{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T13:06:55.526625Z",
     "start_time": "2025-11-05T13:06:55.505124Z"
    }
   },
   "source": [
    "import sys\n",
    "\n",
    "if '/mnt/d/PyProject/NewQuant/' not in sys.path:\n",
    "    sys.path.append('/mnt/d/PyProject/NewQuant/')\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[autoreload of src.strategies.AreaReversal.AreaReversalStrategy failed: Traceback (most recent call last):\n",
      "  File \"D:\\Python\\conda\\envs\\quant\\Lib\\site-packages\\IPython\\extensions\\autoreload.py\", line 322, in check\n",
      "    elif self.deduper_reloader.maybe_reload_module(m):\n",
      "         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"D:\\Python\\conda\\envs\\quant\\Lib\\site-packages\\IPython\\extensions\\deduperreload\\deduperreload.py\", line 545, in maybe_reload_module\n",
      "    new_source_code = f.read()\n",
      "                      ^^^^^^^^\n",
      "UnicodeDecodeError: 'gbk' codec can't decode byte 0x89 in position 364: illegal multibyte sequence\n",
      "]\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "4f7e4b438cea750e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T13:06:55.549940Z",
     "start_time": "2025-11-05T13:06:55.531734Z"
    }
   },
   "source": [
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "import itertools\n",
    "from typing import Dict, Any, List, Tuple, Optional\n",
    "import multiprocessing  # 导入 multiprocessing 模块\n",
    "import math  # 保留 math 导入，因为您的策略内部可能需要用到数学函数\n",
    "\n",
    "# 导入所有必要的模块\n",
    "# 请确保这些导入路径与您的项目结构相符\n",
    "from src.analysis.grid_search_analyzer import GridSearchAnalyzer\n",
    "from src.analysis.result_analyzer import ResultAnalyzer\n",
    "from src.common_utils import generate_parameter_range\n",
    "from src.data_manager import DataManager\n",
    "from src.backtest_engine import BacktestEngine\n",
    "# 导入策略类\n",
    "\n",
    "import builtins\n",
    "\n",
    "\n",
    "origin_print = print\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T13:06:55.568284Z",
     "start_time": "2025-11-05T13:06:55.553269Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "jupyter": {
     "is_executing": true
    },
    "ExecuteTime": {
     "start_time": "2025-11-05T13:06:55.572303Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# --- 主执行块 ---\n",
    "# 这是多进程代码的入口点，必须在 'if __name__ == \"__main__\":' 保护块中\n",
    "# 确保 autoreload 启用 (在Jupyter Notebook中使用，纯Python脚本运行时可移除)\n",
    "# %load_ext autoreload\n",
    "# %autoreload 2\n",
    "\n",
    "from src.strategies.AreaReversal.AreaReversalStrategy import AreaReversalStrategy\n",
    "from src.strategies.AreaReversal.utils import run_single_backtest\n",
    "\n",
    "# --- 全局配置 ---\n",
    "data_file_path = \"D:/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min15.csv\"\n",
    "\n",
    "\n",
    "initial_capital = 100000.0\n",
    "slippage_rate = 0.0000\n",
    "commission_rate = 0.0000\n",
    "global_config = {\n",
    "    'symbol': 'MA',\n",
    "}\n",
    "# 确保每个合约的tick_size在这里定义或获取\n",
    "RB_TICK_SIZE = 1.0  # 螺纹钢的最小变动单位\n",
    "\n",
    "# --- 定义参数网格 ---\n",
    "param1_name = \"ma_period\"\n",
    "param1_values = generate_parameter_range(start=5, end=25, step=2)\n",
    "param2_name = \"area_window\"\n",
    "param2_values = generate_parameter_range(start=5, end=25, step=2)\n",
    "# param2_name = \"dominance_multiplier\"\n",
    "# param2_values = generate_parameter_range(start=0, end=5, step=0.5)\n",
    "optimization_metric = 'total_return'\n",
    "\n",
    "# 生成所有参数组合\n",
    "param_combinations = list(itertools.product(param1_values, param2_values))\n",
    "total_combinations = len(param_combinations)\n",
    "print(f\"总计 {total_combinations} 种参数组合需要回测。\")\n",
    "\n",
    "all_results: List[Dict[str, Any]] = []\n",
    "grid_results: List[Dict[str, Any]] = []\n",
    "\n",
    "# 准备传递给每个子进程的公共配置字典\n",
    "common_config_for_processes = {\n",
    "    'main_symbol': global_config['symbol'],\n",
    "    'symbol': global_config['symbol'],\n",
    "    'data_path': data_file_path,\n",
    "    'initial_capital': initial_capital,\n",
    "    'slippage_rate': slippage_rate,\n",
    "    'commission_rate': commission_rate,\n",
    "    'start_time': datetime(2022, 1, 1),  # 回测起始时间\n",
    "    'end_time': datetime(2024, 6, 1),  # 回测结束时间\n",
    "    'roll_over_mode': True,  # 保持换月模式\n",
    "    'param1_name': param1_name,\n",
    "    'param2_name': param2_name,\n",
    "    'optimization_metric': optimization_metric,\n",
    "    'strategy': AreaReversalStrategy,\n",
    "    'order_direction': ['BUY', 'SELL'],\n",
