{ "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": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "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" ] }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plot.ly" }, "data": [ { "mode": "lines", "name": "Balance", "type": "scatter", "x": [ "2020-01-03", "2020-01-06", "2020-01-07", 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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 }