439 lines
108 KiB
Plaintext
439 lines
108 KiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"id": "522f09ca7b3fe929",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-26T11:24:28.437374Z",
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"start_time": "2025-11-26T11:24:28.405444Z"
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}
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},
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"source": [
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"import sys\n",
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"\n",
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"if '/mnt/d/PyProject/NewQuant/' not in sys.path:\n",
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" sys.path.append('/mnt/d/PyProject/NewQuant/')\n",
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"\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"\n"
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],
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"outputs": [],
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"execution_count": 1
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},
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{
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"cell_type": "code",
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"id": "4f7e4b438cea750e",
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-26T11:24:29.366534Z",
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"start_time": "2025-11-26T11:24:28.437374Z"
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}
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},
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"source": [
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"import pandas as pd\n",
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"from datetime import datetime\n",
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"import itertools\n",
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"from typing import Dict, Any, List, Tuple, Optional\n",
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"import multiprocessing # 导入 multiprocessing 模块\n",
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"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",
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"\n",
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"# 导入所有必要的模块\n",
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"# 请确保这些导入路径与您的项目结构相符\n",
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"from src.analysis.grid_search_analyzer import GridSearchAnalyzer\n",
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"from src.analysis.result_analyzer import ResultAnalyzer\n",
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"from src.common_utils import generate_parameter_range\n",
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"from src.data_manager import DataManager\n",
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"from src.backtest_engine import BacktestEngine\n",
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"# 导入策略类\n",
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"\n",
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"import builtins\n",
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"\n",
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"\n",
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"origin_print = print\n",
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"\n"
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],
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"outputs": [],
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"execution_count": 2
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-26T11:24:29.384793Z",
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"start_time": "2025-11-26T11:24:29.368538Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"def slient_print(*args):\n",
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" pass\n"
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],
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"id": "f903fd2761d446cd",
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"outputs": [],
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"execution_count": 3
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-26T11:27:01.326922Z",
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"start_time": "2025-11-26T11:24:29.388796Z"
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}
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},
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"cell_type": "code",
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"source": [
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"from src.strategies.Spectral.utils import run_single_backtest\n",
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"# --- 主执行块 ---\n",
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"# 这是多进程代码的入口点，必须在 'if __name__ == \"__main__\":' 保护块中\n",
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"# 确保 autoreload 启用 (在Jupyter Notebook中使用，纯Python脚本运行时可移除)\n",
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"# %load_ext autoreload\n",
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"# %autoreload 2\n",
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"\n",
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"from src.strategies.Spectral.SpectralTrendStrategy2 import SpectralTrendStrategy\n",
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"\n",
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"# --- 全局配置 ---\n",
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"data_file_path = \"D:/PyProject/NewQuant/data/data/KQ_m@CZCE_MA/KQ_m@CZCE_MA_min15.csv\"\n",
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"\n",
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"\n",
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"initial_capital = 100000.0\n",
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"slippage_rate = 0.0000\n",
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"commission_rate = 0.0000\n",
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"global_config = {\n",
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" 'symbol': 'MA',\n",
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"}\n",
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"# 确保每个合约的tick_size在这里定义或获取\n",
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"RB_TICK_SIZE = 1.0 # 螺纹钢的最小变动单位\n",
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"\n",
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"# --- 定义参数网格 ---\n",
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"param1_name = \"spectral_window_days\"\n",
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"param1_values = generate_parameter_range(start=2, end=12, step=1)\n",
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"# param1_name = \"exit_threshold\"\n",
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"# param1_values = generate_parameter_range(start=0.1, end=0.9, step=0.1)\n",
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"param2_name = \"trend_strength_threshold\"\n",
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"param2_values = generate_parameter_range(start=0.1, end=0.9, step=0.1)\n",
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"# param2_name = \"dominance_multiplier\"\n",
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"# param2_values = generate_parameter_range(start=0, end=5, step=0.5)\n",
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"optimization_metric = 'total_return'\n",
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"\n",
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"# 生成所有参数组合\n",
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"param_combinations = list(itertools.product(param1_values, param2_values))\n",
