NewQuant/futures_trading_strategies/ru/Spectral/search_SpectralTrendStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T19:15:23.597932Z",
     "start_time": "2025-11-26T19:15:23.579880Z"
    }
   },
   "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.indicators.indicators 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 0xaf in position 775: illegal multibyte sequence\n",
      "]\n"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "cell_type": "code",
   "id": "4f7e4b438cea750e",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T19:15:23.614735Z",
     "start_time": "2025-11-26T19:15:23.600936Z"
    }
   },
   "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": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T19:15:23.631058Z",
     "start_time": "2025-11-26T19:15:23.614735Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T19:17:55.871507Z",
     "start_time": "2025-11-26T19:15:23.631058Z"
    }
   },
   "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@SHFE_rb/KQ_m@SHFE_rb_min15.csv\"\n",
    "\n",
    "\n",
    "initial_capital = 100000.0\n",
    "slippage_rate = 0.0000\n",
    "commission_rate = 0.0000\n",
    "global_config = {\n",
    "    'symbol': 'rb',\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",
    "}\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": 24
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-26T19:17:56.065022Z",
     "start_time": "2025-11-26T19:17:55.882808Z"
    }
   },
   "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 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total_return) ---\n",
      "main_symbol                          rb\n",
      "trade_volume                          1\n",
      "spectral_window_days                  9\n",
      "trend_strength_threshold            0.8\n",
      "order_direction             [BUY, SELL]\n",
      "enable_log                        False\n",
      "low_freq_days                         9\n",
      "high_freq_days                        4\n",
      "exit_threshold                      0.5\n",
      "初始资金                           100000.0\n",
      "最终资金                           100917.0\n",
      "总收益率                            0.00917\n",
      "年化收益率                          0.002623\n",
      "最大回撤                           0.005553\n",
      "夏普比率                           0.138434\n",
      "卡玛比率                           0.472418\n",
      "总交易次数                               104\n",
      "交易成本                                0.0\n",
      "总实现盈亏                             916.0\n",
      "胜率                             0.576923\n",
      "盈亏比                            1.095259\n",
      "盈利交易次数                               30\n",
      "亏损交易次数                               22\n",
      "平均每次盈利                             92.4\n",
      "平均每次亏损                       -84.363636\n",
      "initial_capital                100000.0\n",
      "final_capital                  100917.0\n",
      "total_return                    0.00917\n",
      "annualized_return              0.002623\n",
      "max_drawdown                   0.005553\n",
      "sharpe_ratio                   0.138434\n",
      "calmar_ratio                   0.472418\n",
      "sortino_ratio                  0.142283\n",
      "total_trades                        104\n",
      "transaction_costs                   0.0\n",
      "total_realized_pnl                916.0\n",
      "win_rate                       0.576923\n",
      "profit_loss_ratio              1.095259\n",
      "winning_trades_count                 30\n",
      "losing_trades_count                  22\n",
      "avg_profit_per_trade               92.4\n",
      "avg_loss_per_trade           -84.363636\n",
      "Name: 70, 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: 0.8\n",
      "  total_return: 0.0092\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": 25
  }
 ],
 "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
}