NewQuant/futures_trading_strategies/SA/Spectral/search_SpectralTrendStrategy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "id": "522f09ca7b3fe929",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-06T15:17:42.402991Z",
     "start_time": "2025-11-06T15:17:42.369481Z"
    }
   },
   "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": "2025-11-06T15:17:43.407501Z",
     "start_time": "2025-11-06T15:17:42.408994Z"
    }
   },
   "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": "2025-11-06T15:17:43.431465Z",
     "start_time": "2025-11-06T15:17:43.412037Z"
    }
   },
   "cell_type": "code",
   "source": [
    "\n",
    "def slient_print(*args):\n",
    "    pass\n"
   ],
   "id": "f903fd2761d446cd",
   "outputs": [],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-11-06T15:23:19.406712Z",
     "start_time": "2025-11-06T15:17:43.436160Z"
    }
   },
   "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.TestStrategy.TestStrategy import AccelBreakoutStrategy\n",
    "from src.strategies.RsiStrategy.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 = \"breakout_period\"\n",
    "param1_values = generate_parameter_range(start=5, end=105, step=10)\n",
    "param2_name = \"trend_period\"\n",
    "param2_values = generate_parameter_range(start=5, end=105, step=10)\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': AccelBreakoutStrategy,\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 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": "2025-11-06T15:23:19.636729Z",
     "start_time": "2025-11-06T15:23:19.420221Z"
    }
   },
   "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   breakout_period       121 non-null    int64  \n",
      " 3   trend_period          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",
      "\n",
      "--- 最优参数组合 (按total_return) ---\n",
      "main_symbol                      MA\n",
      "trade_volume                      1\n",
      "breakout_period                  95\n",
      "trend_period                     65\n",
      "order_direction         [BUY, SELL]\n",
      "enable_log                    False\n",
      "初始资金                       100000.0\n",
      "最终资金                        99453.0\n",
      "总收益率                       -0.00547\n",
      "年化收益率                     -0.001573\n",
      "最大回撤                         0.0111\n",
      "夏普比率                      -0.086754\n",
      "卡玛比率                      -0.141717\n",
      "总交易次数                           238\n",
      "交易成本                            0.0\n",
      "总实现盈亏                        -547.0\n",
      "胜率                         0.364407\n",
      "盈亏比                        1.441499\n",
      "盈利交易次数                           43\n",
      "亏损交易次数                           75\n",
      "平均每次盈利                    60.581395\n",
      "平均每次亏损                   -42.026667\n",
      "initial_capital            100000.0\n",
      "final_capital               99453.0\n",
      "total_return               -0.00547\n",
      "annualized_return         -0.001573\n",
      "max_drawdown                 0.0111\n",
      "sharpe_ratio              -0.086754\n",
      "calmar_ratio              -0.141717\n",
      "sortino_ratio              -0.10056\n",
      "total_trades                    238\n",
      "transaction_costs               0.0\n",
      "total_realized_pnl           -547.0\n",
      "win_rate                   0.364407\n",
      "profit_loss_ratio          1.441499\n",
      "winning_trades_count             43\n",
      "losing_trades_count              75\n",
      "avg_profit_per_trade      60.581395\n",
      "avg_loss_per_trade       -42.026667\n",
      "Name: 105, dtype: object\n",
      "\n",
      "--- 动态网格搜索完成 ---\n",
      "\n",
      "--- 最佳参数组合 ---\n",
      "  breakout_period: 95\n",
      "  trend_period: 65\n",
      "  total_return: -0.0055\n",
      "[5, 105, 5, 105]\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
}