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"total_combinations = len(param_combinations)\n",
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"print(f\"总计 {total_combinations} 种参数组合需要回测。\")\n",
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"\n",
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"all_results: List[Dict[str, Any]] = []\n",
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"grid_results: List[Dict[str, Any]] = []\n",
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"\n",
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"# 准备传递给每个子进程的公共配置字典\n",
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"common_config_for_processes = {\n",
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" 'main_symbol': global_config['symbol'],\n",
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" 'symbol': global_config['symbol'],\n",
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" 'data_path': data_file_path,\n",
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" 'initial_capital': initial_capital,\n",
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" 'slippage_rate': slippage_rate,\n",
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" 'commission_rate': commission_rate,\n",
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" 'start_time': datetime(2022, 1, 1), # 回测起始时间\n",
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" 'end_time': datetime(2024, 6, 1), # 回测结束时间\n",
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" 'roll_over_mode': True, # 保持换月模式\n",
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" 'param1_name': param1_name,\n",
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" 'param2_name': param2_name,\n",
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" 'optimization_metric': optimization_metric,\n",
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" 'strategy': SpectralTrendStrategy,\n",
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" 'order_direction': ['BUY', 'SELL'],\n",
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" # 'hawkes_entry_percent': 0.9,\n",
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" 'stop_loss_tick': 5\n",
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"}\n",
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"\n",
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"# 确定要使用的进程数量 (通常是CPU核心数)\n",
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"num_processes = int(multiprocessing.cpu_count() * 0.6)\n",
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"if num_processes < 1:\n",
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" num_processes = 1\n",
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"\n",
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"print(f\"--- 启动多进程网格搜索，使用 {num_processes} 个进程 ---\")\n",
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"\n",
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"builtins.print = slient_print\n",
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"\n",
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"# 创建一个进程池\n",
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"with multiprocessing.Pool(processes=num_processes) as pool:\n",
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" # 准备 run_single_backtest 函数的参数列表\n",
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" # 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",
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" args_for_starmap = [\n",
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" (combo, common_config_for_processes) for combo in param_combinations\n",
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" ]\n",
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"\n",
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" run_single_backtest(*args_for_starmap[0])\n",
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"\n",
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" # 使用 starmap() 来并行执行 run_single_backtest 函数\n",
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" # starmap 是阻塞的，会等待所有任务完成并返回结果列表\n",
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" for i, result_entry in enumerate(pool.starmap(run_single_backtest, args_for_starmap)):\n",
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" if result_entry: # 确保结果不为空\n",
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" all_results.append(result_entry)\n",
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" # 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",
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" if 'error' not in result_entry:\n",
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" grid_results.append(\n",
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" {\n",
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" param1_name: result_entry.get(param1_name),\n",
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" param2_name: result_entry.get(param2_name),\n",
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" optimization_metric: result_entry.get(optimization_metric, 0.0),\n",
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" }\n",
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" )\n",
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" else:\n",
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" # 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",
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" grid_results.append(\n",
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" {\n",
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" param1_name: result_entry.get(param1_name),\n",
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" param2_name: result_entry.get(param2_name),\n",
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" optimization_metric: float('-inf'), # 用负无穷表示失败\n",
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" 'error_message': result_entry['error']\n",
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" }\n",
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" )\n",
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"\n",
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"builtins.print = origin_print\n",
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"print(\"\\n--- 网格搜索回测完毕 ---\")"
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],
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"id": "aed5938660e42b20",
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"总计 99 种参数组合需要回测。\n",
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"--- 启动多进程网格搜索，使用 12 个进程 ---\n",
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"\n",
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"--- 网格搜索回测完毕 ---\n"
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]
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}
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],
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"execution_count": 4
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},
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{
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"metadata": {
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"ExecuteTime": {
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"end_time": "2025-11-26T11:27:01.556370Z",
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"start_time": "2025-11-26T11:27:01.340011Z"
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}
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},
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"cell_type": "code",
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"source": [
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"\n",
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"# --- 5. 后处理和最佳结果选择 ---\n",
