NewQuant/futures_trading_strategies/FG/Spectral/search_SpectralTrendStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-01T14:23:28.047129Z",
     "start_time": "2025-12-01T14:23:28.010527Z"
    }
   },
   "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": "2025-12-01T14:23:28.959919Z",
     "start_time": "2025-12-01T14:23:28.047129Z"
    }
   },
   "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": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-01T14:23:29.014133Z",
     "start_time": "2025-12-01T14:23:28.964923Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-12-01T14:26:00.534133Z",
     "start_time": "2025-12-01T14:23:29.023140Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from futures_trading_strategies.MA.Spectral.utils import run_single_backtest\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.MA.Spectral.SpectralTrendStrategy3 import SpectralTrendStrategy\n",
    "\n",
    "# --- 全局配置 ---\n",
    "data_file_path = \"D:/PyProject/NewQuant/data/data/KQ_m@CZCE_FG/KQ_m@CZCE_FG_min15.csv\"\n",
    "\n",
    "\n",
    "initial_capital = 100000.0\n",
    "slippage_rate = 0.0000\n",
    "commission_rate = 0.0000\n",
    "global_config = {\n",
    "    'symbol': 'FG',\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 = \"spectral_window_days\"\n",
    "param1_values = generate_parameter_range(start=2, end=12, step=1)\n",
    "# param1_name = \"exit_threshold\"\n",
    "# param1_values = generate_parameter_range(start=0.1, end=0.9, step=0.1)\n",
    "param2_name = \"trend_strength_threshold\"\n",
    "param2_values = generate_parameter_range(start=0.1, end=0.9, step=0.1)\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': SpectralTrendStrategy,\n",
    "    'order_direction': ['BUY', 'SELL'],\n",
    "    # 'hawkes_entry_percent': 0.9,\n",
    "    'stop_loss_tick': 5,\n",
    "    'reverse': True,\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": "aed5938660e42b20",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "总计 99 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": "2025-12-01T14:26:00.832438Z",
     "start_time": "2025-12-01T14:26:00.555354Z"
    }
   },
   "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: 99 entries, 0 to 98\n",
      "Data columns (total 43 columns):\n",
      " #   Column                    Non-Null Count  Dtype  \n",
      "---  ------                    --------------  -----  \n",
      " 0   main_symbol               99 non-null     object \n",
      " 1   trade_volume              99 non-null     int64  \n",
      " 2   spectral_window_days      99 non-null     int64  \n",
      " 3   trend_strength_threshold  99 non-null     float64\n",
      " 4   order_direction           99 non-null     object \n",
      " 5   enable_log                99 non-null     bool   \n",
      " 6   low_freq_days             99 non-null     int64  \n",
      " 7   high_freq_days            99 non-null     int64  \n",
      " 8   exit_threshold            99 non-null     float64\n",
      " 9   reverse                   99 non-null     bool   \n",
      " 10  初始资金                      99 non-null     float64\n",
      " 11  最终资金                      99 non-null     float64\n",
      " 12  总收益率                      99 non-null     float64\n",
      " 13  年化收益率                     99 non-null     float64\n",
      " 14  最大回撤                      99 non-null     float64\n",
      " 15  夏普比率                      99 non-null     float64\n",
      " 16  卡玛比率                      99 non-null     float64\n",
      " 17  总交易次数                     99 non-null     int64  \n",
      " 18  交易成本                      99 non-null     float64\n",
      " 19  总实现盈亏                     99 non-null     float64\n",
      " 20  胜率                        99 non-null     float64\n",
      " 21  盈亏比                       99 non-null     float64\n",
      " 22  盈利交易次数                    99 non-null     int64  \n",
      " 23  亏损交易次数                    99 non-null     int64  \n",
      " 24  平均每次盈利                    99 non-null     float64\n",
      " 25  平均每次亏损                    99 non-null     float64\n",
      " 26  initial_capital           99 non-null     float64\n",
      " 27  final_capital             99 non-null     float64\n",
      " 28  total_return              99 non-null     float64\n",
      " 29  annualized_return         99 non-null     float64\n",
      " 30  max_drawdown              99 non-null     float64\n",
      " 31  sharpe_ratio              99 non-null     float64\n",
      " 32  calmar_ratio              99 non-null     float64\n",
      " 33  sortino_ratio             99 non-null     float64\n",
      " 34  total_trades              99 non-null     int64  \n",
      " 35  transaction_costs         99 non-null     float64\n",
      " 36  total_realized_pnl        99 non-null     float64\n",
      " 37  win_rate                  99 non-null     float64\n",
      " 38  profit_loss_ratio         99 non-null     float64\n",
      " 39  winning_trades_count      99 non-null     int64  \n",
      " 40  losing_trades_count       99 non-null     int64  \n",
      " 41  avg_profit_per_trade      99 non-null     float64\n",
      " 42  avg_loss_per_trade        99 non-null     float64\n",
      "dtypes: bool(2), float64(29), int64(10), object(2)\n",
      "memory usage: 32.0+ KB\n",
      "None\n",
      "\n",
      "--- 最优参数组合 (按total_return) ---\n",
      "main_symbol                          FG\n",
      "trade_volume                          1\n",
      "spectral_window_days                  9\n",
      "trend_strength_threshold            0.6\n",
      "order_direction             [BUY, SELL]\n",
      "enable_log                        False\n",
      "low_freq_days                         9\n",
      "high_freq_days                        4\n",
      "exit_threshold                      0.3\n",
      "reverse                            True\n",
      "初始资金                           100000.0\n",
      "最终资金                           100944.0\n",
      "总收益率                            0.00944\n",
      "年化收益率                            0.0027\n",
      "最大回撤                           0.003935\n",
      "夏普比率                           0.180775\n",
      "卡玛比率                           0.686206\n",
      "总交易次数                               350\n",
      "交易成本                                0.0\n",
      "总实现盈亏                             943.0\n",
      "胜率                             0.293103\n",
      "盈亏比                            3.080478\n",
      "盈利交易次数                               51\n",
      "亏损交易次数                              123\n",
      "平均每次盈利                        85.176471\n",
      "平均每次亏损                       -27.650407\n",
      "initial_capital                100000.0\n",
      "final_capital                  100944.0\n",
      "total_return                    0.00944\n",
      "annualized_return                0.0027\n",
      "max_drawdown                   0.003935\n",
      "sharpe_ratio                   0.180775\n",
      "calmar_ratio                   0.686206\n",
      "sortino_ratio                  0.224555\n",
      "total_trades                        350\n",
      "transaction_costs                   0.0\n",
      "total_realized_pnl                943.0\n",
      "win_rate                       0.293103\n",
      "profit_loss_ratio              3.080478\n",
      "winning_trades_count                 51\n",
      "losing_trades_count                 123\n",
      "avg_profit_per_trade          85.176471\n",
      "avg_loss_per_trade           -27.650407\n",
      "Name: 68, dtype: object\n",
      "\n",
      "--- 动态网格搜索完成 ---\n",
      "\n",
      "--- 最佳参数组合 ---\n",
      "  spectral_window_days: 9\n",
      "  trend_strength_threshold: 0.6\n",
      "  total_return: 0.0094\n",
      "[2, 12, 0.1, 0.9]\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
}