ00.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(vip13, {})" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2025-01-30 22:14:21.167367\t开始加载历史数据\n", "2025-01-30 22:14:21.167367\t加载进度：# [0%]\n", "2025-01-30 22:14:21.167367\t加载进度：# [10%]\n", "2025-01-30 22:14:21.167367\t加载进度：## [20%]\n", "2025-01-30 22:14:21.167367\t加载进度：### [30%]\n", "2025-01-30 22:14:21.167367\t加载进度：#### [40%]\n", "2025-01-30 22:14:21.167367\t加载进度：##### [50%]\n", "2025-01-30 22:14:21.167367\t加载进度：###### [60%]\n", "2025-01-30 22:14:21.167367\t加载进度：####### [70%]\n", "2025-01-30 22:14:21.167367\t加载进度：######## [80%]\n", "2025-01-30 22:14:21.167367\t加载进度：######### [90%]\n", "2025-01-30 22:14:21.167367\t加载进度：########## [100%]\n", "2025-01-30 22:14:21.167367\t历史数据加载完成，数据量：0\n", "2025-01-30 22:14:21.167367\t策略初始化完成\n", "2025-01-30 22:14:21.167367\t开始回放历史数据\n", "2025-01-30 22:14:21.167367\t历史数据回放结束\n", "2025-01-30 22:14:21.167367\t开始计算逐日盯市盈亏\n", "2025-01-30 22:14:21.167367\t回测成交记录为空\n" ] }, { "ename": "KeyError", "evalue": "\"None of ['date'] are in the columns\"", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", "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", "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", "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", "\u001b[1;31mKeyError\u001b[0m: \"None of ['date'] are in the columns\"" ] } ], "source": [ "engine.load_data()\n", "engine.run_backtesting()\n", "df = engine.calculate_result()\n", "engine.calculate_statistics()\n", "engine.show_chart()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": false }, "outputs": [], "source": [ "# '''用于参数优化'''\n", "# setting = OptimizationSetting()\n", "# setting.set_target(\"sharpe_ratio\")\n", "# setting.add_parameter(\"boll_n\", 5, 60, 5)\n", "# setting.add_parameter(\"offset\", 0.5,3, 0.5)\n", "# setting.add_parameter(\"trailing_stop_rate\", 5, 100, 5)\n", "# setting.add_parameter(\"x\", 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": null, "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 }