NewQuant/futures_trading_strategies/SA/ITrend/search_ITrendStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-25T14:31:20.133709Z",
     "start_time": "2026-01-25T14:31:20.098376Z"
    }
   },
   "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": [],
   "execution_count": 1
  },
  {
   "cell_type": "code",
   "id": "4f7e4b438cea750e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-25T14:31:21.085348Z",
     "start_time": "2026-01-25T14:31:20.137715Z"
    }
   },
   "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",
    "origin_print = print\n",
    "\n"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-25T14:31:21.107923Z",
     "start_time": "2026-01-25T14:31:21.085348Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-25T14:33:58.111630Z",
     "start_time": "2026-01-25T14:31:21.113322Z"
    }
   },
   "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 futures_trading_strategies.SA.ITrend.ITrendStrategy import ITrendStrategy\n",
    "from src.strategies.utils import run_single_backtest\n",
    "\n",
    "# --- 全局配置 ---\n",
    "data_file_path = \"D:/PyProject/NewQuant/data/data/KQ_m@CZCE_SA/KQ_m@CZCE_SA_min15.csv\"\n",
    "\n",
    "initial_capital = 100000.0\n",
    "slippage_rate = 0.0000\n",
    "commission_rate = 0.0000\n",
    "global_config = {\n",
    "    'symbol': 'SA',\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 = \"length\"\n",
    "param1_values = generate_parameter_range(start=10, end=60, step=5)\n",
    "# param1_name = \"exit_threshold\"\n",
    "# param1_values = generate_parameter_range(start=0.1, end=4.1, step=0.4)\n",
    "param2_name = \"range_fraction\"\n",
    "param2_values = generate_parameter_range(start=1, end=31, step=3)\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': ITrendStrategy,\n",
    "    # 'hawkes_entry_percent': 0.9,\n",
    "    'strategy_params': {\n",
    "        # 'fisher_period': 81\n",
    "    }\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",
    "                        **result_entry,\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",
    "                        **result_entry,\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": "aed5938660e42b20",
   "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",
      "\n",
      "--- 网格搜索回测完毕 ---\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2026-01-25T14:33:58.341336Z",
     "start_time": "2026-01-25T14:33:58.123304Z"
    }
   },
   "cell_type": "code",
   "source": [
    "optimization_metric = 'total_return'\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'] > 300)\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 38 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   length                121 non-null    int64  \n",
      " 3   range_fraction        121 non-null    int64  \n",
      " 4   enable_log            121 non-null    bool   \n",
      " 5   初始资金                  121 non-null    float64\n",
      " 6   最终资金                  121 non-null    float64\n",
      " 7   总收益率                  121 non-null    float64\n",
      " 8   年化收益率                 121 non-null    float64\n",
      " 9   最大回撤                  121 non-null    float64\n",
      " 10  夏普比率                  121 non-null    float64\n",
      " 11  卡玛比率                  121 non-null    float64\n",
      " 12  总交易次数                 121 non-null    int64  \n",
      " 13  交易成本                  121 non-null    float64\n",
      " 14  总实现盈亏                 121 non-null    float64\n",
      " 15  胜率                    121 non-null    float64\n",
      " 16  盈亏比                   121 non-null    float64\n",
      " 17  盈利交易次数                121 non-null    int64  \n",
      " 18  亏损交易次数                121 non-null    int64  \n",
      " 19  平均每次盈利                121 non-null    float64\n",
      " 20  平均每次亏损                121 non-null    float64\n",
      " 21  initial_capital       121 non-null    float64\n",
      " 22  final_capital         121 non-null    float64\n",
      " 23  total_return          121 non-null    float64\n",
      " 24  annualized_return     121 non-null    float64\n",
      " 25  max_drawdown          121 non-null    float64\n",
      " 26  sharpe_ratio          121 non-null    float64\n",
      " 27  calmar_ratio          121 non-null    float64\n",
      " 28  sortino_ratio         121 non-null    float64\n",
      " 29  total_trades          121 non-null    int64  \n",
      " 30  transaction_costs     121 non-null    float64\n",
      " 31  total_realized_pnl    121 non-null    float64\n",
      " 32  win_rate              121 non-null    float64\n",
      " 33  profit_loss_ratio     121 non-null    float64\n",
      " 34  winning_trades_count  121 non-null    int64  \n",
      " 35  losing_trades_count   121 non-null    int64  \n",
      " 36  avg_profit_per_trade  121 non-null    float64\n",
      " 37  avg_loss_per_trade    121 non-null    float64\n",
      "dtypes: bool(1), float64(27), int64(9), object(1)\n",
      "memory usage: 35.2+ KB\n",
      "None\n",
      "\n",
      "--- 最优参数组合 (按total_return) ---\n",
      "main_symbol                    SA\n",
      "trade_volume                    1\n",
      "length                         40\n",
      "range_fraction                  4\n",
      "enable_log                  False\n",
      "初始资金                     100000.0\n",
      "最终资金                     100864.0\n",
      "总收益率                      0.00864\n",
      "年化收益率                    0.002472\n",
      "最大回撤                     0.018438\n",
      "夏普比率                     0.100988\n",
      "卡玛比率                      0.13408\n",
      "总交易次数                        1688\n",
      "交易成本                          0.0\n",
      "总实现盈亏                       863.0\n",
      "胜率                       0.332925\n",
      "盈亏比                      2.179513\n",
      "盈利交易次数                        272\n",
      "亏损交易次数                        545\n",
      "平均每次盈利                  39.327206\n",
      "平均每次亏损                 -18.044037\n",
      "initial_capital          100000.0\n",
      "final_capital            100864.0\n",
      "total_return              0.00864\n",
      "annualized_return        0.002472\n",
      "max_drawdown             0.018438\n",
      "sharpe_ratio             0.100988\n",
      "calmar_ratio              0.13408\n",
      "sortino_ratio            0.140057\n",
      "total_trades                 1688\n",
      "transaction_costs             0.0\n",
      "total_realized_pnl          863.0\n",
      "win_rate                 0.332925\n",
      "profit_loss_ratio        2.179513\n",
      "winning_trades_count          272\n",
      "losing_trades_count           545\n",
      "avg_profit_per_trade    39.327206\n",
      "avg_loss_per_trade     -18.044037\n",
      "Name: 67, dtype: object\n",
      "\n",
      "--- 动态网格搜索完成 ---\n",
      "\n",
      "--- 最佳参数组合 ---\n",
      "  length: 40\n",
      "  range_fraction: 4\n",
      "  total_return: 0.0086\n",
      "[10, 60, 1, 31]\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
}