    "    # 'hawkes_entry_percent': 0.9,\n",
    "    'stop_loss_tick': 5\n",
    "}\n",
    "\n",
    "# 确定要使用的进程数量 (通常是CPU核心数)\n",
    "num_processes = int(multiprocessing.cpu_count() * 0.6)\n",
    "if num_processes < 1:\n",
    "    num_processes = 1\n",
    "\n",
    "print(f\"--- 启动多进程网格搜索，使用 {num_processes} 个进程 ---\")\n",
    "\n",
    "builtins.print = slient_print\n",
    "\n",
    "# 创建一个进程池\n",
    "with multiprocessing.Pool(processes=num_processes) as pool:\n",
    "    # 准备 run_single_backtest 函数的参数列表\n",
    "    # starmap 需要一个可迭代对象，其中每个元素是传递给目标函数的参数元组\n",
    "    args_for_starmap = [\n",
    "        (combo, common_config_for_processes) for combo in param_combinations\n",
    "    ]\n",
    "\n",
    "    run_single_backtest(*args_for_starmap[0])\n",
    "\n",
    "    # 使用 starmap() 来并行执行 run_single_backtest 函数\n",
    "    # starmap 是阻塞的，会等待所有任务完成并返回结果列表\n",
    "    for i, result_entry in enumerate(pool.starmap(run_single_backtest, args_for_starmap)):\n",
    "        if result_entry:  # 确保结果不为空\n",
    "            all_results.append(result_entry)\n",
    "            # 仅将成功的（无错误的）结果添加到用于网格分析的列表中\n",
    "            if 'error' not in result_entry:\n",
    "                grid_results.append(\n",
    "                    {\n",
    "                        param1_name: result_entry.get(param1_name),\n",
    "                        param2_name: result_entry.get(param2_name),\n",
    "                        optimization_metric: result_entry.get(optimization_metric, 0.0),\n",
    "                    }\n",
    "                )\n",
    "            else:\n",
    "                # 对于失败的组合，将其优化指标设置为一个特殊值，便于识别\n",
    "                grid_results.append(\n",
    "                    {\n",
    "                        param1_name: result_entry.get(param1_name),\n",
    "                        param2_name: result_entry.get(param2_name),\n",
    "                        optimization_metric: float('-inf'),  # 用负无穷表示失败\n",
    "                        'error_message': result_entry['error']\n",
    "                    }\n",
    "                )\n",
    "\n",
    "builtins.print = origin_print\n",
    "print(\"\\n--- 网格搜索回测完毕 ---\")"
   ],
   "id": "3428ed77effae112",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总计 121 种参数组合需要回测。\n",
      "--- 启动多进程网格搜索，使用 12 个进程 ---\n"
     ]
    }
   ],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-05T09:00:43.930695Z",
     "start_time": "2025-11-05T09:00:43.637014Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "# --- 5. 后处理和最佳结果选择 ---\n",
    "if all_results:\n",
    "    results_df = pd.DataFrame(all_results)\n",
    "    # print(\"\\n--- 所有回测结果汇总 ---\")\n",
    "    # # 确保打印时浮点数格式化\n",
    "    # pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
    "    # print(results_df.to_string())\n",
    "\n",
    "    # 找到最佳组合 (排除有错误的)\n",
    "    # 过滤掉包含 'error' 键的行，或者 'error' 键的值不为空的行\n",
    "    # 同时确保优化指标是数值，并且不为无穷大\n",
    "    print(results_df.info())\n",
    "    successful_results_df = results_df[(pd.to_numeric(results_df[optimization_metric], errors='coerce').notna()) &\n",
    "                                       (pd.to_numeric(results_df[optimization_metric], errors='coerce') != float(\n",
    "                                           '-inf'))\n",
    "                                       ].copy()  # 使用 .copy() 避免 SettingWithCopyWarning\n",
    "\n",
    "    if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
    "        # 确保优化指标列是数值类型\n",
    "        successful_results_df[optimization_metric] = pd.to_numeric(successful_results_df[optimization_metric],\n",
    "                                                                   errors='coerce')\n",
    "\n",
    "        if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
    "            # 过滤掉NaN值，如果所有夏普比率都是NaN，则可能没有有效结果\n",
    "            normal_results = successful_results_df[\n",
    "                (results_df['total_trades'] > 100)\n",
    "                # &\n",
    "                # (results_df['profit_factor'] < 3.)\n",
    "            ]\n",
    "            if len(normal_results) > 0:\n",
    "                best_result = normal_results.loc[(normal_results[optimization_metric].idxmax())]\n",
    "                print(f\"\\n--- 最优参数组合 (按{optimization_metric}) ---\")\n",
    "                print(best_result)\n",
    "            else:\n",
    "                print('ERROR!!!!!!!!!!!!!!!!!!!!')\n",
    "\n",
    "        # 找到最大值的索引\n",
    "        # best_result = successful_results_df.loc[successful_results_df[optimization_metric].idxmax()]\n",