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"if all_results:\n",
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" results_df = pd.DataFrame(all_results)\n",
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" # print(\"\\n--- 所有回测结果汇总 ---\")\n",
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" # # 确保打印时浮点数格式化\n",
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" # pd.set_option('display.float_format', lambda x: '%.4f' % x)\n",
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" # print(results_df.to_string())\n",
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"\n",
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" # 找到最佳组合 (排除有错误的)\n",
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" # 过滤掉包含 'error' 键的行，或者 'error' 键的值不为空的行\n",
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" # 同时确保优化指标是数值，并且不为无穷大\n",
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" print(results_df.info())\n",
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" successful_results_df = results_df[(pd.to_numeric(results_df[optimization_metric], errors='coerce').notna()) &\n",
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" (pd.to_numeric(results_df[optimization_metric], errors='coerce') != float(\n",
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" '-inf'))\n",
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" ].copy() # 使用 .copy() 避免 SettingWithCopyWarning\n",
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"\n",
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" if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
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" # 确保优化指标列是数值类型\n",
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" successful_results_df[optimization_metric] = pd.to_numeric(successful_results_df[optimization_metric],\n",
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" errors='coerce')\n",
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"\n",
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" if not successful_results_df.empty and optimization_metric in successful_results_df.columns:\n",
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" # 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",
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" normal_results = successful_results_df[\n",
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" (results_df['total_trades'] > 100)\n",
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" # &\n",
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" # (results_df['profit_factor'] < 3.)\n",
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" ]\n",
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" if len(normal_results) > 0:\n",
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" best_result = normal_results.loc[(normal_results[optimization_metric].idxmax())]\n",
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" print(f\"\\n--- 最优参数组合 (按{optimization_metric}) ---\")\n",
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" print(best_result)\n",
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" else:\n",
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" print('ERROR!!!!!!!!!!!!!!!!!!!!')\n",
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"\n",
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" # 找到最大值的索引\n",
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" # best_result = successful_results_df.loc[successful_results_df[optimization_metric].idxmax()]\n",
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" # print(f\"\\n--- 最优参数组合 (按 {optimization_metric}) ---\")\n",
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" # print(best_result)\n",
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"\n",
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" # 导出到CSV\n",
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" output_filename = f\"grid_search_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.csv\"\n",
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" # results_df.to_csv(output_filename, index=False, encoding='utf-8')\n",
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" # print(f\"\\n所有结果已导出到: {output_filename}\")\n",
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"\n",
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" # 打印枢轴表\n",
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" grid_df = pd.DataFrame(grid_results)\n",
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" # 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",
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" grid_df[optimization_metric] = pd.to_numeric(grid_df[optimization_metric], errors='coerce')\n",
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"\n",
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" pivot_table = grid_df.pivot_table(\n",
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" index=param1_name, columns=param2_name, values=optimization_metric\n",
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" )\n",
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" # print(f\"\\n{optimization_metric} 网格结果 (Pivoted):\")\n",
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" # print(pivot_table.to_string())\n",
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" else:\n",
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" 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",
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"else:\n",
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" print(\"没有可用的回测结果。\")\n",
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"print(\"\\n--- 动态网格搜索完成 ---\")\n",
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"\n",
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"# --- 6. 可视化 (依赖 GridSearchAnalyzer) ---\n",
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"if grid_results:\n",
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" grid_analyzer = GridSearchAnalyzer(grid_results, optimization_metric)\n",
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|
|
" 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 MA\n",
|
|||
|
|
"trade_volume 1\n",
|
|||
|
|
"spectral_window_days 2\n",
|
|||
|
|
"trend_strength_threshold 0.2\n",
|
|||
|
|
"order_direction [BUY, SELL]\n",
|
|||
|
|
"enable_log False\n",
|
|||
|
|
"low_freq_days 2\n",
|
|||
|
|
"high_freq_days 1\n",
|
|||
|
|
"exit_threshold 0.0\n",
|
|||
|
|
"初始资金 100000.0\n",
|
|||
|
|
"最终资金 101726.0\n",
|
|||
|
|
"总收益率 0.01726\n",
|
|||
|
|
"年化收益率 0.004924\n",
|
|||
|
|
"最大回撤 0.006538\n",
|
|||
|
|
"夏普比率 0.241619\n",
|
|||
|
|
"卡玛比率 0.753058\n",
|
|||
|
|
"总交易次数 174\n",
|
|||
|
|
"交易成本 0.0\n",
|
|||
|
|
"总实现盈亏 1725.0\n",
|
|||
|
|
"胜率 0.586207\n",
|
|||
|
|
"盈亏比 1.226912\n",
|
|||
|
|
"盈利交易次数 51\n",
|
|||
|
|
"亏损交易次数 36\n",
|
|||
|
|
"平均每次盈利 79.647059\n",
|
|||
|
|
"平均每次亏损 -64.916667\n",
|
|||
|
|
"initial_capital 100000.0\n",
|
|||
|
|
"final_capital 101726.0\n",
|
|||
|
|
"total_return 0.01726\n",
|
|||
|
|
"annualized_return 0.004924\n",
|
|||
|
|
"max_drawdown 0.006538\n",
|
|||
|
|
"sharpe_ratio 0.241619\n",
|
|||
|
|
"calmar_ratio 0.753058\n",
|
|||
|
|
"sortino_ratio 0.329565\n",
|
|||
|
|
"total_trades 174\n",
|
|||
|
|
"transaction_costs 0.0\n",
|
|||
|
|
"total_realized_pnl 1725.0\n",
|
|||
|
|
"win_rate 0.586207\n",
|
|||
|
|
"profit_loss_ratio 1.226912\n",
|
|||
|
|
"winning_trades_count 51\n",
|
|||
|
|
"losing_trades_count 36\n",
|
|||
|
|
"avg_profit_per_trade 79.647059\n",
|
|||
|
|
"avg_loss_per_trade -64.916667\n",
|
|||
|
|
"Name: 1, dtype: object\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 动态网格搜索完成 ---\n",
|
|||
|
|
"\n",
|
|||
|
|
"--- 最佳参数组合 ---\n",
|
|||
|
|
" spectral_window_days: 2\n",
|
|||
|
|
" trend_strength_threshold: 0.2\n",
|
|||
|
|
" total_return: 0.0173\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
|
|||
|
|
}
|