NewQuant/futures_trading_strategies/TA/Spectral/search_SpectralTrendStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-29T09:25:24.270690Z",
     "start_time": "2025-11-29T09:25:24.252025Z"
    }
   },
   "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"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "id": "4f7e4b438cea750e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-29T09:25:24.290336Z",
     "start_time": "2025-11-29T09:25:24.270690Z"
    }
   },
   "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": "2025-11-29T09:25:24.311582Z",
     "start_time": "2025-11-29T09:25:24.295003Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-29T09:27:56.898865Z",
     "start_time": "2025-11-29T09:25:24.318062Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from src.strategies.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 src.strategies.Spectral.SpectralTrendStrategy2 import SpectralTrendStrategy\n",
    "\n",
    "# --- 全局配置 ---\n",
    "data_file_path = \"D:/PyProject/NewQuant/data/data/KQ_m@CZCE_TA/KQ_m@CZCE_TA_min15.csv\"\n",
    "\n",
    "\n",
    "initial_capital = 10000.0\n",
    "slippage_rate = 0.0000\n",
    "commission_rate = 0.0000\n",
    "global_config = {\n",
    "    'symbol': 'TA',\n",
    "}\n",
    "# 确保每个合约的tick_size在这里定义或获取\n",
    "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",
    "\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",
    "    'strategy': SpectralTrendStrategy,\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:\n",
    "            all_results.append(result_entry)\n",
    "\n",
    "            if 'error' not in result_entry:\n",
    "                # 成功：直接解压\n",
    "                grid_results.append({\n",
    "                    param1_name: result_entry.get(param1_name),\n",
    "                    param2_name: result_entry.get(param2_name),\n",
    "                    **result_entry,\n",
    "                })\n",
    "            else:\n",
    "                # 失败：解压并添加标记\n",
    "                grid_results.append({\n",
    "                    param1_name: result_entry.get(param1_name),\n",
    "                    param2_name: result_entry.get(param2_name),\n",
    "                    **result_entry,\n",
    "                    'optimization_score': float('-inf'),  # 覆盖优化指标\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": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-29T09:31:41.821814Z",
     "start_time": "2025-11-29T09:31:41.641532Z"
    }
   },
   "cell_type": "code",
   "source": [
    "optimization_metric = 'sharpe_ratio'\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'] > 200)\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 42 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   初始资金                      99 non-null     float64\n",
      " 10  最终资金                      99 non-null     float64\n",
      " 11  总收益率                      99 non-null     float64\n",
      " 12  年化收益率                     99 non-null     float64\n",
      " 13  最大回撤                      99 non-null     float64\n",
      " 14  夏普比率                      99 non-null     float64\n",
      " 15  卡玛比率                      99 non-null     float64\n",
      " 16  总交易次数                     99 non-null     int64  \n",
      " 17  交易成本                      99 non-null     float64\n",
      " 18  总实现盈亏                     99 non-null     float64\n",
      " 19  胜率                        99 non-null     float64\n",
      " 20  盈亏比                       99 non-null     float64\n",
      " 21  盈利交易次数                    99 non-null     int64  \n",
      " 22  亏损交易次数                    99 non-null     int64  \n",
      " 23  平均每次盈利                    99 non-null     float64\n",
      " 24  平均每次亏损                    99 non-null     float64\n",
      " 25  initial_capital           99 non-null     float64\n",
      " 26  final_capital             99 non-null     float64\n",
      " 27  total_return              99 non-null     float64\n",
      " 28  annualized_return         99 non-null     float64\n",
      " 29  max_drawdown              99 non-null     float64\n",
      " 30  sharpe_ratio              99 non-null     float64\n",
      " 31  calmar_ratio              99 non-null     float64\n",
      " 32  sortino_ratio             99 non-null     float64\n",
      " 33  total_trades              99 non-null     int64  \n",
      " 34  transaction_costs         99 non-null     float64\n",
      " 35  total_realized_pnl        99 non-null     float64\n",
      " 36  win_rate                  99 non-null     float64\n",
      " 37  profit_loss_ratio         99 non-null     float64\n",
      " 38  winning_trades_count      99 non-null     int64  \n",
      " 39  losing_trades_count       99 non-null     int64  \n",
      " 40  avg_profit_per_trade      99 non-null     float64\n",
      " 41  avg_loss_per_trade        99 non-null     float64\n",
      "dtypes: bool(1), float64(29), int64(10), object(2)\n",
      "memory usage: 31.9+ KB\n",
      "None\n",
      "\n",
      "--- 最优参数组合 (按sharpe_ratio) ---\n",
      "main_symbol                          TA\n",
      "trade_volume                          1\n",
      "spectral_window_days                  3\n",
      "trend_strength_threshold            0.6\n",
      "order_direction             [BUY, SELL]\n",
      "enable_log                        False\n",
      "low_freq_days                         3\n",
      "high_freq_days                        1\n",
      "exit_threshold                      0.3\n",
      "初始资金                            10000.0\n",
      "最终资金                            12033.0\n",
      "总收益率                             0.2033\n",
      "年化收益率                          0.054553\n",
      "最大回撤                           0.157829\n",
      "夏普比率                           0.151252\n",
      "卡玛比率                           0.345649\n",
      "总交易次数                               228\n",
      "交易成本                                0.0\n",
      "总实现盈亏                            2032.0\n",
      "胜率                             0.587719\n",
      "盈亏比                             0.91339\n",
      "盈利交易次数                               67\n",
      "亏损交易次数                               47\n",
      "平均每次盈利                       130.731343\n",
      "平均每次亏损                       -143.12766\n",
      "initial_capital                 10000.0\n",
      "final_capital                   12033.0\n",
      "total_return                     0.2033\n",
      "annualized_return              0.054553\n",
      "max_drawdown                   0.157829\n",
      "sharpe_ratio                   0.151252\n",
      "calmar_ratio                   0.345649\n",
      "sortino_ratio                  0.193264\n",
      "total_trades                        228\n",
      "transaction_costs                   0.0\n",
      "total_realized_pnl               2032.0\n",
      "win_rate                       0.587719\n",
      "profit_loss_ratio               0.91339\n",
      "winning_trades_count                 67\n",
      "losing_trades_count                  47\n",
      "avg_profit_per_trade         130.731343\n",
      "avg_loss_per_trade           -143.12766\n",
      "Name: 14, dtype: object\n",
      "\n",
      "--- 动态网格搜索完成 ---\n",
      "\n",
      "--- 最佳参数组合 ---\n",
      "  spectral_window_days: 4\n",
      "  trend_strength_threshold: 0.7\n",
      "  sharpe_ratio: 0.2012\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": 11
  }
 ],
 "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
}