    "        # print(f\"\\n--- 最优参数组合 (按 {optimization_metric}) ---\")\n",
    "        # print(best_result)\n",
    "\n",
    "        # 导出到CSV\n",
    "        output_filename = f\"grid_search_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv\"\n",
    "        # results_df.to_csv(output_filename, index=False, encoding='utf-8')\n",
    "        # print(f\"\\n所有结果已导出到: {output_filename}\")\n",
    "\n",
    "        # 打印枢轴表\n",
    "        grid_df = pd.DataFrame(grid_results)\n",
    "        # 确保优化指标列是数值类型，非数值的（如 -inf）在pandas中可能被正确处理\n",
    "        grid_df[optimization_metric] = pd.to_numeric(grid_df[optimization_metric], errors='coerce')\n",
    "\n",
    "        pivot_table = grid_df.pivot_table(\n",
    "            index=param1_name, columns=param2_name, values=optimization_metric\n",
    "        )\n",
    "        # print(f\"\\n{optimization_metric} 网格结果 (Pivoted):\")\n",
    "        # print(pivot_table.to_string())\n",
    "    else:\n",
    "        print(f\"\\n没有成功的组合结果可供分析，或优化指标 '{optimization_metric}' 不在结果中，或所有组合均失败。\")\n",
    "else:\n",
    "    print(\"没有可用的回测结果。\")\n",
    "print(\"\\n--- 动态网格搜索完成 ---\")\n",
    "\n",
    "# --- 6. 可视化 (依赖 GridSearchAnalyzer) ---\n",
    "if grid_results:\n",
    "    grid_analyzer = GridSearchAnalyzer(grid_results, optimization_metric)\n",
    "    grid_analyzer.find_best_parameters()  # 这会找到并打印最佳参数\n",
    "    grid_analyzer.plot_heatmap()  # 这会绘制热力图\n",
    "else:\n",
    "    print(\"\\n没有生成任何网格搜索结果，无法进行分析。\")"
   ],
   "id": "a1c18a2776fcaba2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 121 entries, 0 to 120\n",
      "Data columns (total 39 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   main_symbol           121 non-null    object \n",
      " 1   trade_volume          121 non-null    int64  \n",
      " 2   ma_period             121 non-null    int64  \n",
      " 3   area_window           121 non-null    int64  \n",
      " 4   order_direction       121 non-null    object \n",
      " 5   enable_log            121 non-null    bool   \n",
      " 6   初始资金                  121 non-null    float64\n",
      " 7   最终资金                  121 non-null    float64\n",
      " 8   总收益率                  121 non-null    float64\n",
      " 9   年化收益率                 121 non-null    float64\n",
      " 10  最大回撤                  121 non-null    float64\n",
      " 11  夏普比率                  121 non-null    float64\n",
      " 12  卡玛比率                  121 non-null    float64\n",
      " 13  总交易次数                 121 non-null    int64  \n",
      " 14  交易成本                  121 non-null    float64\n",
      " 15  总实现盈亏                 121 non-null    float64\n",
      " 16  胜率                    121 non-null    float64\n",
      " 17  盈亏比                   121 non-null    float64\n",
      " 18  盈利交易次数                121 non-null    int64  \n",
      " 19  亏损交易次数                121 non-null    int64  \n",
      " 20  平均每次盈利                121 non-null    float64\n",
      " 21  平均每次亏损                121 non-null    float64\n",
      " 22  initial_capital       121 non-null    float64\n",
      " 23  final_capital         121 non-null    float64\n",
      " 24  total_return          121 non-null    float64\n",
      " 25  annualized_return     121 non-null    float64\n",
      " 26  max_drawdown          121 non-null    float64\n",
      " 27  sharpe_ratio          121 non-null    float64\n",
      " 28  calmar_ratio          121 non-null    float64\n",
      " 29  sortino_ratio         121 non-null    float64\n",
      " 30  total_trades          121 non-null    int64  \n",
      " 31  transaction_costs     121 non-null    float64\n",
      " 32  total_realized_pnl    121 non-null    float64\n",
      " 33  win_rate              121 non-null    float64\n",
      " 34  profit_loss_ratio     121 non-null    float64\n",
      " 35  winning_trades_count  121 non-null    int64  \n",
      " 36  losing_trades_count   121 non-null    int64  \n",
      " 37  avg_profit_per_trade  121 non-null    float64\n",
      " 38  avg_loss_per_trade    121 non-null    float64\n",
      "dtypes: bool(1), float64(27), int64(9), object(2)\n",
      "memory usage: 36.2+ KB\n",
      "None\n",
      "ERROR!!!!!!!!!!!!!!!!!!!!\n",
      "\n",
      "--- 动态网格搜索完成 ---\n",
      "\n",
      "--- 最佳参数组合 ---\n",
      "  ma_period: 5\n",
      "  area_window: 11\n",
      "  total_return: 0.0090\n",
      "[5, 25, 5, 25]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 5
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "quant",
   "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": "3.12.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}