增加交易策略、交易指标、量化库代码等文件夹

1.交易策略/1.CTA策略/1.松鼠策略/2.松鼠SF09_基于成交量的阶梯均线过滤震荡行情/使用文稿/vnpy/vip09.ipynb

@@ -0,0 +1,132 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'vnpy_ctastrategy.strategies.vip09'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrader\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OptimizationSetting\n\u001b[0;32m      4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbacktesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BacktestingEngine\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstrategies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvip09\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vip09\n\u001b[0;32m      6\u001b[0m \u001b[38;5;66;03m# from vip09 import vip09\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'vnpy_ctastrategy.strategies.vip09'"
+     ]
+    }
+   ],
+   "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.vip09 import vip09\n",
+    "from vip09 import vip09"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "engine = BacktestingEngine()\n",
+    "engine.set_parameters(\n",
+    "    vt_symbol=\"rb00.SHFE\",\n",
+    "    interval=\"1m\",\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(vip09, {})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "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(\"Length\", 5, 100, 5)\n",
+    "# setting.add_parameter(\"N\", 5, 100, 5)\n",
+    "# setting.add_parameter(\"X\", 1, 20, 1)\n",
+    "# setting.add_parameter(\"TS\", 5, 100, 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
+}