463 lines
109 KiB
Plaintext
463 lines
109 KiB
Plaintext
|
{
|
|||
|
|
"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
|
|||
|
|
}
|